Black spot person prediction method, system and device based on machine learning and medium

Abstract

The invention discloses a black spot personnel prediction method, system and equipment based on machine learning and a medium, belongs to the technical field of business arrangement and machine learning, and aims to solve the technical problem of how to predict whether corresponding personnel are to become black spot personnel or not according to existing index data of different types of personnel such as test personnel, installation personnel and maintenance personnel. In order to improve the evaluation index in advance and effectively avoid the personnel which are finally evaluated as black spots, the technical scheme comprises the following steps of: summarizing and counting historical index data of class personnel of a model to be trained to obtain summarized training data; preprocessing the summarized training data to obtain preprocessed summarized training data; performing model training of a logistic regression algorithm on the preprocessed summarized training data to obtain features, models and evaluation indexes of corresponding personnel categories; inputting the latest personnel index data into the prediction model of the corresponding category to obtain a prediction result of whether the corresponding personnel are black spot personnel; and updating a black spot personnel detail table based on a prediction result.

Description

Technical Field

[0001] The present invention relates to the technical field of business orchestration and machine learning, and in particular to a method, system, device and medium for predicting black spot personnel based on machine learning. Background Art

[0002] With the continuous development of the business, the work conditions of different categories of personnel such as testing, installation and maintenance have a great impact on the development of the business. Each category of personnel has statistical work indicators, such as the number of installation work orders, on-site timeliness rate, etc. These indicators are intuitive reflections of the impact on customer feedback on the business. Testers and installation and maintenance personnel with low service timeliness, service standardization, construction standardization and service satisfaction can be considered as black spot personnel. However, the existing technology is still blank on how to predict whether different categories of personnel such as testing, installation and maintenance are about to become black and white personnel based on their current indicators.

[0003] Therefore, how to use the existing indicator data of different categories of personnel such as testing, installation and maintenance to predict whether the corresponding personnel are about to become blackspot personnel, so that they can improve their evaluation indicators in advance and effectively avoid being evaluated as blackspot personnel in the end is a technical problem that needs to be solved urgently. Summary of the invention

[0004] The technical task of the present invention is to provide a black spot personnel prediction method, system, equipment and medium based on machine learning to solve the problem of how to use the existing indicator data of different categories of personnel such as testing, installation and maintenance to predict whether the corresponding personnel are about to become black spot personnel, so that they can improve the evaluation indicators in advance and effectively avoid the problem of being evaluated as a black spot personnel in the end.

[0005] The technical task of the present invention is achieved in the following way: a method for predicting black spot personnel based on machine learning, the method is as follows:

[0006] Summarize and count the historical indicator data of the category personnel for which the model is to be trained to obtain the summary training data;

[0007] Preprocessing the aggregated training data to obtain the preprocessed aggregated training data;

[0008] Perform model training of the logistic regression algorithm on the preprocessed summary training data to obtain the characteristics, model and evaluation indicators of the corresponding personnel category;

[0009] Input the latest personnel index data into the prediction model of the corresponding category to obtain the prediction result of whether the corresponding personnel are black spot personnel;

[0010] Based on the prediction results, update the black spot personnel details table;

[0011] Based on the updated black spot personnel details table, update the regional black spot personnel monitoring table.

[0012] Preferably, the pretreatment is as follows:

[0013] Feature markers;

[0014] Outlier handling;

[0015] Label tag.

[0016] Preferably, detailed data on monitoring of black spot personnel is displayed based on the prediction results.

[0017] Preferably, data on black spot warning personnel of different categories in the area are displayed based on the updated black spot personnel details table.

[0018] Preferably, the black spot personnel details table is used to obtain the indicator information set of the corresponding personnel.

[0019] More preferably, the regional black spot personnel monitoring table is used to obtain the black spot personnel data of the corresponding area.

[0020] A black spot personnel prediction system based on machine learning, the system comprising:

[0021] The data aggregation module is used to aggregate and count the historical indicator data of the category personnel to be trained and obtain aggregate training data;

[0022] A data preprocessing module, used to preprocess the summary training data and obtain the preprocessed summary training data;

[0023] The model training module is used to perform model training of the logistic regression algorithm on the preprocessed summary training data to obtain the characteristics, model and evaluation indicators of the corresponding personnel category;

[0024] The prediction module is used to input the latest personnel index data into the prediction model of the corresponding category to obtain the prediction result of whether the corresponding personnel is a black spot personnel;

[0025] Update module 1, used to update the black spot personnel details table based on the prediction results;

[0026] Update module two is used to update the regional black spot personnel monitoring table based on the updated black spot personnel details table.

[0027] Preferably, the data preprocessing module includes feature marking, outlier processing and label marking;

[0028] Based on the prediction results of the prediction module, the detailed monitoring data of black spot personnel is displayed;

[0029] According to the updated black spot personnel details table in update module 2, the data of different categories of black spot warning personnel in the area are displayed.

