CN108154392A - A kind of data analysing method, device and medium - Google Patents
A kind of data analysing method, device and medium Download PDFInfo
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- CN108154392A CN108154392A CN201711407261.5A CN201711407261A CN108154392A CN 108154392 A CN108154392 A CN 108154392A CN 201711407261 A CN201711407261 A CN 201711407261A CN 108154392 A CN108154392 A CN 108154392A
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- 238000000034 method Methods 0.000 title claims abstract description 61
- 238000012417 linear regression Methods 0.000 claims abstract description 47
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 27
- 238000007405 data analysis Methods 0.000 claims abstract description 23
- 238000007621 cluster analysis Methods 0.000 claims abstract description 17
- 238000004590 computer program Methods 0.000 claims description 9
- 241000288113 Gallirallus australis Species 0.000 claims description 6
- 241000938605 Crocodylia Species 0.000 claims description 5
- 238000010276 construction Methods 0.000 claims description 5
- 238000003064 k means clustering Methods 0.000 claims description 5
- 238000012216 screening Methods 0.000 claims description 3
- 230000000153 supplemental effect Effects 0.000 abstract description 22
- 238000004458 analytical method Methods 0.000 abstract description 21
- 230000000694 effects Effects 0.000 abstract description 5
- 238000013459 approach Methods 0.000 description 5
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- 241000406668 Loxodonta cyclotis Species 0.000 description 2
- 238000012512 characterization method Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
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- 240000007594 Oryza sativa Species 0.000 description 1
- 235000007164 Oryza sativa Nutrition 0.000 description 1
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- 230000015572 biosynthetic process Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
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- 238000002360 preparation method Methods 0.000 description 1
- 238000003672 processing method Methods 0.000 description 1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0201—Market modelling; Market analysis; Collecting market data
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0201—Market modelling; Market analysis; Collecting market data
- G06Q30/0202—Market predictions or forecasting for commercial activities
Abstract
The invention discloses a kind of data analysing method, device and medium, the step of this method, includes：Obtain the target component set of each target object；Each target component set is converted into vector model；Wherein, each element in target component set is the coordinate in vector model；Cluster analysis is carried out to vector model by clustering algorithm, each vector model is classified, and generate corresponding disaggregated model；Vector model in each disaggregated model is configured to equation of linear regression, to be predicted according to equation of linear regression the variation tendency of target component.As it can be seen that this method can the relatively accurate supplemental characteristic to target object carry out comprehensive analysis and classification, and realize the prediction to the supplemental characteristic variation tendency of target object.In addition, the present invention also provides a kind of data analysis set-up and medium, advantageous effect is as described above.
Description
Technical field
The present invention relates to data analysis field, more particularly to a kind of data analysing method, device and medium.
Background technology
With gradually universal, a large amount of shopping at network platform emergence of Internet technology, more and more data quilts
It is presented on network, the understanding and analysis of data is provided a great convenience for people.
If the supplemental characteristic of sale article can be all shown by the shopping platforms such as used car, second-hand house to user, use
The comparison that family will often carry out the article in multiple platforms all aspects of the parameters data when selecting article is analyzed, and then estimate out
The article is classified the parameters situation of situation and all kinds of articles in the market in the market.Current data analysing method is past
It is artificially that parameters data are recorded, and then carry out comprehensive analysis to supplemental characteristic by user toward being.With shopping platform
Scene it is found that the quantity of current item supplemental characteristic is often more huge, using manual method it is difficult to accurately to each article
Supplemental characteristic carry out comprehensive analysis and classify, and the variation tendency of the supplemental characteristic of article can not also be carried out pre-
It surveys.
It can be seen that provide a kind of data analysing method, with relative system accurately to the supplemental characteristic of target object into
The comprehensive analysis of row and classification, and realize the prediction to the supplemental characteristic variation tendency of target object, it is people in the art
Member's urgent problem to be solved.
