Note: Descriptions are shown in the official language in which they were submitted.
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MANAGEMENT OF A PORTFOLIO OF ASSETS
CROSS REFERENCE TO RELATED APPLICATIONS
100011 This application claims the benefit of U.S. Provisional
Patent Application No.
63/127,559, titled "CONTEXTUAL ROLLUP OF INDUSTRIAL METRICS," and filed on
December 18, 2020, U.S. Provisional Patent Application No. 63/133,652, titled
"MANAGEMENT OF A PORTFOLIO OF ASSETS WITH CENTRALIZED CONTROL,"
and filed on January 4, 2021, and India Patent Application No. 202111038629,
titled
"VIRTUAL ASSISTANT FOR A PORTFOLIO OF ASSETS," and filed on August 26,
2021, the entireties of which are hereby incorporated by reference.
TECHNICAL FIELD
[0002] The present disclosure relates generally to real-time asset
analytics, and more
particularly to real-time asset analytics for a portfolio of assets.
BACKGROUND
[0003] Traditionally, data analytics and/or digital
transformation of data related to
assets generally involves human interaction. However, often times a
specialized worker (e.g.,
a manager) is responsible for a large portfolio of assets (e.g., 1000
buildings each with 100
assets such as a boiler, a chiller, a pump, sensors, etc.). Therefore, it is
generally difficult to
identify and/or fix issues with the large portfolio of assets. For example, in
certain scenarios,
multiple assets (e.g., 25 assets) from the large portfolio of assets may have
an issue.
Furthermore, a limited amount of time is traditionally spent on modeling of
data related to
assets to, for example, provide insights related to the data. As such,
computing resources
related to data analytics and/or digital transformation of data related to
assets are traditionally
employed in an inefficient manner.
SUMMARY
[0004] The details of some embodiments of the subject matter
described in this
specification are set forth in the accompanying drawings and the description
below. Other
features, aspects, and advantages of the subject matter will become apparent
from the
description, the drawings, and the claims.
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[0005] In an embodiment, a system comprises one or more processors,
a memory, and
one or more programs stored in the memory. The one or more programs comprise
instructions configured to receive a request to generate a dashboard
visualization associated
with a portfolio of assets. The request comprises an asset descriptor
describing one or more
assets in the portfolio of assets. In response to the request, the one or more
programs
comprise instructions configured to obtain, based on the asset descriptor,
aggregated data
associated with the portfolio of assets. In response to the request, the one
or more programs
also comprise instructions configured to determine metrics for an asset
hierarchy associated
with the portfolio of assets based on a model related to a time series mapping
of attributes for
the aggregated data. In response to the request, the one or more programs also
comprise
instructions configured to provide the dashboard visualization to an
electronic interface of a
computing device, the dashboard visualization comprising the metrics for an
asset hierarchy
associated with the portfolio of assets.
[0006] In another embodiment, a method comprises, at a device with
one or more
processors and a memory, receiving a request to generate a dashboard
visualization
associated with a portfolio of assets. The request comprises an asset
descriptor describing
one or more assets in the portfolio of assets. In response to the request, the
method comprises
obtaining, based on the asset descriptor, aggregated data associated with the
portfolio of
assets. In response to the request, the method also comprises determining
metrics for an asset
hierarchy associated with the portfolio of assets based on a model related to
a time series
mapping of attributes for the aggregated data. In response to the request, the
method also
comprises providing the dashboard visualization to an electronic interface of
a computing
device, the dashboard visualization comprising the metrics for an asset
hierarchy associated
with the portfolio of assets.
[0007] In yet another embodiment, a non-transitory computer-readable
storage medium
comprises one or more programs for execution by one or more processors of a
device. The
one or more programs include instructions which, when executed by the one or
more
processors, cause the device to receive a request to generate a dashboard
visualization
associated with a portfolio of assets. The request comprises an asset
descriptor describing
one or more assets in the portfolio of assets. In response to the request, the
one or more
programs include instructions which, when executed by the one or more
processors, cause the
device to obtain, based on the asset descriptor, aggregated data associated
with the portfolio
of assets. In response to the request, the one or more programs also include
instructions
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which, when executed by the one or more processors, cause the device to
determine metrics
for an asset hierarchy associated with the portfolio of assets based on a
model related to a
time series mapping of attributes for the aggregated data. In response to the
request, the one
or more programs also include instructions which, when executed by the one or
more
processors, cause the device to provide the dashboard visualization to an
electronic interface
of a computing device, the dashboard visualization comprising the metrics for
an asset
hierarchy associated with the portfolio of assets.
BRIEF DESCRIPTION OF THE DRAWINGS
[0008] The description of the illustrative embodiments can be read in
conjunction with
the accompanying figures. It will be appreciated that for simplicity and
clarity of illustration,
elements illustrated in the figures have not necessarily been drawn to scale.
For example, the
dimensions of some of the elements are exaggerated relative to other elements.
Embodiments
incorporating teachings of the present disclosure are shown and described with
respect to the
figures presented herein, in which:
[0009] FIG. 1 illustrates an exemplary networked computing system
environment, in
accordance with one or more embodiments described herein;
[0010] FIG. 2 illustrates a schematic block diagram of a framework
of an IoT platform of
the networked computing system, in accordance with one or more embodiments
described
herein;
[0011] FIG. 3 illustrates a system that provides an exemplary
environment, in accordance
with one or more embodiments described herein;
[0012] FIG. 4 illustrates another system that provides an exemplary
environment, in
accordance with one or more embodiments described herein;
[0013] FIG. 5 illustrates an exemplary computing device, in accordance with
one or more
embodiments described herein;
[0014] FIG. 6 illustrates an exemplary centralized control
database, in accordance with
one or more embodiments described herein;
[0015] FIG. 7 illustrates an exemplary system, in accordance with
one or more
embodiments described herein;
[0016] FIG. 8 illustrates another exemplary system, in accordance
with one or more
embodiments described herein;
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[0017] FIG. 9 illustrates an exemplary system associated with
digital twins, in accordance
with one or more embodiments described herein;
[0018] FIG. 10 illustrates an exemplary system associated with a
dashboard visualization,
in accordance with one or more embodiments described herein;
[0019] FIG. 12 illustrates another exemplary system associated with a
dashboard
visualization, in accordance with one or more embodiments described herein;
[0020] FIG. 13 illustrates an exemplary system associated with a
voice input, in
accordance with one or more embodiments described herein;
[0021] FIG. 14 illustrates an exemplary system associated with
natural language
processing with respect to a voice input, in accordance with one or more
embodiments
described herein;
[0022] FIG. 15 illustrates an exemplary electronic interface, in
accordance with one or
more embodiments described herein;
[0023] FIG. 16 illustrates another exemplary electronic interface,
in accordance with one
or more embodiments described herein;
[0024] FIG. 17 illustrates another exemplary electronic interface,
in accordance with one
or more embodiments described herein;
[0025] FIG. 18 illustrates another exemplary electronic interface,
in accordance with one
or more embodiments described herein;
[0026] FIG. 19 illustrates another exemplary electronic interface, in
accordance with one
or more embodiments described herein;
[0027] FIG. 20 illustrates another exemplary electronic interface,
in accordance with one
or more embodiments described herein;
[0028] FIG. 21 illustrates another exemplary electronic interface,
in accordance with one
or more embodiments described herein;
[0029] FIG. 22 illustrates another exemplary electronic interface,
in accordance with one
or more embodiments described herein;
[0030] FIG. 23 illustrates another exemplary electronic interface,
in accordance with one
or more embodiments described herein;
[0031] FIG. 24 illustrates a schematic view of a material handling system
including
LiDAR based vision system, in accordance with one or more embodiments
described herein;
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[0032] FIG. 25 illustrates a schematic view of a target area of the
material handling
system including the LiDAR based vision system, in accordance with one or more
embodiments described herein;
[0033] FIG. 26 illustrates an example scenario depicting monitoring
of an operation
performed by a worker in a material handling environment by using LiDAR based
vision
system, in accordance with one or more embodiments described herein;
[0034] FIG. 27 illustrates another example scenario depicting
another operation
performed in a material handling environment that can be monitored by using
LiDAR based
vision system, in accordance with one or more embodiments described herein;
[0035] FIG. 28 illustrates a flow diagram for creating create a dashboard
visualization of
metrics for an asset hierarchy associated with a portfolio of assets, in
accordance with one or
more embodiments described herein;
[0036] FIG. 29 illustrates a flow diagram for aggregating data
across a portfolio of assets
to create a dashboard visualization of prioritized actions for the portfolio
of assets, in
accordance with one or more embodiments described herein;
[0037] FIG. 30 illustrates a flow diagram for performing a natural
language query to
obtain data across a portfolio of assets and to create a dashboard
visualization report for the
portfolio of assets, in accordance with one or more embodiments described
herein;
[0038] FIG. 31 illustrates a flow diagram for generating a voice
input to create a
dashboard visualization report for a portfolio of assets, in accordance with
one or more
embodiments described herein; and
[0039] FIG. 32 illustrates a functional block diagram of a computer
that may be
configured to execute techniques described in accordance with one or more
embodiments
described herein.
DETAILED DESCRIPTION
[0040] Reference will now be made in detail to embodiments,
examples of which are
illustrated in the accompanying drawings. In the following detailed
description, numerous
specific details are set forth in order to provide a thorough understanding of
the various
described embodiments. However, it will be apparent to one of ordinary skill
in the art that
the various described embodiments may be practiced without these specific
details. In other
instances, well-known methods, procedures, components, circuits, and networks
have not
been described in detail so as not to unnecessarily obscure aspects of the
embodiments. The
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term "or" is used herein in both the alternative and conjunctive sense, unless
otherwise
indicated. The terms "illustrative," "example," and "exemplary" are used to be
examples
with no indication of quality level. Like numbers refer to like elements
throughout.
[0041] The phrases "in an embodiment," "in one embodiment,"
"according to one
embodiment," and the like generally mean that the particular feature,
structure, or
characteristic following the phrase can be included in at least one embodiment
of the present
disclosure, and can be included in more than one embodiment of the present
disclosure
(importantly, such phrases do not necessarily refer to the same embodiment).
[0042] The word "exemplary" is used herein to mean "serving as an
example, instance, or
illustration." Any implementation described herein as "exemplary" is not
necessarily to be
construed as preferred or advantageous over other implementations.
[0043] If the specification states a component or feature "can,"
"may," "could," "should,"
"would," "preferably," "possibly," "typically," "optionally," "for example,"
"often," or
"might" (or other such language) be included or have a characteristic, that
particular
component or feature is not required to be included or to have the
characteristic. Such
component or feature can be optionally included in some embodiments, or it can
be excluded.
[0044] In general, the present disclosure provides for an "Internet-
of-Things" or "IoT"
platform for enterprise performance management that uses real-time accurate
models and
visual analytics to deliver intelligent actionable recommendations for
sustained peak
performance of an enterprise or organization. The IoT platform is an
extensible platform that
is portable for deployment in any cloud or data center environment for
providing an
enterprise-wide, top to bottom view, displaying the status of processes,
assets, people, and
safety. Further, the IoT platform of the present disclosure supports end-to-
end capability to
execute digital twins against process data and to translate the output into
actionable insights,
as detailed in the following description.
[0045] Traditionally, data analytics and/or digital
transformation of data related to
assets generally involves human interaction. However, often times a
specialized worker (e.g.,
a manager) is responsible for a large portfolio of assets (e.g., 1000
buildings each with 100
assets such as a boiler, a chiller, a pump, sensors, etc.). Therefore, it is
generally difficult to
identify and/or fix issues with the large portfolio of assets. For example, in
certain scenarios,
multiple assets (e.g., 25 assets) from the large portfolio of assets may have
an issue.
Furthermore, a limited amount of time is traditionally spent on modeling of
data related to
assets to, for example, provide insights related to the data. As such,
computing resources
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related to data analytics and/or digital transformation of data related to
assets are traditionally
employed in an inefficient manner.
[0046] As an example, it is generally desirable for management
personnel (e.g.,
executives, managers, etc.) to be provided with an understanding of which
assets from a
portfolio of assets require service, which assets from a portfolio of assets
should be serviced
first, etc. For example, it is often desirable for management personnel (e.g.,
executives, plant
managers, etc.) to be provided with a common view of rolled up metrics related
to an
industrial environment (e.g., an industrial plant) to, for example, increase
asset and/or
operation performance. However, in spite of various dashboard technology
available today,
metrics displayed do not provide insight to improve and/or adjust execution
strategy by
management personnel (e.g., executives, plant managers, etc.) without
depending on
technical personnel (e.g., engineers, etc.). Hence, management personnel
(e.g., executives,
plant managers, etc.) generally heavily depend on engineering analysis, which
is generally
involves extensive and/or time-consuming analysis in order to obtain metrics
for an industrial
environment. Additionally, it is generally desirable for management personnel
(e.g.,
executives, managers, etc.) to be provided with improved technology to
facilitate servicing of
assets from a portfolio of assets. For example, traditional dashboard
technology generally
involves manual configuration of the dashboard to, for example, provide
different insights for
assets. Furthermore, traditional dashboard technology employed with dashboard
data
modelling of assets is generally implemented outside of a core application
and/or asset
model. Therefore, it is generally difficult to execute data modelling for
assets in an efficient
and/or accurate manner.
[0047] Thus, to address these and/or other issues, technologies
and/or techniques to
manage a portfolio of assets is provided. In various embodiments, the
technologies and/or
techniques disclosed herein are employed to manage asset performance to, for
example,
improve performance of one or more assets in a portfolio of assets.
[0048] In one or more embodiments, management of a portfolio of
assets with centralized
control to facilitate asset performance management is provided. In various
embodiments,
data associated with one or more assets is ingested, cleaned and aggregated to
provide
aggregated data. Furthermore, in various embodiments, one or more metrics are
determined
from the aggregated data to provide opportunity and/or performance insights
for the assets.
According to various embodiments, a dashboard visualization that presents
issues associated
with one or more assets from a portfolio of assets is provided. In various
embodiments, the
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dashboard visualization is an enterprise application that allows a portfolio
operator to
remotely manage, investigate, and/or resolve issues associated with a
portfolio of assets. In
various embodiments, the dashboard visualization facilitates aggregation of
asset
performance data into a score or metric value such as, for example, a key
performance
indicator (KPI). In various embodiments, the dashboard visualization
additionally or
alternatively facilitates providing recommendations to improve asset
performance. In various
embodiments, the dashboard visualization additionally or alternatively
facilitates remote
control and/or altering of asset set points. In one or more embodiments, the
issues associated
with the one or more assets are ordered such that issues with a largest impact
with respect to
the portfolio of assets is presented first via the dashboard visualization.
Impact may be based
on cost to repair an asset, energy consumption associated with issues related
to the one or
more assets, savings lost associated with issues related to the one or more
assets, etc.
[0049] In various embodiments, a user may employ the dashboard
visualization to
identify issues associated with the portfolio of assets, to make adjustments
with respect to the
portfolio of assets, and/or to make work orders associated with the portfolio
of assets. In
various embodiments, a user may be subscribed to a performance management
category (e.g.,
Energy Optimization, Digitized Maintenance, etc.) to facilitate determining
issues for the
portfolio of assets to be resolved and/or to facilitate determining an
ordering for prioritized
actions related to the portfolio of assets. For example, an ordering of
prioritized actions may
be different for Energy Optimization than Digitized Maintenance. In various
embodiments,
the dashboard visualization provides an alerts list that combines alerts from
an on-premise
building management system (BMS). In various embodiments, cloud analytics is
performed
to group alerts based on issues and/or to prioritize the issues based on one
or more
algorithms. In various embodiments, the dashboard visualization provides an
issue analysis
triage solution that employs one or more data models to automatically present
information to
facilitate analysis and/or actions related to alerts. In various embodiments,
the dashboard
visualization provides a service case management solution that is integrated
into a building
management technical solution to create issue-based cases related alerts
and/or asset links. In
various embodiments, the dashboard visualization centralizes portfolio
operations to a single
location to allow operators to easily understand an operational status of
assets, to investigate
issues related to assets, and/or to make control changes related to assets. As
such, according
to various embodiments, asset and/or workforce use is optimized, and highest
priority issues
related to the portfolio of assets is presented to a user in an optimal
manner. Additionally,
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according to various embodiments, facility operating and/or maintenance costs
are reduced
while also improving equipment up-time, service operational efficiency, and/or
environmental conditions by employing the dashboard visualization.
Additionally, by
employing the dashboard visualization according to various embodiments, remote
triage of
faults and/or remote resolution of asset issues is provided. Additionally,
according to various
embodiments, the dashboard visualization provides centralized capability to
review, manage
and/or control assets.
[0050] In one or more embodiments, a virtual assistant for a
portfolio of assets to
facilitate real-time asset analytics is additionally or alternatively
provided. For instance, in
various embodiments, a smart industrial virtual assistant (e.g., a chatbot,
etc.) to improve
operation and/or maintenance of assets in a portfolio of assets is provided.
In various
embodiments, the dashboard visualization provides visualizations configured
for mobile
devices and/or remote monitoring of the portfolio of assets. In various
embodiments,
conversational artificial intelligence is provided over an enterprise
performance management
application to build the dashboard visualization of portfolio operations for
the portfolio of
assets. In various embodiments, a natural language query is provided to build
the dashboard
visualization and/or real-time asset analytics related to the portfolio of
assets. In various
embodiments, multiple datastores are abstracted to facilitate generation of
the dashboard
visualization and/or real-time asset analytics related to the portfolio of
assets. In various
embodiments, multiple models are integrated and/or a natural language query is
employed to
build the dashboard visualization related to data associated with the multiple
models. In
various embodiments, the dashboard visualization provides visibility to an end-
user across
multiple layers of an enterprise (e.g., multiple layers with respect to the
portfolio of assets,
multiple layers of a warehouse process, multiple layers of an industrial
process, etc.). In
various embodiments, the dashboard visualization provides real-time asset
analytics
associated with sensors (e.g., vibration, power, etc.), control devices (e.g.,
key performance
indicators (KPIs), equipment states, etc.), labor management (e.g.,
allocation, utilization,
quality, etc.), warehouse execution (e.g., orders, routing, etc.), inventory
management (e.g.,
location, quantity, slotting, etc.), and/or one or more other layers of an
enterprise. In various
embodiments, the dashboard visualization provides a high-level view of
respective layers of
an enterprise to facilitate enterprise performance management.
[0051] In various embodiments, the dashboard visualization
facilitates alert and/or case
management related to the portfolio of assets. For example, in various
embodiments, the
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dashboard visualization provides a consolidated view of alerts from analytical
products
and/or directly from on-site systems that are combined into rich service
cases. In various
embodiments, the dashboard visualization facilitates triage and control. For
example, in
various embodiments, the dashboard visualization provides real-time data
and/or historical
trends related to assets. In various embodiments, features, attributes and/or
relationships
associated with the real-time data and/or historical trends are determined
based on one or
more artificial intelligence systems to, for example, trouble-shoot equipment
faults, control
equipment, and/or change set-points to resolve issues within the dashboard
visualization.
[0052] In various embodiments, the dashboard visualization
facilitates display of graphics
and/or other visualizations related to the portfolio of assets. For example,
in various
embodiments, the dashboard visualization provides dynamically generated
graphics that show
configuration of, relationships between, and/or location of assets in the
portfolio of assets to,
for example, enable knowledge associated with remote facilities, aiding of
fault diagnosis,
and/or performing actions related to issues. In various embodiments, the
dashboard
visualization facilitates operations and/or scheduling associated with the
portfolio of assets.
For example, in various embodiments, the dashboard visualization facilitate
temporary or
long-term changes to operational modes of assets can be made through
scheduling changes
and/or manual switching to allow for events, seasonal changes, maintenance
periods and/or
other changes to asset use or operations.
[0053] In various embodiments, the dashboard visualization presents alerts
from different
sources and/or different system types into a single alert screen to provide a
prioritized view of
issues related to a portfolio of assets. According to various embodiments, the
alerts include
alarms from on-premises BMS, security, fire and other systems. Additionally or
alternatively, according to various embodiments, the alerts include alerts
from analytics
and/or rule-based cloud-located systems with respect to current states and/or
historical states
of assets. Additionally or alternatively, according to various embodiments,
the alerts include
alerts from systems monitoring an asset environment and/or health and safety
conditions
associated with assets. Additionally or alternatively, according to various
embodiments, the
alerts include alerts from cyber security systems. Additionally or
alternatively, according to
various embodiments, the alerts include alerts from systems monitoring of the
health of
assets. Additionally or alternatively, according to various embodiments, the
alerts include
manually entered alerts that may arise due to calls from building occupants,
staff, technicians,
etc. In various embodiments, the alerts are logically grouped and/or presented
to an operator
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via the dashboard visualization. In various embodiments, the alerts are
logically grouped
based on location (e.g., geographic areas or buildings) and/or related assets.
In various
embodiments, the alerts are presented via the dashboard visualization such
that the highest
priority issues are at the top of the list of alerts. In various embodiments,
prioritization of the
alerts is determined based on type of asset, type of facility, use and size of
area affected by
the issues, number of assets, number of issues, types assigned priority of
individual alerts,
and/or other features associated with the assets. In various embodiments,
machine learning is
employed to logically grouped and/or present the alerts. In various
embodiments, machine
learning is employed to identify alerts that optimally reflect use by an
operator of the
dashboard visualization.
[0054] In various embodiments, an extensible object model is
employed to provide
automated display of real-time properties and trends related to service cases
into tabular and
graphical displays. Additionally or alternatively, in various embodiments, an
extensible
object model is employed to provide automated generation and display of
equipment
schematic diagrams and configurations using standard or modular diagrams
populated by
model data. Additionally or alternatively, in various embodiments, an
extensible object
model is employed to create a graph model view of relationships between assets
in the
portfolio of assets (e.g., between equipment and/or other assets in the
facilities, between
building and physical spaces within buildings, etc.). Additionally or
alternatively, in various
embodiments, an extensible object model is employed to determine relationships
between
models such that nodes in the graph visually indicates whether the portfolio
of assets is
associated with one or more alarms related to the nodes. Additionally or
alternatively, in
various embodiments, an extensible object model is employed to provide
information
notifications via the nodes with asset data and/or links to other information.
[0055] In various embodiments, the dashboard visualization is provided to
drive and/or
provide opportunity at an asset level, a plant level, a site level, and/or an
enterprise level
based on metrics such as metrics related to safety, risk, energy/utility cost,
overall equipment
effectiveness (OEE), performance indicators, etc. In various embodiments,
metric
monitoring for one or more assets is customizable. For example, in one or more
embodiments, metric monitoring for one or more assets is configurable for
different reporting
intervals of time (e.g., daily metric monitoring (1-24hr), monthly metric
monitoring (first day
of month to last day of month), yearly metric monitoring first month to last
month of a year),
etc.). In another example, in one or more embodiments, a start of a reporting
period for
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metric monitoring and end of a reporting period for metric monitoring is
configurable (e.g.,
metric monitoring starting at 7am and ending at 3pm, metric monitoring
starting at the first
day of the month and ending at the tenth day of the month, metric monitoring
starting in April
and ending in December, etc.).
[0056] In one or more embodiments, contextual rollup of industrial metrics
to facilitate
asset performance management is additionally or alternatively provided. In
various
embodiments, rollup of data related to a model (e.g., by rolling data from
assets) is provided
to drive and/or provide opportunity at an asset level, a plant level, a site
level, and/or an
enterprise level based on metrics such as metrics related to safety, risk,
energy/utility cost,
OEE, performance indicators, etc. In various embodiments, a contribution model
is provided
for controllable analysis to drive one or more actions in order to provide
economic
impacts/savings. For example, in order to improve a specific target metric for
one or more
assets, the contribution model provides one or more insights with respect to
controllable
variables contribution to loss in order to facilitate one or more with respect
to one or more
changes associated with the one or more assets (e.g., such as main steam
temperature
deviation from an expected value provided by a digital twin model cost). In
various
embodiments, the one or more insights are provided to the user via a dashboard
visualization.
