Note: Descriptions are shown in the official language in which they were submitted.
1
Holding Tank Monitoring System Based On Wireless Sensor Network And Monitoring
Method
FIELD OF INVENTION
[001] The current invention in some embodiments thereof, relates to the field
of the Internet
.. of Things and artificial intelligence. More specifically, the present
invention relates to using the
IoT structure and machine learning technologies to build a new water quality
monitoring
system with a real-time monitoring and corrective capability.
RELATED APPLICATIONS
[002] In some aspects, this application may claim benefit from several patent
applications and
prior art literature including:
[003] U510436615B2: A sensing system includes a sensor assembly that is
communicably
connected to a computer system, such as a server or a cloud computing system.
The sensor
assembly includes a plurality of sensors that sense a variety of different
physical phenomena.
The sensor assembly featurizes the raw sensor data and transmits the
featurized data to the
computer system. Through machine learning, the computer system then trains a
classifier to
serve as a virtual sensor for an event that is correlated to the data from one
or more sensor
streams within the featurized sensor data. The virtual sensor can then
subscribe to the relevant
sensor feeds from the sensor assembly and monitor for subsequent occurrences
of the event.
Higher order virtual sensors can receive the outputs from lower order virtual
sensors to infer
nonbinary details about the environment in which the sensor assemblies are
located.
[004] KR20140114089A: The present invention relates to horticultural facility
controlling
system and, more specifically, to horticultural facility monitoring and
controlling system and
controlling method which can monitor environment and mechanical devices in a
horticultural
facility and control the same with a smart device and a computer connected
with internet. The
system in the present invention accurately controls temperature, humidity,
CO2, and light
condition of a horticultural facility according to an optimum control value
based on database
data to have an optimum growing environment for growing steps of crops to
reduce energy and
make remote monitoring of facility environment and multistage control of such
as devices,
Date Recue/Date Received 2021-08-12
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window, and curtain possible from outside to improve the quality of crops
produced in the
horticultural facility, and comprises a camera unit, a sensor unit, a control
means, a local
control unit, and a center server.
[005] CN112003948A: The invention provides an intelligent agricultural
Internet of things
system, which comprises an agricultural data collection module, an
agricultural data analysis
module, a remote server and a data early warning module, wherein the
agricultural data
collection module remotely transmits data to the agricultural data analysis
module through a
satellite, the agricultural data analysis module is in signal connection with
the remote server
through a local area communication network, the remote server collects and
counts agricultural
information and then transmits the agricultural information to the data early
warning module in
an electric signal mode, the data early warning module displays the
agricultural data
information of each planting base, and the data early warning module comprises
agricultural
environment information, planting crop growth information of the same planting
base and
development information of a planting crop of each planting base, the
intelligent agricultural
Internet of things system can help a grower to analyze the planting crop
growth information of
the same planting base and the development information of the planting crop of
each planting
base, thereby improving the judgment capability of the development planning of
the self
organism and improving the agricultural planting efficiency.
[006] CN110825058A: The invention discloses an agricultural real-time
monitoring system,
which comprises a data acquisition system, a data transmission system and an
analysis control
system, wherein the data acquisition system comprises a soil humidity sensor,
a soil nutrient
analyzer, a meteorological monitoring station, an insect condition analysis
and forecast lamp, a
fixed spore capture instrument, a seedling condition and disaster condition
monitoring camera
and a water and fertilizer all-in-one machine, the data transmission system is
a GPRS mobile
wireless network, and the analysis control system is ARM upper computer
monitoring software
and comprises a basic information management module, a system parameter
setting module, a
real-time data monitoring module, a control module, a data dynamic analysis
module and an
abnormity early warning module. The invention can monitor, analyze and
actively deal with the
agricultural system in real time, improve the working efficiency and solve the
labor cost.