Invention content
The object of the present invention is to provide a kind of data analysing method, device and media, and relative system is accurately to target pair
The supplemental characteristic of elephant carries out comprehensive analysis and classification, and realizes the prediction to the supplemental characteristic variation tendency of target object.
In order to solve the above technical problems, the present invention provides a kind of data analysing method, including：
Obtain the target component set of each target object；
Each target component set is converted into vector model；Wherein, each element in target component set is vectorial mould
Coordinate in type；
Cluster analysis is carried out to vector model by clustering algorithm, each vector model is classified, and is generated corresponding
Disaggregated model；
Vector model in each disaggregated model is configured to equation of linear regression, to join according to equation of linear regression to target
Several variation tendencies are predicted.
Preferably, before each target component set is converted into vector model, this method further comprises：
Each target component set is screened according to preset standard.
Preferably, clustering algorithm is specially K-MEANS clustering algorithms.
Preferably, the target component set for obtaining each target object is specially：
Target component set is obtained in a manner that Python reptiles parse network data.
Preferably, this method further comprises：
Daily record is written into equation of linear regression.
Preferably, the vector model in each disaggregated model is configured to equation of linear regression is specially：
Vector model in each disaggregated model is configured to by equation of linear regression by Weka.
In addition, the present invention also provides a kind of data analysis set-up, including：
Gather generation module, for obtaining the target component set of each target object；
Vector model generation module, for each target component set to be converted into vector model；
Cluster execution module, for pass through clustering algorithm to vector model carry out cluster analysis, by each vector model into
Row classification, and generate corresponding disaggregated model；
Regression equation generation module, for the vector model in each disaggregated model to be configured to equation of linear regression, with root
The variation tendency of target component is predicted according to equation of linear regression.
Preferably, which further comprises：
Screening module, for being screened according to preset standard to each target component set.
In addition, the present invention also provides a kind of data analysis set-up, including：
Memory, for storing computer program；
The step of processor, for performing computer program when, realize data analysing method as described above.
In addition, the present invention also provides a kind of computer readable storage medium, meter is stored on computer readable storage medium
Calculation machine program, the step of data analysing method as described above is realized when computer program is executed by processor.
Data analysing method provided by the present invention obtains the target component and group of the target object of pending data analysis
Into parameter sets, it is equivalent to each parameter sets and characterizes corresponding target object, and then parameters set is converted into
The form of vector model, and pass through clustering algorithm and cluster analysis is carried out to vector model, it is realized with this according to each target object
Between parameter differences and comprehensive analysis is carried out to target object and is classified, and then by the vector in each disaggregated model of gained
Model construction is equation of linear regression, and the approach of vector model entirety in each classification can be embodied by equation of linear regression
Value, and then convenient for being predicted according to equation of linear regression the variation tendency of each target component.As it can be seen that this method can be opposite
Comprehensive analysis and classification are accurately carried out to the supplemental characteristic of target object, and realizes the supplemental characteristic to target object
The prediction of variation tendency.In addition, the present invention also provides a kind of data analysis set-up and medium, advantageous effect is as described above.
Description of the drawings
In order to illustrate the embodiments of the present invention more clearly, attached drawing needed in the embodiment will be done simply below
It introduces, it should be apparent that, the accompanying drawings in the following description is only some embodiments of the present invention, for ordinary skill people
For member, without creative efforts, other attached drawings are can also be obtained according to these attached drawings.
Fig. 1 is a kind of flow chart of data analysing method provided in an embodiment of the present invention；
Fig. 2 is the flow chart of another data analysing method provided in an embodiment of the present invention；
Fig. 3 is a kind of data analysis set-up structure chart provided in an embodiment of the present invention.
Specific embodiment
Below in conjunction with the attached drawing in the embodiment of the present invention, the technical solution in the embodiment of the present invention is carried out clear, complete
Site preparation describes, it is clear that described embodiment is only part of the embodiment rather than whole embodiments of the present invention.Based on this
Embodiment in invention, without making creative work, what is obtained is every other by those of ordinary skill in the art
Embodiment belongs to the scope of the present invention.