In various embodiments, a lightweight design for data aggregation, data
storage, and/or data
rollup is provided to provide one or more metrics across an enterprise. In
various
embodiments, one or more overlapping and/or non-overlapping metrics are
defined to
facilitate evaluation of data associated with one or more assets.
[0057] In various embodiments, a hierarchy for role-based metrics
aggregation and/or
reporting is provided. In various embodiments, the hierarchy is mapped to role
to aggregate
relevant metrics for one or more underlying assets. For example, in one or
more
embodiments, asset availability for a maintenance engineer is provided based
on rotating
asset hierarchy and/or instrument asset hierarchy. In various embodiments, a
metrics
evaluator is integrated with streaming data from one or more assets to report
one or more
metrics associated with the one or more assets. In one or more embodiments,
the streaming
data is aggregated and/or rolled over (e.g., moved) between different data
structures and/or
different locations within a data structure. In various embodiments, a dynamic
cache that
aggregates the data is configured as a cascaded waterfall stack to roll over
data using a chain
of responsibility flow pattern starting from an hourly interval of time to a
daily interval of
time to a monthly interval of time. In various embodiments, the dynamic cache
is additionally
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or alternatively configured for rollup of data via a hierarchy of assets such
as plant level,
asset level, site level, area level, etc. In various embodiments, a dynamic
waterfall cache
provides improved cost and/or performance. In various embodiments, data
aggregation
and/or data rollup is provided in real-time (e.g., hour to day, via daily
metrics, etc.). In
various embodiments, the dynamic cache is a time series database and/or a time
series
transaction store for metrics. In various embodiments, performance for data
storage is
improved via dynamic caching. In various embodiments, what-if and/or offline
recalculation
for sensitivity analysis with respect to the dynamic cache is provided. In one
or more
embodiments, one or more portions of a dynamic cache is cloned and/or employed
for rerun
of one or more calculations.
[0058] In various embodiments, a dashboard visualization across
various user identities is
provided via a templated dashboard model using, for example, an extensible
object model In
various embodiments, a dashboard visualization for a particular user identity
(e.g., a
maintenance is reported at various hierarchy levels such as an enterprise
level, a site level, a
plant level, a unit level (e.g., an asset level), etc. In various embodiments,
metrics associated
with a first asset hierarchy level (e.g., an enterprise level) includes
metrics or goals (e.g.,
OEE, etc.). In various embodiments, metrics associated with a second asset
hierarchy level
(e.g., a site level) includes metrics that influence a target goal (e.g.,
availability, energy,
performance, quality). In various embodiments, metrics associated with a third
asset
hierarchy level (e.g., a plant level) includes identification of undesirable
actor assets that
influences targeted goal OEE. In various embodiments, metrics associated with
a fourth asset
hierarchy level (e.g., an asset level) includes events or exception that are
related to a target
goal.
[0059] In various embodiments, a dashboard visualization is
modified based on context
(e.g., a dashboard is changed to energy context and displays a same level of
details based on
modelling of assets and/or metrics via an extensible object model). In various
embodiments,
a configured model is employed to present relevant metrics based on user role,
user context
of invoking the dashboard, and/or hierarchy mapped for a metrics model. In
various
embodiments, a metrics model provides a KPI summary data set related to a
hierarchy of
assets and/or one or more schedules (e.g., one or more intervals of time) to
aggregate and/or
rollup storage of data. In various embodiments, a metrics model provides
rollup of data to
calculate one or more targets and/or to identify opportunity (e.g., actual vs
limit & target) at
asset level, plant level, site level, and/or enterprise level based on metrics
such as safety risk,
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energy/utility cost, OEE by rolling data from assets. In various embodiments,
an application
programming interface is employed to integrate different visualization tools
and/or different
reporting tools (e.g., via the dashboard visualization). In one or more
embodiments, a user-
interactive graphical user interface is generated. For instance, in one or
more embodiments,
the graphical user interface renders a visual representation of the dashboard
visualization. In
one or more embodiments, one or more notifications for user devices are
generated based on
metrics associated with one or more assets at different levels in a hierarchy
of assets.
[0060] In various embodiments, an application programming interface
is employed to
integrate different visualization tools and/or different reporting tools
(e.g., via the dashboard
visualization). In one or more embodiments, a user-interactive graphical user
interface is
generated. For instance, in one or more embodiments, the graphical user
interface renders a
visual representation of the dashboard visualization. In one or more
embodiments, one or
more notifications for user devices are generated based on metrics associated
with one or
more assets of the portfolio of assets.
[0061] In one or more embodiments, the dashboard visualization allows a
user to see how
one or more assets are performing against one or more metrics (e.g., one or
more KPIs). In
one or more embodiments, the dashboard visualization allows a user to identify
what next
steps with respect to assets will provide an optimal return on investment for
the action (e.g.,
repair device #1 vs. device #2) depending on the metrics (e.g., fixing device
#1 will save X%
energy, whereas repairing device #2 will save $Y). In one or more embodiments,
the
dashboard visualization allows a user to view individual assets through the
dashboard (e.g.,
boiler #1 is operating at 90% efficiency, or will fail in X weeks, Y days, Z
hours unless
action is taken; and repairing the boiler #1 within a first interval of time
will save $X,
whereas repairing within a second interval of time will save $Y). In one or
more
embodiments, the dashboard visualization allows a user to change individual
settings for an
asset remotely. In one or more embodiments, the dashboard visualization
notifies a user that
changing settings for an asset from X to Y will save X% energy or $Y.
[0062] As such, by employing one or more techniques disclosed
herein, asset
performance is optimized. Moreover, by employing one or more techniques
disclosed herein,
improved insights for opportunity and/or performance insights for assets is
provided to a user
via improved visual indicators associated with a graphical user interface. For
instance, by
employing one or more techniques disclosed herein, additional and/or improved
asset insights
as compared to capabilities of conventional techniques can be achieved across
a data set.
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Additionally, performance of a processing system associated with data
analytics is improved
by employing one or more techniques disclosed herein. For example, a number of
computing
resources, a number of a storage requirements, and/or number of errors
associated with data
analytics is reduced by employing one or more techniques disclosed herein.
[0063] FIG. 1 illustrates an exemplary networked computing system
environment 100,
according to the present disclosure. As shown in FIG. 1, networked computing
system
environment 100 is organized into a plurality of layers including a cloud 105
(e.g., cloud
layer 105), a network 110 (e.g., network layer 110), and an edge 115 (e.g.,
edge layer 115).
As detailed further below, components of the edge 115 are in communication
with
components of the cloud 105 via network 110.
[0064] In various embodiments, network 110 is any suitable network
or combination of
networks and supports any appropriate protocol suitable for communication of
data to and
from components of the cloud 105 and between various other components in the
networked
computing system environment 100 (e.g., components of the edge 115). According
to
various embodiments, network 110 includes a public network (e.g., the
Internet), a private
network (e.g., a network within an organization), or a combination of public
and/or private
networks. According to various embodiments, network 110 is configured to
provide
communication between various components depicted in FIG. 1. According to
various
embodiments, network 110 comprises one or more networks that connect devices
and/or
components in the network layout to allow communication between the devices
and/or
components. For example, in one or more embodiments, the network 110 is
implemented as
the Internet, a wireless network, a wired network (e.g., Ethernet), a local
area network
(LAN), a Wide Area Network (WANs), Bluetooth, Near Field Communication (NFC),
or any
other type of network that provides communications between one or more
components of the
network layout. In some embodiments, network 110 is implemented using cellular
networks,
satellite, licensed radio, or a combination of cellular, satellite, licensed
radio, and/or
unlicensed radio networks.
[0065] Components of the cloud 105 include one or more computer
systems 120 that
form a so-called "Internet-of-Things" or "IoT" platform 125. It should be
appreciated that
"IoT platform" is an optional term describing a platform connecting any type
of Internet-
connected device, and should not be construed as limiting on the types of
computing systems
useable within IoT platform 125. In particular, in various embodiments,
computer systems
120 includes any type or quantity of one or more processors and one or more
data storage
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devices comprising memory for storing and executing applications or software
modules of
networked computing system environment 100. In one embodiment, the processors
and data
storage devices are embodied in server-class hardware, such as enterprise-
level servers. For
example, in an embodiment, the processors and data storage devices comprise
any type or
combination of application servers, communication servers, web servers, super-
computing
servers, database servers, file servers, mail servers, proxy servers, and/
virtual servers.
Further, the one or more processors are configured to access the memory and
execute
processor-readable instructions, which when executed by the processors
configures the
processors to perform a plurality of functions of the networked computing
system
environment 100.
[0066] Computer systems 120 further include one or more software
components of the
IoT platform 125. For example, in one or more embodiments, the software
components of
computer systems 120 include one or more software modules to communicate with
user
devices and/or other computing devices through network 110. For example, in
one or more
embodiments, the software components include one or more modules 141, models
142,
engines 143, databases 144, services 145, and/or applications 146, which may
be stored in/by
the computer systems 120 (e.g., stored on the memory), as detailed with
respect to FIG. 2
below. According to various embodiments, the one or more processors are
configured to
utilize the one or more modules 141, models 142, engines 143, databases 144,
services 145,
and/or applications 146 when performing various methods described in this
disclosure.
[0067] Accordingly, in one or more embodiments, computer systems
120 execute a cloud
computing platform (e.g., IoT platform 125) with scalable resources for
computation and/or
data storage, and may run one or more applications on the cloud computing
platform to
perform various computer-implemented methods described in this disclosure. In
some
embodiments, some of the modules 141, models 142, engines 143, databases 144,
services
145, and/or applications 146 are combined to form fewer modules, models,
engines,
databases, services, and/or applications. In some embodiments, some of the
modules 141,
models 142, engines 143, databases 144, services 145, and/or applications 146
are separated
into separate, more numerous modules, models, engines, databases, services,
and/or
applications. In some embodiments, some of the modules 141, models 142,
engines 143,
databases 144, services 145, and/or applications 146 are removed while others
are added.
[0068] The computer systems 120 are configured to receive data from
other components
(e.g., components of the edge 115) of networked computing system environment
100 via
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network 110. Computer systems 120 are further configured to utilize the
received data to
produce a result. According to various embodiments, information indicating the
result is
transmitted to users via user computing devices over network 110. In some
embodiments, the
computer systems 120 is a server system that provides one or more services
including
providing the information indicating the received data and/or the result(s) to
the users.
According to various embodiments, computer systems 120 are part of an entity
which include
any type of company, organization, or institution that implements one or more
IoT services.
In some examples, the entity is an IoT platform provider.
[0069] Components of the edge 115 include one or more enterprises
160a-160n each
including one or more edge devices 161a-161n and one or more edge gateways
162a-162n.
For example, a first enterprise 160a includes first edge devices 161a and
first edge gateways
162a, a second enterprise 160b includes second edge devices 161b and second
edge gateways
162b, and an nth enterprise 160n includes nth edge devices 161n and nth edge
gateways
162n. As used herein, enterprises 160a-160n represent any type of entity,
facility, or vehicle,
such as, for example, companies, divisions, buildings, manufacturing plants,
warehouses, real
estate facilities, laboratories, aircraft, spacecraft, automobiles, ships,
boats, military vehicles,
oil and gas facilities, or any other type of entity, facility, and/or entity
that includes any
number of local devices.
[0070] According to various embodiments, the edge devices 161a-161n
represent any of a
variety of different types of devices that may be found within the enterprises
160a-160n.
Edge devices 161a-161n are any type of device configured to access network
110, or be
accessed by other devices through network 110, such as via an edge gateway
162a-162n.
According to various embodiments, edge devices 161a-161n are "IoT devices"
which include
any type of network-connected (e.g., Internet-connected) device. For example,
in one or
more embodiments, the edge devices 161a-161n include assets, sensors,
actuators,
processors, computers, valves, pumps, ducts, vehicle components, cameras,
displays, doors,
windows, security components, boilers, chillers, pumps, HVAC components,
factory
equipment, and/or any other devices that are connected to the network 110 for
collecting,
sending, and/or receiving information. Each edge device 161a-161n includes, or
is otherwise
in communication with, one or more controllers for selectively controlling a
respective edge
device 161a-161n and/or for sending/receiving information between the edge
devices 161a-
161n and the cloud 105 via network 110. With reference to FIG. 2, in one or
more
embodiments, the edge 115 include operational technology (OT) systems 163a-
163n and
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information technology (IT) applications 164a-164n of each enterprise 161a-
161n. The OT
systems 163a-163n include hardware and software for detecting and/or causing a
change,
through the direct monitoring and/or control of industrial equipment (e.g.,
edge devices 161a-
161n), assets, processes, and/or events. The IT applications 164a-164n
includes network,
storage, and computing resources for the generation, management, storage, and
delivery of
data throughout and between organizations.
[0071] The edge gateways 162a-162n include devices for facilitating
communication
between the edge devices 161a-161n and the cloud 105 via network 110. For
example, the
edge gateways 162a-162n include one or more communication interfaces for
communicating
with the edge devices 161a-161n and for communicating with the cloud 105 via
network 110.
According to various embodiments, the communication interfaces of the edge
gateways 162a-
162n include one or more cellular radios, Bluetooth, WiFi, near-field
communication radios,
Ethernet, or other appropriate communication devices for transmitting and
receiving
information. According to various embodiments, multiple communication
interfaces are
included in each gateway 162a-162n for providing multiple forms of
communication between
the edge devices 161a-161n, the gateways 162a-162n, and the cloud 105 via
network 110.
For example, in one or more embodiments, communication are achieved with the
edge
devices 161a-161n and/or the network 110 through wireless communication (e.g.,
WiFi, radio
communication, etc.) and/or a wired data connection (e.g., a universal serial
bus, an onboard
diagnostic system, etc.) or other communication modes, such as a local area
network (LAN),
wide area network (WAN) such as the Internet, a telecommunications network, a
data
network, or any other type of network.
[0072] According to various embodiments, the edge gateways 162a-
162n also include a
processor and memory for storing and executing program instructions to
facilitate data
processing. For example, in one or more embodiments, the edge gateways 162a-
162n are
configured to receive data from the edge devices 161a-161n and process the
data prior to
sending the data to the cloud 105. Accordingly, in one or more embodiments,
the edge
gateways 162a-162n include one or more software modules or components for
providing data
processing services and/or other services or methods of the present
disclosure. With
reference to FIG. 2, each edge gateway 162a-162n includes edge services 165a-
165n and
edge connectors 166a-166n. According to various embodiments, the edge services
165a-
165n include hardware and software components for processing the data from the
edge
devices 161a-161n. According to various embodiments, the edge connectors 166a-
166n
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include hardware and software components for facilitating communication
between the edge
gateway 162a-162n and the cloud 105 via network 110, as detailed above. In
some cases,
any of edge devices 161a-n, edge connectors 166a-n, and edge gateways 162a-n
have their
functionality combined, omitted, or separated into any combination of devices.
In other
words, an edge device and its connector and gateway need not necessarily be
discrete
devices.
[0073] FIG. 2 illustrates a schematic block diagram of framework
200 of the IoT platform
125, according to the present disclosure. The IoT platform 125 of the present
disclosure is a
platform for enterprise performance management that uses real-time accurate
models and
visual analytics to deliver intelligent actionable recommendations and/or
analytics for
sustained peak performance of the enterprise 160a-160n. The IoT platform 125
is an
extensible platform that is portable for deployment in any cloud or data
center environment
for providing an enterprise-wide, top to bottom view, displaying the status of
processes,
assets, people, and safety. Further, the IoT platform 125 supports end-to-end
capability to
execute digital twins against process data and to translate the output into
actionable insights,
using the framework 200, detailed further below.
[0074] As shown in FIG. 2, the framework 200 of the IoT platform
125 comprises a
number of layers including, for example, an IoT layer 205, an enterprise
integration layer
210, a data pipeline layer 215, a data insight layer 220, an application
services layer 225, and
an applications layer 230. The IoT platform 125 also includes a core services
layer 235 and
an extensible object model (EOM) 250 comprising one or more knowledge graphs
251. The
layers 205-235 further include various software components that together form
each layer
205-235. For example, in one or more embodiments, each layer 205-235 includes
one or
more of the modules 141, models 142, engines 143, databases 144, services 145,
applications
146, or combinations thereof. In some embodiments, the layers 205-235 are
combined to
form fewer layers. In some embodiments, some of the layers 205-235 are
separated into
separate, more numerous layers. In some embodiments, some of the layers 205-
235 are
removed while others may be added.
[0075] The IoT platform 125 is a model-driven architecture. Thus,
the extensible object
model 250 communicates with each layer 205-230 to contextualize site data of
the enterprise
160a-160n using an extensible graph based object model (or "asset model"). In
one or more
embodiments, the extensible object model 250 is associated with knowledge
graphs 251
where the equipment (e.g., edge devices 161a-161n) and processes of the
enterprise 160a-
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160n are modeled. The knowledge graphs 251 of EOM 250 are configured to store
the
models in a central location. The knowledge graphs 251 define a collection of
nodes and
links that describe real-world connections that enable smart systems. As used
herein, a
knowledge graph 251: (i) describes real-world entities (e.g., edge devices
161a-161n) and
their interrelations organized in a graphical interface; (ii) defines possible
classes and
relations of entities in a schema; (iii) enables interrelating arbitrary
entities with each other;
and (iv) covers various topical domains. In other words, the knowledge graphs
251 define
large networks of entities (e.g., edge devices 161a-161n), semantic types of
the entities,
properties of the entities, and relationships between the entities. Thus, the
knowledge graphs
251 describe a network of "things" that are relevant to a specific domain or
to an enterprise or
organization. Knowledge graphs 251 are not limited to abstract concepts and
relations, but
can also contain instances of objects, such as, for example, documents and
datasets In some
embodiments, the knowledge graphs 251 include resource description framework
(RDF)
graphs. As used herein, a "RDF graph" is a graph data model that formally
describes the
semantics, or meaning, of information. The RDF graph also represents metadata
(e.g., data
that describes data). According to various embodiments, knowledge graphs 251
also include
a semantic object model. The semantic object model is a subset of a knowledge
graph 251
that defines semantics for the knowledge graph 251. For example, the semantic
object model
defines the schema for the knowledge graph 251.
[0076] As used herein, EOM 250 includes a collection of application
programming
interfaces (APIs) that enables seeded semantic object models to be extended.
For example,
the EOM 250 of the present disclosure enables a customer's knowledge graph 251
to be built
subject to constraints expressed in the customer's semantic object model.
Thus, the
knowledge graphs 251 are generated by customers (e.g., enterprises or
organizations) to
create models of the edge devices 161a-161n of an enterprise 160a-160n, and
the knowledge
graphs 251 are input into the EOM 250 for visualizing the models (e.g., the
nodes and links).
[0077] The models describe the assets (e.g., the nodes) of an
enterprise (e.g., the edge
devices 161a-161n) and describe the relationship of the assets with other
components (e.g.,
the links). The models also describe the schema (e.g., describe what the data
is), and
therefore the models are self-validating. For example, in one or more
embodiments, the
model describes the type of sensors mounted on any given asset (e.g., edge
device 161a-
161n) and the type of data that is being sensed by each sensor. According to
various
embodiments, a KPI framework is used to bind properties of the assets in the
extensible
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object model 250 to inputs of the KPI framework. Accordingly, the IoT platform
125 is an
extensible, model-driven end-to-end stack including: two-way model sync and
secure data
exchange between the edge 115 and the cloud 105, metadata driven data
processing (e.g.,
rules, calculations, and aggregations), and model driven visualizations and
applications. As
used herein, "extensible" refers to the ability to extend a data model to
include new
properties/columns/fields, new classes/tables, and new relations. Thus, the
IoT platform 125
is extensible with regards to edge devices 161a-161n and the applications 146
that handle
those devices 161a-161n. For example, when new edge devices 161a-161n are
added to an
enterprise 160a-160n system, the new devices 161a-161n will automatically
appear in the IoT
platform 125 so that the corresponding applications 146 understand and use the
data from the
new devices 161a-161n.
[0078] In some cases, asset templates are used to facilitate
configuration of instances of
edge devices 161a-161n in the model using common structures. An asset template
defines
the typical properties for the edge devices 161a-161n of a given enterprise
160a-160n for a
certain type of device. For example, an asset template of a pump includes
modeling the
pump having inlet and outlet pressures, speed, flow, etc. The templates may
also include
hierarchical or derived types of edge devices 161a-161n to accommodate
variations of a base
type of device 161a-161n. For example, a reciprocating pump is a
specialization of a base
pump type and would include additional properties in the template. Instances
of the edge
device 161a-161n in the model are configured to match the actual, physical
devices of the
enterprise 160a-160n using the templates to define expected attributes of the
device 161a-
161n. Each attribute is configured either as a static value (e.g., capacity is
1000 BPH) or with
a reference to a time series tag that provides the value. The knowledge graph
251 can
automatically map the tag to the attribute based on naming conventions,
parsing, and
matching the tag and attribute descriptions and/or by comparing the behavior
of the time
series data with expected behavior. In one or more embodiments, each of the
key attribute
contributing to one or more metrics to drive a dashboard is marked with one or
more metric
tags such that a dashboard visualization is generated.
[0079] The modeling phase includes an onboarding process for
syncing the models
between the edge 115 and the cloud 105. For example, in one or more
embodiments, the
onboarding process includes a simple onboarding process, a complex onboarding
process,
and/or a standardized rollout process. The simple onboarding process includes
the
knowledge graph 251 receiving raw model data from the edge 115 and running
context
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discovery algorithms to generate the model. The context discovery algorithms
read the
context of the edge naming conventions of the edge devices 161a-161n and
determine what
the naming conventions refer to. For example, in one or more embodiments, the
knowledge
graph 251 receives "TMP" during the modeling phase and determine that "TMP"
relates to
"temperature." The generated models are then published. The complex onboarding
process
includes the knowledge graph 251 receiving the raw model data, receiving point
history data,
and receiving site survey data. According to various embodiments, the
knowledge graph 251
then uses these inputs to run the context discovery algorithms. According to
various
embodiments, the generated models are edited and then the models are
published. The
standardized rollout process includes manually defining standard models in the
cloud 105 and
pushing the models to the edge 115.
[0080] The IoT layer 205 includes one or more components for device
management, data
ingest, and/or command/control of the edge devices 161a-161n. The components
of the IoT
layer 205 enable data to be ingested into, or otherwise received at, the IoT
platform 125 from
a variety of sources. For example, in one or more embodiments, data is
ingested from the
edge devices 161a-161n through process historians or laboratory information
management
systems. The IoT layer 205 is in communication with the edge connectors 165a-
165n
installed on the edge gateways 162a-162n through network 110, and the edge
connectors
165a-165n send the data securely to the IoT platform 205. In some embodiments,
only
authorized data is sent to the IoT platform 125, and the IoT platform 125 only
accepts data
from authorized edge gateways 162a-162n and/or edge devices 161a-161n.
According to
various embodiments, data is sent from the edge gateways 162a-162n to the IoT
platform 125
via direct streaming and/or via batch delivery. Further, after any network or
system outage,
data transfer will resume once communication is re-established and any data
missed during
the outage will be backfilled from the source system or from a cache of the
IoT platform 125.
According to various embodiments, the IoT layer 205 also includes components
for accessing
time series, alarms and events, and transactional data via a variety of
protocols.
