Note : Les descriptions sont présentées dans la langue officielle dans laquelle elles ont été soumises.
DATA PROCESSING METHOD, PLATFORM AND SYSTEM
BACKGROUND OF THE INVENTION
Technical Field
[0001] =The present application relates to the field of business data
calculation and enquiry, and
more particularly to a data processing method, and corresponding platform and
system.
Description of Related Art
[0002] It is frequently required to use some analytical data as guiding basis
for operations when
merchants sell commodities. These analytical data are mostly obtained on the
basis of
analysis of great quantities of commodity content data by platforms. For
instance, such
data as commodity content quality scores characterizing commodity descriptive
information quality can provide commodity operational guidance for merchants
to sell
material commodities. Such data is obtained by platforms that perform
summarized
analytical calculations on a great deal of commodity content data of great
many merchants.
At present, the summarized analytical calculations on a great deal of
commodity content
data are mostly realized via the mode of Java and the relational database
Mysql. When it
is required for a merchant to enquire the calculation result data, Mysql will
be directly
enquired.
[0003] However, in the ear when e-commerce rapidly develops, colossal volume
of commodity
content data is generated, especially during such large-scale sales
promotional activities
of platforms as "Double 11", "618", "818" and "Double 12" etc., when data
volume
increases even greatly. The mode of Java and the relational database Mysql is
relatively
low in efficiency in the computation of data, when merchants enquire
calculation result
data, the mode of Java and the relational database Mysql also renders low the
enquiring
efficiency. In particular when some complicated enquiring conditions are
encountered,
the enquiring times are essentially in the order of seconds.
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SUMMARY OF THE INVENTION
[0004] The present application provides a data processing method, and
corresponding platform
and system, so as to solve prior-art problem of low efficiency in calculating
and enquiring
commodity content data.
[0005] The present application sets forth the following solutions.
[0006] According to one aspect, there is provided a data processing method
that comprises:
[0007] storing primary commodity content data in a first relational database
through clustering
and sharding;
[0008] creating index data according to the primary commodity content data and
storing the
index data in an index database, wherein the index data includes keyword
fields and query
dimension identification data corresponding to each keyword field; and
[0009] invoking a calculation program to calculate the primary commodity
content data to obtain
calculation result data, and storing the calculation result data in
association with the query
dimension identification data in the first relational database.
[0010] Preferably, the method further comprises:
[0011] receiving an enquiring request of a user;
[0012] parsing the enquiring request to obtain a keyword to be enquired;
[0013] enquiring in the index database to obtain query dimension
identification data
corresponding to the keyword to be enquired to serve as a target
identification; and
[0014] enquiring in the first relational database to obtain calculation result
data corresponding to
the target identification.
[0015] Preferably, the method further comprises:
[0016] storing at least partial data of the calculation result data in
association with the query
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dimension identification data in the index database.
[0017] Preferably, the step of invoking a calculation program to calculate the
primary commodity
content data to obtain calculation result data includes:
[0018] invoking the calculation program to calculate various dimension content
quality scores
of each commodity in at least two content dimensions at the primary commodity
content
data, and calculating a content quality total score of each commodity
according to the
various dimension scores;
[0019] the step of storing the calculation result data in association with the
query dimension
identification data in the first relational database includes:
[0020] storing the various dimension content quality scores of each commodity
and the content
quality total score of each commodity in association with the query dimension
identification data in the first relational database; and
[0021] the step of storing at least partial data of the calculation result
data in association with the
query dimension identification data in the index database includes:
[0022] storing the quality total score of each commodity in association with
the query dimension
identification data in the index database.
[0023] Preferably, the query dimension identification data is a commodity code
and/or a
merchant code.
[0024] Preferably, the method further comprises:
[0025] receiving the primary commodity content data and storing the same in a
second relational
database through clustering and sharding; and
[0026] synchronizing the primary commodity content data in the second
relational database to
the first relational database.
[0027] Preferably, the step of receiving the primary commodity content data
and storing the same
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in a second relational database through clustering and sharding includes:
[0028] receiving the primary commodity content data and storing the same in a
second relational
database through clustering and sharding according to commodity codes.