The core of the present invention is to provide a kind of data analysing method, with relative system accurately to the parameter number of target object
According to the analysis and classification for carrying out synthesis, and realize the prediction to the supplemental characteristic variation tendency of target object.The present invention's is another
One core is to provide a kind of data analysis set-up and medium.
In order to which those skilled in the art is made to more fully understand the present invention program, with reference to the accompanying drawings and detailed description
The present invention is described in further detail.
Embodiment one
Fig. 1 is a kind of flow chart of data analysing method provided in an embodiment of the present invention.It please refers to Fig.1, data analysis side
The specific steps of method include：
Step S10：Obtain the target component set of each target object.
It is understood that this method using each target object as an analysis object, in each target object
There is a series of relevant parameter, user can determine required parameter, i.e. target component, each mesh according to analysing content
The quantity for marking the corresponding target component of object should be depending on the analysis demand of user, and then generates the mesh for including target component
Mark parameter sets.
Step S11：Each target component set is converted into vector model.
Wherein, each element in target component set is the coordinate in vector model.
It should be noted that vector model is a kind of form of expression of multidimensional data, it can be comprehensive by a vector model
Relevant parameter possessed by the existing entity object of zoarium.Due to this method need to the target component in target component set into
The comprehensive analysis of row, therefore each target component set is all separately converted to vector model in this step.In addition, in order to
Make each vector model that can characterize its corresponding target object, and then realize point to the associated parameter data of target object
It analyses, the coordinate in vector model should be each element in target component set.In addition, the vector model of different target object it
Between characterization should be the parameter of same type with the coordinate of dimension.
Step S12：Cluster analysis is carried out to vector model by clustering algorithm, each vector model is classified, and
Generate corresponding disaggregated model.
It should be noted that cluster analysis is that data are assigned to different classes or such a process of cluster, so together
Object in one cluster has very big similitude, and the object between different clusters has certain diversity.Cluster analysis is to pass through number
Simplify a kind of method of data according to modeling.In addition, the type of traditional clustering algorithm specifically include hierarchical clustering method, decomposition method,
Addition method, clustering ordered samples, has overlapping cluster and fuzzy clustering etc. at dynamic state clustering.User can according to actual demand and
Using corresponding clustering algorithm, it is not specifically limited herein.It, can be by all vector models point after carrying out cluster analysis
For multiple types, specific number of types is by user's execution horizontal for characterization cluster set before clustering algorithm is performed
Depending on parameter, it can be made an amendment according to specific requirements.
Step S13：Vector model in each disaggregated model is configured to equation of linear regression, with according to equation of linear regression
The variation tendency of target component is predicted.
It is understood that the structure of equation of linear regression is carried out for the disaggregated model of every one kind in this step,
Due to the overall trend of target component that target object can be understood according to equation of linear regression, on the basis of disaggregated model
Upper structure equation of linear regression can have more accurate embodiment to the variation tendency of target component, be returned convenient for user according to linear
Equation is returned to predict the variation tendency of target component.
For a kind of usage scenario presented below to be specifically described, the target object under the scene is specially the room of second-hand house
Source, specifically used scene are as follows：
The target component set of each source of houses in second-hand house platform is obtained, target component set includes " area ", " room
Valency ", " unit price ", and then target component set is converted into vector model.Such as：" area " of a certain source of houses is " 120 " square
Rice, " room rate " are " 1500000 " member, " unit price " is " 13500 " member, then vector model is specially (120,1500000,13500).
Each source of houses is corresponding there are one vector model, and the corresponding position in each vector model represents identical attribute, into
And each vector model is carried out by cluster analysis by clustering algorithm, the data in the vector model of each source of houses are carried out with comprehensive point
Class obtains only including the disaggregated model with class vector model, and then each disaggregated model is converted into equation of linear regression.