[0081] The enterprise integration layer 210 includes one or more
components for
events/messaging, file upload, and/or REST/OData. The components of the
enterprise
integration layer 210 enable the IoT platform 125 to communicate with third
party cloud
applications 211, such as any application(s) operated by an enterprise in
relation to its edge
devices. For example, the enterprise integration layer 210 connects with
enterprise databases,
such as guest databases, customer databases, financial databases, patient
databases, etc. The
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enterprise integration layer 210 provides a standard application programming
interface (API)
to third parties for accessing the IoT platform 125. The enterprise
integration layer 210 also
enables the IoT platform 125 to communicate with the OT systems 163a-163n and
IT
applications 164a-164n of the enterprise 160a-160n. Thus, the enterprise
integration layer
210 enables the IoT platform 125 to receive data from the third-party
applications 211 rather
than, or in combination with, receiving the data from the edge devices 161a-
161n directly.
[0082] The data pipeline layer 215 includes one or more components
for data
cleansing/enriching, data transformation, data calculations/aggregations,
and/or API for data
streams. Accordingly, in one or more embodiments, the data pipeline layer 215
pre-processes
and/or performs initial analytics on the received data. The data pipeline
layer 215 executes
advanced data cleansing routines including, for example, data correction, mass
balance
reconciliation, data conditioning, component balancing and simulation to
ensure the desired
information is used as a basis for further processing. The data pipeline layer
215 also
provides advanced and fast computation. For example, cleansed data is run
through
enterprise-specific digital twins. According to various embodiments, the
enterprise-specific
digital twins include a reliability advisor containing process models to
determine the current
operation and the fault models to trigger any early detection and determine an
appropriate
resolution. According to various embodiments, the digital twins also include
an optimization
advisor that integrates real-time economic data with real-time process data,
selects the right
feed for a process, and determines optimal process conditions and product
yields.
[0083] According to various embodiments, the data pipeline layer
215 employs models
and templates to define calculations and analytics. Additionally or
alternatively, according to
various embodiments, the data pipeline layer 215 employs models and templates
to define
how the calculations and analytics relate to the assets (e.g., the edge
devices 161a-161n). For
example, in an embodiment, a pump template defines pump efficiency
calculations such that
every time a pump is configured, the standard efficiency calculation is
automatically
executed for the pump. The calculation model defines the various types of
calculations, the
type of engine that should run the calculations, the input and output
parameters, the
preprocessing requirement and prerequisites, the schedule, etc. According to
various
embodiments, the actual calculation or analytic logic is defined in the
template or it may be
referenced. Thus, according to various embodiments, the calculation model is
employed to
describe and control the execution of a variety of different process models.
According to
various embodiments, calculation templates are linked with the asset templates
such that
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when an asset (e.g., edge device 161a-161n) instance is created, any
associated calculation
instances are also created with their input and output parameters linked to
the appropriate
attributes of the asset (e.g., edge device 161a-161n).
[0084] According to various embodiments, the IoT platform 125
supports a variety of
different analytics models including, for example, first principles models,
empirical models,
engineered models, user-defined models, machine learning models, built-in
functions, and/or
any other types of analytics models. Fault models and predictive maintenance
models will
now be described by way of example, but any type of models may be applicable.
[0085] Fault models are used to compare current and predicted
enterprise 160a-160n
performance to identify issues or opportunities, and the potential causes or
drivers of the
issues or opportunities. The IoT platform 125 includes rich hierarchical
symptom-fault
models to identify abnormal conditions and their potential consequences. For
example, in
one or more embodiments, the IoT platform 125 drill downs from a high-level
condition to
understand the contributing factors, as well as determining the potential
impact a lower level
condition may have. There may be multiple fault models for a given enterprise
160a-160n
looking at different aspects such as process, equipment, control, and/or
operations.
According to various embodiments, each fault model identifies issues and
opportunities in
their domain, and can also look at the same core problem from a different
perspective.
According to various embodiments, an overall fault model is layered on top to
synthesize the
different perspectives from each fault model into an overall assessment of the
situation and
point to the true root cause.
[0086] According to various embodiments, when a fault or
opportunity is identified, the
IoT platform 125 provides recommendations about an optimal corrective action
to take.
Initially, the recommendations are based on expert knowledge that has been pre-
programmed
into the system by process and equipment experts. A recommendation services
module
presents this information in a consistent way regardless of source, and
supports workflows to
track, close out, and document the recommendation follow-up. According to
various
embodiments, the recommendation follow-up is employed to improve the overall
knowledge
of the system over time as existing recommendations are validated (or not) or
new cause and
effect relationships are learned by users and/or analytics.
[0087] According to various embodiments, the models are used to
accurately predict what
will occur before it occurs and interpret the status of the installed base.
Thus, the IoT
platform 125 enables operators to quickly initiate maintenance measures when
irregularities
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occur. According to various embodiments, the digital twin architecture of the
IoT platform
125 employs a variety of modeling techniques. According to various
embodiments, the
modeling techniques include, for example, rigorous models, fault detection and
diagnostics
(FDD), descriptive models, predictive maintenance, prescriptive maintenance,
process
optimization, and/or any other modeling technique.
[0088] According to various embodiments, the rigorous models are
converted from
process design simulation. In this manner, process design is integrated with
feed conditions
and production requirement. Process changes and technology improvement provide
business
opportunities that enable more effective maintenance schedule and deployment
of resources
in the context of production needs. The fault detection and diagnostics
include generalized
rule sets that are specified based on industry experience and domain knowledge
and can be
easily incorporated and used working together with equipment models. According
to various
embodiments, the descriptive models identifies a problem and the predictive
models
determines possible damage levels and maintenance options. According to
various
embodiments, the descriptive models include models for defining the operating
windows for
the edge devices 161a-161n.
[0089] Predictive maintenance includes predictive analytics models
developed based on
rigorous models and statistic models, such as, for example, principal
component analysis
(PCA) and partial least square (PLS). According to various embodiments,
machine learning
methods are applied to train models for fault prediction. According to various
embodiments,
predictive maintenance leverages FDD-based algorithms to continuously monitor
individual
control and equipment performance. Predictive modeling is then applied to a
selected
condition indicator that deteriorates in time. Prescriptive maintenance
includes determining
an optimal maintenance option and when it should be performed based on actual
conditions
rather than time-based maintenance schedule. According to various embodiments,
prescriptive analysis selects the right solution based on the company's
capital, operational,
and/or other requirements. Process optimization is determining optimal
conditions via
adjusting set-points and schedules. The optimized set-points and schedules can
be
communicated directly to the underlying controllers, which enables automated
closing of the
loop from analytics to control.
[0090] The data insight layer 220 includes one or more components
for time series
databases (TDSB), relational/document databases, data lakes, blob, files,
images, and videos,
and/or an API for data query. According to various embodiments, when raw data
is received
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at the IoT platform 125, the raw data is stored as time series tags or events
in warm storage
(e.g., in a TSDB) to support interactive queries and to cold storage for
archive purposes.
According to various embodiments, data is sent to the data lakes for offline
analytics
development. According to various embodiments, the data pipeline layer 215
accesses the
data stored in the databases of the data insight layer 220 to perform
analytics, as detailed
above.
[0091] The application services layer 225 includes one or more
components for rules
engines, workflow/notifications, KPI framework, insights (e.g., actionable
insights),
decisions, recommendations, machine learning, and/or an API for application
services. The
application services layer 225 enables building of applications 146a-d. The
applications layer
230 includes one or more applications 146a-d of the IoT platform 125. For
example,
according to various embodiments, the applications 146a-d includes a buildings
application
146a, a plants application 146b, an aero application 146c, and other
enterprise applications
146d. According to various embodiments, the applications 146 includes general
applications
146 for portfolio management, asset management, autonomous control, and/or any
other
custom applications. According to various embodiments, portfolio management
includes the
KPI framework and a flexible user interface (UI) builder. According to various
embodiments, asset management includes asset performance and asset health.
According to
various embodiments, autonomous control includes energy optimization and/or
predictive
maintenance. As detailed above, according to various embodiments, the general
applications
146 is extensible such that each application 146 is configurable for the
different types of
enterprises 160a-160n (e.g., buildings application 146a, plants application
146b, aero
application 146c, and other enterprise applications 146d).
[0092] The applications layer 230 also enables visualization of
performance of the
enterprise 160a-160n. For example, dashboards provide a high-level overview
with drill
downs to support deeper investigations. Recommendation summaries give users
prioritized
actions to address current or potential issues and opportunities. Data
analysis tools support
ad hoc data exploration to assist in troubleshooting and process improvement.
[0093] The core services layer 235 includes one or more services of
the IoT platform 125
According to various embodiments, the core services 235 include data
visualization, data
analytics tools, security, scaling, and monitoring. According to various
embodiments, the
core services 235 also include services for tenant provisioning, single
login/common portal,
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self-service admin, UIlibrary/UI tiles, identity/access/entitlements,
logging/monitoring,
usage metering, API gateway/dev portal, and the IoT platform 125 streams.
[0094] FIG. 3 illustrates a system 300 that provides an exemplary
environment according
to one or more described features of one or more embodiments of the
disclosure. According
to an embodiment, the system 300 includes an asset performance management
computer
system 302 to facilitate a practical application of data analytics technology
and/or digital
transformation technology to provide optimization related to enterprise
performance
management. In one or more embodiments, the asset performance management
computer
system 302 facilitates a practical application of metrics modeling and/or
dynamic cache
storage related to dashboard technology to provide optimization related to
enterprise
performance management In one or more embodiments, the asset performance
management
computer system 302 stores and/or analyzes data that is aggregated from one or
more assets
and/or one or more data sources associated with an enterprise system (e.g., a
building system,
an industrial system or another type of enterprise system). In one or more
embodiments, the
asset performance management computer system 302 facilitates a practical
application of a
virtual assistant related to dashboard technology to provide optimization
related to enterprise
performance management. In one or more embodiments, the asset performance
management
computer system 302 employs artificial intelligence to provide the practical
application of a
virtual assistant related to dashboard technology to provide optimization
related to enterprise
performance management.
[0095] In an embodiment, the asset performance management computer
system 302 is a
server system (e.g., a server device) that facilitates a data analytics
platform between one or
more computing devices, one or more data sources, and/or one or more assets.
In one or
more embodiments, the asset performance management computer system 302 is a
device with
one or more processors and a memory. In one or more embodiments, the asset
performance
management computer system 302 is a computer system from the computer systems
120. For
example, in one or more embodiments, the asset performance management computer
system
302 is implemented via the cloud 105. The asset performance management
computer system
302 is also related to one or more technologies, such as, for example,
enterprise technologies,
connected building technologies, industrial technologies, Internet of Things
(IoT)
technologies, data analytics technologies, digital transformation
technologies, cloud
computing technologies, cloud database technologies, server technologies,
network
technologies, private enterprise network technologies, wireless communication
technologies,
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machine learning technologies, artificial intelligence technologies, digital
processing
technologies, electronic device technologies, computer technologies, supply
chain analytics
technologies, aircraft technologies, industrial technologies, cybersecurity
technologies,
navigation technologies, asset visualization technologies, oil and gas
technologies,
petrochemical technologies, refinery technologies, process plant technologies,
procurement
technologies, and/or one or more other technologies.
[0096] Moreover, the asset performance management computer system
302 provides an
improvement to one or more technologies such as enterprise technologies,
connected building
technologies, industrial technologies, loT technologies, data analytics
technologies, digital
transformation technologies, cloud computing technologies, cloud database
technologies,
server technologies, network technologies, private enterprise network
technologies, wireless
communication technologies, machine learning technologies, artificial
intelligence
technologies, digital processing technologies, electronic device technologies,
computer
technologies, supply chain analytics technologies, aircraft technologies,
industrial
technologies, cybersecurity technologies, navigation technologies, asset
visualization
technologies, oil and gas technologies, petrochemical technologies, refinery
technologies,
process plant technologies, procurement technologies, and/or one or more other
technologies.
In an implementation, the asset performance management computer system 302
improves
performance of a computing device. For example, in one or more embodiments,
the asset
performance management computer system 302 improves processing efficiency of a
computing device (e.g., a server), reduces power consumption of a computing
device (e.g., a
server), improves quality of data provided by a computing device (e.g., a
server), etc.
[0097] The asset performance management computer system 302
includes a data
aggregation component 304, a metrics engine component 306, a prioritized
actions
component 326, a virtual assistant component 336, and/or a dashboard
visualization
component 308. Additionally, in one or more embodiments, the asset performance
management computer system 302 includes a processor 310 and/or a memory 312.
In certain
embodiments, one or more aspects of the asset performance management computer
system
302 (and/or other systems, apparatuses and/or processes disclosed herein)
constitute
executable instructions embodied within a computer-readable storage medium
(e.g., the
memory 312). For instance, in an embodiment, the memory 312 stores computer
executable
component and/or executable instructions (e.g., program instructions).
Furthermore, the
processor 310 facilitates execution of the computer executable components
and/or the
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executable instructions (e.g., the program instructions). In an example
embodiment, the
processor 310 is configured to execute instructions stored in the memory 312
or otherwise
accessible to the processor 310.
[0098] The processor 310 is a hardware entity (e.g., physically
embodied in circuitry)
capable of performing operations according to one or more embodiments of the
disclosure.
Alternatively, in an embodiment where the processor 310 is embodied as an
executor of
software instructions, the software instructions configure the processor 310
to perform one or
more algorithms and/or operations described herein in response to the software
instructions
being executed. In an embodiment, the processor 310 is a single core
processor, a multi-core
processor, multiple processors internal to the asset performance management
computer
system 302, a remote processor (e.g., a processor implemented on a server),
and/or a virtual
machine. In certain embodiments, the processor 310 is in communication with
the memory
312, the data aggregation component 304, the metrics engine component 306, the
prioritized
actions component 326, the virtual assistant component 336 and/or the
dashboard
visualization component 308 via a bus to, for example, facilitate transmission
of data among
the processor 310, the memory 312, the data aggregation component 304, the
metrics engine
component 306, the prioritized actions component 326, the virtual assistant
component 336
and/or the dashboard visualization component 308. The processor 310 may be
embodied in a
number of different ways and, in certain embodiments, includes one or more
processing
devices configured to perform independently. Additionally or alternatively, in
one or more
embodiments, the processor 310 includes one or more processors configured in
tandem via a
bus to enable independent execution of instructions, pipelining of data,
and/or multi-thread
execution of instructions.
[0099] The memory 312 is non-transitory and includes, for example,
one or more volatile
memories and/or one or more non-volatile memories. In other words, in one or
more
embodiments, the memory 312 is an electronic storage device (e.g., a computer-
readable
storage medium). The memory 312 is configured to store information, data,
content, one or
more applications, one or more instructions, or the like, to enable the asset
performance
management computer system 302 to carry out various functions in accordance
with one or
more embodiments disclosed herein. As used herein in this disclosure, the term
"component,- "system,- and the like, is a computer-related entity. For
instance, "a
component," "a system," and the like disclosed herein is either hardware,
software, or a
combination of hardware and software. As an example, a component is, but is
not limited to,
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a process executed on a processor, a processor, circuitry, an executable
component, a thread
of instructions, a program, and/or a computer entity.
[00100] In an embodiment, the asset performance management computer system 302
(e.g.,
the data aggregation component 304 of the asset performance management
computer system
302) receives asset data 314 from the edge devices 161a-161n. In one or more
embodiments,
the edge devices 161a-16ln are associated with a portfolio of assets. For
instance, in one or
more embodiments, the edge devices 161a-161n include one or more assets in a
portfolio of
assets. The edge devices 161a-161n include, in one or more embodiments, one or
more
databases, one or more assets (e.g., one or more building assets, one or more
industrial assets,
etc.), one or more IoT devices (e.g., one or more industrial IoT devices), one
or more
connected building assets, one or more sensors, one or more actuators, one or
more
processors, one or more computers, one or more valves, one or more pumps
(e.g., one or
more centrifugal pumps, etc.), one or more motors, one or more compressors,
one or more
turbines, one or more ducts, one or more heaters, one or more chillers, one or
more coolers,
one or more boilers, one or more furnaces, one or more heat exchangers, one or
more fans,
one or more blowers, one or more conveyor belts, one or more vehicle
components, one or
more cameras, one or more displays, one or more security components, one or
more HVAC
components, industrial equipment, factory equipment, and/or one or more other
devices that
are connected to the network 110 for collecting, sending, and/or receiving
information. In
one or more embodiments, the edge device 161a-161n include, or is otherwise in
communication with, one or more controllers for selectively controlling a
respective edge
device 161a-161n and/or for sending/receiving information between the edge
devices 161a-
161n and the asset performance management computer system 302 via the network
110. The
asset data 314 includes, for example, industrial data, connected building
data, sensor data,
real-time data, historical data, event data, process data, location data,
and/or other data
associated with the edge devices 161a-161n.
[00101] In certain embodiments, at least one edge device from the edge devices
161a-161n
incorporates encryption capabilities to facilitate encryption of one or more
portions of the
asset data 314 Additionally, in one or more embodiments, the asset performance
management computer system 302 (e.g., the data aggregation component 304 of
the asset
performance management computer system 302) receives the asset data 314 via
the network
110. In one or more embodiments, the network 110 is a Wi-Fi network, a Near
Field
Communications (NFC) network, a Worldwide Interoperability for Microwave
Access
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(WiMAX) network, a personal area network (PAN), a short-range wireless network
(e.g., a
Bluetooth network), an infrared wireless (e.g., IrDA) network, an ultra-
wideband (UWB)
network, an induction wireless transmission network, and/or another type of
network. In one
or more embodiments, the edge devices 161a-161n are associated with an
industrial
environment (e.g., a plant, etc.). Additionally or alternatively, in one or
more embodiments,
the edge devices 161a-161n are associated with components of the edge 115 such
as, for
example, one or more enterprises 160a-160n.
[00102] In one or more embodiments, the data aggregation component 304
aggregates the
asset data 314 from the edge devices 161a-161n. For instance, in one or more
embodiments,
the data aggregation component 304 aggregates the asset data 314 into a
centralized control
database 318 configured as a database structure. The centralized control
database 318 is a
cache memory (e.g., a dynamic cache) that dynamically stores the asset data
314 based on
interval of time and/or asset hierarchy level. For instance, in one or more
embodiments, the
centralized control database 318 stores the asset data 314 for one or more
intervals of time
(e.g., 1 minute to 12 minutes, 1 hour to 24 hours, 1 day to 31 days, 1 month
to 12 months,
etc.) and/or for one or more asset hierarchy levels (e.g., asset level, asset
zone, building
level, building zone, plant level, plant zone, industrial site level, etc.).
In a non-limiting
embodiment, the centralized control database 318 stores the asset data 314 for
a first interval
of time (e.g., 1 hour to 24 hours minutes) for a first asset (e.g., a first
asset hierarchy level),
for a second interval of time (e.g., 1 day to 31 days) for the first asset,
and for a third interval
of time (e.g., 1 month to 12 months) for the first asset.
[00103] In an example embodiment, the centralized control database 318 stores
the asset
data 314 for the first interval of time (e.g., 1 hour to 24 hours minutes) for
all assets in a
connected building (e.g., a second asset hierarchy level), for the second
interval of time (e.g.,
1 day to 31 days) for all the assets in the connected building, and for the
third interval of time
(e.g., 1 month to 12 months) for the all the assets in the connected building.
In the example
embodiment, the centralized control database 318 also stores the asset data
314 for the first
interval of time (e.g., 1 hour to 24 hours minutes) for all connected
buildings within a
particular geographic region (e.g., a third asset hierarchy level), for the
second interval of
time (e.g., 1 day to 31 days) for all connected buildings within the
particular geographic
region, and for the third interval of time (e.g., 1 month to 12 months) for
all connected
buildings within the particular geographic region.
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[00104] In another example embodiment, the centralized control database 318
stores the
asset data 314 for the first interval of time (e.g., 1 hour to 24 hours
minutes) for all assets in a
plant (e.g., a second asset hierarchy level), for the second interval of time
(e.g., 1 day to 31
days) for all the assets in the plant, and for the third interval of time
(e.g., 1 month to 12
months) for the all the assets in the plant. In the example embodiment, the
centralized control
database 318 also stores the asset data 314 for the first interval of time
(e.g., 1 hour to 24
hours minutes) for all plants at an industrial site (e.g., a third asset
hierarchy level), for the
second interval of time (e.g., 1 day to 31 days) for all plants at the
industrial site, and for the
third interval of time (e.g., 1 month to 12 months) for all plants at the
industrial site.
[00105] In one or more embodiments, the data aggregation component 304
repeatedly
updates data of the centralized control database 318 based on the asset data
314 provided by
the edge devices 161a-161n during the one or more intervals of time associated
with the
centralized control database 318. For instance, in one or more embodiments,
the data
aggregation component 304 stores new data and/or modified data associated with
the asset
data 314. In one or more embodiments, the data aggregation component 304
repeatedly scans
the edge devices 161a-161n to determine new data for storage in the
centralized control
database 318. In one or more embodiments, the data aggregation component 304
formats one
or more portions of the asset data 314. For instance, in one or more
embodiments, the data
aggregation component 304 provides a formatted version of the asset data 314
to the
centralized control database 318. In an embodiment, the formatted version of
the asset data
314 is formatted with one or more defined formats associated with the one or
more intervals
of time and/or the one or more asset hierarchy levels. A defined format is,
for example, a
structure for data fields of the centralized control database 318. In various
embodiments, the
formatted version of the asset data 314 is stored in the centralized control
database 318.
[00106] In one or more embodiments, the data aggregation component 304
identifies
and/or groups data types associated with the asset data 314 based on the one
or more intervals
of time (e.g., one or more reporting intervals of time) and/or the one or more
asset hierarchy
levels. In one or more embodiments, the data aggregation component 304 employs
batching,
concatenation of the asset data 314, identification of data types, merging of
the asset data
314, grouping of the asset data 314, reading of the asset data 314 and/or
writing of the asset
data 314 to facilitate storage of the asset data 314 within the centralized
control database 318.
In one or more embodiments, the data aggregation component 304 groups data
from the asset
data 314 based on corresponding features and/or attributes of the data. In one
or more
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embodiments, the data aggregation component 304 groups data from the asset
data 314 based
on corresponding identifiers (e.g., a matching asset hierarchy level, a
matching asset, a
matching connected building, etc.) for the asset data 314. In one or more
embodiments, the
data aggregation component 304 employs one or more locality-sensitive hashing
techniques
to group data from the asset data 314 based on similarity scores and/or
calculated distances
between different data in the asset data 314.
[00107] In one or more embodiments, the data aggregation component 304
organizes the
formatted version of the asset data 314 based on a time series mapping of
attributes for the
asset data 314. For instance, in one or more embodiments, the data aggregation
component
304 employs a hierarchical data format technique to organize the formatted
version of the
asset data 314 in the centralized control database 318. In one or more
embodiments, the
centralized control database 318 dynamically stores data (e.g., one or more
portions of the
asset data 314) based on type of data presented via a dashboard visualization.
In one or more
embodiments, data (e.g., one or more portions of the asset data 314)
aggregated from the
edge devices 161a-161n is converted into one or more metrics (e.g., a KPI
metric, a duty KPI,
a duty target KPI) prior to being stored in the centralized control database
318. In one or
more embodiments, a metric (e.g. a KP metrics) consists of aspect data
indicative of an aspect
employed in a model to map an attribute to the metric (e.g., an operating
power asset type
attribute is mapped to a duty aspect, etc.), aggregation data indicative of
information related
to aggregation across time, rollup data indicative of an aggregate metric of
an asset across an
asset at one level as well as across a hierarchy asset, low limit data
indicative of a low-limit
constant derived from a digital twin model in real-time, high limit data
indicative of a high-
limit constant derived from a digital twin model in real-time, target data
indicative of a target
constant derived from a digital twin model in real-time, custom calculation
data indicative of
information related to custom calculations using aggregate data across time or
asset, and/or
other data related to the metric.