[0029] Preferably, the first relational database is Hbase, the second
relational database is Mysql,
the calculation program is Spark, and the index database is Elasticsearch.
[0030] According to another aspect, the present application further provides a
data processing
platform, and the platform comprises a data storage layer and a data
calculation layer, of
which
[0031] the data storage layer is employed for storing primary commodity
content data in a first
relational database through clustering and sharding, and creating index data
according to
the primary commodity content data and storing the index data in an index
database,
wherein the index data includes keyword fields and query dimension
identification data
corresponding to each keyword field; and
[0032] the data calculation layer is employed for invoking a calculation
program to calculate the
primary commodity content data to obtain calculation result data, and storing
the
calculation result data in association with the query dimension identification
data in the
first relational database.
[0033] According to still another aspect, the present application further
provides a computer
system that comprises:
[0034] one or more processor(s); and
[0035] a memory, associated with the one or more processor(s) for storing a
program instruction
that executes the following operations when read and executed by the one or
more
processor(s):
[0036] storing primary commodity content data in a first relational database
through clustering
and sharding;
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[0037] creating index data according to the primary commodity content data and
storing the
index data in an index database, wherein the index data includes keyword
fields and
identification data corresponding to each keyword field; and
[0038] invoking a calculation program to calculate the primary commodity
content data to obtain
calculation result data, and storing the calculation result data in
association with the query
dimension identification data in the first relational database.
[0039] According to the specific embodiments provided by the present
application, the present
application has disclosed the following technical effects:
[0040] the technical solution of the present application enhances computing
efficiency by storing
the commodity primary data in a relational database by clustering and sharding
and
invoking a calculation program to perform calculation, creates an index
database
according to query dimensions, and must enhance the enquiring efficiency by
firstly
indexing before subsequent enquiring. In comparison with the state of the art,
such
solution can quickly provide multi-dimensional queries of the calculation
result data, and
avoids the problem of low efficiency caused by direct enquiry in the
relational database.
[0041] Of course, not any product that implements the present application is
necessarily required
to achieve all of the aforementioned advantages simultaneously.
BRIEF DESCRIPTION OF THE DRAWINGS
[0042] To more clearly describe the technical solutions in the embodiments of
the present
application or the state of the art, drawings required to be used in the
description of the
embodiments will be briefly introduced below. Apparently, the drawings
introduced
below are merely directed to some embodiments of the present invention, while
it is
possible for persons ordinarily skilled in the art to acquire other drawings
based on these
drawings without spending creative effort in the process.
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[0043] Fig. 1 is a view illustrating the structure of the data processing
platform provided by an
embodiment of the present application;
[0044] Fig. 2 is a view schematically illustrating clustering and sharding
provided by an
embodiment of the present application;
[0045] Fig. 3 is a flowchart illustrating synchronization of primary commodity
content data
provided by an embodiment of the present application;
[0046] Fig. 4 is a flowchart illustrating enquiry of commodity content quality
scores provided by
an embodiment of the present application;
[0047] Fig. 5 is a flowchart illustrating the data processing method provided
by an embodiment
of the present application; and
[0048] Fig. 6 is a view illustrating the architecture of the computer system
provided by an
embodiment of the present application.
DETAILED DESCRIPTION OF THE INVENTION
[0049] The technical solutions in the embodiments of the present application
will be more clearly
and comprehensively described below with reference to the accompanying
drawings in
the embodiments of the present application. Apparently, the embodiments as
described
are merely partial, rather than the entire, embodiments of the present
application. All other
embodiments obtainable by persons ordinarily skilled in the art on the basis
of the
embodiments in the present application without spending creative effort shall
all fall
within the protection scope of the present application.
[0050] The present application aims to provide a method of processing
commodity content data,
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whereby after primary commodity content data has been stored in a relational
database
by clustering and sharding, a calculation program is invoked to perform
calculation
through sub-libraries in parallel to enhance computing efficiency, and index
data is
created according to query dimensions to be enquired, so that, during
subsequent
enquiring, identification data can be firstly matched in the index database
and enquiry is
thereafter performed in the relational database, such a solution can quickly
provide multi-
dimensional queries of the calculation result data, and enhances enquiring
efficiency.