Date Recue/Date Received 2021-08-12
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[007] However, the above cited prior art does not provide a real-time sensor
data analysis
using a suitably trained machine learning algorithm, whose output is
actionable signals
processable by an actuator on the holding tank to perform a corrective action
on the holding
tank to maintain the environment at optimal conditions.
Date Recue/Date Received 2021-08-12
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BACKGROUND OF THE INVENTION
[009] With the aquaculture industrialization and improvement of precision
farming, for
industrialized farming, keeping track of dynamic changes in the water quality
environment in
time is an important problem to be solved urgently. Each aquaculture animal or
plant needs a
water quality environment suitable for its survival. If the water quality
environment can meet
requirements, aquaculture animals and plants can grow and reproduce. If the
water in the water
quality environment is contaminated somehow, or some water quality indexes
exceed the range
of adaptation and tolerance of aquaculture animals or plants, a large number
of aquaculture
animals and plants may die, resulting in direct economic losses.
[0010] In recent years, a great many scholars have contributed a great deal of
research on
aquaculture monitoring technologies, and the precision management level of
traditional
aquaculture has been effectively improved. In the prior art, single sensors
are often used for
data detection, for example, flow meters are mounted on the water inlet and
outlet pipes, and
salinity and dissolved oxygen concentration sensors are mounted in the tank.
Moreover, signal
transmitters and data display units can only be mounted in the vicinity of the
sensors. In some
improvements, sensor data may be collected and transmitted to a remote central
computer for
remote monitoring. However, there is still need for manual periodic inspection
of record data,
and emergency measures are taken after abnormalities are found.
[0011] Moreover, there is no connection between the sensors and between the
sensors and
actuators, resulting in a lack of a unified monitoring and control mechanism,
so it is impossible
to use computer and network technologies for automatic control. In most cases,
manual
observation or periodic inspection is required to measure and record data,
which is laborious,
error-prone, and poor real-time performance. To solve these problems
associated with the prior
art and to improve the overall technology, the current invention discloses a
holding tank
monitoring system based on a wireless sensor network and a monitoring method
provided
thereof characterized by an unattended operation and automated execution by
obtaining
environmental data of a holding tank in real-time, and analyzing the data
using a suitably
trained machine learning algorithm and causing a control action on the
actuator to perform a
corrective action on the holding tank for optimal operation of the system.
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SUMMARY
[0013] The following summary is an explanation of some of the general
inventive steps for the
device, method, manufacture and apparatus in the description. This summary is
not an
extensive overview of the invention and does not intend to limit the scope
beyond what is
described and claimed as a summary.
[0014] The current invention in some embodiments thereof, relates to the field
of the Internet
of Things and artificial intelligence. More specifically, the present
invention relates to using an
IoT structure and a machine learning model to build a new water quality
monitoring system
with a real-time monitoring and corrective capability.
[0015] In summary, the disclosed system comprises of a holding tank capable of
supporting an
aquatic life form, a plurality of sensors on the holding tank capable of
determining at least one
or more conditions in the holding tank, and capable of transmitting detected
condition to a
remote computer, a micro-controller, an actuator with a corrective measure for
the at least one
or more conditions in the holding tank determined by the sensor and a
communication module
with a network receiver, the module capable of receiving signals from a remote
computer,
where received signals are processed by the micro-controller to activate the
actuator to perform
a corrective measure for the at least one or more conditions in the holding
tank determined by
the sensor.
[0016] Further comprised in the system is a remote computer comprising of at
least a processor,
memory and storage, capable of receiving from above sensors detected condition
for at least
one or more conditions in the holding tank. The computer receives the
conditions from the
sensor, stores received condition in a database. The computer uses a suitably
trained machine
learning algorithm to determine the conditions that would be optimal for the
aquatic life form
in the holding tank and generates a corrective signal e.g. temperature,
turbidity, mineral
composition etc, and transmits the signal to the communication module, which
makes the
actuator to take a corrective action, and finally a monitoring and control
device.
[0017] In an exemplary embodiment, the holding tank is any such tank capable
of supporting
an aquatic life form e.g. fish, plants or any such in an aqueous environment.