[00108] In one or more embodiments, the asset performance management computer
system 302 (e.g., the prioritized actions component 326 of the asset
performance
management computer system 302) receives a request 320. In an embodiment, the
request
320 is a request to generate a dashboard visualization associated with a
portfolio of assets.
For instance, in one or more embodiments, the request 320 is a request to
generate a
dashboard visualization associated with the edge devices 161a-161n (e.g., the
edge devices
161a-161n included in a portfolio of assets).
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[00109] In one or more embodiments, the request 320 includes one or more asset
descriptors that describe one or more assets in the portfolio of assets. For
instance, in one or
more embodiments, the request 320 includes one or more asset descriptors that
describe the
edge devices 161a-161n. An asset descriptor includes, for example, an asset
name, an asset
identifier, an asset level and/or other information associated with an asset.
Additionally or
alternatively, in one or more embodiments, the request 320 includes one or
more user
identifiers describing a user role for a user associated with access of a
dashboard
visualization. A user identifier includes, for example, an identifier for a
user role name (e.g.,
a manager, an executive, a maintenance engineer, a process engineer, etc.).
Additionally or
alternatively, in one or more embodiments, the request 320 includes one or
more metrics
context identifiers describing context for the metrics. A metrics context
identifier includes,
for example, an identifier for a plant performance metric, an asset
performance metric, a goal
(e.g., review production related to one or more assets, etc.). Additionally or
alternatively, in
one or more embodiments, the request 320 includes one or more time interval
identifier
describing an interval of time for the metrics. A time interval identifier
describes, for
example, an interval of time for aggregated data such as hourly, daily,
monthly, yearly etc. In
one or more embodiments, a time interval identifier is a reporting time
identifier describing
an interval of time for the metrics.
[00110] In one or more embodiments, the request 320 is a voice input. In an
embodiment,
the voice input includes and/or initiates a request to generate a dashboard
visualization
associated with the portfolio of assets. For instance, in one or more
embodiments, the voice
input includes and/or initiates a request to generate a dashboard
visualization associated with
the edge devices 161a-161n (e.g., the edge devices 161a-161n included in a
portfolio of
assets). In one or more embodiments, the voice input comprises voice input
data associated
with the request to generate the dashboard visualization. For example, in one
or more
embodiments, the voice input data associated with the voice input comprises
one or more
asset insight requests associated with the portfolio of assets. In an
embodiment, the one or
more asset insight requests include a phrase provided via the voice input
data. In another
embodiment, the one or more asset insight requests include a question provided
via the voice
input data. For instance, in an embodiment, a user can speak a phrase or a
question via a
computing device to provide the voice input data associated with the voice
input.
[00111] In one or more embodiments, the voice input includes one or more
attributes (e.g.,
asset insight attributes, a metrics context identifier, etc.) associated with
the one or more asset
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insight requests. For instance, in one or more embodiments, the voice input
includes, for
example, an identifier for a plant performance metric, an asset performance
metric indicator,
a goal indicator, etc. In an example, for a phrase "What was the production
and quality of
product A?", the word "production" can be a first attribute and the word
"quality" can be a
second attribute. In one or more embodiments, the voice input additionally or
alternatively
includes one or more asset descriptors that describe one or more assets in the
portfolio of
assets. For instance, in one or more embodiments, the voice input additionally
or
alternatively includes one or more asset descriptors that describe the edge
devices 161a-161n.
An asset descriptor includes, for example, an asset name, an asset identifier,
an asset level
and/or other information associated with an asset. Additionally or
alternatively, in one or
more embodiments, the voice input includes the one or more user identifiers
describing a user
role for a user associated with access of a dashboard visualization
Additionally or
alternatively, in one or more embodiments, the voice input includes time data
describing a
time and/or an interval of time for the metrics and/or one or more asset
insights.
[00112] In one or more embodiments, in response to the request 320, the
metrics engine
component 306 determines one or more metrics for an asset hierarchy associated
with the
portfolio of assets. For instance, in one or more embodiments, the metrics
engine component
306 determines one or more metrics for an asset hierarchy associated with the
edge devices
161a-161n in response to the request 320. In one or more embodiments, the
metrics engine
component 306 converts a portion of the asset data 314 into a metric for the
portion of the
asset data 314 and stores the metric for the portion of the asset data 314
into the centralized
control database 318. In one or more embodiments, the metrics engine component
306
determines the one or more metrics for the asset hierarchy based on a model
related to a time
series mapping of attributes for the asset data 314. For example, in one or
more
embodiments, the metrics engine component 306 determines the one or more
metrics for the
asset hierarchy based on time series mapping of attributes for the asset data
314 with respect
to the centralized control database 318.
[00113] In one or more embodiments, in response to the request 320, the
prioritized
actions component 326 determines prioritized actions for the portfolio of
assets based on
attributes for the aggregated data stored in the centralized control database
318. In an
embodiment, the prioritized actions indicate which assets from the portfolio
of assets should
be serviced first. For example, in an embodiment, the prioritized actions
indicate a first asset
from the portfolio of assets that should be serviced first, a second asset
from the portfolio of
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assets that should be serviced second, a third asset from the portfolio of
assets that should be
serviced third, etc. In one or more embodiments, the prioritized actions is a
list of prioritized
actions for the portfolio of assets based on impact to the portfolio. For
instance, in one or
more embodiments, the prioritized actions component 326 ranks, based on impact
of
respective prioritized actions with respect to the portfolio of assets, the
prioritized actions to
generate the list of the prioritized actions. In one or more embodiments, the
prioritized
actions component 326 groups the prioritized actions for the portfolio of
assets based on
relationships, features, and/or attributes between the aggregated data. In one
or more
embodiments, the prioritized actions component 326 determines the prioritized
actions for the
portfolio of assets based on a digital twin model associated with one or more
assets from the
portfolio of assets. Additionally or alternatively, in one or more
embodiments, the prioritized
actions component 326 determines the prioritized actions for the portfolio of
assets based on
a digital twin model associated with an operator identity associated with one
or more assets
from the portfolio of assets.
[00114] In one or more embodiments, the prioritized actions component 326
determines
the list of the prioritized actions for the portfolio of assets based on
metrics associated with
the aggregated data. In certain embodiments, in response to the request 320,
the prioritized
actions component 326 determines one or more metrics for an asset hierarchy
associated with
the portfolio of assets. For instance, in one or more embodiments, the
prioritized actions
component 326 determines one or more metrics for an asset hierarchy associated
with the
edge devices 161a-161n in response to the request 320. In one or more
embodiments, the
prioritized actions component 326 converts a portion of the asset data 314
into a metric for
the portion of the asset data 314 and stores the metric for the portion of the
asset data 314 into
the centralized control database 318. In one or more embodiments, the
prioritized actions
component 326 determines the one or more metrics for the asset hierarchy based
on a model
related to a time series mapping of attributes, features, and/or relationships
for the asset data
314 For example, in one or more embodiments, the prioritized actions component
326
determines the one or more metrics for the asset hierarchy based on time
series mapping of
attributes, features, and/or relationships for the asset data 314 with respect
to the centralized
control database 318.
[00115] In one or more embodiments, in response to the request 320, the
virtual assistant
component 336 performs a natural language query with respect to the voice
input data to
obtain the one or more attributes associated with the one or more asset
insight requests. For
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example, in one or more embodiments, the virtual assistant component 336
performs natural
language processing with respect to the voice input data to obtain the one or
more attributes
associated with the one or more asset insight requests. In one or more
embodiments, the
virtual assistant component 336 converts the voice input data into a text
string such that the
text string associated with one or more textual elements. In one or more
embodiments, the
virtual assistant component 336 employs natural language processing (e.g., one
or more
natural language processing techniques) to determine textual data associated
with the voice
input data. In one or more embodiments, the virtual assistant component 336
queries a
natural language database based on the voice input to determine the one or
more attributes
associated with the one or more asset insight requests. In one or more
embodiments, the
virtual assistant component 336 provides the one or more attributes, one or
more tags, one or
more labels, one or more classifications, and/or one or more other inferences
with respect to
the voice input data. For example, in one or more embodiments, the virtual
assistant
component 336 performs part-of-speech tagging with respect to the voice input
data to obtain
the one or more attributes, one or more tags, one or more labels, one or more
classifications,
and/or one or more other inferences with respect to the voice input data. In
one or more
embodiments, the virtual assistant component 336 performs one or more natural
language
processing queries with respect to the centralized control database 318 based
on the one or
more tags, the one or more labels, the one or more classifications, the one or
more attributes,
and/or the one or more other inferences with respect to the voice input data.
[00116] In one or more embodiments, the virtual assistant component 336
employs one or
more machine learning techniques to facilitate determination of the one or
more attributes,
the one or more tags, the one or more labels, the one or more classifications,
and/or the one or
more other inferences with respect to the voice input data. For instance, in
one or more
embodiments, the virtual assistant component 336 performs a fuzzy matching
technique with
respect to the voice input data to determine the one or more attributes
associated with the one
or more asset insight requests. Additionally or alternatively, in one or more
embodiments,
the virtual assistant component 336 provides the voice input data to a neural
network model
configured for determining the one or more attributes associated with the one
or more asset
insight requests.
[00117] In one or more embodiments, the virtual assistant component 336
obtains
aggregated data associated with the portfolio of assets based on the one or
more attributes, the
one or more labels, the one or more tags, the one or more classifications, /or
the one or more
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other inferences with respect to the voice input data. Additionally, in one or
more
embodiments, the virtual assistant component 336 determines one or more asset
insights for
the portfolio of assets based on the aggregated data. In one or more
embodiments, the virtual
assistant component 336 groups, based on the one or more attributes, the
aggregated data
based on one or more relationships between assets from the portfolio of
assets. In one or
more embodiments, the virtual assistant component 336 applies the one or more
attributes to
at least a first model associated with a first type of asset insight and a
second model
associated with a second type of asset insight. In one or more embodiments,
the virtual
assistant component 336 aggregates first output data from the first model and
second output
data from the second model to determine at least a portion of the aggregated
data. In one or
more embodiments, in response to the voice input, the virtual assistant
component 336
determines prioritized actions for the portfolio of assets based on the one or
more attributes.
In certain embodiments, in response to the voice input, the virtual assistant
component 336
determines one or more metrics for an asset hierarchy associated with the
portfolio of assets.
For instance, in one or more embodiments, the virtual assistant component 336
determines
one or more metrics for an asset hierarchy associated with the edge devices
161a-161n in
response to the voice input.
[00118] In one or more embodiments, in response to the request 320, the
dashboard
visualization component 308 generates dashboard visualization data 322
associated with the
one or more metrics for the asset hierarchy. For instance, in one or more
embodiments, the
dashboard visualization component 308 provides the dashboard visualization to
an electronic
interface of a computing device based on the dashboard visualization data 322.
In one or
more embodiments, the dashboard visualization data 322 and/or the dashboard
visualization
associated with the dashboard visualization data 322 includes the metrics for
an asset
hierarchy associated with the portfolio of assets. In one or more embodiments,
in response to
the request 320, the dashboard visualization component 308 associates aspects
of the asset
data 314 and/or metrics associated with the asset data 314 stored in the
centralized control
database 318 to provide the one or more metrics. For example, in one or more
embodiment,
in response to the voice input, the dashboard visualization component 308
associates aspects
of the asset data 314 and/or metrics associated with the asset data 314 stored
in the
centralized control database 318 to provide the one or more metrics. In an
aspect, the
dashboard visualization component 308 determines the aspects of the asset data
314 and/or
metrics associated with the asset data 314 stored in the centralized control
database 318 based
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on the time series structure and/or the hierarchy structure of asset level of
the centralized
control database 318.
[00119] In one or more embodiments, the dashboard visualization data 322
and/or the
dashboard visualization associated with the dashboard visualization data 322
includes the
prioritized actions for the portfolio of assets. In one or more embodiments,
the dashboard
visualization data 322 and/or the dashboard visualization associated with the
dashboard
visualization data 322 includes the list of the prioritized actions. In one or
more
embodiments, the dashboard visualization data 322 and/or the dashboard
visualization
associated with the dashboard visualization data 322 includes the grouping of
the prioritized
actions for the portfolio of assets. In one or more embodiments, the dashboard
visualization
data 322 and/or the dashboard visualization associated with the dashboard
visualization data
322 includes the metrics for an asset hierarchy associated with the portfolio
of assets
[00120] In one or more embodiments, in response to the voice input, the
dashboard
visualization component 308 generates the dashboard visualization data 322
associated with
the one or more metrics for the asset hierarchy. In one or more embodiments,
the dashboard
visualization data 322 and/or the dashboard visualization associated with the
dashboard
visualization data 322 is configured based on the one or more attributes
associated with the
voice input. In one or more embodiments, the dashboard visualization data 322
and/or the
dashboard visualization associated with the dashboard visualization data 322
includes a
dashboard visualization element configured to present sensor data related to
the portfolio of
assets, a dashboard visualization element configured to present control data
related to the
portfolio of assets, a dashboard visualization element configured to present
labor
management data related to the portfolio of assets, a dashboard visualization
element
configured to present warehouse execution data related to the portfolio of
assets, a dashboard
visualization element configured to present inventory data related to the
portfolio of assets, a
dashboard visualization element configured to present warehouse management
data related to
the portfolio of assets, a dashboard visualization element configured to
present machine
control data related to the portfolio of assets, and/or one or more other
dashboard
visualization elements associated with the one or more asset insights.
[00121] Additionally, in one or more embodiments, the dashboard visualization
component 308 performs one or more actions based on the metrics. For instance,
in one or
more embodiments, the dashboard visualization component 308 generates
dashboard
visualization data 322 associated with the one or more actions. In an
embodiment, an action
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includes generating a user-interactive electronic interface that renders a
visual representation
of the one or more metrics. In another embodiment, an action from the one or
more actions
includes transmitting, to a computing device, one or more notifications
associated with the
one or more metrics. In another embodiment, an action from the one or more
actions
includes providing an optimal process condition for an asset associated with
the asset data
314. For example, in another embodiment, an action from the one or more
actions includes
adjusting a set-point and/or a schedule for an asset associated with the asset
data 314. In
another embodiment, an action from the one or more actions includes one or
more corrective
action to take for an asset associated with the asset data 314. In another
embodiment, an
action from the one or more actions includes providing an optimal maintenance
option for an
asset associated with the asset data 314. In another embodiment, an action
from the one or
more actions includes an action associated with the application services layer
225, the
applications layer 230, and/or the core services layer 235.
[00122] Additionally, in one or more embodiments, the dashboard visualization
component 308 performs one or more actions based on the prioritized actions
for the portfolio
of assets. In an embodiment, an action includes generating a user-interactive
electronic
interface that renders a visual representation of the prioritized actions for
the portfolio of
assets and/or the one or more metrics. In another embodiment, an action from
the one or
more actions includes transmitting, to a computing device, one or more
notifications
associated with the prioritized actions for the portfolio of assets and/or the
one or more
metrics. In one or more embodiments, the dashboard visualization data 322
and/or the
dashboard visualization associated with the dashboard visualization data 322
configures the
dashboard visualization for remote control of one or more assets from the
portfolio of assets
based on the one or more attributes associated with the voice input. In one or
more
embodiments, the dashboard visualization data 322 and/or the dashboard
visualization
associated with the dashboard visualization data 322 configures a three-
dimensional (3D)
model of an asset from the portfolio of assets for the dashboard visualization
based on the one
or more attributes associated with the voice input (e.g., the voice input
associated with the
request 320) In one or more embodiments, the dashboard visualization data 322
and/or the
dashboard visualization associated with the dashboard visualization data 322
filters one or
more events associated with the asset related to the 3D model based on the one
or more
attributes associated with the voice input. In one or more embodiments, the
dashboard
visualization data 322 and/or the dashboard visualization associated with the
dashboard
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visualization data 322 configures the dashboard visualization for real-time
collaboration
between two or more computing devices based on the one or more attributes
associated with
the voice input.
[00123] FIG. 4 illustrates a system 300' that provides an exemplary
environment
according to one or more described features of one or more embodiments of the
disclosure.
In an embodiment, the system 300' corresponds to an alternate embodiment of
the system
300 shown in FIG. 3. According to an embodiment, the system 300' includes the
asset
performance management computer system 302, the edge devices 161a-161n, the
centralized
control database 318 and/or a computing device 402. In one or more
embodiments, the asset
performance management computer system 302 is in communication with the edge
devices
161a-161n and/or the computing device 402 via the network 110. The computing
device 402
is a mobile computing device, a smartphone, a tablet computer, a mobile
computer, a desktop
computer, a laptop computer, a workstation computer, a wearable device, a
virtual reality
device, an augmented reality device, or another type of computing device
located remote
from the asset performance management computer system 302. In one or more
embodiments, the computing device 402 generates the request 320. For example,
in one or
more embodiments, the request 320 is generated via a visual display (e.g., a
user interface) of
the computing device 402. In one or more embodiments, the computing device 402
generates
the voice input. For example, in one or more embodiments, the voice input
(e.g., the voice
input associated with the request 320) is generated via one or more
microphones of the
computing device 402 and/or one or more microphones communicatively coupled to
the
computing device 402.
[00124] In one or more embodiments, the dashboard visualization component 308
communicates the dashboard visualization data 322 to the computing device 402.
For
example, in one or more embodiments, the dashboard visualization data 322
includes one or
more visual elements for a visual display (e.g., a user-interactive electronic
interface) of the
computing device 402 that renders a visual representation of the one or more
metrics. In one
or more embodiments, the dashboard visualization data 322 includes one or more
visual
elements for a visual display (e.g., a user-interactive electronic interface)
of the computing
device 402 that renders a visual representation of the prioritized actions for
the portfolio of
assets and/or the one or more metrics associated with the portfolio of assets.
In certain
embodiments, the visual display of the computing device 402 displays one or
more graphical
elements associated with the dashboard visualization data 322 (e.g., the one
or more metrics).
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In another example, in one or more embodiments, the dashboard visualization
data 322
includes one or notifications associated with the one or more metrics and/or
the prioritized
actions for the portfolio of assets. In one or more embodiments, the dashboard
visualization
data 322 allows a user associated with the computing device 402 to make
decisions and/or
perform one or more actions with respect to the prioritized actions for the
portfolio of assets
and/or the one or more metrics associated with the portfolio of assets. In one
or more
embodiments, the dashboard visualization data 322 allows a user associated
with the
computing device 402 to control the one or more portions of the assets of the
portfolio of
assets (e.g., one or more portions of the edge devices 161a-161n). In one or
more
embodiments, the dashboard visualization data 322 allows a user associated
with the
computing device 402 to generate one or more work orders for the one or more
assets of the
portfolio of assets.
[00125] FIG. 5 illustrates a system 500 according to one or more embodiments
of the
disclosure. The system 500 includes the computing device 402. In one or more
embodiments, the computing device 402 employs mobile computing, augmented
reality,
cloud-based computing, IoT technology and/or one or more other technologies to
provide
performance data, video, audio, text, graphs, charts, real-time data,
graphical data, one or
more communications, one or more messages, one or more notifications, and/or
other media
data associated with the one or more metrics. The computing device 402
includes
mechanical components, electrical components, hardware components and/or
software
components to facilitate determining prioritized actions and/or one or more
metrics
associated with the asset data 314. In the embodiment shown in FIG. 5, the
computing
device 402 includes a visual display 504, one or more speakers 506, one or
more cameras
508, one or more microphones 510, a global positioning system (GPS) device
512, a
gyroscope 514, one or more wireless communication devices 516, and/or a power
supply 518.
[00126]
In an embodiment, the visual display 504 is a display that facilitates
presentation
and/or interaction with one or more portions of the dashboard visualization
data 322. In one
or more embodiments, the computing device 402 displays an electronic interface
(e.g., a
graphical user interface) associated with an asset performance management
platform In one
or more embodiments, the visual display 504 is a visual display that renders
one or more
interactive media elements via a set of pixels. The one or more speakers 506
include one or
more integrated speakers that project audio. The one or more cameras 508
include one or
more cameras that employ autofocus and/or image stabilization for photo
capture and/or real-
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time video. The one or more microphones 510 include one or more digital
microphones that
employ active noise cancellation to capture audio data. In one or more
embodiments, at least
a portion of the voice input is generated via the one or more microphones 510.
The GPS
device 512 provides a geographic location for the computing device 402. The
gyroscope 514
provides an orientation for the computing device 402. The one or more wireless
communication devices 516 includes one or more hardware components to provide
wireless
communication via one or more wireless networking technologies and/or one or
more short-
wavelength wireless technologies. The power supply 518 is, for example, a
power supply
and/or a rechargeable battery that provides power to the visual display 504,
the one or more
speakers 506, the one or more cameras 508, the one or more microphones 510,
the GPS
device 512, the gyroscope 514, and/or the one or more wireless communication
devices 516.
In certain embodiments, the dashboard visualization data 322 associated with
the one or more
metrics, the prioritized actions and/or the one or more asset insights related
to the portfolio of
assets is presented via the visual display 504 and/or the one or more speakers
506.
[00127] FIG. 6 illustrates a system 600 according to one or more described
features of one
or more embodiments of the disclosure. In an embodiment, the system 600
includes a non-
limiting embodiment of the centralized control database 318. In one or more
embodiments,
the centralized control database 318 stores data (e.g., one or more portions
of the asset data
314) aggregated from the edge devices 161a-161n. The centralized control
database 318 is a
cache memory (e.g., a database structure) that dynamically stores data (e.g.,
one or more
portions of the asset data 314) based on interval of time and/or asset
hierarchy level. For
instance, in one or more embodiments, the centralized control database 318
stores data (e.g.,
one or more portions of the asset data 314) for one or more intervals of time
(e.g., 1 minute to
12 minutes, 1 hour to 24 hours, 1 day to 31 days, 1 month to 12 months, etc.)
and/or for one
or more asset hierarchy levels (e.g., asset level, plant level, industrial
site level, etc.). In one
or more embodiments, the centralized control database 318 is related to a time
series mapping
of attributes for data (e.g., one or more portions of the asset data 314)
aggregated from the
edge devices 161a-161n. In one or more embodiments, the centralized control
database 318
dynamically stores data (e.g., one or more portions of the asset data 314)
based on type of
data presented via a dashboard visualization. In one or more embodiments, data
(e.g., one or
more portions of the asset data 314) aggregated from the edge devices 161a-
161n is
converted into one or more metrics (e.g., a KPI metric, a duty KPI, a duty
target KPI) prior to
being stored in the centralized control database 318. In one or more
embodiments, a metric
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(e.g. a KP metrics) consists of aspect data indicative of an aspect employed
in a model to map
an attribute to the metric (e.g., an operating power asset type attribute is
mapped to a duty
aspect, etc.), aggregation data indicative of information related to
aggregation across time,
rollup data indicative of an aggregate metric of an asset across an asset at
one level as well as
across a hierarchy asset, low limit data indicative of a low-limit constant
derived from a
digital twin model in real-time, high limit data indicative of a high-limit
constant derived
from a digital twin model in real-time, target data indicative of a target
constant derived from
a digital twin model in real-time, custom calculation data indicative of
information related to
custom calculations using aggregate data across time or asset, and/or other
data related to the
metric.
