CN111461446A - Prediction method and device for complaint reporting case based on machine learning - Google Patents
Prediction method and device for complaint reporting case based on machine learning Download PDFInfo
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Abstract
The invention relates to a prediction method and a prediction device for complaint reporting cases based on machine learning, which comprises the steps of obtaining influence factor indexes of cases, calculating corresponding weights of the influence factor indexes, and determining a first weight factor; wherein, the influencing factor indexes comprise a plurality of indexes; calculating the weight of each classification under each influence factor index in the influence factor index, and determining a second weight factor; obtaining a prediction model by combining the first weight factor and the second weight factor; and predicting the complaint case to be predicted through the prediction model to obtain a score distribution, so that the prediction result of the complaint case to be predicted can be obtained. The method and the system can reasonably predict the occurrence condition of the complaint reporting case from different dimensions, further help law enforcement personnel to more effectively process the complaint reporting case, help related personnel to timely find the reason of frequent occurrence of the complaint reporting case, and improve the working efficiency of the law enforcement personnel in managing the complaint reporting case.
Description
Technical Field
The invention belongs to the technical field of law, and particularly relates to a prediction method and a prediction device of a complaint report case based on machine learning.
Background
The complaint report is a window reflecting the social condition and plays an important role in constructing a harmonious society. At present, the analysis of the prediction of complaint reports is relatively backward, and there is no systematic analysis method. In the law enforcement process of law enforcement departments, the prediction of the complaint reporting cases is very important, the method has important influence on effectively improving the efficiency of the law enforcement personnel in managing the complaint reporting cases, and is also helpful for helping the law enforcement personnel to analyze the reason of frequent complaint reporting cases in the dimensions of administrative divisions, time periods, industries and the like so as to better serve people. Therefore, there is a need for predictive analysis of complaint reporting cases.
Disclosure of Invention
In view of the above, the present invention provides a prediction method and device for complaint reporting cases based on machine learning, so as to solve the problem of low efficiency of law enforcement officers in managing complaint reporting cases in the prior art.
In order to achieve the purpose, the invention adopts the following technical scheme: a prediction method of complaint reporting cases based on machine learning comprises the following steps:
acquiring influence factor indexes of a case, calculating corresponding weights of the influence factor indexes, and determining a first weight factor; wherein the influence factor indexes comprise a plurality of indexes;
calculating the weight of each classification under each influence factor index in the influence factor index, and determining a second weight factor;
obtaining a prediction model by combining the first weight factor and the second weight factor;
and predicting the complaint case to be predicted through the prediction model to obtain a score distribution, namely obtaining the prediction result of the complaint case to be predicted.
Further, the obtaining of the influence factor index of the case and calculating the weight corresponding to the influence factor index, and determining the first weight factor, before, further includes:
acquiring case information;
processing the case information to generate structured data and storing the structured data in a database;
the structured data is processed.
Further, the processing the case information to generate structured data and storing the structured data in a database includes:
determining influence factor indexes of complaint reporting cases, and arranging the influence factor indexes into structured data;
storing the structured data to a database.
Further said processing said structured data, comprising:
missing data in the database is filled.
Further, calculating the weight corresponding to the influence factor index by adopting a principal component analysis method, and determining a first weight factor.
Further, calculating the corresponding weight of each classification under each influence factor index in the influence factor index by adopting a principal component analysis method, and determining a second weight factor.
Further, the influence factor indicators include:
at least one of the industry or field to which the case belongs, the case occurrence division and the case occurrence time.
Further, the prediction model is a scoring function;
and the scoring function predicts according to the industry or the field to which the case belongs, the case occurrence division and the case occurrence time.
The embodiment of the application provides a prediction device of complaint report case based on machine learning, includes:
the first calculation module is used for acquiring influence factor indexes of cases, calculating corresponding weights of the influence factor indexes and determining a first weight factor; wherein the influence factor indexes comprise a plurality of indexes;
the second calculation module is used for calculating the weight of each classification under each influence factor index in the influence factor indexes and determining a second weight factor;
the combination module is used for combining the first weight factor and the second weight factor to obtain a prediction model;
and the prediction module is used for predicting the complaint case to be predicted through the prediction model to obtain a score distribution, namely the prediction result of the complaint case to be predicted.
Further, the method also comprises the following steps:
the acquisition module is used for acquiring case information;
the storage module is used for processing the case information to generate structured data and storing the structured data in a database;
a processing module for the structured data.