Data analysing method provided by the present invention obtains the target component and group of the target object of pending data analysis
Into parameter sets, it is equivalent to each parameter sets and characterizes corresponding target object, and then parameters set is converted into
The form of vector model, and pass through clustering algorithm and cluster analysis is carried out to vector model, it is realized with this according to each target object
Between parameter differences and comprehensive analysis is carried out to target object and is classified, and then by the vector in each disaggregated model of gained
Model construction is equation of linear regression, and the approach of vector model entirety in each classification can be embodied by equation of linear regression
Value, and then convenient for being predicted according to equation of linear regression the variation tendency of each target component.As it can be seen that this method can be opposite
Comprehensive analysis and classification are accurately carried out to the supplemental characteristic of target object, and realizes the supplemental characteristic to target object
The prediction of variation tendency.
Embodiment two
Fig. 2 is the flow chart of another data analysing method provided in an embodiment of the present invention.In Fig. 2 step S10 to S13 with
Fig. 1 is identical, and details are not described herein.
As shown in Fig. 2, as a preferred embodiment, before each target component set is converted into vector model,
This method further comprises：
Step S20：Each target component set is screened according to preset standard.
It is understood that it is to ensure target component according to the purpose that preset standard screens each target component set
The availability of set, and then ensure the accuracy of data analysis.Preset standard should be depending on actual conditions, for example, can be
" each target component in target component set must be complete, and cannot be emoticon, URL link, picture etc.,
It is only the text formatting of specification ", the effect of above-mentioned preset standard by way of example only is limited not as specific.Certainly, it is sieved
The target component set for not meeting preset standard selected can also subsequent modification be the target component set for meeting preset standard
It to reuse, but does not limit herein, basic goal is the availability in order to ensure each target component set.
In addition, as a preferred embodiment, clustering algorithm is specially K-MEANS clustering algorithms.
It should be noted that since K-MEANS clustering algorithms have faster convergence rate, gathered by K-MEANS
Class algorithm can more efficiently carry out the cluster analysis of vector model, and then promote whole data analysis efficiency.In addition, make
It is less therefore relatively easy for the calling of algorithm with the required parameter of K-MEANS clustering algorithms.
In addition, as a preferred embodiment, the target component set for obtaining each target object is specially：
Target component set is obtained in a manner that Python reptiles parse network data.
Since the diction of Python is succinct, and the method base of existing mainstream can be compatible with, therefore pass through Python
The period that language carries out Python reptile exploitations is short, and write Python reptiles function is more powerful, therefore passes through
The mode of Python reptiles parsing network data obtains target component set, can improve whole acquisition efficiency and reliable
Property.
As shown in Fig. 2, as a preferred embodiment, this method further comprises：
Step S21：Daily record is written into equation of linear regression.
It is understood that record is often played the role of in daily record, after equation of linear regression is written daily record, user can be with
Equation of linear regression is repeatedly obtained by way of reading daily record, to carry out corresponding subsequent operation, and then realizes linear regression
The nonexpondable effect of once generation of equation avoids repeatedly generating the whole resource overhead caused by equation of linear regression.
In addition, as a preferred embodiment, the vector model in each disaggregated model is configured to linear regression side
Journey is specially：
Vector model in each disaggregated model is configured to by equation of linear regression by Weka.
Weka systems summarize the machine learning algorithm of forefront and data prediction tool, so that user can be quickly clever
It is applied to new data set according to processing method by existing livingly.It provides comprehensive support for the whole process of data mining,
Including being ready for data, statistical estimation Learning Scheme, input data and the visualization of learning effect, therefore will be each by Weka
Vector model in disaggregated model is configured to equation of linear regression more high efficient and reliable.In addition Weka is free and non-commercialization
, therefore the advantage of lower cost used.
Embodiment three
Hereinbefore the embodiment of data analysing method is described in detail, the present invention also provides a kind of with being somebody's turn to do
The corresponding data analysis set-up of method, since the embodiment of device part is corresponded with the embodiment of method part, dress
Put part embodiment refer to method part embodiment description, wouldn't repeat here.
Fig. 3 is a kind of data analysis set-up structure chart provided in an embodiment of the present invention.Data provided in an embodiment of the present invention
Analytical equipment specifically includes：
Gather generation module 10, for obtaining the target component set of each target object.