[0051] As shown in Fig. 1, which is a view illustrating the structure of the
data processing
platform in one of the embodiments of the present application, included are an
Mysql
database, an Hbase database, a Spark calculation program for calculation, a
search engine
Elasticsearch, a remote service framework RFS, and an enquiring merchant.
[0052] The Mysql database serves as the database to receive the primary
commodity content data,
and stores in itself colossal volume of the primary commodity content data
through the
mode of clustering and sharding. It is specifically possible to complete
clustering and
sharding according to commodity codes, and the specific operation thereof will
be
described later in detail.
[0053] The Hbase database is used to perform synchronization according to the
data in the Mysql
database. It can specifically complete the synchronization through data
replication and a
data exchange platform. Having synchronized, the Hbase database stores the
primary
commodity content data according to the mode of clustering and sharding.
[0054] In other embodiments of the present application, the primary commodity
content data can
be directly stored in the Hbase database, without having to pass through the
Mysql
database. However, the mode of passing through the Mysql database on the one
hand
takes into consideration the stability of data backup, and on the other hand
takes into
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consideration that other business processes should rely on the Mysql database
for
operations.
[0055] Result data to be subsequently enquired is calculated, index is
created, and an association
relation is established between the index and the result data obtained by
calculation, so
that the result data can be further enquired out according to index data:
[0056] In the index database Elasticsearch are stored such keyword fields for
enquiry as
commodity brands, and such identification data to which the keyword fields
correspond
as commodity codes. Based on such index, the query keyword input by a user
(merchant)
can be matched with the corresponding commodity code.
[0057] The Spark calculation program is used to base on the number segments of
commodity
codes to perform MapReduce (a programming model for parallel operations on
large-
scale datasets (greater than 1TB)) on the primary commodity content data of
each cluster
according to an expression rule, so as to obtain a calculation result, for
instance, to
calculate commodity content quality scores. After the calculation result has
been obtained,
the calculation result and such identification data as commodity codes are
stored in the
Hbase database.
[0058] Through the foregoing steps is created an association between the index
data in
Elasticsearch and the calculation result data in the Hbase database through
the
identification data.
[0059] When the user inputs a query keyword, the RSF firstly enquires in the
index to determine
matched identification data, such as commodity codes, and hence determines the
calculation result data in the Hbase database according to the commodity
codes.
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[0060] The aforementioned creation of the index can be independent of the
calculation process,
and it is of course also possible in the present application to store at least
a part of the
calculation result in the index database. When this part of the result is
enquired, the
enquiry can be completed merely through Elasticsearch, while it is not
required to further
enquire in the Hbase database.
[0061] As should be noted, the aforementioned Mysql database, Hbase database,
Spark
calculation program, and search engine Elasticsearch can all be replaced with
modules of
similar functions, and Fig. 1 merely illustrates a specific system structure
of the present
application.
[0062] Taking for example the system and the calculation of commodity content
quality scores
illustrated in Fig. 1, the process of storing primary commodity content data
through
clustering and sharding, the process of synchronizing commodity content data,
the
process of calculating commodity content quality scores, the process of
synchronizing
commodity content quality scores, the process of creating index, and the
process of
enquiring commodity content quality scores are described in detail below:
[0063] the primary commodity content data is stored through clustering and
sharding:
[0064] the primary commodity content data is stored in 4 clusters of Mysql
according to number
segments of commodity codes, the results by getting modulus of 10 according to
the last
two digits of the commodity codes are stored in 10 sub-libraries of each
cluster, and the
results by getting remainder of 10 according to the last one digit of the
commodity codes
are stored in 10 sub-tables of each sub-library, thusly more than one billion
commodity
content data are dispersed in several hundreds of sub-tables. Fig. 2 is a view
schematically
illustrating clustering and sharding.
[0065] For instance, number segments of commodity codes stored in each cluster
are defined
thus that: commodity data from number segment 000000000000000000 to number
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segment 000000000500000000 are stored in cluster 1; commodity data from number
segment 000000000500000001 to number segment 000000000100000000 are stored in
cluster 2; commodity data from number segment 000000001000000001 to number
segment 000000001500000000 are stored in cluster 3; and commodity data from
number
segment 000000001500000001 to number segment 000000002000000000 are stored in
cluster 4.
