CN111461446B - Prediction method and device for complaint report cases based on machine learning - Google Patents
Prediction method and device for complaint report cases based on machine learning Download PDFInfo
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Abstract
The invention relates to a prediction method and a device for complaint report cases based on machine learning, comprising the steps of obtaining influence factor indexes of the cases, calculating corresponding weights of the influence factor indexes, and determining first weight factors; wherein the influencing factor index comprises a plurality of indexes; calculating the corresponding weight of each category under each influence factor index in the influence factor index, and determining a second weight factor; combining the first weight factor and the second weight factor to obtain a prediction model; predicting the complaint cases to be predicted through the prediction model to obtain a grading distribution, and obtaining the prediction results of the complaint cases to be predicted. The method and the system can predict the occurrence of the complaint report from different dimensions, further help law enforcement personnel to effectively process the complaint report, help related personnel to discover the frequent reasons of the complaint report in time, and improve the working efficiency of the law enforcement personnel for managing the complaint report.
Description
Technical Field
The invention belongs to the technical field of complaint report management, and particularly relates to a machine learning-based complaint report case prediction method and device.
Background
At present, analysis of complaint report predictions is relatively late, and no systematic analysis method exists. Therefore, it is necessary to conduct predictive analysis on complaint report cases.
Disclosure of Invention
In view of the above, the invention aims to overcome the defects of the prior art, and provides a machine learning-based complaint report prediction method and a machine learning-based complaint report prediction device, so as to solve the problem of low efficiency of managing complaint report by law enforcement personnel in the prior art.
In order to achieve the above purpose, the invention adopts the following technical scheme: a machine learning-based complaint report case prediction method comprises the following steps:
Acquiring influence factor indexes of cases, calculating corresponding weights of the influence factor indexes, and determining first weight factors; wherein the influence factor index comprises a plurality of;
calculating the corresponding weight of each category under each influence factor index in the influence factor index, and determining a second weight factor;
combining the first weight factor and the second weight factor to obtain a prediction model;
predicting the complaint cases to be predicted through the prediction model to obtain a scoring distribution, and obtaining the prediction results of the complaint cases to be predicted.
Further, the acquiring the influence factor index of the case, calculating the corresponding weight of the influence factor index, and determining the first weight factor further includes:
acquiring case information;
Processing the case information to generate structured data and storing the structured data into a database;
Processing the structured data.
Further, processing the case information to generate structured data and storing the structured data in a database, including:
determining influence factor indexes of complaint report cases, and sorting the influence factor indexes into structural data;
and storing the structured data in a database.
Further said processing said structured data comprises:
Filling the missing data in the database.
Further, a principal component analysis method is adopted to calculate the corresponding weight of the influence factor index, and a first weight factor is determined.
Further, a principal component analysis method is adopted to calculate the corresponding weight of each category under each influence factor index in the influence factor index, and a second weight factor is determined.
Further, the influence factor index includes:
At least one of the industries or the fields, the case occurrence regions and the case occurrence time to which the case belongs.
Further, the prediction model is a scoring function;
The scoring function predicts according to the industry or the field to which the case belongs, the case occurrence region and the case occurrence time.
The embodiment of the application provides a machine learning-based complaint report case prediction device, which comprises:
The first calculation module is used for obtaining the influence factor index of the case, calculating the corresponding weight of the influence factor index and determining a first weight factor; wherein the influence factor index comprises a plurality of;
The second calculation module is used for calculating the corresponding weight of each category under each influence factor index in the influence factor index 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;
the prediction module is used for predicting the complaint case to be predicted through the prediction model to obtain a grading distribution, and the prediction result of the complaint case to be predicted is obtained.
Further, the method further 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 into a database;
and the processing module is used for the structured data.