Vector model generation module 11, for each target component set to be converted into vector model.
Execution module 12 is clustered, cluster analysis is carried out to vector model for passing through clustering algorithm, by each vector model
Classify, and generate corresponding disaggregated model.
Regression equation generation module 13, for the vector model in each disaggregated model to be configured to equation of linear regression, with
The variation tendency of target component is predicted according to equation of linear regression.
Data analysis set-up provided by the present invention obtains the target component and group of the target object of pending data analysis
Into parameter sets, it is equivalent to each parameter sets and characterizes corresponding target object, and then parameters set is converted into
The form of vector model, and pass through clustering algorithm and cluster analysis is carried out to vector model, it is realized with this according to each target object
Between parameter differences and comprehensive analysis is carried out to target object and is classified, and then by the vector in each disaggregated model of gained
Model construction is equation of linear regression, and the approach of vector model entirety in each classification can be embodied by equation of linear regression
Value, and then convenient for being predicted according to equation of linear regression the variation tendency of each target component.As it can be seen that the present apparatus can be opposite
Comprehensive analysis and classification are accurately carried out to the supplemental characteristic of target object, and realizes the supplemental characteristic to target object
The prediction of variation tendency.
On the basis of embodiment three, which further includes：
Screening module, for being screened according to preset standard to each target component set.
Example IV
The present invention also provides a kind of data analysis set-up, including：
Memory, for storing computer program；
The step of processor, for performing computer program when, realize data analysing method as described above.
Data analysis set-up provided by the present invention obtains the target component and group of the target object of pending data analysis
Into parameter sets, it is equivalent to each parameter sets and characterizes corresponding target object, and then parameters set is converted into
The form of vector model, and pass through clustering algorithm and cluster analysis is carried out to vector model, it is realized with this according to each target object
Between parameter differences and comprehensive analysis is carried out to target object and is classified, and then by the vector in each disaggregated model of gained
Model construction is equation of linear regression, and the approach of vector model entirety in each classification can be embodied by equation of linear regression
Value, and then convenient for being predicted according to equation of linear regression the variation tendency of each target component.As it can be seen that the present apparatus can be opposite
Comprehensive analysis and classification are accurately carried out to the supplemental characteristic of target object, and realizes the supplemental characteristic to target object
The prediction of variation tendency.
The present invention also provides a kind of computer readable storage medium, computer journey is stored on computer readable storage medium
Sequence, the step of data analysing method as described above is realized when computer program is executed by processor.
The computer readable storage medium of data analysis provided by the present invention obtains the target pair of pending data analysis
The target component and composition parameter set of elephant are equivalent to each parameter sets and characterize corresponding target object, and then will be each
A parameter sets are converted into the form of vector model, and pass through clustering algorithm and carry out cluster analysis to vector model, are realized with this
Comprehensive analysis is carried out according to the parameter differences between each target object to target object to classify, and then by each of gained
Vector model in disaggregated model is configured to equation of linear regression, can be embodied by equation of linear regression vectorial in each classification
The approach of model entirety, and then convenient for being predicted according to equation of linear regression the variation tendency of each target component.As it can be seen that
This computer readable storage medium can the relatively accurate supplemental characteristic to target object carry out comprehensive analysis and classification, and
And realize prediction to the supplemental characteristic variation tendency of target object.
A kind of data analysing method provided by the present invention, device and medium are described in detail above.Specification
In each embodiment described by the way of progressive, the highlights of each of the examples are it is different from other embodiment it
Locate, just to refer each other for identical similar portion between each embodiment.For device disclosed in embodiment, due to itself and reality
Apply that method disclosed in example is corresponding, so description is fairly simple, reference may be made to the description of the method.It should refer to
Go out, it for those skilled in the art, without departing from the principle of the present invention, can also be to the present invention
Some improvement and modification can also be carried out, these improvement and modification are also fallen within the protection scope of the claims of the present invention.