[0066] The sub-library of a cluster to which each commodity belongs is
defined: a corresponding
sub-library is designated according to the results by performing modulo
operation to the
last two digits of the commodity code with 10.
[0067] The sub-table of a sub-library of a cluster to which each commodity
belongs is defined:
a corresponding sub-table is designated according to the results by performing
remainder
operation to the last one digit of the commodity code with 10.
[0068] For instance, commodity code 000000001500000023 belongs to sub-table 4,
sub-library
3, cluster 4.
[0069] Synchronization of the primary commodity content data:
[0070] synchronization of the primary commodity content data is classified
into three types:
quasi real-time incremental update, daily incremental update, and weekly total
update, of
which both the daily incremental update and the weekly total update are
directed to fault
toleration.
[0071] As shown in Fig. 3, specifically, a real-time data replication system
(RDRS) platform can
be defined to synchronize Mysql data to HBase in quasi real time, and a data
exchange
platform IDE can be defined to synchronize Mysql data to HBase daily
incrementally and
weekly totally:
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[0072] The RDRS platform synchronizes commodity content data to HBase by
parsing binlog
information of the Mysql database cluster in quasi real time.
[0073] The data exchange platform synchronizes commodity content information
incremental
data to HBase daily, and makes comparison and correction with the quasi real-
time HBase
commodity content data.
[0074] The data exchange platform synchronizes total commodity data to HBase
weekly, and
makes comparison and correction with the current HBase commodity content data.
[0075] Calculation of commodity content quality scores:
[0076] the commodity content quality is mainly affected by 7 content
dimensions, namely basic
information, parameter information, category information, master map
information, title
information, selling point information, and detailed information. The Spark
program
bases on the expression rule to perform parallel calculation on each sub-
library, calculates
out the scores of the basic information, parameter information, category
information,
master map information, title information, selling point information, and
detailed
information of the entire sub-library commodities, and finally summarizes and
writes the
entire dimension scores in Hive (a data warehouse tool of Hadoop),
specifically:
[0077] the scores of the basic information, parameter information, category
information, master
map information, title information, selling point information, and detailed
information of
the entire sub-libraries are firstly calculated out by means of MapReduce
according to the
sub-libraries. Calculation according to the sub-libraries mainly aims to
reduce excessive
data skew, to hence enhance computing efficiency.
[0078] The scores of the basic information, parameter information, category
information, master
map information, title information, selling point information, and detailed
information
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are merged together to obtain a total score.
[0079] The following is directed to tests of computing efficiencies of the
present application and
the prior-art technology:
[0080] One million pieces of data to be calculated, ten million pieces of data
to be calculated,
and one hundred million pieces of data to be calculated are inserted into a
calculation
table of commodity quality estimation. Calculations are subsequently performed
on the
basis ofjava+Mysql and Spark+HBase, respectively. The test results are
recorded in Table
1.
[0081] Table 1. Comparison of Spark+HBase and java Computing Efficiencies
Pieces of Data Recorded Sp ark+HB
ase Java+Mysq
1,000,000 30 minutes
8 hours
10,000,000 2 hours
3 days
100,000,000 5 hours
30 days
[0082] As can be seen from the test results, the calculation based on the
combination of
Spark+HBase greatly enhances computing efficiency, and the computing
efficiency still
exhibits excellent performance even when the number of pieces of data is
increased by
folds.
[0083] Synchronization of commodity content quality scores:
[0084] the various scores are summarized and calculated according to set query
dimensions, such
as commodities and merchants, to obtain the corresponding total score, for
example, the
total score of a certain commodity or the total score of a certain merchant.
Of course,
other dimensions can also be utilized. Thereafter, such data as the commodity
content
quality scores of various dimensions, the commodity content quality total
score, and the
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scores summarized according to set query dimensions are synchronized to HBase.
[0085] Creation of query dimension index:
[0086] index data is created according to such query dimensions as commodity
codes and
merchant codes, and the index data includes keyword fields and corresponding
query
dimension identification data, such as commodity brands and the corresponding
commodity codes.