[00128] In an embodiment illustrated in FIG. 6, the centralized
control database 318
includes a first set of data structures 602 associated with a first asset
hierarchy level (e.g., a
site) in an industrial environment, a second set of data structures 604
associated with a second
asset hierarchy level (e.g., a plant) in the industrial environment, and a
third set of data
structures 606 associated with a third asset hierarchy level (e.g., units) in
the industrial
environment. For instance, in one or more embodiments, the centralized control
database 318
organizes and/or stores data for the first asset hierarchy level (e.g., the
site) in the first set of
data structures 602. In an embodiment, the data aggregation component 304
aggregates
and/or repeatedly updates data for the first asset hierarchy level (e.g., the
site) per interval of
time. In one or more embodiments, the data aggregation component 304
repeatedly
aggregates data for the first asset hierarchy level (e.g., the site) per hour
and stores the
aggregated data in a first data structure 608 of the first set of data
structures 602 until an end
of a first cycle (e.g., an end of a 24 hour cycle) is satisfied. Additionally,
in one or more
embodiments, the data aggregation component 304 repeatedly aggregates data for
the first
asset hierarchy level (e.g., the site) per day and stores the aggregated data
in a second data
structure 610 of the first set of data structures 602 until an end of a second
cycle (e.g., an end
of a 31 day cycle) is satisfied. In one or more embodiments, the data
aggregation component
304 also repeatedly aggregates data for the first asset hierarchy level (e.g.,
the site) per month
and stores the aggregated data in a third data structure 612 of the first set
of data structures
602 until an end of a third cycle (e.g., an end of a 12 month cycle) is
satisfied. In one or more
embodiments, a change to the first data structure 608 initiates a change to
the second data
structure 610 and/or the third data structure 612. Additionally or
alternatively, in one or more
embodiments, a change to the second data structure 610 initiates a change to
the first data
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structure 608 and/or the third data structure 612. Additionally or
alternatively, in one or more
embodiments, a change to the third data structure 612 initiates a change to
the first data
structure 608 and/or the second data structure 610.
[00129] In one or more embodiments, a portion of the data in the first data
structure 608 is
moved within the first data structure 608 and/or into another data structure
(e.g., the second
data structure 610) in response to an end of a cycle being satisfied. For
instance, in an
embodiment, a portion of the data in the first data structure 608 that
corresponds to a data
field for an interval of time from 0-1 hour is moved to another data field in
the first data
structure 608 for an interval of time from 1-2 hour in response to a cycle
that corresponds to
the interval of time from 0-1 hour being satisfied. In another embodiment, a
portion of the
data in the first data structure 608 that corresponds to data fields for an
interval of time from
0-24 hours is moved to another data field in the second data structure 610 for
an interval of
time from 0-1 day in response to a cycle that corresponds to the interval of
time from 0-24
hours being satisfied. Similarly, in one or more embodiments, a portion of the
data in the
second data structure 610 is moved within the second data structure 610 and/or
into another
data structure (e.g., the third data structure 612) in response to an end of a
cycle being
satisfied. For instance, in an embodiment, a portion of the data in the second
data structure
610 that corresponds to a data field for an interval of time from 0-1 day is
moved to another
data field in the second data structure 610 for an interval of time from 1-2
day in response to
a cycle that corresponds to the interval of time from 0-1 day being satisfied.
In another
embodiment, a portion of the data in the second data structure 610 that
corresponds to data
fields for an interval of time from 0-31 days is moved to another data field
in the third data
structure 612 for an interval of time from 0-1 month in response to a cycle
that corresponds to
the interval of time from 0-31 days being satisfied. Similarly, in one or more
embodiments, a
portion of the data in the third data structure 612 is moved within the third
data structure 612
and/or into another data structure in response to an end of a cycle being
satisfied. For
instance, in an embodiment, a portion of the data in the third data structure
612 that
corresponds to a data field for an interval of time from 0-1 month is moved to
another data
field in the third data structure 612 for an interval of time from 1-2 months
in response to a
cycle that corresponds to the interval of time from 0-1 month being satisfied.
[00130] Additionally, in one or more embodiments, the centralized control
database 318
organizes and/or stores data for the second asset hierarchy level (e.g., the
plant) in the second
set of data structures 604. In an embodiment, the data aggregation component
304 aggregates
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and/or repeatedly updates data for the second asset hierarchy level (e.g., the
plant) per
interval of time. In one or more embodiments, the data aggregation component
304
repeatedly aggregates data for the second asset hierarchy level (e.g., the
plant) per hour and
stores the aggregated data in a first data structure 614 of the second set of
data structures 604
until an end of a first cycle (e.g., an end of a 24 hour cycle) is satisfied.
Additionally, in one
or more embodiments, the data aggregation component 304 repeatedly aggregates
data for the
second asset hierarchy level (e.g., the plant) per day and stores the
aggregated data in a
second data structure 616 of the second set of data structures 604 until an
end of a second
cycle (e.g., an end of a 31 day cycle) is satisfied. In one or more
embodiments, the data
aggregation component 304 also repeatedly aggregates data for the second asset
hierarchy
level (e.g., the plant) per month and stores the aggregated data in a third
data structure 618 of
the second set of data structures 604 until an end of a third cycle (e.g., an
end of a 12 month
cycle) is satisfied. In one or more embodiments, a change to the first data
structure 614
initiates a change to the second data structure 616 and/or the third data
structure 618.
Additionally or alternatively, in one or more embodiments, a change to the
second data
structure 616 initiates a change to the first data structure 614 and/or the
third data structure
618. Additionally or alternatively, in one or more embodiments, a change to
the third data
structure 618 initiates a change to the first data structure 614 and/or the
second data structure
616.
[00131] In one or more embodiments, a portion of the data in the first data
structure 614 is
moved within the first data structure 614 and/or into another data structure
(e.g., the second
data structure 616) in response to an end of a cycle being satisfied. For
instance, in an
embodiment, a portion of the data in the first data structure 614 that
corresponds to a data
field for an interval of time from 0-1 hour is moved to another data field in
the first data
structure 614 for an interval of time from 1-2 hour in response to a cycle
that corresponds to
the interval of time from 0-1 hour being satisfied. In another embodiment, a
portion of the
data in the first data structure 614 that corresponds to data fields for an
interval of time from
0-24 hours is moved to another data field in the second data structure 616 for
an interval of
time from 0-1 day in response to a cycle that corresponds to the interval of
time from 0-24
hours being satisfied. Similarly, in one or more embodiments, a portion of the
data in the
second data structure 616 is moved within the second data structure 616 and/or
into another
data structure (e.g., the third data structure 618) in response to an end of a
cycle being
satisfied. For instance, in an embodiment, a portion of the data in the second
data structure
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616 that corresponds to a data field for an interval of time from 0-1 day is
moved to another
data field in the second data structure 616 for an interval of time from 1-2
day in response to
a cycle that corresponds to the interval of time from 0-1 day being satisfied.
In another
embodiment, a portion of the data in the second data structure 616 that
corresponds to data
fields for an interval of time from 0-31 days is moved to another data field
in the third data
structure 618 for an interval of time from 0-1 month in response to a cycle
that corresponds to
the interval of time from 0-31 days being satisfied. Similarly, in one or more
embodiments, a
portion of the data in the third data structure 618 is moved within the third
data structure 618
and/or into another data structure in response to an end of a cycle being
satisfied. For
instance, in an embodiment, a portion of the data in the third data structure
618 that
corresponds to a data field for an interval of time from 0-1 month is moved to
another data
field in the third data structure 618 for an interval of time from 1-2 months
in response to a
cycle that corresponds to the interval of time from 0-1 month being satisfied.
[00132] Additionally, in one or more embodiments, the centralized control
database 318
organizes and/or stores data for the third asset hierarchy level (e.g., the
units) in the third set
of data structures 606. In an embodiment, the data aggregation component 304
aggregates
and/or repeatedly updates data for the third asset hierarchy level (e.g., the
assets) per interval
of time. In one or more embodiments, the data aggregation component 304
repeatedly
aggregates data for the third asset hierarchy level (e.g., the assets) per
hour and stores the
aggregated data in a first data structure 620 of the third set of data
structures 606 until an end
of a first cycle (e.g., an end of a 24 hour cycle) is satisfied. Additionally,
in one or more
embodiments, the data aggregation component 304 repeatedly aggregates data for
the third
asset hierarchy level (e.g., the assets) per day and stores the aggregated
data in a second data
structure 622 of the third set of data structures 606 until an end of a second
cycle (e.g., an end
of a 31 day cycle) is satisfied. In one or more embodiments, the data
aggregation component
304 also repeatedly aggregates data for the third asset hierarchy level (e.g.,
the assets) per
month and stores the aggregated data in a third data structure 624 of the
third set of data
structures 606 until an end of a third cycle (e.g., an end of a 12 month
cycle) is satisfied. In
one or more embodiments, a change to the first data structure 620 initiates a
change to the
second data structure 622 and/or the third data structure 624. Additionally or
alternatively, in
one or more embodiments, a change to the second data structure 622 initiates a
change to the
first data structure 620 and/or the third data structure 624. Additionally or
alternatively, in
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one or more embodiments, a change to the third data structure 624 initiates a
change to the
first data structure 620 and/or the second data structure 622.
[00133] In one or more embodiments, a portion of the data in the first data
structure 620 is
moved within the first data structure 620 and/or into another data structure
(e.g., the second
data structure 622) in response to an end of a cycle being satisfied. For
instance, in an
embodiment, a portion of the data in the first data structure 620 that
corresponds to a data
field for an interval of time from 0-1 hour is moved to another data field in
the first data
structure 620 for an interval of time from 1-2 hour in response to a cycle
that corresponds to
the interval of time from 0-1 hour being satisfied. In another embodiment, a
portion of the
data in the first data structure 620 that corresponds to data fields for an
interval of time from
0-24 hours is moved to another data field in the second data structure 622 for
an interval of
time from 0-1 day in response to a cycle that corresponds to the interval of
time from 0-24
hours being satisfied. Similarly, in one or more embodiments, a portion of the
data in the
second data structure 622 is moved within the second data structure 622 and/or
into another
data structure (e.g., the third data structure 624) in response to an end of a
cycle being
satisfied. For instance, in an embodiment, a portion of the data in the second
data structure
622 that corresponds to a data field for an interval of time from 0-1 day is
moved to another
data field in the second data structure 622 for an interval of time from 1-2
day in response to
a cycle that corresponds to the interval of time from 0-1 day being satisfied.
In another
embodiment, a portion of the data in the second data structure 622 that
corresponds to data
fields for an interval of time from 0-31 days is moved to another data field
in the third data
structure 624 for an interval of time from 0-1 month in response to a cycle
that corresponds to
the interval of time from 0-31 days being satisfied. Similarly, in one or more
embodiments, a
portion of the data in the third data structure 624 is moved within the third
data structure 624
and/or into another data structure in response to an end of a cycle being
satisfied. For
instance, in an embodiment, a portion of the data in the third data structure
624 that
corresponds to a data field for an interval of time from 0-1 month is moved to
another data
field in the third data structure 624 for an interval of time from 1-2 months
in response to a
cycle that corresponds to the interval of time from 0-1 month being satisfied.
[00134] FIG. 7 illustrates a system 700 according to one or more described
features of one
or more embodiments of the disclosure. According to various embodiments, the
system 700
corresponds to a model (e.g., a contribution model, an extensible object
model, a metrics
model, an asset model, another type of model, etc.) related to a time series
mapping of
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attributes for aggregated data. In an embodiment, the system 700 includes an
asset 702. In
one or more embodiments, the asset 702 corresponds to an edge device from the
edge devices
161a-161n. Furthermore, in one or more embodiments, the asset 702 corresponds
to one or
more assets (e.g., one or more industrial assets), one or more databases, one
or more IoT
devices (e.g., one or more industrial IoT devices), one or more sensors, one
or more
actuators, one or more processors, one or more computers, one or more valves,
one or more
pumps (e.g., one or more centrifugal pumps, etc.), one or more motors, one or
more
compressors, one or more turbines, one or more ducts, one or more heaters, one
or more
coolers, one or more boilers, one or more furnaces, one or more heat
exchangers, one or more
fans, one or more blowers, one or more conveyor belts, one or more vehicle
components, one
or more cameras, one or more displays, one or more security components, one or
more
HVAC components, factory equipment, and/or one or more other devices. In
addition, in one
or more embodiments, the asset 702 is associated with one or more asset
hierarchy levels for
an industrial environment. For instance, in an embodiment, the asset 702 is an
asset or a sub-
asset within an area of a plant at an industrial site.
[00135] In an embodiment, the metrics engine component 306 generates an asset
metric
attribute 704 for the asset 702. For instance, in an embodiment, the asset
metric attribute 704
is an asset API attribute for the asset 702 such as an API metric for the for
the asset 702, an
opportunity metric for the asset 702, a safety risk value for the asset 702,
an energy/utility
cost value for the asset 702, a plant performance metric for the asset 702, an
overall
equipment effectiveness for the asset 702, a fault summary metric for the
asset 702, a fault
status metric for the asset 702, a design duty metric for the asset 702, a
design fouling
resistance metric for the asset 702, a frequency of failing metric for the
asset 702, and/or
another type of metric related to performance of the for the asset 702. In one
or more
embodiments, the asset metric attribute 704 is provided to the centralized
control database
318 for storage in the centralized control database 318. In one or more
embodiments, the
asset metric attribute 704 is provided to one or more asset hierarchy levels
(e.g., plant, unit,
area, asset, sub-asset, etc.) associated with the centralized control database
318 (e.g., via a
rollup process related to distribution of the asset metric attribute 704 to
the one or more asset
hierarchy levels). In one or more embodiments, the asset metric attribute 704
is provided to
the centralized control database 318 via a time series query 712 of one or
more data structures
of the centralized control database 318. In one or more embodiments, the asset
metric
attribute 704 and/or the time series query 712 are employed to provide a
contextual rollup
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714 of industrial metrics associated with the centralized control database 318
to facilitate
asset performance management with respect to the asset 702.
[00136] Additionally, in an embodiment, the metrics engine component 306
generates a
marker tag 706 for the asset metric attribute 704. According to one or more
embodiments,
the marker tag 706 is a tag that identifies the asset 702 and/or the asset
metric attribute 704
for the asset 702. In one or more embodiments, the marker tag 706 is employed
to facilitate
presentation of data associated with the asset 702 and/or the asset metric
attribute 704 via a
dashboard visualization. For instance, in an embodiment, a dashboard
visualization includes
a KPI summary dataset 708 associated with one or more dashboard reports
related to the asset
702 and/or one or more other assets in an industrial environment. In one or
more
embodiments, a KPI dataset 710 from the KPI summary dataset 708 is determined,
generated
and/or rendered based on the marker tag 706 associated with the asset 702
and/or the asset
metric attribute 704 for the asset 702.
[00137] FIG. 8 illustrates a system 800 according to one or more described
features of one
or more embodiments of the disclosure. According to various embodiments, the
system 800
is related to asset performance management using a configured model to present
relevant
metrics to a user based on user role, user context associated with invoking
one or more
metrics via a dashboard visualization, and/or a hierarchy mapped for a metrics
model.
According to an embodiment illustrated in FIG. 8, the system 800 includes a
first user 802
(e.g., a plant manager), a second user 804 (e.g., a maintenance engineer), a
third user 806
(e.g., a process & energy engineer) and a fourth user 808 (e.g., an
application configuration
engineer). In an embodiment, the first user 802 is associated with a first
computing device
that generates a request to review plant performance and/or opportunity to
improve with
respect to a plant asset hierarchy 810 that includes a hierarchy of asset in a
plant.
Furthermore, in an embodiment, a dashboard visualization associated with the
plant asset
hierarchy 810 provides one or more metrics (e.g., production metrics, OEE
metrics,
availability metrics, performance metrics, quality metrics, energy metrics
and/or other
metrics) via a visual display of the first computing device associated with
the first user 802.
[00138] In another embodiment, the second user 804 is associated with a second
computing device that generates a request to review asset performance and/or
opportunity to
improve with respect to a rotating asset hierarchy 812 that includes a
hierarchy of assets.
Furthermore, in an embodiment, a dashboard visualization associated with the
rotating asset
hierarchy 812 provides one or more metrics (e.g., OEE metrics, availability
metrics, quality
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metrics, energy metrics, fault metrics, and/or other metrics) via a visual
display of the second
computing device associated with the second user 804. In one or more
embodiments, the
fourth user 808 is additionally or alternatively able to view one or more
metrics (e.g., OEE
metrics, availability metrics, quality metrics, energy metrics, fault metrics,
and/or other
metrics) associated with the dashboard visualization via a visual display of a
computing
device associated with the fourth user 808. In yet another embodiment, the
third user 806 is
associated with a third computing device that generates a request to review
production,
energy consumption and efficiency/loss with respect to an energy monitoring
hierarchy 814
that includes a hierarchy of assets. Furthermore, in an embodiment, a
dashboard visualization
associated with the energy monitoring hierarchy 814 provides one or more
metrics via a
visual display of the third computing device associated with the third user
806.
[00139] FIG. 9 illustrates a system 900 according to one or more described
features of one
or more embodiments of the disclosure. In an embodiment, the system 900
includes a digital
asset twin 902 and a digital operator twin 904. In one or more embodiments,
the digital asset
twin 902 is associated with one or more assets from the portfolio of assets.
Furthermore, in
one or more embodiments, the digital operator twin 904 is associated with an
operator
identity associated with one or more assets from the portfolio of assets. In
certain
embodiments, the edge devices 161a-161n includes the digital asset twin 902.
For example,
in an embodiment, the digital asset twin 902 is a digital simulation (e.g., a
digital replication)
of an asset (e.g., a boiler, etc.) from the portfolio of assets.
[00140] In an exemplary embodiment, the asset performance management computer
system 302 determines that temperature for the digital asset twin 902 is 5
degrees out of
range. Furthermore, in the exemplary embodiment, the asset performance
management
computer system 302 recommends modification of a temperature set-point
associated with
the digital asset twin 902. For example, in the exemplary embodiment, the
asset performance
management computer system 302 recommends changing the temperature set-point
to 65
degrees and the asset performance management computer system 302 generates a
work order
associated with the temperature set-point change. In one or more embodiments,
the asset
performance management computer system 302 sends a notification associated
with the
temperature set-point change to the digital operator twin 904. In certain
embodiments, the
digital operator twin 904 is a digital simulation of an operator. For example,
in certain
embodiments, the digital operator twin 904 corresponds to the computing device
402.
Additionally, in the exemplary embodiment, the digital operator twin 904
receives the
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notification associated with the temperature set-point change. In one or more
embodiments,
the digital operator twin 904 analyzes the work order with respect to a set of
previously
generated work orders. In one or more embodiments, the digital operator twin
904
additionally or alternatively analyzes an asset zone, occupant status, and/or
one or more
predefined configurations for the asset associated with the digital asset twin
902. In one or
more embodiments, the digital operator twin 904 sends one or more control
setting changes
to the digital asset twin 902. In one or more embodiments, the digital
operator twin 904
additionally or alternatively confirms the set point changes and/or removes a
manual override
associated with the work order. In one or more embodiments, the digital
operator twin 904
additionally or alternatively monitors for any manual override reoccurrence.
In one or more
embodiments, the digital operator twin 904 additionally or alternatively
closes the work order
in response to the set point changes. As such, in certain embodiments, an
operator is notified
of an issue associated with an asset (e.g., the digital asset twin 902) and,
in certain
embodiments, is provided with predefined operating configurations and/or
service case
documentation.
[00141] In another exemplary embodiment, the asset performance management
computer
system 302 determines that an upper valve for the digital asset twin 902 has
failed.
Furthermore, in this exemplary embodiment, the asset performance management
computer
system 302 recommends replacement of the upper valve associated with the
digital asset twin
902. For example, in this exemplary embodiment, the asset performance
management
computer system 302 recommends replacement of the upper valve and the asset
performance
management computer system 302 generates a work order associated with the
upper valve
replacement. In one or more embodiments, the asset performance management
computer
system 302 sends a notification associated with the upper valve replacement to
the digital
operator twin 904. Additionally, in this exemplary embodiment, the digital
operator twin 904
receives the notification associated with the upper valve replacement. In one
or more
embodiments, the digital operator twin 904 analyzes the work order with
respect to a set of
previously generated work orders. In one or more embodiments, the digital
operator twin 904
additionally or alternatively determines an optimal service technician for the
upper valve
replacement. In one or more embodiments, the digital operator twin 904 sends a
work order
associated with the upper valve replacement to a computing device associated
with the
service technician. In one or more embodiments, the digital operator twin 904
additionally or
alternatively generates one or more system security keys for the service
technician associated
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with the work order. In one or more embodiments, the digital operator twin 904
additionally
or alternatively closes the work order in response to the upper valve
replacement. In one or
more embodiments, the digital operator twin 904 additionally or alternatively
performs an
auto-test associated with the new upper valve for the asset (e.g., the digital
asset twin 902).
In one or more embodiments, the digital operator twin 904 additionally or
alternatively pays a
bill associated with the upper valve replacement. As such, in certain
embodiments, a service
technician is sourced, dispatched, performs a repair associated with an asset,
and/or is paid by
employing the digital asset twin 902 and/or the digital operator twin 904.
[00142] FIG. 10 illustrates a system 1000 according to one or more described
features of
one or more embodiments of the disclosure. The system 1000 illustrates
functionality
provided via a dashboard visualization according to one or more embodiments of
the
disclosure. In an embodiment, a dashboard visualization associated with the
dashboard
visualization data 322 provides functionality 1002 associated with viewing
issues across a
portfolio of assets. In one or more embodiments, a portfolio status is based
on one or more
alerts and/or one or more service cases. In one or more embodiments, one or
more details
associated with issues across a portfolio of assets is provided. In another
embodiment, a
dashboard visualization associated with the dashboard visualization data 322
provides
functionality 1004 associated with viewing a prioritized and/or grouped alert
list for a
portfolio of assets. In one or more embodiments, one or more analytics alerts
and/or one or
more alarms (e.g., one or more BMS alarms) are provided via the dashboard
visualization. In
one or more embodiments, alerts are grouped into common issues associated with
assets. In
one or more embodiments, priorities associated with the portfolio of assets
are presented
based on factors associated with the assets to facilitate generation of one or
more actions for
the portfolio of assets. In one or more embodiments, one or more notifications
(e.g., one or
more web-app notifications, one or more mobile notifications, etc.) are
provided.
[00143] In another embodiment, a dashboard visualization associated with the
dashboard
visualization data 322 provides functionality 1006 associated with triaging a
selected issue.
In one or more embodiments, one or more alerts across several assets is
provided. In one or
more embodiments, live asset properties (e.g., value, status, trends, service
cases, etc.) are
displayed via the dashboard visualization. In one or more embodiments, a
predicted root
cause of an issue associated with the portfolio of assets is provided via the
dashboard
visualization. In one or more embodiments, insights and/or logs are recorded
for one or more
previously generated services cases and/or one or more new service cases. In
another
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embodiment, a dashboard visualization associated with the dashboard
visualization data 322
provides functionality 1008 associated with a response to an issue related to
the portfolio of
assets. In one or more embodiments, one or more control changes (e.g. set-
points, status,
automatic control changes, manual control changes, etc.) can be made via the
dashboard
visualization. In one or more embodiments, a service case can be assigned to
an operator
(e.g., a service technician) via the dashboard visualization. In another
embodiment, a
dashboard visualization associated with the dashboard visualization data 322
provides
functionality 1010 associated with review of services cases. In one or more
embodiments, a
service case view provided via the dashboard visualization facilitates viewing
services cases,
updating service cases, performing actions with respect to service cases,
and/or closing
services cases. In one or more embodiments, the dashboard visualization
provides for reports
on service case trends for on-going improvements with respect to the portfolio
of assets.