By adopting the technical scheme, the invention can achieve the following beneficial effects:
the method can reasonably predict the occurrence condition of the complaint reporting case from different dimensions, and further help law enforcement personnel to more effectively process the complaint reporting case, help related personnel to timely find the reason why the complaint reporting case frequently occurs, and improve the working efficiency of the law enforcement personnel to manage the complaint reporting case.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic diagram illustrating steps of a method for predicting a complaint report case based on machine learning according to the present invention;
FIG. 2 is a schematic flow chart of the method for predicting complaint reporting cases based on machine learning according to the present invention;
FIG. 3 is a schematic structural diagram of a prediction device for complaint reporting cases based on machine learning according to the present invention;
fig. 4 is a schematic structural diagram of a computer device in a hardware operating environment related to the prediction method for complaint reporting cases based on machine learning according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be described in detail below. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be derived by a person skilled in the art from the examples given herein without any inventive step, are within the scope of the present invention.
A specific method and apparatus for predicting a complaint report based on machine learning provided in the embodiments of the present application will be described with reference to the accompanying drawings.
As shown in fig. 1, the method for predicting a complaint report based on machine learning provided in the embodiment of the present application includes:
s101, acquiring influence factor indexes of a case, calculating corresponding weights of the influence factor indexes, and determining a first weight factor; wherein the influence factor indexes comprise a plurality of indexes;
acquiring influence factor indexes in a case, wherein the influence factor indexes are at least one, and can be one or more of the industry or the field to which the case belongs, the occurrence division of the case, the occurrence year and month of the case and the like, and calculating the proportion of each influence factor index in the case, namely determining a first weight factor. The first weighting factor is a general term of the proportion of each influence factor index in the case.
S102, calculating the weight of each classification under each influence factor index in the influence factor index, and determining a second weight factor;
and a plurality of classifications are also arranged under each influence factor index, specifically, the classification is the distribution of the specific values of the influence factors under the influence factor index, and the weight of each classification in the influence factor index is calculated, namely, a second weight factor is determined. The second weight factor is a general term for the weight of each classification in the influencer index.
S103, obtaining a prediction model by combining the first weight factor and the second weight factor;
as can be seen from steps S101 and S102, the first weight factor and the second weight factor are two levels, and the first weight factor and the second weight factor are combined to obtain a scoring function, i.e., a prediction model.
And S104, predicting the complaint case to be predicted through the prediction model to obtain a score distribution, namely obtaining the prediction result of the complaint case to be predicted.
And inputting the complaint case to be predicted into the prediction model, outputting a score distribution by the prediction model, and obtaining the prediction result of the complaint case to be predicted according to the score distribution condition. Specifically, the scoring function is calculated by determining influence factors through a first weight factor, determining a second weight factor through the obtained influence factors, counting the number of reported cases classified by each influence factor in a related case library, multiplying the number of reported cases in each classification by each second weight factor to obtain a first value, multiplying the number of cases which are not reported in each classification by each second weight factor to obtain a second value, subtracting the first value from the second value to obtain a difference value, comparing the difference value with a threshold value, and determining a final result, wherein the threshold value is a preset value.
The working principle of the prediction method of the complaint reporting case based on machine learning is as follows: acquiring influence factor indexes of a case, calculating corresponding weights of the influence factor indexes, and determining a first weight factor; wherein the influence factor indexes comprise a plurality of indexes; calculating the weight of each classification under each influence factor index in the influence factor index, and determining a second weight factor; obtaining a prediction model by combining the first weight factor and the second weight factor; and predicting the complaint case to be predicted through the prediction model to obtain a score distribution, namely obtaining the prediction result of the complaint case to be predicted.
The prediction method for the complaint reporting case based on machine learning can objectively carry out targeted subdivision prediction on the complaint reporting case based on one or more of administrative divisions, industries/fields and complaint reporting time.
In some embodiments, as shown in fig. 2, the obtaining an influence factor indicator of a case, calculating a weight corresponding to the influence factor indicator, and determining a first weight factor further includes:
acquiring case information;
processing the case information to generate structured data and storing the structured data in a database;
the structured data is processed.
Specifically, key information of case information conditions is extracted, and all important indexes related to complaint reports, namely all influence factor indexes are extracted, wherein the influence factor indexes at least comprise one of the following indexes: the industry or field to which the case belongs, the occurrence division of the case, the occurrence year and month of the case, and the like. And (4) sorting all the influence factor indexes into structured data, storing the structured data into a database, and processing the structured data.
Preferably, the processing the case information to generate structured data and storing the structured data in a database includes:
determining influence factor indexes of complaint reporting cases, and arranging the influence factor indexes into structured data;
storing the structured data to a database.
Preferably, the processing the structured data includes:
missing data in the database is filled.