[0087] The creation of such index can be based on the process of synchronizing
the commodity
content quality score data, when the commodity content quality score data is
calculated
and obtained and synchronized to HBase, correspondence relations between the
keyword
fields in the primary commodity data and the query dimension identification
data are
created, and the total score data summarized and obtained according to such
query
dimensions as commodity codes and merchant codes is synchronized to the index
data.
[0088] The relevant calculation result data of Elasticsearch and HBase, such
as the commodity
content quality score data, are all incrementally updated.
[0089] Enquiry of commodity content quality scores.
[0090] with respect to data of differently typed enquiring conditions required
by the user,
corresponding enquiring interfaces and request parameters are required,
corresponding
commodity codes and merchant codes are then firstly obtained from
Elasticsearch
according to the enquiring conditions, the required data is thereafter
enquired out of
HBase according to the enquired commodity codes and merchant codes, and the
data that
conforms to the conditions is finally returned to the user after integration
and filtration,
specifically:
[0091] the remote service framework (RSF) is firstly defined to provide remote
query service to
the enquirer component and to define the query service, for processing queries
of
merchants. It invokes the RSF service to perform various types of iterative
queries
according to the enquiring condition input by a merchant, and then gets
intersection of
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the results of various sub-enquiring conditions, wherein the sub-queries are
concurrent
queries.
[0092] Fig. 4 is a flowchart illustrating the process of enquiring commodity
content quality
scores, and the process includes the following steps:
[0093] a client end sends out a query service request of commodity quality
scores;
[0094] an enquiring server expression-parses the query service request of
commodity quality
scores sent by the client end;
[0095] the enquiring server submits the parsed query request to an
Elasticsearch cluster ¨ a
cluster is set for Elasticsearches in this embodiment to avoid single-point
failure of the
machine;
[0096] the Elasticsearch cluster returns a query result (commodity codes +
merchant codes) to
the enquiring server;
[0097] the enquiring sewer submits the query request to an HBase cluster
according to the query
result returned from the Elasticsearch cluster;
[0098] the HBase cluster returns a final query result corresponding to the
commodity codes and
the merchant codes to the enquiring server; and
[0099] the enquiring server returns the final query result to the client end.
[0100] =The following is directed to tests of enquiring efficiencies of the
present application and
the prior-art technology:
[0101] One million pieces of data to be calculated, ten million pieces of data
to be calculated,
and one hundred million pieces of data to be calculated are inserted into a
calculation
table of commodity quality estimation. Calculations are subsequently performed
on the
basis ofJava+Mysql and Spark+HBase, respectively.
[0102] As can be seen from the test results, the calculation based on the
combination of
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Spark+HBase greatly enhances computing efficiency, and the computing
efficiency still
exhibits excellent performance even when the number of pieces of data is
increased by
folds.
[0103] One million pieces of data, ten million pieces of data, one hundred
million pieces of data,
and one billion pieces of data are respectively inserted into different tables
of
Elasticsearch and HBase, and 15 fields are recorded for each piece. Enquiry is
subsequently performed on the basis of java+Mysql and Elasticsearch+HBase,
respectively.
[0104] The test results are recorded in Table 2.
[0105] Table 2. Comparison of Elasticsearch+HBase and Java+Mysql Enquiring
Efficiencies
Pieces of Data Recorded Elasticsearch+HBase
Java+Mysql
1,000,000 125ms
0.564s
10,000,000 140ms
2.543s
100,000,000 162ms
timeout exception
1,000,000,000 190ms
timeout exception
[0106] As can be seen from the test results, the enquiry based on the
combination of
Elasticsearch+HBase greatly enhances enquiring efficiency, and the enquiring
efficiency
still exhibits excellent performance even when the number of pieces of data is
increased
by folds.
[0107] Embodiment 1
[0108] As previously mentioned, the aforementioned various databases or
calculation program
Spark can be replaced with modules of similar functions, and the calculation
results can
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also be set as data other than commodity content quality scores according to
requirements
of users. On the basis thereof, Embodiment 1 of the present application
provides a data
processing method, as shown in Fig. 5, the method comprises the following
steps:
[0109] S51 - storing primary commodity content data in a first relational
database through
clustering and sharding;
[0110] S52 - creating index data according to the primary commodity content
data and storing
the index data in an index database, wherein the index data includes keyword
fields and
query dimension identification data corresponding to each keyword field; and
[0111] S53 - invoking a calculation program to calculate the primary commodity
content data to
obtain calculation result data, and storing the calculation result data in
association with
the query dimension identification data in the first relational database.