[00144] FIG. 11 illustrates a system 1100 according to one or more described
features of
one or more embodiments of the disclosure. The system 1100 illustrates an
operator
workflow facilitated via a dashboard visualization, in accordance with one or
more
embodiments of the disclosure. At step 1102, a grouped and/or prioritized
alert list is
presented via a dashboard visualization for review. At step 1104, one or more
alerts from the
grouped and/or prioritized alert list is selected for review. At step 1106, it
is determined if an
alert is already active in a service case. If yes, it is determined at step
1108 if the alert should
be assigned to the active service case. If no, at step 1110, information for
the asset related to
the alert is presented via the dashboard visualization for review. Returning
to step 1108, if it
is determined that the alert should be assigned to the active case, a comment
is added to the
existing service case at step 1112. However, if it is determined that the
alert should not be
assigned to the active case, information for the asset related to the alert is
presented via the
dashboard visualization for review at step 1110. At step 1114, information
related to
properties, trends, associated equipment, live service cases and/or closed
service cases are
presented via the dashboard visualization for review. At step 1116, a
resolution route for the
alert is decided via the dashboard visualization. If it is determined to send
a worker to the
field associated with the asset, at step 1118, a new service case is created
and/or comments
are added via the dashboard visualization. At step 1120, alerts are assigned
to a service case.
At step 1122, an urgency indicator, a priority indicator and/or a
recommendation is added to
the service case. At step 1124, the service case is assigned to a site team
(e.g., mechanical
team, electrical team, controls team, etc.) based on attributes of the issue.
At step 1126, the
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service case is closed in response to the issue being resolved. However, it is
determined at
step 1116 to centrally resolve an issue associated with the asset, changes
and/or reasons for
the issue is noted in a new service case at step 1128. At step 1130, control
actions are
performed to resolve the issue. At step 1132, alerts are assigned to a service
case. At step
1134, it is determined whether the issue is fully resolved. If no, then an
urgency indicator, a
priority indicator and/or a recommendation is added to the service case at
step 1122.
However, if yes, the service case is closed at step 1136.
[00145] FIG. 12 illustrates a system 1200 according to one or more described
features of
one or more embodiments of the disclosure. The system 1200 includes a voice
input 320a
that corresponds to an exemplary voice input (e.g., the request 320) generated
by the
computing device 402 and/or provided to the asset performance management
computer
system 302. For instance, the voice input 320a includes voice input data that
corresponds to a
phrase "What is the performance index of k101." In one or more embodiments,
the virtual
assistant component 336 employs natural language processing and/or performs a
natural
language query to determine an attribute 1202 that corresponds to "performance
index" and
an asset identifier 1204 that corresponds to "k101." The system 1200 also
includes a voice
input 320b that corresponds to another exemplary voice input generated by the
computing
device 402 and/or provided to the asset performance management computer system
302. For
instance, the voice input 320b includes voice input data that corresponds to a
phrase "What
was the production and quality of ethyne cracker for last 2 weeks." In one or
more
embodiments, the virtual assistant component 336 employs natural language
processing
and/or performs a natural language query to determine a first attribute 1202
that corresponds
to "production," a second attribute 1202 that corresponds to "quality," an
asset identifier
1204 that corresponds to "ethyne cracker," and time data 1206 that corresponds
to "last 2
weeks."
[00146] FIG. 13 illustrates a system 1300 according to one or more described
features of
one or more embodiments of the disclosure. In one or more embodiments, the
system 1300 is
employed to produce a structured query for a data API based on a natural
language query.
The system 1300 includes an input query 1302 that corresponds to a voice input
(e.g., the
request 320), for example. In one or more embodiments, a slot model 1304
and/or an intent
model 1306 associated with natural language processing is employed to
determine a phrase
that does not include time data (e.g., simple 1308 that corresponds to voice
input 320a) and/or
a phrase that include times data (e.g., simple time 1312 that corresponds to
voice input
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320b). In one or more embodiments, the slot model 1304 tags one or more words
associated
with the input query 1302 with one or more slots. For instance, in one or more
embodiments,
the slot model 1304 is configured to identify meaning of individual words
associated with the
input query 1302 via slot detection. In one or more embodiments, each word is
provided to a
slot to understand which type of information is being shared with the word. In
one example,
the input query 1302 includes an input sentence -give me the capacity of DCC
plant" and the
slot model 1304 determines a first slot (e.g., an attribute name slot) that
corresponds to
"capacity" and a second slot (e.g., an asset name slot) that corresponds to
"DCC". Other
tokens may be labeled as -others". In one or more embodiments, the intent
model 1306
labels a sentence associated with the input query 1302 with an intent. For
example, the intent
model 1306 translates a type of query associated with the input query 1302.
[00147] In one or more embodiments, the slot model 1304 includes one or more
encoders
and/or one or more decoders configured for slot-filling. Additionally or
alternatively, in one
or more embodiments, the intent model 1306 includes one or more encoders
and/or one or
more decoders configured for intent classification. In one or more
embodiments, respective
encoders for the slot model 1304 and/or the intent model 1306 include an
embedding layer, a
long short-term memory (LSTM) layer, and/or a linear layer (e.g., a linear
layer for
classification). In one or more embodiments, respective decoders for the slot
model 1304
and/or the intent model 1306 employ hidden states provide by the encoders. In
one or more
embodiments, hidden states associated with two or more encoders are provided
as input to a
decoder of the slot model 1304 and/or a decoder of the intent model 1306. As
such, in one or
more embodiments, processing performed by the slot model 1304 and the intent
model 1306
are interdependent. In one or more embodiments, the slot model 1304 and/or the
intent
model 1306 are repeatedly trained based on training data and/or test data
associated with
words tagged with a slot and/or labeled sentences until a certain accuracy
(e.g., a certain Fl
score) is achieved. In one or more embodiments, the training data includes an
input sentence
in natural language along with corresponding slot labels and/or an intent. For
example, a
portion of the training data may include "What:0 is:0 the:0 quality:B-
attribute name of: 0
cdu:B-asset name inlet:I-asset name stream:E-asset name <=> simple" where each
word is
followed by a slot after the colon ":", and the intent is located at the end.
In one or more
embodiments, every word present in the training data is added to a dictionary
of the slot
model 1304 and/or the intent model 1306, where each word is mapped to a
number.
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[00148] In one or more embodiments, an action 1310 is performed with respect
to simple
1308 and/or simple time 1312 to facilitate asset parsing via asset parser
1318, KPI parsing
via KPI parser 1320, and/or time parsing via time parser 1324. In one or more
embodiments,
the asset parser 1318 employs asset data 1316 to facilitate the asset parsing.
In one or more
embodiments, the KPI parser 1320 employs KPI data 1322 to facilitate the KPI
parsing. In
one or more embodiments, depending on an intent of the input query 1302, a
corresponding
action function is invoked via the action 1310. For example, an action
function may be
configured for each possible intent. In one or more embodiments, each action
function
contains parsers for the slots present in a query of the respective intent.
Respective parsers
identify words having a certain slot and is then employed with respect to
query of a database
to determine a closest match. In one or more embodiments, a closest match is
determined
based on a fuzzy matching technique to account for spelling errors, different
writing styles
employed by respective users, and/or different spoken language employed by
respective
users.
[00149] In one or more embodiments, the time parser 1324 employs data provided
by a
time model 1314. In one or more embodiments, the time model 1314 is configured
to
classify a format in which time data has been given in the input query 1302.
In one or more
embodiments, the time model 1314 is configured to classify a relative time
format and/or an
absolute time format. In one or more embodiments, the time model 13 14 is a
trained neural
network model configured to identify time information. For instance, in one or
more
embodiments, the time model 1314 is a fully connected feed forward neural
network that
includes two or more hidden layers. In one or more embodiments, input provided
to the time
model 1314 includes a set of words created based on the input query 1302.
Additionally or
alternatively, the input provided to the time model 1314 includes time data
which contains
one or more words that can be employed to identify time associated with the
input query
1302. In one or more embodiments, a size of an input layer of the time model
1314
corresponds to a size of the time data. Furthermore, in one or more
embodiments, a size of
an output layer of the time model 1314 corresponds to a number of time formats
to be
identified by the time model 1314. In one or more embodiments, the time model
1314 is
configured to classify the time information into a fixed format. In response
to identification
of a time format, in one or more embodiments, the time parser 1324 is
configured to calculate
a start time and an end time based on data provided by the time model 1314. In
one or more
embodiments, training data for the time model 1314 is generated using a random
time data
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generator. In one or more embodiments, a query handler 1328 is employed to
perform a
search with respect to the centralized control database 318 based on one or
more attributes,
one or more asset identifiers, and/or time data provided by the asset parser
1318, the KPI
parser 1320, and/or the time parser 1324. Additionally, in one or more
embodiments, an
output query 1330 associated with aggregated data and/or one or more asset
insights is
provided in response to one or more searches performed via the query handler
1328. The
output query 1330 is, for example, a structured query associated with a syntax
of the input
query 1302 (e.g., a syntax of the query language). For example, in one or more
embodiments, the output query 1330 is a query string created by combining
asset parser
information, KPI parser information, time parser information, and/or other
information into
the syntax. In one or more embodiments, the output query 1330 (e.g., the
syntax of the
output query 1330) is configured for employment by a data API.
[00150] FIG. 14 illustrates an exemplary electronic interface 1400 according
to one or
more embodiments of the disclosure. In an embodiment, the electronic interface
1400 is an
electronic interface of the computing device 402 that is presented via the
visual display 504.
In one or more embodiments, a data visualization is presented via the
electronic interface
1400. In certain embodiments, the data visualization presented via the
electronic interface
1400 presents a visualization of plant health for an industrial plant. In one
or more
embodiments, the asset performance management computer system 302 receives the
request
320 via the electronic interface 1400. Furthermore, in one or more
embodiments, the
dashboard visualization component 308 provides the dashboard visualization
data 322 to the
electronic interface 1400. According to an embodiment illustrated in FIG. 14,
the electronic
interface 1400 presents first metrics data 1402 associated with plant
performance for a
hierarchy of assets associated with one or more intervals of time (e.g., per
year, per 6 months,
per 1 month, per week, per day, etc.), second metrics data 1404 associated
with key KPIs for
the hierarchy of assets associated with the one or more intervals of time,
third metrics data
1406 associated with a fault summary and/or a fault status for the hierarchy
of assets
associated with the one or more intervals of time, and fourth metrics data
1408 associated
with frequently failing assets for the hierarchy of assets associated with the
one or more
intervals of time. Additionally, in certain embodiments, the electronic
interface 1400
includes a notification center 1410 that presents one or more notifications
associated with the
hierarchy of assets and/or one or more other assets from a portfolio of
assets.
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[00151] FIG. 15 illustrates an exemplary electronic interface 1500 according
to one or
more embodiments of the disclosure. In an embodiment, the electronic interface
1500 is an
electronic interface of the computing device 402 that is presented via the
visual display 504.
In one or more embodiments, a dashboard visualization is presented via the
electronic
interface 1500. In certain embodiments, the data visualization presented via
the electronic
interface 1500 presents a visualization of alerts grouped by asset to
facilitate analysis of a
portfolio of assets via the dashboard visualization associated with the
electronic interface
1500.
[00152] FIG. 16 illustrates an exemplary electronic interface 1600 according
to one or
more embodiments of the disclosure. In an embodiment, the electronic interface
1600 is an
electronic interface of the computing device 402 that is presented via the
visual display 504.
In one or more embodiments, a dashboard visualization is presented via the
electronic
interface 1600. In certain embodiments, the data visualization presented via
the electronic
interface 1600 presents a visualization of a view list of service cases
grouped by asset to
facilitate analysis of a portfolio of assets via the dashboard visualization
associated with the
electronic interface 1600.
[00153] FIG. 17 illustrates an exemplary electronic interface 1700 according
to one or
more embodiments of the disclosure. In an embodiment, the electronic interface
1700 is an
electronic interface of the computing device 402 that is presented via the
visual display 504.
In one or more embodiments, a dashboard visualization is presented via the
electronic
interface 1700. In certain embodiments, the data visualization presented via
the electronic
interface 1700 presents a visualization of details of a service case and real-
time values of
properties for associated assets to facilitate analysis of a portfolio of
assets via the dashboard
visualization associated with the electronic interface 1700.
[00154] FIG. 18 illustrates an exemplary electronic interface 1800
according to one or
more embodiments of the disclosure. In an embodiment, the electronic interface
1800 is an
electronic interface of the computing device 402 that is presented via the
visual display 504.
In one or more embodiments, a dashboard visualization is presented via the
electronic
interface 1800. In certain embodiments, the data visualization presented via
the electronic
interface 1800 presents a visualization of service cases related to assets to
facilitate analysis
of a portfolio of assets via the dashboard visualization associated with the
electronic interface
1800.
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[00155] FIG. 19 illustrates an exemplary electronic interface 1900 according
to one or
more embodiments of the disclosure. In an embodiment, the electronic interface
1900 is an
electronic interface of the computing device 402 that is presented via the
visual display 504.
In one or more embodiments, a dashboard visualization is presented via the
electronic
interface 1900. In certain embodiments, the data visualization presented via
the electronic
interface 1900 presents a visualization of trends of digital and/or analog
properties related to
assets to facilitate analysis of a portfolio of assets via the dashboard
visualization associated
with the electronic interface 1900.
[00156] FIG. 20 illustrates an exemplary electronic interface 2000 according
to one or
more embodiments of the disclosure. In an embodiment, the electronic interface
2000 is an
electronic interface of the computing device 402 that is presented via the
visual display 504
In one or more embodiments, a dashboard visualization is presented via the
electronic
interface 2000. In certain embodiments, the data visualization presented via
the electronic
interface 2000 presents a visualization of control properties related to
assets to facilitate
analysis of a portfolio of assets via the dashboard visualization associated
with the electronic
interface 2000.
[00157] FIG. 21 illustrates an exemplary electronic interface 2100 according
to one or
more embodiments of the disclosure. In an embodiment, the electronic interface
2100 is an
electronic interface of the computing device 402 that is presented via the
visual display 504.
In one or more embodiments, a dashboard visualization is presented via the
electronic
interface 2100. In certain embodiments, the data visualization presented via
the electronic
interface 2100 presents one or more asset insights 2102 and/or one or more
notifications 2104
via the dashboard visualization associated with the electronic interface 2100.
[00158] FIG. 22 illustrates an exemplary electronic interface 2200 according
to one or
more embodiments of the disclosure In an embodiment, the electronic interface
2200 is an
electronic interface of the computing device 402 that is presented via the
visual display 504
In one or more embodiments, a dashboard visualization is presented via the
electronic
interface 2200. In certain embodiments, the data visualization presented via
the electronic
interface 2200 presents one or more asset insights 2202 and/or one or more
notifications 2204
via the dashboard visualization associated with the electronic interface 2200.
[00159] FIG. 23 illustrates an exemplary electronic interface 2300 according
to one or
more embodiments of the disclosure. In an embodiment, the electronic interface
2300 is an
electronic interface of the computing device 402 that is presented via the
visual display 504.
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In one or more embodiments, a dashboard visualization is presented via the
electronic
interface 2300. In certain embodiments, the data visualization presented via
the electronic
interface 2300 presents one or more asset insights 2302 and/or one or more
notifications 2304
via the dashboard visualization associated with the electronic interface 2300.
In one or more
embodiments, the data visualization presented via the electronic interface
2300 includes a 3D
model 2306 associated with an asset from a portfolio of assets. In one or more
embodiments,
one or more events associated with the 3D model 2306 can be filtered and/or
information
associated with the one or more events can be displayed in response to
selection of one or
more interactive buttons associated with the 3D model 2306.
[00160] FIG. 24 illustrates a schematic view of a material handling system
2400, in
accordance with one or more embodiments described herein. In one or more
embodiments,
the material handling system 2400 corresponds to an enterprise from the
enterprises 160a-n.
In one or more embodiments, the material handling system 2400 includes one or
more assets
from a portfolio of assets. The material handling system 2400 includes at
least one vision
system 2402 with one or more LiDAR based sensors 2404, according to an example
embodiment. The material handling system 2400 may correspond to a material
handling
environment for example, but not limited to, a distribution center, a shipping
station, a
warehouse, an inventory, etc. According to some example embodiments, the
material
handling system 2400 includes one or more conveyors for handling various items
such as,
cartons, totes, shipping packages, boxes etc. As illustrated, the material
handling system 2400
includes a sorter portion 2406 for selectively identifying, sorting and/or
diverting one or more
articles 2408 to one of the destinations 2410, such as, but not limited to,
takeaway conveyors,
chutes, and the like. In some examples, the diverted articles may be sent to
shipping 2412 for
shipping to a destination, for example, a store. While the example as shown in
FIG. 24 may
illustrate a paddle sorter, it is noted that the scope of the present
disclosure is not limited to a
paddle sorter. In some examples, the material handling system 2400 may include
other types
of sorter(s) may be implemented, including, but not limited to, pusher/puller
sorters, pop-up
transfer sorters, and/or cross-belt sorters.
[00161] Although the LiDAR sensors 2404 are illustrated to be located within
the vision
system 2402, however, according to various example embodiments described
herein, multiple
LiDAR based sensors are installed at various sections of the material handling
system 2400.
In other words, the LiDAR sensors 2404 may be positioned at various different
sections (e.g.
workstations) within the material handling system 2400. Further, in one or
more
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embodiments, these LiDAR based sensors are communicatively coupled (e.g.
remotely
connected) to the vision system 2402, via a communication network (e.g.
wireless or wired
network).
[00162]
Referring to FIG. 24, illustratively, a first LiDAR sensor unit 2404-1 is
installed
near an area corresponding to an automated storage and retrieval system (ASRS)
2422.
Similarly, a second LiDAR sensor unit 2404-2 may be installed near another
area
corresponding to a singulation system along the sorter 2406. In another
example, similar
LiDAR based sensor units may be located at the shipping station 2412 or at
various other
positions (not shown) along the sorter 2406. Accordingly, the material
handling system 2400
may include many more such LiDAR sensor units that are installed or mounted at
various
sections (e.g. dedicated zones) of a material handling environment. As stated
before, in one or
more embodiments, these sensor units are communicatively coupled to the vision
system
2402, via the communication network. These LiDAR based sensor units may be
capable of
capturing a data stream (e.g. 3D data stream) representative of a 3D scan of
that area where
the respective LiDAR sensor unit is located. In one or more embodiments, the
data stream is
used by the vision system 2402 to monitor, one or more articles 2414,
machines, and/or
workers present in various sections of the material handling system 2400.
[00163] As illustrated, in one or more embodiments, the material handling
system 2400
includes a sorter portion (e.g. the sorter 2406) that receives the one or more
articles 2414
from an induction portion 2416. In some examples, the induction portion 2416
is associated
with a singulation system 2418 that is configured to generate spacing between
the one or
more articles 2414. For example, the induction portion 2416 may comprise
various
mechanical components e.g. configurations of belt units and/or mechanical
actuators with end
effectors, which may create the required spacing between the one or more
articles 2414. In
accordance with some example embodiments, LiDAR based sensors of the LiDAR
sensor
unit 2404-2 may capture a 3D scan of various operations and/or activities that
may be
performed on the singulation system 2418.
[00164] In some examples, the induction portion 2416 receives articles 2414
from a merge
portion 2420, as shown in FIG. 24. The merge portion 2420 may have multiple
accumulation
lanes and/or conveyors for releasing articles in a slug and/or zipper fashion
onto the induction
portion 2416. In some examples, the merge portion 2420 may receive the one or
more articles
2414 from a receiving system and/or an automated storage and retrieval system
(ASRS)
2422. Additionally, or alternatively, the merge portion 2420 may receive the
one or more
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articles from other sources. In some example embodiments, the ASRS 2422 may
also include
a separate vision system (VS1) 2424 with one or more LiDAR based sensor units
(similar to
2404-1, 2404-2) that may be installed at various locations within the ASRS
2422.
[00165] According to some example embodiments, the LiDAR sensors 2404 of the
vision
system 2402 are configured for scanning a target area of the material handling
environment
and generate one or more data streams. In some example embodiments, a
processor of the
vision system 2402 may utilize a data stream to construct 3D point cloud that
may represent a
3D-scan of the target area. As an example, a data stream recorded by these
LiDAR sensors
may capture various operations of a material handling site e.g. movement of
the one or more
articles 2414, e.g. from the induction portion 2416 towards the sorter portion
2406 or from
the ASRS 2422 to the merge portion 2420, and so on. Further, data streams from
various
LiDAR sensors 2404 may also capture operations and/or actions performed by
various
machines of the material handling site. For instance, in an example, the data
stream may
capture movement of various mechanical components e.g. conveyor belts etc. of
the
singulation system. Furthermore, the data streams may also capture operations
performed by
one or more workers in that target area.
[00166] According to some example embodiments, one or more components of the
example material handling system 2400, such as, but not limited to, the sorter
portion 2406,
the induction portion 2416, the merge portion 2420, the vision system 2402,
and/or the like,
may be communicably coupled to at least one of a central system e.g., a
distribution center
(DC) execution system 2426 (or a warehouse management system, a labor
management
system, a machine control system, and/or another system) and/or a controller
2428. In one or
more embodiments, the controller 2428 is configured for machine control. The
term
"communicably coupled'' refers to two or more components (for example, but not
limited to,
the sorter portion 2406, the induction portion 2416, the merge portion 2420,
the vision system
2402, the DC execution system 2426 and the controller 2428 as shown in FIG.
24) being
connected through wired means (for example but not limited to, wired Ethernet)
and/or
wireless means (for example but not limited to, Wi-Fi, Bluetooth, ZigBee),
such that data
and/or information may be transmitted to and/or received from these components
[00167] FIG. 25 illustrates a schematic view 2500 of a target area of the
material handling
system 2400 including the LiDAR based vision system, according to an example
embodiment. The target area may correspond to an area of a distribution center
(DC). In one
or more embodiments, the DC may receive goods in bulk from various
manufacturers,
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suppliers, stores, shipping stations, and the like, and may store and/or
handle received goods
until the goods are further picked and/or shipped. Further, the received goods
may be
transferred into totes and/or containers of appropriate size, shape, material,
etc. for storage
and/or further processing. In accordance with some example embodiments
described herein,
the DC may include a vision system 2501 that may be communicatively coupled,
via a
network 2503, to multiple LiDAR based sensor units VS1, VS2, VS3, VS4, etc.,
as illustrated
in FIG. 25. Similar to as described earlier in reference to FIG. 24, these
LiDAR based sensor
units (VS1-VSn) may be capable of recording data streams including 3D scan of
a target area.
The network 2503 may correspond to a wired or wireless communication network.
In one or
more embodiments, the vision system 101 corresponds to an asset from a
portfolio of assets.
[00168] Illustratively, in some example embodiments, the DC may have a
replenishment
area 2502 for replenishing one or more containers 2504 with goods arriving at
the
replenishment area 2502 in multiple stock keeping units (SKUs) 2506. The term
'replenishment area as used herein may refer to an area, system, workstation,
and the like in
the DC for transferring goods from the multiple SKUs 2506 into one or more
containers
2504. The replenishment area 2502 may have a collaborative system of multiple
material
handling devices and systems, such as, but not limited to, infeed conveyors,
outfeed
conveyors, goods to operator workstations, devices, staging units, and the
like. Transferring
goods from an SKU into the containers 2504 may be automated, for example, may
be done
by a robotic tool, and/or may be a manual process carried out by an operator,
such as
operators 2508 and 2510, as shown in FIG. 25. In accordance with some example
embodiments described herein, one or more LiDAR based sensors are associated
with the
replenishment area 2502 to perform a 3D scan that captures activities,
operations, devices,
and/or workers in the replenishment area 2502. Accordingly, in one or more
embodiments,
there are multiple vision systems that may be associated with different
sections of the DC. In
one or more embodiments, these vision systems employ LiDAR based sensors to
record the
activities related to operators, items, and/or machines within the respective
section. As an
example, as illustrated in FIG. 25, a vision system unit VS2 with one or more
LiDAR sensors
204 is associated with the replenishment area 2502.