Specifically, the structured data is supplemented, the missing data comprises basic information of a complaint reporting applicant, wherein the basic information can be identity information and the like, the missing data can also comprise other related data, the application is not limited herein, the missing data is filled so that the structured data is more complete, and when the complaint reporting case is predicted, the occurrence condition of the complaint reporting case can be predicted reasonably through different dimensions.
Preferably, the corresponding weight of the influence factor index is calculated by adopting a Principal Component Analysis (PCA), and a first weight factor is determined.
Preferably, the corresponding weight of each classification under each influence factor index in the influence factor index is calculated by using a Principal Component Analysis (PCA), and a second weight factor is determined.
Principal Component Analysis (PCA), a statistical method. A group of variables which are possibly correlated are converted into a group of linearly uncorrelated variables through orthogonal transformation, and the group of converted variables are called principal components.
Preferably, the influence factor indicators include:
at least one of the industry or field to which the case belongs, the case occurrence division and the case occurrence time.
Preferably, the prediction model is a scoring function;
and the scoring function predicts according to the industry or the field to which the case belongs, the case occurrence division and the case occurrence time.
This application can follow different dimensions and carry out the prediction of rationality to the emergence condition of the case of reporting a complaint, and then help law enforcement personnel more effective processing case of reporting a complaint, and help relevant personnel in time discover the reason that the case of reporting a complaint frequently sent, can predict the case of reporting a complaint of different time spaces and trade fields, use this as law enforcement personnel and regard as the early warning reference, and in time take measures, have important using value, the work efficiency of the case of reporting a complaint of law enforcement personnel management is improved.
As shown in fig. 3, an embodiment of the present application provides a prediction apparatus for a complaint report case based on machine learning, including:
a first calculating module 301, configured to obtain an influence factor indicator of a case, calculate a weight corresponding to the influence factor indicator, and determine a first weight factor; wherein the influence factor indexes comprise a plurality of indexes;
a second calculating module 302, configured to calculate a weight corresponding to each category in each influence factor indicator under each influence factor indicator, and determine a second weight factor;
a combining module 303, configured to combine the first weighting factor and the second weighting factor to obtain a prediction model;
the prediction module 304 is configured to predict the complaint case to be predicted through the prediction model to obtain a score distribution, i.e., a prediction result of the complaint case to be predicted.
Preferably, the method further comprises the following steps:
an obtaining module 305, configured to obtain case information;
the storage module 306 is used for processing the case information to generate structured data and storing the structured data in a database;
a processing module 307 for the structured data.
The working principle of the prediction device for complaint reporting cases based on machine learning provided by the application is that the acquisition module 305 acquires case information; the storage module 306 processes the case information to generate structured data and stores the structured data in a database; the processing module 307 is configured to process the structured data, and the first calculating module 301 obtains an influence factor index of a case, calculates a weight corresponding to the influence factor index, and determines a first weight factor; wherein the influence factor indexes comprise a plurality of indexes; the second calculating module 302 calculates the weight of each category under each influence factor index corresponding to the influence factor index, and determines a second weight factor; the combination module 303 combines the first weight factor and the second weight factor to obtain a prediction model; the prediction module 304 predicts the complaint case to be predicted through the prediction model to obtain a score distribution, namely a prediction result of the complaint case to be predicted.
As shown in fig. 4, the present application provides a computer device comprising: the memory, which may include volatile memory on a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). The computer device stores an operating system, and the memory is an example of a computer-readable medium. The computer program, when executed by a processor, causes the processor to perform a big data based administrative penalty discretion rationality assessment method, the structure shown in fig. 4 is a block diagram of only a part of the structure relating to the solution of the present application, and does not constitute a limitation of the computer device to which the solution of the present application is applied, a specific computer device may comprise more or less components than shown in the figure, or combine certain components, or have a different arrangement of components.
In one embodiment, the prediction method for the complaint report based on machine learning provided by the present application can be implemented in the form of a computer program, and the computer program can be run on a computer device as shown in fig. 4.
In some embodiments, the computer program, when executed by the processor, causes the processor to perform the steps of: acquiring influence factor indexes of a case, calculating corresponding weights of the influence factor indexes, and determining a first weight factor; wherein the influence factor indexes comprise a plurality of indexes; calculating the weight of each classification under each influence factor index in the influence factor index, and determining a second weight factor; obtaining a prediction model by combining the first weight factor and the second weight factor; and predicting the complaint case to be predicted through the prediction model to obtain a score distribution, namely obtaining the prediction result of the complaint case to be predicted.