By adopting the technical scheme, the invention has the following beneficial effects:
The method and the system can predict rationality of occurrence conditions of complaints from different dimensions, further help law enforcement personnel to effectively process the complaints, help related personnel to discover reasons of frequent complaints in time, and improve working efficiency of the law enforcement personnel to manage the complaints.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of the steps of a method for predicting a complaint report based on machine learning according to the present invention;
FIG. 2 is a flow chart of a method for predicting a complaint report based on machine learning according to the present invention;
FIG. 3 is a schematic diagram of a machine learning-based complaint report predicting device according to the present invention;
Fig. 4 is a schematic structural diagram of a computer device of a hardware operating environment related to a machine learning-based complaint report case prediction method of 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 will be apparent that the described embodiments are only some, but not all, embodiments of the invention. All other embodiments, based on the examples herein, which are within the scope of the invention as defined by the claims, will be within the scope of the invention as defined by the claims.
The following describes a specific machine learning-based complaint report prediction method and device provided in the embodiment of the application with reference to the accompanying drawings.
As shown in fig. 1, the method for predicting a complaint report case based on machine learning provided in the embodiment of the present application includes:
S101, acquiring influence factor indexes of cases, calculating corresponding weights of the influence factor indexes, and determining first weight factors; wherein the influence factor index comprises a plurality of;
The method comprises the steps of obtaining influence factor indexes in cases, wherein at least one influence factor index is one or more of industries or fields to which the cases belong, case occurrence regions, case occurrence years and months and the like, and calculating the proportion of each influence factor index in the case, namely determining a first weight factor. The first weight factor is a generic term for the specific weight of each influencing factor indicator in the case.
S102, calculating the corresponding weight of each category under each influence factor index in the influence factor index, and determining a second weight factor;
there are also multiple classifications under each influence factor indicator, specifically, the classification is the distribution of specific values of the influence factors under the influence factor indicator, and the weight of each classification in the influence factor indicator is calculated, namely, the second weight factor is determined. The second weight factor is a generic term for the weight of each category in the impact factor indicator.
S103, combining the first weight factor and the second weight factor to obtain a prediction model;
As can be seen from steps S101 and S102, the first weight factor and the second weight factor are two-stage, and the scoring function, i.e., the prediction model, is obtained by combining the first weight factor and the second weight factor.
S104, predicting the complaint case to be predicted through the prediction model to obtain a grading distribution, and obtaining a prediction result of the complaint case to be predicted.
Inputting the complaint cases to be predicted into a prediction model, outputting a grading distribution by the prediction model, and obtaining the prediction results of the complaint cases to be predicted according to the grading distribution. Specifically, the calculation method of the scoring function includes determining an influence factor through a first weight factor, determining a second weight factor through the obtained influence factor, counting the number of reported cases classified by each influence factor in a related case library, multiplying the number of reported cases classified by each second weight factor to obtain a first value, multiplying the number of non-reported cases classified 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 machine learning-based complaint report case prediction method is as follows: acquiring influence factor indexes of cases, calculating corresponding weights of the influence factor indexes, and determining first weight factors; wherein the influence factor index comprises a plurality of; calculating the corresponding weight of each category under each influence factor index in the influence factor index, and determining a second weight factor; combining the first weight factor and the second weight factor to obtain a prediction model; predicting the complaint cases to be predicted through the prediction model to obtain a scoring distribution, and obtaining the prediction results of the complaint cases to be predicted.
According to the machine learning-based complaint report case prediction method, the complaint report case can be objectively subdivided and predicted in a targeted manner based on one or more of administrative division, industry/field and complaint report time.
In some embodiments, as shown in fig. 2, the acquiring the impact factor indicator of the case and calculating the weight corresponding to the impact factor indicator, and determining the first weight factor further includes:
acquiring case information;
Processing the case information to generate structured data and storing the structured data into a database;
Processing the structured data.
Specifically, key information of case information conditions is extracted, and all important indexes related to complaint report, namely all influence factor indexes are extracted, wherein the influence factor indexes at least comprise one of the following indexes: the case belongs to the industry or the field, the case generation region, the case generation year and month, and the like. And (3) finishing all the influence factor indexes into structured data, storing the structured data into a database, and processing the structured data.
Preferably, processing the case information to generate structured data and storing the structured data in a database includes:
determining influence factor indexes of complaint report cases, and sorting the influence factor indexes into structural data;
and storing the structured data in a database.
Preferably, the processing the structured data includes:
Filling the missing data in the database.