For purposes of
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this this disclosure the aquatic life form anticipated is a fish, however,
other life form are also
anticipated. Water quality is the most important factor affecting fish health
and performance in
aquaculture production systems. There is need to understand the water quality
requirements of
the fish under culture very well. Fish live and are totally dependent on the
water they live in for
.. all their needs. Different fish species have different and specific range
of water quality aspects
(temperature, pH, oxygen concentration, salinity, hardness, etc.) within which
they can survive,
grow and reproduce.
[0018] Further, according to the current disclosure, the sensors in the
holding tank are capable
of determining at least one or more conditions in the holding tank, and
capable of transmitting
detected condition to a remote computer. Some of these sensors could be
capable of detecting
one or more of the following: Temperature, Turbidity, Water pH and acidity,
Alkalinity and
hardness, Dissolved gases: oxygen, carbon dioxide, nitrogen,
Ammonia content, Toxic
materials among others
[0019] According to an exemplary arrangement, the micro-controller is
configured to operate
the actuator to make a corrective measure for the at least one or more
conditions in the holding
tank based on a corrective signal received from a remote computer configured
with a suitable
machine learning algorithm, where the corrective signal is derived from
learning based on a
plurality of inputs from a plurality of holding tanks.
[0020] According to an exemplary arrangement, the actuator comprises of a
corrective measure
for the at least one or more conditions in the holding tank determined by the
sensor, for
example, it could be heater to increase temperature, a fan to reduce
temperature, a filter to
remove ammonia, a mechanism to pass air into the tank to increase dissolved
oxygen, a filter to
remove suspended particles, a chemical to alter to pH etc. The actuator
receives a corrective
signal from a micro-controller.
[0021] According to an exemplary arrangement, the communication module
comprises of a
network receiver, the module capable of receiving signals from a remote
computer, where
received signals are processed by the micro-controller to activate the
actuator to perform a
corrective measure for the at least one or more conditions in the holding tank
determined by the
sensor.
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[0022] According to an exemplary arrangement, the remote computer comprised of
at least a
processor, memory and storage. The memory of the remote computer is configured
with a
suitably trained machine learning algorithm. Such an algorithm could comprise
of any such
trained algorithm such as: Linear Regression, Logistic Regression, Decision
Tree, SVM, Naive
Bayes, kNN, K-Means, Random Forest, Dimensionality Reduction, Gradient
Boosting
algorithms etc.
[0023] Additionally, the productivity of all holding tanks is measured and
recorded in a
suitable storage device, so as to be able to determine the correlation between
detected
conditions and the productivity. For purposes of this disclosure, the
productivity could be the
average fish size, the quantity of produced fish, the rate of reproduction,
disease rate, the
weight of fish produced or even mortality rate of fish.
[0024] The remote computer receives the conditions from the sensor, stores
received
condition in a database. The computer uses a suitably trained machine learning
algorithm to
determine the conditions that would be optimal for the aquatic life form in
the holding tank and
generates a corrective signal e.g. temperature, turbidity, mineral composition
etc, and transmits
the signal to the communication module, which makes the actuator to take a
corrective action.
The corrective action is automatically generated, and the signals provided are
improved over
time based on a learning model and thus offer a novel and patentable solution.
[0025] According to an exemplary arrangement, a method of training the ML
algorithm is
described as comprising of the steps of:
[0026] Collecting the productivity data of the aquatic life in a holding tank
for a plurality of
tanks
[0027] Collect the corresponding sensor conditions correlating to the
productivity data
[0028] Split the data into training data set and validation data set
[0029] Using a suitable algorithm, use the training data set of sensor
conditions to predict the
productivity and obtain a ML model
[0030] Use the validation data set to verify the accuracy of the model
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[0031] Select the best ML model for your data set
BRIEF DESCRIPTION OF THE DRAWINGS
[0032] The novel features believed to be characteristic of the illustrative
embodiments are set
forth in the appended claims. The illustrative embodiments, however, as well
as a preferred
mode of use, further objectives and descriptions thereof, will best be
understood by reference to
the following detailed description of one or more illustrative embodiments of
the present
disclosure when read in conjunction with the accompanying drawings, wherein:
[0033] Fig. 1 of the diagrams is a schematic architectural view of the system
as described
according to the current invention.