[00169] According to said example embodiments, an SKU 2506 may include goods
of a
similar type, size, shape, and/or any other common characteristic. In an
embodiment, one or
more SKUs 2506 may be grouped together and stacked on a pallet 2512, as shown
in FIG. 25.
The SKUs 2506 may be grouped based on a common characteristic, such as type of
goods.
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Additionally, or alternatively, mixed SKUs 2506 may be grouped randomly and
placed on the
pallet 2512. The SKUs 2506 may be grouped and stacked on the pallet 2512 at
the DC for
ease of handling. In some embodiments, each SKU 2506 and each pallet 2512 may
include a
respective identifier (e.g. a barcode label, RFID tag) that is scanned at the
replenishment area
2502. The scanned information indicates, in one or more embodiments, a
location of the
pallet 2512 at the replenishment area 2502. In some example embodiments, one
or more
LiDAR based sensor units may also be located in the DC to perform 3D scan of
an area
including the SKUs 2506 and/or pallets 2512. Illustratively, in an example,
two vision system
units VS1 and VS4 with LiDAR sensors may be located to track activities,
operations, and/or
characteristics associated with the SKUs 2506 and/or the pallets 2512.
[00170] In accordance with one or more embodiments, the replenishment area
2502
includes a gravity flow rack 2514 for staging and/or conveying one or more
containers 2504.
Further, the replenishment area 2502 may include multiple replenishment zones.
The gravity
flow rack 2514 may be placed between different replenishment zones, such that
the gravity
flow rack 2514 may convey replenished containers from a first replenishment
zone 2516 to a
second replenishment zone 2518 and convey empty containers back from the
second
replenishment zone 2518 to the first replenishment zone 2516. The gravity flow
rack 2514
may also function as a staging area for the empty and/or filled containers
2504 until the
containers 2504 are handled by the operator 2508 and/or a robotic tool. In
accordance with
some example embodiments, the vision system unit VS2 may scan the area
including the
gravity flow rack 2514.
[00171] The replenishment area 2502 may further include one or more devices
2520. The
devices 2520 may refer to any portable and/or fixed device (e.g. a human
machine interface
TIMI) that may be communicably coupled to a central controller (e.g. the
controller 2428). In
some examples, the devices 2520 may include an input/output interface which
may be used
for assisting the operator 2508 in the replenishment process. According one or
more
embodiments, the devices 2520 correspond to or include for example, but not
limited to,
scanners, imagers, displays, computers, communication devices, headsets, and
the like.
According to some example embodiments, the devices 2520 may further receive
data,
commands, workflows, etc. from the central controller and/or any other device
that may be
communicably coupled to the devices 2520. According to some example
embodiments, the
vision system units VS1 and VS5 using the LiDAR based sensors may perform a 3D
scan of
area including the one or more devices 2520.
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[00172] According to some example embodiments, the data stream captured by the
vision
system 2501 may monitor various activities, operations, individuals, and/or
equipment in the
DC. For instance, the data stream may be used to monitor arrival of the
pallets 2512 having
one or more SKUs 2506 at the replenishment area 2502 of the DC. Further, the
data stream
may monitor scanning of a pallet identifier and/or an SKU identifier using the
devices 2520
by any of the operators 2508 and/or 2510. In some example embodiments, the
data stream
captured by the LiDAR sensors 204 of the vision system 2501 may also include
an operation
by a robotic tool (not shown) and/or the operators (2508, 2510) to pick one or
more of the
containers 2504 on the gravity flow rack 2514 for replenishing the one or more
containers
2504 with the goods that may be in the SKU 2506 and/or the pallet 2512.
Further, in some
example embodiments, the data stream captured by the LiDAR sensors 204 of the
vision
system units VS2, VS3, and/or VS4 may include conveyance or movement of the
one or
more containers 2504 that may be on the gravity flow rack 2514. In this
aspect, the containers
2504 may be conveyed from the first replenishment zone 2516 to the second
replenishment
zone 2518 through the gravity flow rack 2514. In some example embodiments, the
data
stream may also include monitoring of empty container(s) that may be placed on
the gravity
flow rack 2514 for transferring back to the first replenishment zone 2516 for
receiving goods
from a next SKU and/or pallet. In an example embodiment, the data stream also
includes
movement of some containers to one or more shuttle totes that can be moved for
storing
goods in an Automated Storage and Retrieval System (ASRS) in the DC.
[00173] FIG. 26 illustrates an example scenario 2600 depicting monitoring of
an operation
performed by a worker in a material handling environment by using LiDAR based
vision
system (e.g. the vision system 2402), according to an example embodiment. In
some example
embodiments, the operation may be performed in a replenishment zone of a
distribution
center. FIG. 26 illustrates an example of a replenishment zone 2602 of a
distribution center.
As described earlier, in one or more embodiments, a material handling
environment includes
a plurality of vision systems. Illustratively, in some example embodiments, a
distribution
center DC includes a plurality of vision systems (2601, 2603, 2607 etc.). Each
of these vision
systems (2601-2607) include one or more LiDAR based sensors that may be
installed and/or
mounted at various sections of the material handling environment. In this
aspect, each of
these vision systems 2601-2607 are capable of capturing a data stream (i.e. a
3D scan) of a
target area. In one or more embodiments, the vision systems 2601-2607
correspond to
respective assets from a portfolio of assets.
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[00174] According to some example embodiments, the operation monitored by
using
LiDAR based vision systems corresponds to replenishing of one or more
containers. The
containers may be placed on a gravity flow rack 2608 and, in one or more
embodiments, is
replenished with goods from the one or more SKUs 2610 that may be arriving at
a
replenishment area of the replenishment zone 2602. According to some example
embodiments, there may be different sizes of containers for replenishment in
the DC. For
instance, a first set of containers 2604 may be of moderate size, whereas a
second set of
containers 2606 may be smaller than the first set of containers 2604, and a
third set of
containers 2605 may be larger than containers of the first set of containers
2604. In one or
more embodiments, the replenishment of containers is based on a size of the
containers.
According to one or more embodiments, each of the containers 2604, 2606, 2605
have an
associated container identifier (not shown). The container identifier may
refer to a unique
identifier that may be used to identify a particular container, such as, but
not limited to, a
serial number, a barcode label, RFID tag, etc. The container identifier may
include
information regarding the container, such as, but not limited to, type, size,
capacity, weight,
shape, and the like.
[00175] In accordance with said example embodiments, a container identifier
for a
container may be scanned before performing each replenishment operation for
that container.
By scanning the container identifier, a central controller (e.g. the
controller 2428) and/or any
other computing device in the DC, may track an occupied volume of the
container. Further,
based on this information, the central controller may calculate a current
capacity i.e. based on
a maximum capacity of the container and the occupied volume. Said that, in
accordance with
said example embodiments, to maximize storage capacity and overall efficiency,
it may be
desired to pick appropriately sized container(s) from various sized containers
for storing
goods from the SKUs 2610.
[00176] FIG. 27 illustrates another example scenario 2700 depicting another
operation
performed in a material handling environment that is monitored by using LiDAR
based
vision system (e.g. the vision system 2402), according to an example
embodiment. FIG. 27
illustrates a perspective view of a second replenishment zone 2702 of the
distribution center
(DC), in accordance with one or more embodiments of the present disclosure.
Illustratively,
in some example embodiments, a distribution center DC includes a plurality of
vision
systems (2701, 2703, 2705 etc.). Each of these vision systems (2701-2705)
includes one or
more LiDAR based sensors that may be installed and/or mounted at various
sections of the
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material handling environment. In this aspect, each of these vision systems
2701-2705 is
configured to capture a data stream (i.e. a 3D scan) of a target area. In one
or more
embodiments, the plurality of vision systems (2701-2705) correspond to
respective assets
from a portfolio of assets. In accordance with some example embodiments, the
data stream
from the LiDAR sensor-based vision system captures an operation related to a
replenishment
process in the second replenishment zone 2702.
[00177] According to some example embodiments, a replenishment process
illustrated in
FIG. 27 includes replenishing of one or more containers from a second set of
containers 2704
with goods from the replenished first set of containers 2706 that may be
arriving at the
second replenishment zone 2702 (e.g. through the gravity flow rack 2708). In
some example
embodiments, the second set of containers 2704 may correspond to shuttle totes
used in an
ASRS (e.g., the ASRS 2422) that may be having multiple compartments of
different size. The
shuttle totes may be partially filled or empty and may be used to store goods
in a storage
facility, such as the ASRS 2422 as illustrated in FIG. 24.
[00178] FIG. 28 illustrates a method 2800 for creating create a dashboard
visualization of
metrics for an asset hierarchy associated with a portfolio of assets, in
accordance with one or
more embodiments described herein. The method 2800 is associated with the
asset
performance management computer system 302, for example. For instance, in one
or more
embodiments, the method 2800 is executed at a device (e.g. the asset
performance
management computer system 302) with one or more processors and a memory. In
one or
more embodiments, the method 2800 begins at block 2802 that receives (e.g., by
the metrics
engine component 306 and/or the dashboard visualization component 308) a
request to
generate a dashboard visualization associated with a portfolio of assets, the
request
comprising an asset descriptor describing one or more assets in the portfolio
of assets. The
request to generate the dashboard visualization provides one or more technical
improvements
such as, but not limited to, facilitating interaction with a computing device
and/or extended
functionality for a computing device.
[00179] At block 2804, it is determined whether the request is
processed. If no, block
2804 is repeated to determine whether the request is processed. If yes, the
method 2800
proceeds to block 2806. In response to the request, block 2806 that obtains,
based on the
asset descriptor, aggregated data associated with the portfolio of assets. The
obtaining the
aggregated data based on the asset descriptor provides one or more technical
improvements
such as, but not limited to, extended functionality for a computing device.
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[00180] The method 2800 also includes a block 2808 that, in response to the
request,
determines (e.g., by the metrics engine component 306) metrics for an asset
hierarchy
associated with the portfolio of assets based on a model related to a time
series mapping of
attributes for the aggregated data. The determining the metrics for the asset
hierarchy
provides one or more technical improvements such as, but not limited to,
improving accuracy
of the dashboard visualization.
[00181] The method 2800 also includes a block 2810 that, in response
to the request,
provides (e.g., by the dashboard visualization component 308) the dashboard
visualization to
an electronic interface of a computing device, the dashboard visualization
comprising the
metrics for an asset hierarchy associated with the portfolio of assets. The
providing the
dashboard visualization with the metrics provides one or more technical
improvements such
as, but not limited to, what and/or how to present information via a computing
device.
[00182] In one or more embodiments, the request additionally or alternatively
includes a
user identifier describing a user role for a user associated with access of
the dashboard
visualization via the electronic interface. Furthermore, in one or more
embodiments, the
obtaining the aggregated data additionally or alternatively includes obtaining
the aggregated
data based on the user identifier. The obtaining the aggregated data based on
the user
identifier provides one or more technical improvements such as, but not
limited to, extended
functionality for a computing device. In one or more embodiments, the method
2800 also
includes configuring the dashboard visualization based on the user identifier.
The
configuring the dashboard visualization based on the user identifier provides
one or more
technical improvements such as, but not limited to, what and/or how to present
information
via a computing device.
[00183] In one or more embodiments, the request additionally or alternatively
includes a
metrics context identifier describing context for the metrics. Furthermore, in
one or more
embodiments, the obtaining the aggregated data includes obtaining the
aggregated data based
on the metrics context identifier. The obtaining the aggregated data based on
the metrics
context identifier provides one or more technical improvements such as, but
not limited to,
extended functionality for a computing device. In one or more embodiments,
different types
of aggregates such as maximum, minimum, count, sum, and/or average are
supported.
Additionally, in one or more embodiments, a calculation is custom defined
based on the
metrics being aggregated at different levels to, for example, provide improved
extensibility.
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[00184] In one or more embodiments, the request additionally or alternatively
includes a
time interval identifier (e.g., a reporting time interval identifier)
describing an interval of time
for the metrics. Furthermore, in one or more embodiments, the obtaining the
aggregated data
includes obtaining the aggregated data based on the time interval identifier
(e.g., the reporting
time interval identifier). The obtaining the aggregated data based on the time
interval
identifier (e.g., the reporting time interval identifier) provides one or more
technical
improvements such as, but not limited to, extended functionality for a
computing device.
[00185] In one or more embodiments, the method 2800 also includes determining
a list of
prioritized actions for the portfolio of assets based on the metrics.
Additionally, in one or
more embodiments, the method 2800 also includes providing the list of
prioritized actions to
the electronic interface via the dashboard visualization. The providing the
list of prioritized
actions to the electronic interface provides one or more technical
improvements such as, but
not limited to, what and/or how to present information via a computing device.
[00186] In one or more embodiments, the determining the metrics includes
determining
the metrics for different hierarchy level of assets. Furthermore, in one or
more embodiments,
the providing the dashboard visualization includes providing the metrics for
the different
hierarchy level of assets. The providing the metrics for the different
hierarchy level of assets
provides one or more technical improvements such as, but not limited to, what
and/or how to
present information via a computing device.
[00187] In one or more embodiments, the method 2800 also includes aggregating
multiple
types of metrics for the portfolio of assets based on the aggregated data. The
aggregating the
multiple types of metrics provides one or more technical improvements such as,
but not
limited to, improving accuracy of the dashboard visualization.
[00188] In one or more embodiments, the method 2800 also includes determining
an alerts
list associated with one or more recommendations for the portfolio of assets
based on the
metrics. Additionally, in one or more embodiments, the method 2800 also
includes providing
the alerts list to the electronic interface via the dashboard visualization.
The providing the
alerts list to the electronic interface provides one or more technical
improvements such as,
but not limited to, what and/or how to present information via a computing
device.
[00189] In one or more embodiments, the method 2800 also includes modeling of
the
aggregated data based on different hierarchy level of assets. The modeling of
the aggregated
data provides one or more technical improvements such as, but not limited to,
extending
functionality of the dashboard visualization.
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[00190] In one or more embodiments, the method 2800 also includes configuring
the
dashboard visualization to facilitate viewing of performance of the portfolio
of assets with
respect to different hierarchy level of assets. The providing the configuring
the dashboard
visualization provides one or more technical improvements such as, but not
limited to,
extending functionality of the dashboard visualization and providing what
and/or how to
present information via a computing device.
[00191] In one or more embodiments, the method 2800 also includes mapping the
attributes for the aggregated data via a dynamic cache that stores the
attributes for the
aggregated data. The dynamic cache provides one or more technical improvements
such as,
but not limited to, faster storage via the dynamic cache, improving accuracy
of the metrics
provided via the dashboard visualization, and improving efficiency of storage
of data and/or
retrieval of data for the dashboard visualization.
[00192] In one or more embodiments, the method 2800 also includes dynamically
caching
the aggregated data in a dynamic cache based on different hierarchy level of
assets. The
dynamic caching provides one or more technical improvements such as, but not
limited to,
faster storage via the dynamic cache, improving accuracy of the metrics
provided via the
dashboard visualization, and improving efficiency of storage of data and/or
retrieval of data
for the dashboard visualization.
[00193] In one or more embodiments, the method 2800 also includes configuring
the
dashboard visualization to provide individual control of the one or more
assets in the
portfolio of assets via the dashboard visualization. The control of the one or
more assets
provides one or more technical improvements such as, but not limited to,
necessary
interaction with the dashboard visualization and/or improved performance of
the one or more
assets.
[00194] In one or more embodiments, the method 2800 also includes configuring
the
dashboard visualization to facilitate creation of one or more work orders for
the one or more
assets in the portfolio of assets. The creation of the one or more work orders
provides one or
more technical improvements such as, but not limited to, necessary interaction
with the
dashboard visualization and/or improved performance of the one or more assets.
[00195] In one or more embodiments, the method 2800 also includes configuring
the
metrics across different hierarchy instances. The configuring the metrics
provides one or
more technical improvements such as, but not limited to, improving accuracy of
the
dashboard visualization. In one or more embodiments, the configuring the
metrics comprises
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selecting one or more metrics for presentation via the dashboard
visualization. In one or
more embodiments, the configuring the metrics additionally or alternatively
comprises
configuring a view of the metrics via the dashboard visualization. For
example, in one or
more embodiments, metrics rollup across different hierarchy instances is
dynamically
configured, one or more exclusions with respect to the metrics is determined,
different views
with respect to the metrics are dynamically configured, and/or metrics rollup
calculation of
the metrics is dynamically configured. In one or more embodiments, the
configuring the
metrics additionally or alternatively comprises performing a metrics
calculation in real-time
with respect to presentation of the metrics via the dashboard visualization.
In one or more
embodiments, the configuring the metrics additionally or alternatively
comprises providing
current metrics data and historical trend data for the asset hierarchy
associated with the
portfolio of assets. For example, in one or more embodiments, metrics
calculation and/or
rollup is provided in real time to provide, for example, accurate aggregated
metrics values
with reduced storage requirements (e.g., min 67 values for 1 year) and/or to
provide
visualization of current metric and historical trends related to the portfolio
of assets.
[00196] FIG. 29 illustrates a method 2900 for aggregating data across a
portfolio of assets
to create a dashboard visualization of prioritized actions for the portfolio
of assets, in
accordance with one or more embodiments described herein. The method 2900 is
associated
with the asset performance management computer system 302, for example. For
instance, in
one or more embodiments, the method 2900 is executed at a device (e.g. the
asset
performance management computer system 302) with one or more processors and a
memory.
In one or more embodiments, the method 2900 begins at block 2902 that receives
(e.g., by the
prioritized actions component 326 and/or the dashboard visualization component
308) a
request to generate a dashboard visualization associated with a portfolio of
assets, the request
comprising an asset descriptor describing one or more assets in the portfolio
of assets. The
request to generate the dashboard visualization provides one or more technical
improvements
such as, but not limited to, facilitating interaction with a computing device
and/or extended
functionality for a computing device.
[00197] At block 2904, it is determined whether the request is
processed. If no, block
2904 is repeated to determine whether the request is processed. If yes, the
method 2900
proceeds to block 2906. In response to the request, block 2906 that obtains,
based on the
asset descriptor, aggregated data associated with the portfolio of assets. The
obtaining the
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aggregated data based on the asset descriptor provides one or more technical
improvements
such as, but not limited to, extended functionality for a computing device.
[00198] The method 2900 also includes a block 2908 that, in response to the
request,
determines (e.g., by the prioritized actions component 326) prioritized
actions for the
portfolio of assets based on attributes for the aggregated data. The
determining the
prioritized actions for the portfolio of assets provides one or more technical
improvements
such as, but not limited to, improving accuracy of the dashboard
visualization. In one or
more embodiments, the determining the prioritized actions for the portfolio of
assets includes
determining the prioritized actions for the portfolio of assets based on a
digital twin model
associated with one or more assets from the portfolio of assets. Additionally
or alternatively,
in one or more embodiments, the determining the prioritized actions for the
portfolio of assets
includes determining the prioritized actions for the portfolio of assets based
on a digital twin
model associated with an operator identity associated with one or more assets
from the
portfolio of assets.
[00199] The method 2900 also includes a block 2910 that, in response to the
request,
provides (e.g., by the dashboard visualization component 308) the dashboard
visualization to
an electronic interface of a computing device, the dashboard visualization
comprising the
prioritized actions for the portfolio of assets. The providing the dashboard
visualization with
the prioritized actions for the portfolio of assets provides one or more
technical
improvements such as, but not limited to, what and/or how to present
information via a
computing device.
[00200] In one or more embodiments, the request additionally or alternatively
includes a
user identifier describing a user role for a user associated with access of
the dashboard
visualization via the electronic interface. Furthermore, in one or more
embodiments, the
obtaining the aggregated data additionally or alternatively includes obtaining
the aggregated
data based on the user identifier. The obtaining the aggregated data based on
the user
identifier provides one or more technical improvements such as, but not
limited to, extended
functionality for a computing device. In one or more embodiments, the method
2900 also
includes configuring the dashboard visualization based on the user identifier.
The
configuring the dashboard visualization based on the user identifier provides
one or more
technical improvements such as, but not limited to, what and/or how to present
information
via a computing device.
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[00201] In one or more embodiments, the request additionally or alternatively
includes a
metrics context identifier describing context for metrics. Furthermore, in one
or more
embodiments, the obtaining the aggregated data includes obtaining the
aggregated data based
on the metrics context identifier. The obtaining the aggregated data based on
the metrics
context identifier provides one or more technical improvements such as, but
not limited to,
extended functionality for a computing device. In one or more embodiments,
different types
of aggregates such as maximum, minimum, count, sum, and/or average are
supported.
Additionally, in one or more embodiments, a calculation is custom defined
based on the
metrics being aggregated at different levels to, for example, provide improved
extensibility.
[00202] In one or more embodiments, the request additionally or alternatively
includes a
time interval identifier (e.g., a reporting time interval identifier)
describing an interval of time
for the metrics. Furthermore, in one or more embodiments, the obtaining the
aggregated data
includes obtaining the aggregated data based on the time interval identifier
(e.g., the reporting
time interval identifier). The obtaining the aggregated data based on the time
interval
identifier (e.g., the reporting time interval identifier) provides one or more
technical
improvements such as, but not limited to, extended functionality for a
computing device.
[00203] In one or more embodiments, the method 2900 also includes grouping the
prioritized actions for the portfolio of assets based on relationships between
the aggregated
data, the dashboard visualization configuring the prioritized actions based on
the grouping of
the prioritized actions for the portfolio of assets. The grouping the
prioritized actions
provides one or more technical improvements such as, but not limited to, what
and/or how to
present information via a computing device.
[00204] In one or more embodiments, the method 2900 also includes ranking,
based on
impact of respective prioritized actions with respect to the portfolio of
assets, the prioritized
actions to generate a list of the prioritized actions. Additionally or
alternatively, in one or
more embodiments, the method 2900 also includes providing the list of the
prioritized actions
to the electronic interface via the dashboard visualization The ranking
provides one or more
technical improvements such as, but not limited to, what and/or how to present
information
via a computing device.
[00205] In one or more embodiments, the method 2900 also includes determining
a list of
the prioritized actions for the portfolio of assets based on metrics
associated with the
aggregated data. Additionally or alternatively, in one or more embodiments,
the method
2900 also includes providing the list of prioritized actions to the electronic
interface via the
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dashboard visualization. The determining the list of the prioritized actions
provides one or
more technical improvements such as, but not limited to, what and/or how to
present
information via a computing device.
[00206] In one or more embodiments, the method 2900 also includes determining
a list of
prioritized actions for the portfolio of assets based on the metrics.
Additionally, in one or
more embodiments, the method 2900 also includes providing the list of
prioritized actions to
the electronic interface via the dashboard visualization. The providing the
list of prioritized
actions to the electronic interface provides one or more technical
improvements such as, but
not limited to, what and/or how to present information via a computing device.
[00207] In one or more embodiments, the determining the metrics includes
determining
the metrics for different hierarchy level of assets Furthermore, in one or
more embodiments,
the providing the dashboard visualization includes providing the metrics for
the different
hierarchy level of assets. The providing the metrics for the different
hierarchy level of assets
provides one or more technical improvements such as, but not limited to, what
and/or how to
present information via a computing device.
[00208] In one or more embodiments, the method 2900 also includes aggregating
multiple
types of metrics for the portfolio of assets based on the aggregated data. The
aggregating the
multiple types of metrics provides one or more technical improvements such as,
but not
limited to, improving accuracy of the dashboard visualization.