Specifically, the structured data is supplemented, the missing data comprises basic information of the complaint reporting applicant, wherein the basic information can be identity information and the like, and the missing data can also comprise other related data.
Preferably, the main component analysis PCA is adopted to calculate the corresponding weight of the influence factor index, and the first weight factor is determined.
Preferably, a principal component analysis PCA is adopted to calculate the corresponding weight of each category under each influence factor index in the influence factor index, and a second weight factor is determined.
Principal component analysis (PRINCIPAL COMPONENT ANALYSIS, PCA) is a statistical method. A set of variables which may have correlation is converted into a set of variables which are not linearly correlated through positive-negative conversion, and the converted set of variables is called a main component.
Preferably, the influence factor index includes:
At least one of the industries or the fields, the case occurrence regions and the case occurrence time to which the case belongs.
Preferably, the prediction model is a scoring function;
The scoring function predicts according to the industry or the field to which the case belongs, the case occurrence region and the case occurrence time.
The method and the system can predict the occurrence of the complaint report cases from different dimensions, further help law enforcement personnel to effectively process the complaint report cases, help related personnel to find the reasons of frequent complaint report cases in time, and predict the complaint report cases in different time spaces and industry fields, thereby taking the complaint report cases as early warning references for the law enforcement personnel, taking measures in time, having important application value and improving the working efficiency of the law enforcement personnel for managing the complaint report cases.
As shown in fig. 3, an embodiment of the present application provides a machine learning-based complaint report predicting apparatus, including:
The first calculating module 301 is configured to obtain an impact factor indicator of a case, calculate a weight corresponding to the impact factor indicator, and determine a first weight factor; wherein the influence factor index comprises a plurality of;
A second calculation module 302, configured to calculate a weight corresponding to each category under each influence factor indicator in the influence factor indicator, and determine a second weight factor;
A combination module 303, configured to combine the first weight factor and the second weight factor to obtain a prediction model;
The prediction module 304 is configured to predict a complaint case to be predicted according to the prediction model, so as to obtain a score distribution, and obtain a prediction result of the complaint case to be predicted.
Preferably, the method further comprises:
an acquiring module 305, configured to acquire case information;
the storage module 306 is configured to process the case information to generate structured data and store the structured data in a database;
a processing module 307 for the structured data.
The working principle of the prediction device for complaints and reports 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 processes the structured data, and the first computing module 301 obtains the impact factor index of the case, calculates the weight corresponding to the impact factor index, and determines a first weight factor; wherein the influence factor index comprises a plurality of; the second calculation module 302 calculates the corresponding weight of each category under each influence factor index in 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 scoring distribution, and then a prediction result of the complaint case to be predicted is obtained.
As shown in fig. 4, the present application provides a computer device including: the memory and processor may also include a network interface, the memory storing a computer program, the memory may include volatile memory, random Access Memory (RAM) and/or nonvolatile memory in a computer readable medium, such as Read Only Memory (ROM) or flash RAM. The computer device stores an operating system, with memory being an example of a computer-readable medium. The computer program, when executed by a processor, causes the processor to perform a method for discretionary evaluation of administrative penalties based on big data, the structure shown in fig. 4 is merely a block diagram of a part of the structure related to the inventive solution and does not constitute a limitation of the computer device to which the inventive solution is applied, and a specific computer device may include more or less components than those shown in the drawings, or may combine some components, or have a different arrangement of components.
In one embodiment, the machine learning-based complaint report prediction method provided by the present application may be implemented in the form of a computer program that 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 cases, calculating corresponding weights of the influence factor indexes, and determining first weight factors; wherein the influence factor index comprises a plurality of; calculating the corresponding weight of each category under each influence factor index in the influence factor index, and determining a second weight factor; combining the first weight factor and the second weight factor to obtain a prediction model; predicting the complaint cases to be predicted through the prediction model to obtain a scoring distribution, and obtaining the prediction results of the complaint cases 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 also proposes 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 cases, calculating corresponding weights of the influence factor indexes, and determining first weight factors; wherein the influence factor index comprises a plurality of; calculating the corresponding weight of each category under each influence factor index in the influence factor index, and determining a second weight factor; combining the first weight factor and the second weight factor to obtain a prediction model; predicting the complaint cases to be predicted through the prediction model to obtain a scoring distribution, and obtaining the prediction results of the complaint cases to be predicted.