[0034] Fig. 2 of the diagrams illustrates the method of training a machine
learning algorithm
according to the current invention.
[0035] Fig. 3 of the diagrams illustrates a method of using the trained
machine learning
algorithm according to the current invention.
[0036] Fig. 4 of the diagrams illustrates the mechanism of activating a
control mechanism
performed by the actuator for a corrective mechanism according to the current
invention.
[0037] Fig. 5 of the diagrams a control mechanism performed by the monitoring
and control
device according to the current invention.
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DETAILED DESCRIPTION OF THE DRAWINGS
[0038] Hereinafter, the preferred embodiment of the present invention will be
described in
detail with reference to the accompanying drawings. The terminologies or words
used in the
description and the claims of the present invention should not be interpreted
as being limited
.. merely to their common and dictionary meanings. On the contrary, they
should be interpreted
based on the meanings and concepts of the invention in keeping with the scope
of the invention
based on the principle that the inventor(s) can appropriately define the terms
in order to
describe the invention in the best way.
[0039] It is to be understood that the form of the invention shown and
described herein is to be
taken as a preferred embodiment of the present invention, so it does not
expressly limit the
technical spirit and scope of this invention. Accordingly, it should be
understood that various
changes and modifications may be made to the invention without departing from
the spirit and
scope thereof.
[0040] In a first embodiment according to Fig. 1 of the diagrams is a
schematic architectural
view of the system as described according to the current invention.
Illustrated in the figure is a
holding tank 1, a micro-controller 2, a sensor or plurality of sensors 3, an
actuator or plurality
of actuators 4, a remote computer or server 5 with a storage device 6, a
monitoring and control
device 7, a networks 8, a signal receiver and transmitter module 9, an aquatic
life 10 as well as
a computer-implemented module 50 configured on the remote computer 5, said
module
comprising a suitably trained machine learning algorithm to determine the
conditions that
would be optimal for the aquatic life form in the holding tank and generates a
corrective signal
e.g. temperature, turbidity, mineral composition etc, and transmits the signal
to the
communication module, which makes the actuator to take a corrective action.
[0041] The holding tank 1 is any such tank capable of supporting an aquatic
life form e.g. fish,
plants or any such in an aqueous environment. There are numerous instances
where fish is the
preferred example of aquatic life in this disclosure, however, other life form
are also anticipated.
As an example, water quality is the most important factor affecting fish
health and performance
in aquaculture production systems, and the same is true to may aquatic life
forms. Good water
quality refers to what the fish life thrives best. This means that a farmer
must understand the
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water quality requirements of the fish under culture very well. Fish live in
and are totally
dependent on the water they live in for all their needs. Additionally,
different fish species have
different and specific range of water quality aspects, which may include one
or more of the
temperature, pH, oxygen concentration, salinity, hardness, etc. within which
they can survive,
thrive, grow and reproduce.
[0042] Within these tolerance limits, each species has its own optimum range,
that is, the range
within which it performs best. It is therefore very important for fish
producers to ensure that the
physical and chemical conditions of the water remain, as much as possible,
within the optimum
range of the fish under culture all the time. Outside these optimum ranges,
fish will exhibit poor
growth, erratic behaviour, and disease symptoms or parasite infestations.
Under extreme cases,
or where the poor conditions remain for prolonged periods of time, fish
mortality may occur.
Holding tank water contains two major groups of substances: (a) suspended
particles made of
non-living particles and very small plants and animals, the plankton, and (b)
dissolved
substances made of gases, minerals and organic compounds.