[00209] In one or more embodiments, the method 2900 also includes determining
an alerts
list associated with one or more recommendations for the portfolio of assets
based on the
prioritized actions for the portfolio of assets. Additionally, in one or more
embodiments, the
method 2900 also includes providing the alerts list to the electronic
interface via the
dashboard visualization. The providing the alerts list to the electronic
interface provides one
or more technical improvements such as, but not limited to, what and/or how to
present
information via a computing device.
[00210] In one or more embodiments, the method 2900 also includes modeling of
the
aggregated data based on different hierarchy level of assets. The modeling of
the aggregated
data provides one or more technical improvements such as, but not limited to,
extending
functionality of the dashboard visualization.
[00211] In one or more embodiments, the method 2900 also includes configuring
the
dashboard visualization to facilitate viewing of performance of the portfolio
of assets with
respect to different hierarchy level of assets. The providing the configuring
the dashboard
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visualization provides one or more technical improvements such as, but not
limited to,
extending functionality of the dashboard visualization and providing what
and/or how to
present information via a computing device.
[00212] In one or more embodiments, the method 2900 also includes configuring
the
dashboard visualization to provide individual control of the one or more
assets in the
portfolio of assets via the dashboard visualization. The control of the one or
more assets
provides one or more technical improvements such as, but not limited to,
necessary
interaction with the dashboard visualization and/or improved performance of
the one or more
assets.
[00213] In one or more embodiments, the method 2900 also includes configuring
the
dashboard visualization to facilitate creation of one or more work orders for
the one or more
assets in the portfolio of assets. The creation of the one or more work orders
provides one or
more technical improvements such as, but not limited to, necessary interaction
with the
dashboard visualization and/or improved performance of the one or more assets.
[00214] FIG. 30 illustrates a method 3000 for performing a natural language
query to
obtain data across a portfolio of assets and to create a dashboard
visualization report for the
portfolio of assets, in accordance with one or more embodiments described
herein. The
method 3000 is associated with the asset performance management computer
system 302, for
example. For instance, in one or more embodiments, the method 3000 is executed
at a device
(e.g., the asset performance management computer system 302) with one or more
processors
and a memory. In one or more embodiments, the method 3000 begins at block 3002
that
receives (e.g., by the virtual assistant component 336 and/or the dashboard
visualization
component 308) a voice input, the voice input comprising a request to generate
a dashboard
visualization associated with a portfolio of assets, the voice input
comprising voice input
data, the voice input data comprising one or more asset insight requests
associated with the
portfolio of assets. The voice input provides one or more technical
improvements such as,
but not limited to, facilitating interaction with a computing device and/or
extended
functionality for a computing device.
[00215] At block 3004, it is determined whether the request is
processed. If no, block
3004 is repeated to determine whether the voice input is processed. If yes,
the method 3000
proceeds to block 3006. In response to the voice input, the method 3000
includes a block
3006 that performs (e.g., by the virtual assistant component 336) a natural
language query
with respect to the voice input data, the natural language query obtaining one
or more
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attributes associated with the one or more asset insight requests. The natural
language query
provides one or more technical improvements such as, but not limited to,
extended
functionality for a computing device and/or improving accuracy of a dashboard
visualization.
[00216] In certain embodiments, the performing the natural language query
comprises
querying a natural language database based on the voice input to determine the
one or more
attributes associated with the one or more asset insight requests. In certain
embodiments, the
performing the natural language query comprises classifying one or more
portions of the
voice input with a tag to determine the one or more attributes associated with
the one or more
asset insight requests. In certain embodiments, the performing the natural
language query
comprises performing a fuzzy matching technique with respect to the voice
input data to
determine the one or more attributes associated with the one or more asset
insight requests.
In certain embodiments, the performing the natural language query comprises
providing the
voice input data to a neural network model configured for determining the one
or more
attributes associated with the one or more asset insight requests. In certain
embodiments, the
performing the natural language query comprises obtaining one or more asset
identifiers
associated with the one or more asset insight requests, and the obtaining the
aggregated data
comprising obtaining the aggregated data based on the one or more asset
identifiers. In
certain embodiments, the performing the natural language query comprises
obtaining time
data associated with the one or more asset insight requests, and the obtaining
the aggregated
data comprising obtaining the aggregated data based on the time data.
[00217]
The method 3000 also includes a block 3008 that, in response to the voice
input,
obtains (e.g., by the virtual assistant component 336) aggregated data
associated with the
portfolio of assets abed on the one or more attributes. The obtaining the
aggregated data
provides one or more technical improvements such as, but not limited to,
improving accuracy
of the dashboard visualization. In certain embodiments, the obtaining the
aggregated data
comprising grouping, based on the one or more attributes, the aggregated data
based on one
or more relationships between assets from the portfolio of assets. In certain
embodiments,
the obtaining the aggregated data comprising aggregating first output data
from the first
model and second output data from the second model to determine at least a
portion of the
aggregated data.
[00218] The method 3000 also includes a block 3010 that, in response to the
voice input,
determines (e.g., by the virtual assistant component 336) one or more asset
insights related to
the portfolio of assets based on the aggregated data. The determining the one
or more asset
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insights provides one or more technical improvements such as, but not limited
to, improving
accuracy of the dashboard visualization.
[00219] The method 3000 also includes a block 3012 that, in response to the
voice input,
provides (e.g., by the dashboard visualization component 308) the dashboard
visualization to
an electronic interface of a computing device, the dashboard visualization
comprising the one
or more asset insights for the portfolio of assets. The providing the
dashboard visualization
with the prioritized actions for the portfolio of assets provides one or more
technical
improvements such as, but not limited to, what and/or how to present
information via a
computing device.
[00220] In certain embodiments, the providing the dashboard visualization
comprises
providing a dashboard visualization element configured to present sensor data
related to the
portfolio of assets. In certain embodiments, the providing the dashboard
visualization
comprises providing a dashboard visualization element configured to present
control data
related to the portfolio of assets. In certain embodiments, the providing the
dashboard
visualization comprises providing a dashboard visualization element configured
to present
labor management data related to the portfolio of assets. In certain
embodiments, the
providing the dashboard visualization comprises providing a dashboard
visualization element
configured to present warehouse execution data related to the portfolio of
assets. In certain
embodiments, the providing the dashboard visualization comprises providing a
dashboard
visualization element configured to present inventory data related to the
portfolio of assets.
[00221] In certain embodiments, the providing the dashboard visualization
comprises
providing a list of prioritized actions for the portfolio of assets based on
the one or more asset
insights. In certain embodiments, the providing the dashboard visualization
comprises
providing one or more metrics for the portfolio of assets based on the one or
more asset
insights. In certain embodiments, the method 3000 additionally or
alternatively includes
determining one or more actions associated with the portfolio of assets based
on the one or
more metrics. In certain embodiments, the providing the dashboard
visualization comprises
providing an alerts list associated with the one or more asset insights for
the portfolio of
assets. In certain embodiments, the method 3000 additionally or alternatively
includes
configuring the dashboard visualization based on the one or more attributes
associated with
the voice input. In certain embodiments, the method 3000 additionally or
alternatively
includes configuring the dashboard visualization for remote control of one or
more assets
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from the portfolio of assets based on the one or more attributes associated
with the voice
input.
[00222] In certain embodiments, the method 3000 additionally or alternatively
includes
configuring a 3D model of an asset from the portfolio of assets for the
dashboard
visualization based on the one or more attributes associated with the voice
input. In certain
embodiments, the method 3000 additionally or alternatively includes filtering
one or more
events associated with the asset related to the 3D model based on the one or
more attributes
associated with the voice input. In certain embodiments, the method 3000
additionally or
alternatively includes configuring the dashboard visualization for real-time
collaboration
between two or more computing devices based on the one or more attributes
associated with
the voice input. In certain embodiments, the method 3000 additionally or
alternatively
includes applying the one or more attributes to at least a first model
associated with a first
type of asset insight and a second model associated with a second type of
asset insight.
[00223] FIG. 31 illustrates a method 3100 for generating a voice input to
create a
dashboard visualization report for a portfolio of assets, in accordance with
one or more
embodiments described herein. The method 3100 is associated with the computing
device
402, for example. For instance, in one or more embodiments, the method 3100 is
executed at
a device (e.g., the computing device 402) with one or more processors and a
memory. In one
or more embodiments, the method 3100 begins at block 3102 that generates
(e.g., by the
computing device 402) a voice input, the voice input comprising a request to
generate a
dashboard visualization associated with a portfolio of assets, the voice input
comprising voice
input data, the voice input data comprising one or more asset insight requests
associated with
the portfolio of assets. The voice input provides one or more technical
improvements such
as, but not limited to, facilitating interaction with a computing device
and/or extended
functionality for a computing device.
[00224] At block 3104, it is determined whether the request is
processed. If no, block
3104 is repeated to determine whether the voice input is processed. If yes,
the method 3100
proceeds to block 3106. In response to the voice input, the method 3100
includes a block
3106 that receives (e.g., by the computing device 402) one or more dashboard
visualization
elements associated with one or more asset insights related to the portfolio
of assets, the one
or more dashboard visualization elements generated based on one or more
attributes
associated with the voice input data. The one or more dashboard visualization
elements
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provide one or more technical improvements such as, but not limited to,
extended
functionality for a computing device and/or improving accuracy of a dashboard
visualization.
[00225] The method 3100 includes a block 3108 that, in response to the voice
input,
renders (e.g., by the computing device 402) the one or more dashboard
visualization elements
via the dashboard visualization for an electronic interface of a computing
device, the
dashboard visualization comprising the one or more asset insights related to
the portfolio of
assets. The rendering the one or more dashboard visualization elements
provides one or more
technical improvements such as, but not limited to, what and/or how to present
information
via a computing device.
[00226] In certain embodiments, the rendering the one or more dashboard
visualization
elements comprises rendering a dashboard visualization element configured to
present sensor
data related to the portfolio of assets In certain embodiments, the rendering
the one or more
dashboard visualization elements comprises rendering a dashboard visualization
element
configured to present control data related to the portfolio of assets. In
certain embodiments,
the rendering the one or more dashboard visualization elements comprises
rendering a
dashboard visualization element configured to present labor management data
related to the
portfolio of assets. In certain embodiments, the rendering the one or more
dashboard
visualization elements comprises rendering a dashboard visualization element
configured to
present warehouse execution data related to the portfolio of assets. In
certain embodiments,
the rendering the one or more dashboard visualization elements comprises
rendering a
dashboard visualization element configured to present inventory data related
to the portfolio
of assets.
[00227] In certain embodiments, the rendering the dashboard visualization
comprises
rendering a list of prioritized actions for the portfolio of assets. In
certain embodiments, the
rendering the dashboard visualization comprises rendering a visualization
associated with one
or more metrics for the portfolio of assets In certain embodiments, the method
3100
additionally or alternatively includes initiating one or more actions
associated with the
portfolio of assets via the dashboard visualization. In certain embodiments,
the rendering the
dashboard visualization comprises rendering an alerts list associated with the
one or more
asset insights for the portfolio of assets. In certain embodiments, the method
3100
additionally or alternatively includes providing remote control of one or more
assets from the
portfolio of assets via the dashboard visualization
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[00228] In certain embodiments, the rendering the dashboard visualization
comprises
rendering a 3D model of an asset from the portfolio of assets for the
dashboard visualization.
In certain embodiments, the rendering the dashboard visualization comprises
rendering a
visualization associated with one or more events for the asset related to the
3D model. In
certain embodiments, the method 3100 additionally or alternatively includes
initiating real-
time collaboration between two or more computing devices via the dashboard
visualization.
[00229] In some example embodiments, certain ones of the operations herein can
be
modified or further amplified as described below. Moreover, in some
embodiments additional
optional operations can also be included. It should be appreciated that each
of the
modifications, optional additions or amplifications described herein can be
included with the
operations herein either alone or in combination with any others among the
features described
herein.
[00230] The foregoing method descriptions and the process flow diagrams are
provided
merely as illustrative examples and are not intended to require or imply that
the steps of the
various embodiments must be performed in the order presented. As will be
appreciated by
one of skill in the art the order of steps in the foregoing embodiments can be
performed in
any order. Words such as "thereafter," "then," "next," etc. are not intended
to limit the order
of the steps; these words are simply used to guide the reader through the
description of the
methods. Further, any reference to claim elements in the singular, for
example, using the
articles "a,'' "an" or "the" is not to be construed as limiting the element to
the singular.
[00231] FIG. 32 depicts an example system 3200 that may execute techniques
presented
herein. FIG. 32 is a simplified functional block diagram of a computer that
may be
configured to execute techniques described herein, according to exemplary
embodiments of
the present disclosure. Specifically, the computer (or "platform" as it may
not be a single
physical computer infrastructure) may include a data communication interface
3260 for
packet data communication. The platform also may include a central processing
unit
("CPU") 3220, in the form of one or more processors, for executing program
instructions.
The platform may include an internal communication bus 3210, and the platform
also may
include a program storage and/or a data storage for various data files to be
processed and/or
communicated by the platform such as ROM 3230 and RAM 3240, although the
system 3200
may receive programming and data via network communications. The system 3200
also may
include input and output ports 3250 to connect with input and output devices
such as
keyboards, mice, touchscreens, monitors, displays, etc. Of course, the various
system
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functions may be implemented in a distributed fashion on a number of similar
platforms, to
distribute the processing load. Alternatively, the systems may be implemented
by appropriate
programming of one computer hardware platform.
[00232] The general discussion of this disclosure provides a brief,
general description of a
suitable computing environment in which the present disclosure may be
implemented. In one
embodiment, any of the disclosed systems, methods, and/or graphical user
interfaces may be
executed by or implemented by a computing system consistent with or similar to
that
depicted and/or explained in this disclosure. Although not required, aspects
of the present
disclosure are described in the context of computer-executable instructions,
such as routines
executed by a data processing device, e.g., a server computer, wireless
device, and/or
personal computer. Those skilled in the relevant art will appreciate that
aspects of the present
disclosure can be practiced with other communications, data processing, or
computer system
configurations, including: Internet appliances, hand-held devices (including
personal digital
assistants ("PDAs")), wearable computers, all manner of cellular or mobile
phones (including
Voice over IP ("VoIP") phones), dumb terminals, media players, gaming devices,
virtual
reality devices, multi-processor systems, microprocessor-based or programmable
consumer
electronics, set-top boxes, network PCs, mini-computers, mainframe computers,
and the like.
Indeed, the terms "computer," "server," and the like, are generally used
interchangeably
herein, and refer to any of the above devices and systems, as well as any data
processor.
[00233] Aspects of the present disclosure may be embodied in a special purpose
computer
and/or data processor that is specifically programmed, configured, and/or
constructed to
perform one or more of the computer-executable instructions explained in
detail herein.
While aspects of the present disclosure, such as certain functions, are
described as being
performed exclusively on a single device, the present disclosure also may be
practiced in
distributed environments where functions or modules are shared among disparate
processing
devices, which are linked through a communications network, such as a Local
Area Network
("LAN"), Wide Area Network ("WAN"), and/or the Internet. Similarly, techniques
presented
herein as involving multiple devices may be implemented in a single device. In
a distributed
computing environment, program modules may be located in both local and/or
remote
memory storage devices.
[00234] Aspects of the present disclosure may be stored and/or distributed on
non-
transitory computer-readable media, including magnetically or optically
readable computer
discs, hard-wired or preprogrammed chips (e.g., EEPROM semiconductor chips),
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nanotechnology memory, biological memory, or other data storage media.
Alternatively,
computer implemented instructions, data structures, screen displays, and other
data under
aspects of the present disclosure may be distributed over the Internet and/or
over other
networks (including wireless networks), on a propagated signal on a
propagation medium
(e.g., an electromagnetic wave(s), a sound wave, etc.) over a period of time,
and/or they may
be provided on any analog or digital network (packet switched, circuit
switched, or other
scheme).
[00235] Program aspects of the technology may be thought of as "products" or
"articles of
manufacture" typically in the form of executable code and/or associated data
that is carried
on or embodied in a type of machine-readable medium. "Storage" type media
include any or
all of the tangible memory of the computers, processors or the like, or
associated modules
thereof, such as various semiconductor memories, tape drives, disk drives and
the like, which
may provide non-transitory storage at any time for the software programming.
All or
portions of the software may at times be communicated through the Internet or
various other
telecommunication networks. Such communications, for example, may enable
loading of the
software from one computer or processor into another, for example, from a
management
server or host computer of the mobile communication network into the computer
platform of
a server and/or from a server to the mobile device. Thus, another type of
media that may bear
the software elements includes optical, electrical and electromagnetic waves,
such as used
across physical interfaces between local devices, through wired and optical
landline networks
and over various air-links. The physical elements that carry such waves, such
as wired or
wireless links, optical links, or the like, also may be considered as media
bearing the
software. As used herein, unless restricted to non-transitory, tangible
"storage" media, terms
such as computer or machine "readable medium" refer to any medium that
participates in
providing instructions to a processor for execution.
[00236] It is to be appreciated that 'one or more' includes a function being
performed by
one element, a function being performed by more than one element, e.g., in a
distributed
fashion, several functions being performed by one element, several functions
being
performed by several elements, or any combination of the above.
[00237] Moreover, it will also be understood that, although the terms
first, second, etc. are,
in some instances, used herein to describe various elements, these elements
should not be
limited by these terms. These terms are only used to distinguish one element
from another.
For example, a first contact could be termed a second contact, and, similarly,
a second
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contact could be termed a first contact, without departing from the scope of
the various
described embodiments. The first contact and the second contact are both
contacts, but they
are not the same contact.
[00238] The terminology used in the description of the various described
embodiments
herein is for the purpose of describing particular embodiments only and is not
intended to be
limiting. As used in the description of the various described embodiments and
the appended
claims, the singular forms "a", "an" and "the" are intended to include the
plural forms as
well, unless the context clearly indicates otherwise. It will also be
understood that the term
-and/or" as used herein refers to and encompasses any and all possible
combinations of one
or more of the associated listed items. It will be further understood that the
terms "includes,"
"including," "comprises," and/or "comprising," when used in this
specification, specify the
presence of stated features, integers, steps, operations, elements, and/or
components, but do
not preclude the presence or addition of one or more other features, integers,
steps,
operations, elements, components, and/or groups thereof
[00239] As used herein, the term "if' is, optionally, construed to mean "when"
or 'upon"
or "in response to determining- or "in response to detecting," depending on
the context.
Similarly, the phrase "if it is determined" or "if [a stated condition or
event] is detected" is,
optionally, construed to mean "upon determining" or "in response to
determining" or "upon
detecting [the stated condition or event]" or "in response to detecting [the
stated condition or
event]," depending on the context.
[00240] The systems, apparatuses, devices, and methods disclosed
herein are described in
detail by way of examples and with reference to the figures. The examples
discussed herein
are examples only and are provided to assist in the explanation of the
apparatuses, devices,
systems, and methods described herein. None of the features or components
shown in the
drawings or discussed below should be taken as mandatory for any specific
implementation
of any of these the apparatuses, devices, systems or methods unless
specifically designated as
mandatory. For ease of reading and clarity, certain components, modules, or
methods may be
described solely in connection with a specific figure. In this disclosure, any
identification of
specific techniques, arrangements, etc. are either related to a specific
example presented or
are merely a general description of such a technique, arrangement, etc.
Identifications of
specific details or examples are not intended to be, and should not be,
construed as mandatory
or limiting unless specifically designated as such. Any failure to
specifically describe a
combination or sub-combination of components should not be understood as an
indication
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that any combination or sub-combination is not possible. It will be
appreciated that
modifications to disclosed and described examples, arrangements,
configurations,
components, elements, apparatuses, devices, systems, methods, etc. can be made
and may be
desired for a specific application. Also, for any methods described,
regardless of whether the
method is described in conjunction with a flow diagram, it should be
understood that unless
otherwise specified or required by context, any explicit or implicit ordering
of steps
performed in the execution of a method does not imply that those steps must be
performed in
the order presented but instead may be performed in a different order or in
parallel.
1002411 Throughout this disclosure, references to components or modules
generally refer
to items that logically can be grouped together to perform a function or group
of related
functions. Like reference numerals are generally intended to refer to the same
or similar
components. Components and modules can be implemented in software, hardware,
or a
combination of software and hardware. The term "software" is used expansively
to include
not only executable code, for example machine-executable or machine-
interpretable
instructions, but also data structures, data stores and computing instructions
stored in any
suitable electronic format, including firmware, and embedded software. The
terms
"information" and "data" are used expansively and includes a wide variety of
electronic
information, including executable code; content such as text, video data, and
audio data,
among others; and various codes or flags. The terms "information," "data," and
"content" are
sometimes used interchangeably when permitted by context.
[00242] The hardware used to implement the various illustrative
logics, logical blocks,
modules, and circuits described in connection with the aspects disclosed
herein can include a
general purpose processor, a digital signal processor (DSP), a special-purpose
processor such
as an application specific integrated circuit (ASIC) or a field programmable
gate array
(FPGA), a programmable logic device, discrete gate or transistor logic,
discrete hardware
components, or any combination thereof designed to perform the functions
described herein.
A general-purpose processor can be a microprocessor, but, in the alternative,
the processor
can be any processor, controller, microcontroller, or state machine. A
processor can also be
implemented as a combination of computing devices, e.g., a combination of a
DSP and a
microprocessor, a plurality of microprocessors, one or more microprocessors in
conjunction
with a DSP core, or any other such configuration. Alternatively, or in
addition, some steps or
methods can be performed by circuitry that is specific to a given function.
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[00243] In one or more example embodiments, the functions described herein can
be
implemented by special-purpose hardware or a combination of hardware
programmed by
firmware or other software. In implementations relying on firmware or other
software, the
functions can be performed as a result of execution of one or more
instructions stored on one
or more non-transitory computer-readable media and/or one or more non-
transitory
processor-readable media. These instructions can be embodied by one or more
processor-
executable software modules that reside on the one or more non-transitory
computer-readable
or processor-readable storage media. Non-transitory computer-readable or
processor-readable
storage media can in this regard comprise any storage media that can be
accessed by a
computer or a processor. By way of example but not limitation, such non-
transitory
computer-readable or processor-readable media can include random access memory
(RAM),
read-only memory (ROM), electrically erasable programmable read-only memory
(EEPROM), FLASH memory, disk storage, magnetic storage devices, or the like.
Disk
storage, as used herein, includes compact disc (CD), laser disc, optical disc,
digital versatile
disc (DVD), floppy disk, and Blu-ray discTM, or other storage devices that
store data
magnetically or optically with lasers. Combinations of the above types of
media are also
included within the scope of the terms non-transitory computer-readable and
processor-
readable media. Additionally, any combination of instructions stored on the
one or more non-
transitory processor-readable or computer-readable media can be referred to
herein as a
computer program product.
[00244] Many modifications and other embodiments of the inventions set forth
herein will
come to mind to one skilled in the art to which these inventions pertain
having the benefit of
teachings presented in the foregoing descriptions and the associated drawings.
Although the
figures only show certain components of the apparatus and systems described
herein, it is
understood that various other components can be used in conjunction with the
supply
management system. Therefore, it is to be understood that the inventions are
not to be limited
to the specific embodiments disclosed and that modifications and other
embodiments are
intended to be included within the scope of the appended claims. Moreover, the
steps in the
method described above can not necessarily occur in the order depicted in the
accompanying
diagrams, and in some cases one or more of the steps depicted can occur
substantially
simultaneously, or additional steps can be involved. Although specific terms
are employed
herein, they are used in a generic and descriptive sense only and not for
purposes of
limitation.
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[00245] It is intended that the specification and examples be considered as
exemplary
only, with a true scope and spirit of the disclosure being indicated by the
following claims.
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