[0112] Preferably, the method further comprises:
[0113] receiving an enquiring request of a user;
[0114] parsing the enquiring request to obtain a keyword to be enquired;
[0115] enquiring in the index database to obtain query dimension
identification data
corresponding to the keyword to be enquired to serve as a target
identification; and
[0116] enquiring in the first relational database to obtain calculation result
data corresponding to
the target identification.
[0117] Further, the method can further comprise:
[0118] storing at least partial data of the calculation result data in
association with the query
dimension identification data in the index database.
[0119] In another preferred embodiment, the method further comprises:
receiving the primary
commodity content data and storing the same in a second relational database
through
clustering and sharding ¨ specifically, clustering and sharding can be
performed
according to commodity codes; and
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[0120] synchronizing the primary commodity content data in the second
relational database to
the first relational database.
[0121] Embodiment 2
[0122] Corresponding to the aforementioned method, the present application
further provides a
data processing platform, and the platform comprises a data storage layer and
a data
calculation layer, of which
[0123] the data storage layer is employed for storing primary commodity
content data in a first
relational database through clustering and sharding, and creating index data
according to
the primary commodity content data and storing the index data in an index
database,
wherein the index data includes keyword fields and query dimension
identification data
corresponding to each keyword field; and
[0124] the data calculation layer is employed for invoking a calculation
program to calculate the
primary commodity content data to obtain calculation result data, and storing
the
calculation result data in association with the query dimension identification
data in the
first relational database.
[0125] In a preferred embodiment, the data processing platform further
comprises a data
application layer for receiving an enquiring request of a user for parsing to
obtain a
keyword to be enquired, enquiring in the index database to obtain query
dimension
identification data corresponding to the keyword to be enquired to serve as a
target
identification, and enquiring in the first relational database to obtain
calculation result
data corresponding to the target identification, so as to return the result
data to the user.
[0126] In a preferred embodiment, the storage layer is further employed for
storing at least partial
data of the calculation result data in association with the query dimension
identification
data in the index database.
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[0127] In a preferred embodiment, the storage layer is further employed for
receiving the primary
commodity content data and storing the same in a second relational database
through
clustering and sharding, and synchronizing the primary commodity content data
in the
second relational database to the first relational database.
[0128] Embodiment 3
[0129] Corresponding to the aforementioned method and platform, Embodiment 3
of the present
application further provides a computer system that comprises:
[0130] one or more processor(s); and
[0131] a memory, associated with the one or more processor(s) for storing a
program instruction
that executes the following operations when read and executed by the one or
more
processor(s):
[0132] storing primary commodity content data in a first relational database
through clustering
and sharding;
[0133] creating index data according to the primary commodity content data and
storing the
index data in an index database, wherein the index data includes keyword
fields and
identification data corresponding to each keyword field; and
[0134] calculating the primary commodity content data through a calculation
program to obtain
calculation result data, and storing the calculation result data in
association with the query
dimension identification data in the first relational database.
[0135] Fig. 6 exemplarily illustrates the framework of a computer system that
can specifically
include a processor 1510, a video display adapter 1511, a magnetic disk driver
1512, an
input/output interface 1513, a network interface 1514, and a memory 1520. The
processor
1510, the video display adapter 1511, the magnetic disk driver 1512, the
input/output
interface 1513, the network interface 1514, and the memory 1520 can be
communicably
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connected with one another via a communication bus 1530.
[0136] =The processor 1510 can be embodied as a general CPU (Central
ProcElasticsearchsing
Unit), a microprocessor, an ASIC (Application Specific Integrated Circuit), or
one or
more integrated circuit(s) for executing relevant program(s) to realize the
technical
solutions provided by the present application.