In summary, the invention provides a method for predicting rationality of occurrence of complaints from different dimensions, thereby helping law enforcement personnel to more effectively process the complaints, helping related personnel to discover reasons of frequent complaints in time and improving working efficiency of the law enforcement personnel to manage the complaints.
It can be understood that the above-provided device embodiments correspond to the above-described method embodiments, and corresponding specific details may be referred to each other, which is not described herein again.
It will be appreciated by those skilled in the art that 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, magnetic 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 flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations 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 foregoing is merely illustrative of the present invention, and the present invention is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (6)
1. The machine learning-based complaint report case prediction method is characterized by comprising the following steps:
Acquiring influence factor indexes of cases, calculating corresponding weights of the influence factor indexes, and determining first weight factors; the influence factor indexes comprise: at least one of industries or fields to which the cases belong, case occurrence regions and case occurrence times;
Calculating the corresponding weight of each category under each influence factor index in the influence factor index, and determining a second weight factor; calculating the corresponding weight of each category under each influence factor index in the influence factor index by adopting a principal component analysis method, and determining a second weight factor;
combining the first weight factor and the second weight factor to obtain a prediction model;
Predicting the complaint cases to be predicted through the prediction model to obtain a scoring distribution, namely obtaining the prediction results of the complaint cases to be predicted; wherein the predictive model is a scoring function; the calculation method of the scoring function comprises the following steps: determining influence factors through the first weight factors, determining second weight factors through the obtained influence factors, counting the number of reported cases of each influence factor classification in a related case library, multiplying the number of reported cases of each classification by each second weight factor to obtain a first value, multiplying the number of non-reported cases of 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 method comprises the steps of obtaining the influence factor index of the case, calculating the corresponding weight of the influence factor index, and determining a first weight factor, wherein the method further comprises the following steps:
acquiring case information;
Processing the case information to generate structured data and storing the structured data into a database;
Processing the structured data.
2. The method of claim 1, wherein processing the case information to generate structured data and storing the structured data in a database comprises:
determining influence factor indexes of complaint report cases, and sorting the influence factor indexes into structural data;
and storing the structured data in a database.
3. The method of predicting as recited in claim 1, wherein said processing said structured data comprises:
Filling the missing data in the database.
4. The method for predicting according to claim 1, wherein,
And calculating the corresponding weight of the influence factor index by adopting a principal component analysis method, and determining a first weight factor.
5. The method for predicting according to claim 1, wherein,
The scoring function predicts according to the industry or the field to which the case belongs, the case occurrence region and the case occurrence time.
6. The utility model provides a prediction device of complaint report case based on machine learning which characterized in that includes:
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 into a database;
the processing module is used for processing the structured data;
The first calculation module is used for obtaining the influence factor index of the case, calculating the corresponding weight of the influence factor index and determining a first weight factor; wherein the influence factor index comprises a plurality of;
The second calculation module is used for calculating the corresponding weight of each category under each influence factor index in the influence factor index and determining a second weight factor; calculating the corresponding weight of each category under each influence factor index in the influence factor index by adopting a principal component analysis method, 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;
The prediction module is used for predicting the complaint case to be predicted through the prediction model to obtain a scoring distribution, namely a prediction result of the complaint case to be predicted; wherein the predictive model is a scoring function; the calculation method of the scoring function comprises the following steps: determining influence factors through the first weight factors, determining second weight factors through the obtained influence factors, counting the number of reported cases of each influence factor classification in a related case library, multiplying the number of reported cases of each classification by each second weight factor to obtain a first value, multiplying the number of non-reported cases of each classification by the second weight factors 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.
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| CN109961248A (en) * | 2017-12-25 | 2019-07-02 | 顺丰科技有限公司 | Waybill complains prediction technique, device, equipment and its storage medium |
| 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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