It should also be noted that, in the present specification, relational terms such as first and second and the like be used merely to by
One entity or operation are distinguished with another entity or operation, without necessarily requiring or implying these entities or operation
Between there are any actual relationship or orders.Moreover, term " comprising ", "comprising" or its any other variant meaning
Covering non-exclusive inclusion, so that process, method, article or equipment including a series of elements not only include that
A little elements, but also including other elements that are not explicitly listed or further include for this process, method, article or
The intrinsic element of equipment.In the absence of more restrictions, the element limited by sentence "including a ...", is not arranged
Except also there are other identical elements in the process, method, article or apparatus that includes the element.
Claims (10)
1. a kind of data analysing method, which is characterized in that including：
Obtain the target component set of each target object；
Each target component set is converted into vector model；Wherein, each element in the target component set is institute
State the coordinate in vector model；
Cluster analysis is carried out to the vector model by clustering algorithm, each vector model is classified, and generates
Corresponding disaggregated model；
Vector model in each disaggregated model is configured to equation of linear regression, with according to the equation of linear regression to institute
The variation tendency for stating target component is predicted.
2. according to the method described in claim 1, it is characterized in that, each target component set is converted into vector described
Before model, this method further comprises：
Each target component set is screened according to preset standard.
3. according to the method described in claim 1, it is characterized in that, the clustering algorithm is specially K-MEANS clustering algorithms.
4. according to the method described in claim 1, it is characterized in that, the target component set for obtaining each target object is specific
For：
The target component set is obtained in a manner that Python reptiles parse network data.
5. according to the method described in claim 1, it is characterized in that, this method further comprises：
Daily record is written into the equation of linear regression.
6. according to the method described in claim 1-5 any one, which is characterized in that it is described by each disaggregated model to
Measure model construction is specially for equation of linear regression：
The vector model in each disaggregated model is configured to by the equation of linear regression by Weka.
7. a kind of data analysis set-up, which is characterized in that including：
Gather generation module, for obtaining the target component set of each target object；
Vector model generation module, for each target component set to be converted into vector model；
Execution module is clustered, cluster analysis is carried out to the vector model for passing through clustering algorithm, by each vectorial mould
Type is classified, and generates corresponding disaggregated model；
Regression equation generation module, for the vector model in each disaggregated model to be configured to equation of linear regression, with root
The variation tendency of the target component is predicted according to the equation of linear regression.
8. device according to claim 7, which is characterized in that the device further comprises：
Screening module, for being screened according to preset standard to each target component set.
9. a kind of data analysis set-up, which is characterized in that including：
Memory, for storing computer program；
Processor realizes such as claim 1 to 6 any one of them data analysing method during for performing the computer program
The step of.
10. a kind of computer readable storage medium, which is characterized in that be stored with computer on the computer readable storage medium
Program is realized when the computer program is executed by processor such as claim 1 to 6 any one of them data analysing method
Step.
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| CN201711407261.5A CN108154392A (en) | 2017-12-22 | 2017-12-22 | A kind of data analysing method, device and medium |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109063736A (en) * | 2018-06-29 | 2018-12-21 | 考拉征信服务有限公司 | Data classification method, device, electronic equipment and computer readable storage medium |
| CN109582550A (en) * | 2018-09-29 | 2019-04-05 | 阿里巴巴集团控股有限公司 | A kind of method, apparatus and server obtaining full dose business scenario failure collection |
-
2017
- 2017-12-22 CN CN201711407261.5A patent/CN108154392A/en active Pending
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109063736A (en) * | 2018-06-29 | 2018-12-21 | 考拉征信服务有限公司 | Data classification method, device, electronic equipment and computer readable storage medium |
| CN109582550A (en) * | 2018-09-29 | 2019-04-05 | 阿里巴巴集团控股有限公司 | A kind of method, apparatus and server obtaining full dose business scenario failure collection |
| CN109582550B (en) * | 2018-09-29 | 2022-04-26 | 创新先进技术有限公司 | Method, device and server for acquiring full-service scene fault set |
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