[0030] An electronic device comprising: a memory and at least one processor;

[0031] Wherein, the memory stores a computer program;

[0032] The at least one processor executes the computer program stored in the memory, so that the at least one processor performs the black spot personnel prediction method based on machine learning as described above.

[0033] A computer-readable storage medium having a computer program stored therein, wherein the computer program can be executed by a processor to implement the black spot personnel prediction method based on machine learning as described above.

[0034] The black spot personnel prediction method, system, device and medium based on machine learning of the present invention have the following advantages:

[0035] (1) The present invention predicts the current indicators of different types of personnel such as testers, installers and maintenance personnel, and can determine whether they are about to become black spot personnel. According to the abnormal personnel warning list, personnel are urged to make timely rectifications according to the corresponding indicators, so as to increase the attention of relevant management personnel in a timely manner, so as to avoid being listed as black spot personnel on the final evaluation date;

[0036] (II) The present invention can update the personnel black spot prediction results according to the scheduled tasks, conduct personnel black spot monitoring, and output an abnormal personnel warning list to urge the test personnel to make timely corrections.

[0037] (III) The present invention can predict whether a tester or maintenance personnel is a black spot personnel based on their existing indicator data, so as to improve the evaluation indicators in advance and avoid being evaluated as a black spot personnel in the end. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The present invention is further described below in conjunction with the accompanying drawings.

[0039] Attached Figure 1 The flowchart of the black spot personnel prediction method based on machine learning is shown in FIG. DETAILED DESCRIPTION

[0040] The black spot personnel prediction method, system, device and medium based on machine learning of the present invention are described in detail below with reference to the drawings and specific embodiments of the specification.

[0041] Embodiment 1:

[0042] As attached Figure 1 As shown, this embodiment provides a method for predicting black spot personnel based on machine learning, and the method is specifically as follows:

[0043] S1. Summarize and count the historical indicator data of the category personnel to be trained to obtain summary training data;

[0044] S2. Preprocess the summary training data to obtain the preprocessed summary training data;

[0045] S3, performing model training of the logistic regression algorithm on the preprocessed summary training data to obtain the characteristics, model and evaluation index of the corresponding personnel category;

[0046] S4. Input the latest personnel index data into the prediction model of the corresponding category to obtain the prediction result of whether the corresponding personnel are black spot personnel;

[0047] S5. Based on the prediction results, update the black spot personnel details table;

[0048] S6. Based on the updated black spot personnel details table, update the regional black spot personnel monitoring table.

[0049] The preprocessing in step S2 of this embodiment is specifically as follows:

[0050] S201, feature mark;

[0051] S202, outlier processing;

[0052] S203: Label marking.

[0053] According to the prediction results in step S4 of this embodiment, detailed data on monitoring of black spot personnel are displayed.

[0054] According to the updated black spot personnel details table in step S5 of this embodiment, data of black spot warning personnel of different categories in the area are displayed.

[0055] The black spot personnel details table in step S5 of this embodiment is used to obtain the indicator information set of the corresponding personnel.

[0056] The regional black spot personnel monitoring table in step S6 of this embodiment is used to obtain the black spot personnel data of the corresponding area.

[0057] Embodiment 2:

[0058] This embodiment provides a black spot personnel prediction system based on machine learning, the system comprising:

[0059] The data aggregation module is used to aggregate and count the historical indicator data of the category personnel to be trained and obtain aggregate training data;

[0060] A data preprocessing module, used to preprocess the summary training data and obtain the preprocessed summary training data;

[0061] The model training module is used to perform model training of the logistic regression algorithm on the preprocessed summary training data to obtain the characteristics, model and evaluation indicators of the corresponding personnel category;

[0062] The prediction module is used to input the latest personnel index data into the prediction model of the corresponding category to obtain the prediction result of whether the corresponding personnel is a black spot personnel;

[0063] Update module 1, used to update the black spot personnel details table based on the prediction results;

[0064] Update module two is used to update the regional black spot personnel monitoring table based on the updated black spot personnel details table.

[0065] The data preprocessing module in this embodiment includes feature marking, outlier processing and label marking.

[0066] According to the prediction results of the prediction module in this embodiment, the detailed data of black spot personnel monitoring is displayed.

[0067] According to the black spot personnel details table updated by the updating module 2 in this embodiment, data of black spot warning personnel of different categories in the area are displayed.

[0068] Embodiment 3:

[0069] This embodiment also provides an electronic device, including: a memory and a processor;

[0070] Wherein, the memory stores computer-executable instructions;

[0071] The processor executes the computer-executable instructions stored in the memory, so that the processor executes the blackspot personnel prediction method based on machine learning in any embodiment of the present invention.

[0072] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor may be a microprocessor or any conventional processor, etc.