[0043] It should be understood that the composition of holding tank water
changes
continuously, depending on climatic and seasonal changes, the flow of water,
and on how a
holding tank is used. It is the aim of good management to control the
composition to yield the
best conditions for the fish. For producers to be able to maintain ideal
holding tank water
quality conditions, they must understand the physical and chemical components
contributing to
good or bad water quality. Sensors can provide a great deal of understanding
on these
conditions.
[0044] Further, the micro-controller 2 is configured to operate the actuator 4
to make a
corrective measure for the at least one or more conditions in the holding tank
based on a
corrective signal received from the remote computer 5 configured with a
suitable machine
learning algorithm, where the corrective signal is derived from learning based
on a plurality of
inputs preferably from a plurality of holding tanks. It should be noted that
the corrective
mechanism is activated at any such preferred frequency and autonomously
without a manual
input, but rather based on a signal from the remote computer.
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[0045] Now, the sensor or plurality of sensors 3 according to the current
disclosure, is capable
of determining at least one or more conditions in the holding tank. Preferably
located in the
holding tank and capable of transmitting detected condition to a remote
computer. Some of
these sensors could be capable of detecting one or more of the following:
Temperature,
Turbidity, Water pH and acidity, Alkalinity and hardness, Dissolved gases:
oxygen, carbon
dioxide, nitrogen, Ammonia content or Toxic materials.
[0046] The actuator or plurality of actuators 4 comprise of a mechanism for
performing a
corrective measure for the at least one or more conditions in the holding tank
determined by the
sensor 3. For example, it could be heater to heat the water and as such
increase the temperature,
a fan to blow the surface of the water to reduce temperature, a filter to
remove ammonia, a
mechanism to pass air into the tank to increase the amounts of dissolved
oxygen, a filter to
remove suspended particles or a chemical to alter to pH etc. The actuator
receives a corrective
signal from the micro-controller 2. it should be noted that the signal is
received autonomously
and the micro-controller activates the actuator actively, but it is also
anticipated that there could
be an element of human control for the system.
[0047] Further in the figure is a a remote computer or server 5 with a storage
device 6, wherein
according to the current invention, the remote computer 5 comprises of at
least a processor,
memory and storage. The memory of the remote computer is configured with a
suitably trained
machine learning algorithm, which makes up the computer-implemented module 50.
Such
module comprises an algorithm that could comprise of any such trained
algorithm such as:
Linear Regression, Logistic Regression, Decision Tree, SVM, Naive Bayes, kNN,
K-Means,
Random Forest, Dimensionality Reduction Algorithms, Gradient Boosting
algorithms (GBM,
XGBoost, LightGBM, CatBoost), among others. On the other hand, the storage
device 6
comprises of a database capable of receiving from above sensors detected
condition for at least
one or more conditions in the holding tank. For each detected condition, there
are inputs from
multiple holding tanks, probably at different locations. However, the setup
could also work for
a single holding tank.
[0048] Furthermore, the productivity of all holding tanks (if more than one
are included) is
measured and recorded, so as to be able to determine the correlation between
detected
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conditions and the productivity. This is preferably performed using the
monitoring and control
device 7. For purposes of this disclosure, the productivity could be the
average fish size, the
quantity of produced fish, the rate of reproduction, disease rate, the weight
of fish produced or
even mortality rate of fish. However, it should be understood that similar
metrics for other
aquatic life forms could be measured as well, and this only forms a suitable
example for a
person skilled in the art to perform the invention.
[0049] Further still is a network 8, which may comprise of one or more
mechanisms capable
of transmitting data between any number of computers or any such devices with
network
interfaces. In the current invention, such networks could comprise broadband
networks, fibre
optic, Ethernet, cabling, electromagnetic waves etc.
[0050] Further still is a signal receiver and transmitter module 9, which
comprises of a network
receiver, the module capable of receiving signals from a remote computer,
where received
signals are processed by the micro-controller 2 to activate the actuator 4 to
perform a corrective
measure for the at least one or more conditions in the holding tank 1 as
determined by the
sensor 3.