The present application also provides a computer storage medium, examples of which include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassette storage or other magnetic storage devices, or any other non-transmission medium, that can be used to store information that can be accessed by a computing device.
In some embodiments, the present invention further provides a computer-readable storage medium storing a computer program, which when executed by a processor, causes the processor to perform the steps of: acquiring influence factor indexes of a case, calculating corresponding weights of the influence factor indexes, and determining a first weight factor; wherein the influence factor indexes comprise a plurality of indexes; calculating the weight of each classification under each influence factor index in the influence factor index, and determining a second weight factor; obtaining a prediction model by combining the first weight factor and the second weight factor; and predicting the complaint case to be predicted through the prediction model to obtain a score distribution, namely obtaining the prediction result of the complaint case to be predicted.
In conclusion, the invention provides a method which can reasonably predict the occurrence condition of the complaint reporting cases from different dimensions, further help law enforcement personnel to more effectively process the complaint reporting cases, help related personnel to timely find the reason of frequent complaint reporting cases, and improve the working efficiency of the law enforcement personnel in managing the complaint reporting cases.
It is to be understood that the apparatus embodiments provided above correspond to the method embodiments described above, and corresponding specific contents may be referred to each other, which are not described herein again.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (10)
1. A prediction method of complaint reporting cases based on machine learning is characterized by comprising the following steps:
acquiring influence factor indexes of a case, calculating corresponding weights of the influence factor indexes, and determining a first weight factor; wherein the influence factor indexes comprise a plurality of indexes;
calculating the weight of each classification under each influence factor index in the influence factor index, and determining a second weight factor;
obtaining a prediction model by combining the first weight factor and the second weight factor;
and predicting the complaint case to be predicted through the prediction model to obtain a score distribution, namely obtaining the prediction result of the complaint case to be predicted.
2. The prediction method according to claim 1, wherein the obtaining of the influence factor indicators of the cases and calculating the corresponding weights of the influence factor indicators, determining the first weight factor, further comprises:
acquiring case information;
processing the case information to generate structured data and storing the structured data in a database;
the structured data is processed.
3. The prediction method of claim 2, wherein the processing the case information to generate structured data and storing the structured data in a database comprises:
determining influence factor indexes of complaint reporting cases, and arranging the influence factor indexes into structured data;
storing the structured data to a database.
4. The prediction method of claim 2, wherein the processing the structured data comprises:
missing data in the database is filled.
5. The prediction method according to claim 1,
and calculating the weight corresponding to the influence factor index by adopting a principal component analysis method, and determining a first weight factor.
6. The prediction method according to claim 1,
and calculating the weight of each classification in the influence factor indexes under each influence factor index by adopting a principal component analysis method, and determining a second weight factor.
7. The prediction method according to any one of claims 1 to 6, wherein the influence factor indicators include:
at least one of the industry or field to which the case belongs, the case occurrence division and the case occurrence time.
8. The prediction method according to claim 7,
the prediction model is a scoring function;
and the scoring function predicts according to the industry or the field to which the case belongs, the case occurrence division and the case occurrence time.
9. A prediction device for a complaint reporting case based on machine learning, comprising:
the first calculation module is used for acquiring influence factor indexes of cases, calculating corresponding weights of the influence factor indexes and determining a first weight factor; wherein the influence factor indexes comprise a plurality of indexes;
the second calculation module is used for calculating the weight of each classification under each influence factor index in the influence factor indexes and determining a second weight factor;
the combination module is used for combining the first weight factor and the second weight factor to obtain a prediction model;
and the prediction module is used for predicting the complaint case to be predicted through the prediction model to obtain a score distribution, namely the prediction result of the complaint case to be predicted.
10. The prediction apparatus according to claim 9, further comprising:
the acquisition module is used for acquiring case information;
the storage module is used for processing the case information to generate structured data and storing the structured data in a database;
a processing module for the structured data.
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| CN109766359A (en) * | 2018-12-26 | 2019-05-17 | 科大国创软件股份有限公司 | A kind of credit main body comprehensive analysis management system and method |
| CN109858702A (en) * | 2019-02-14 | 2019-06-07 | 中国联合网络通信集团有限公司 | Client upgrades prediction technique, device, equipment and the readable storage medium storing program for executing complained |
| CN110033356A (en) * | 2019-04-22 | 2019-07-19 | 跑哪儿科技(成都)有限公司 | A kind of couple of user is worth the method, apparatus and system being ranked up |
| CN110197332A (en) * | 2019-05-30 | 2019-09-03 | 重庆跃途科技有限公司 | A kind of overall control of social public security evaluation method |
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