[0073] The memory can be used to store computer programs and / or modules. The processor realizes various functions of the electronic device by running or executing the computer programs and / or modules stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required for a function, etc.; the data storage area can store data created according to the use of the terminal, etc. In addition, the memory can also include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (SMC), a secure digital (SD) card, a flash memory card, at least one disk storage period, a flash memory device, or other volatile solid-state storage devices.

[0074] Embodiment 4:

[0075] This embodiment also provides a computer-readable storage medium, in which a plurality of instructions are stored, and the instructions are loaded by a processor, so that the processor executes the black spot personnel prediction method based on machine learning in any embodiment of the present invention. Specifically, a system or device equipped with a storage medium can be provided, on which a software program code that implements the functions of any of the above embodiments is stored, and a computer (or CPU or MPU) of the system or device reads and executes the program code stored in the storage medium.

[0076] In this case, the program code itself read from the storage medium can realize the function of any one of the above-mentioned embodiments, and thus the program code and the storage medium storing the program code constitute a part of the present invention.

[0077] The storage medium embodiments for providing the program code include a floppy disk, a hard disk, a magneto-optical disk, an optical disk (such as CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RYM, DVD-RW, DVD+RW), a magnetic tape, a non-volatile memory card, and a ROM. Alternatively, the program code can be downloaded from a server computer via a communication network.

[0078] In addition, it should be clear that the functions of any of the above embodiments can be implemented not only by executing the program code read by the computer, but also by enabling an operating system operating on the computer to complete part or all of the actual operations based on instructions from the program code.

[0079] In addition, it can be understood that the program code read from the storage medium is written to a memory provided in an expansion board inserted into the computer or written to a memory provided in an expansion unit connected to the computer, and then based on the instructions of the program code, a CPU installed on the expansion board or the expansion unit is enabled to perform part or all of the actual operations, thereby realizing the functions of any of the above-mentioned embodiments.

[0080] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for predicting black spot personnel based on machine learning, characterized in that: The method is as follows: Summarize and count the historical indicator data of the category personnel for which the model is to be trained to obtain the summary training data; Preprocessing the aggregated training data to obtain the preprocessed aggregated training data; Perform model training of the logistic regression algorithm on the preprocessed summary training data to obtain the characteristics, model and evaluation indicators of the corresponding personnel category; Input the latest personnel index data into the prediction model of the corresponding category to obtain the prediction result of whether the corresponding personnel are black spot personnel; Based on the prediction results, update the black spot personnel details table; Based on the updated black spot personnel details table, update the regional black spot personnel monitoring table.

2. The method for predicting black spot personnel based on machine learning according to claim 1, characterized in that: The preprocessing is as follows: Feature markers; Outlier handling; Label tag.

3. The method for predicting black spot personnel based on machine learning according to claim 1, characterized in that: Based on the prediction results, the detailed data of black spot personnel monitoring is displayed.

4. The method for predicting black spot personnel based on machine learning according to claim 1, characterized in that: According to the updated black spot personnel details table, the data of different categories of black spot warning personnel in the area are displayed.

5. The method for predicting black spot personnel based on machine learning according to claim 1, characterized in that: The black spot personnel details table is used to obtain the indicator information set of the corresponding personnel.

6. The method for predicting black spot personnel based on machine learning according to any one of claims 1 to 5, characterized in that: The regional black spot personnel monitoring table is used to obtain the black spot personnel data of the corresponding area.

7. A black spot personnel prediction system based on machine learning, characterized in that: The system includes: The data aggregation module is used to aggregate and count the historical indicator data of the category personnel to be trained and obtain aggregate training data; A data preprocessing module, used to preprocess the summary training data and obtain the preprocessed summary training data; The model training module is used to perform model training of the logistic regression algorithm on the preprocessed summary training data to obtain the characteristics, model and evaluation indicators of the corresponding personnel category; The prediction module is used to input the latest personnel index data into the prediction model of the corresponding category to obtain the prediction result of whether the corresponding personnel is a black spot personnel; Update module 1, used to update the black spot personnel details table based on the prediction results; Update module two is used to update the regional black spot personnel monitoring table based on the updated black spot personnel details table.

8. The black spot personnel prediction system based on machine learning according to claim 7 is characterized in that: The data preprocessing module includes feature marking, outlier processing and label marking; Based on the prediction results of the prediction module, the detailed monitoring data of black spot personnel is displayed; According to the updated black spot personnel details table in update module 2, the data of different categories of black spot warning personnel in the area are displayed.

9. An electronic device, characterized in that: include: memory and at least one processor; Wherein, the memory stores a computer program; The at least one processor executes the computer program stored in the memory, so that the at least one processor executes the black spot personnel prediction method based on machine learning as described in any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which can be executed by a processor to implement the black spot personnel prediction method based on machine learning as described in any one of claims 1 to 6.

Images