[0051] In a second embodiment according to the Fig. 2 of the diagrams, it is
illustrated a
method of training a machine learning algorithm according to the current
invention. The
method comprises of a step 20 of collecting the productivity data of the
aquatic life in a holding
tank or a plurality of tanks. The next step 21 entails the collecting of
corresponding sensor
conditions correlating to the productivity data. In the step 22 is the
splitting of the productivity
data and corresponding sensor conditions data into a training data set and
validation data set.
Further in the step 23 is the selection of a suitable algorithm, and
thereafter using the training
data set of sensor conditions to predict the productivity and obtain a ML
model. Subsequently
in step 24, is using the validation data set to verify the accuracy of the
model. Finally, in the
step 25, the method involves the selection of the best ML model for your data
set.
[0052] In a further embodiment according to the Fig. 3 of the diagrams, it is
illustrated a
method of using the trained machine learning algorithm according to the
current invention. The
method comprises of a step 30 of receiving at a computing device with a memory
configured
with a suitably trained machine learning algorithm the input from one or more
sensor at a
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holding tank, where the sensor detect at least one condition at the holding
tank. The subsequent
step 31 entails determining from the input from one or more sensor at a
holding tank, if the
condition is optimal, and if not optimal, perform the subsequent step 32 of
determining the
amount of correction required to bring back the at least one condition at the
holding tank to an
optimal level. In the subsequent step 33, it is generated a corrective signal
identifying the
correction to be made at the holding tank. Finally in 34, a corrective signal
is transmitted for
corrective action by the actuator.
[0053] Specifically, a remote computer receives the conditions from the
sensor, stores
received condition in a database. The computer uses a suitably trained machine
learning
.. algorithm to determine the conditions that would be optimal for the aquatic
life form in the
holding tank and generates a corrective signal e.g. temperature, turbidity,
mineral composition
etc, and transmits the signal to the communication module, which makes the
actuator to take a
corrective action. The corrective action is automatically generated, and the
signals provided are
improved over time based on a learning model.
.. [0054] In the subsequent embodiment according to the Fig. 4 of the diagrams
is a mechanism
of activating a control mechanism performed by the actuator for a corrective
mechanism
according to the current invention. The method comprises of a step 40 of
receiving a corrective
signal from a suitably configured computer, preferably a remote computer. In
the subsequent
step 41 is the determining the correct actuator for performing the corrective
action. It is
preferable that a micro-controller makes the determination of the correct
actuator, and making
the actuator perform the corrective action. Finally, in the step 42 is making
the actuator to
perform a corrective action to bring the at least one condition in the holding
tank to optimal
level.
[0055] In a final embodiment exemplified by the Fig. 5 of the diagrams is
illustrated a control
mechanism performed by the monitoring and control device according to the
current invention.
The first step 50 entails receiving at a monitoring and control device a
predicted productivity as
determined by a suitably trained machine learning model. Next in the step 51
is the receiving
the determined conditions at the holding tank by one or more sensors a said
holding tank.
Finally in the step 52 is the transmitting a desired productivity at the
holding tank by the
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monitoring and control device, wherein the remote computer 5 with its memory
configured
with a suitably configured machine learning algorithm determines the
conditions at the holding
tank for attaining the productivity received from the monitoring and control
device, and
transmit an actuation signal for the action of the actuator at the holding
tank.
[0056] Although a preferred embodiment of the present invention has been
described for
illustrative purposes, those skilled in the art will appreciate that various
modifications,
additions and substitutions are possible, without departing from the scope and
spirit of the
invention as disclosed in the accompanying claims.
INDUSTRIAL APPLICATION
[0057] The current invention relates to the use and manufacture intelligent
holding tanks for
aquaculture and remote monitoring and control of the environmental conditions
at water tanks.
Date Recue/Date Received 2021-08-12