[0137] The memory 1520 can be embodied in such a form as an ROM (Read Only
Memory), an
RANI (Random AccElasticsearchs Memory), a static storage device, or a dynamic
storage
device. The memory 1520 can store an operating system 1521 for controlling the
running
of a computer system 1500, and a basic input/output system (BIOS) for
controlling lower-
level operations of the computer system 1500. In addition, the memory 1520 can
also
store a web browser 1523, a data storage administration system 1524, and an
icon font
processing system 1525, etc. The icon font processing system 1525 can be an
application
program that specifically realizes the aforementioned various step operations
in the
embodiments of the present application. To sum it up, when the technical
solutions
provided by the present application are to be realized via software or
firmware, the
relevant program codes are stored in the memory 1520, and invoked and executed
by the
processor 1510.
[0138] The input/output interface 1513 is employed to connect with an
input/output module to
realize input and output of information. The input/output module can be
equipped in the
device as a component part (not shown in the drawings), and can also be
externally
connected with the device to provide corresponding functions. The input means
can
include a keyboard, a mouse, a touch screen, a microphone, and various sensors
etc., and
the output means can include a display screen, a loudspeaker, a vibrator, an
indicator light
etc.
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[0139] The network interface 1514 is employed to connect to a communication
module (not
shown in the drawings) to realize intercommunication between the current
device and
other devices. The communication module can realize communication in a wired
mode
(via USB, network cable, for example) or in a wireless mode (via mobile
network, WIFI,
Bluetooth, etc.).
[0140] The bus 1530 includes a passageway transmitting information between
various
component parts of the device (such as the processor 1510, the video display
adapter 1511,
the magnetic disk driver 1512, the input/output interface 1513, the network
interface 1514,
and the memory 1520).
[0141] Additionally, the computer system 1500 may further obtain information
of specific
collection conditions from a virtual resource object collection condition
information
database 1541 for judgment on conditions, and so on.
[0142] As should be noted, although merely the processor 1510, the video
display adapter 1511,
the magnetic disk driver 1512, the input/output interface 1513, the network
interface 1514,
the memory 1520, and the bus 1530 are illustrated for the aforementioned
device, the
device may further include other component parts prerequisite for realizing
normal
running during specific implementation. In addition, as can be understood by
persons
skilled in the art, the aforementioned device may as well only include
component parts
necessary for realizing the solutions of the present application, without
including the
entire component parts as illustrated.
[0143] As can be known through the description to the aforementioned
embodiments, it is clearly
learnt by person skilled in the art that the present application can be
realized through
software plus a general hardware platform. Based on such understanding, the
technical
solutions of the present application, or the contributions made thereby over
the state of
CA 03154438 2022-4-11
the art, can be essentially embodied in the form of a software product, and
such a
computer software product can be stored in a storage medium, such as an
ROM/RAM, a
magnetic disk, an optical disk etc., and includes plural instructions enabling
a computer
equipment (such as a personal computer, a server, or a network device etc.) to
execute the
methods described in various embodiments or some sections of the embodiments
of the
present application.
[0144] =The various embodiments are progressively described in the
Description, identical or
similar sections among the various embodiments can be inferred from one
another, and
each embodiment stresses what is different from other embodiments.
Particularly, with
respect to the system or system embodiment, since it is essentially similar to
the method
embodiment, its description is relatively simple, and the relevant sections
thereof can be
inferred from the corresponding sections of the method embodiment. The system
or
system embodiment as described above is merely exemplary in nature, units
therein
described as separate parts can be or may not be physically separate, parts
displayed as
units can be or may not be physical units, that is to say, they can be located
in a single
site, or distributed over a plurality of network units. It is possible to base
on practical
requirements to select partial modules or the entire modules to realize the
objectives of
the embodied solutions. It is understandable and implementable by persons
ordinarily
skilled in the art without spending creative effort in the process.
[0145] The data processing method and corresponding platform and system
provided by the
present application are described in detail above, specific examples are used
in this paper
to enunciate the principles and modes of execution of the present application,
and
descriptions of the aforementioned embodiments are merely meant to help
understand the
method and kernel conception of the present application; at the same time, to
persons
ordinarily skilled in the art, there may be variations in both the specific
modes of
execution and the range of application based on the conception of the present
application.
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To sum it up, the contents of the current Description shall not be understood
to restrict
the present application.
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