Employee evaluation method, device and equipment and storage medium

Abstract

The invention discloses an employee evaluation method, device and equipment and a storage medium, and belongs to the technical field of artificial intelligence, and the method comprises the steps: determining a target factor corresponding to an evaluation index; obtaining target data corresponding to each evaluation index from the original data of the target employee according to the target factor corresponding to each evaluation index; inputting the target data corresponding to each evaluation index into a comprehensive evaluation model to obtain a target evaluation result of the target employee; wherein the comprehensive evaluation model is composed of sub-evaluation models corresponding to the evaluation indexes. The employee evaluation efficiency is improved, and the objectivity and accuracy of the employee evaluation result are improved.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to an employee evaluation method, device, equipment and storage medium. Background Art

[0002] Employees are one of the most important capital and resources of an enterprise, and their performance directly affects the development and competitiveness of the enterprise. In order to reasonably and fairly evaluate and motivate employees, many companies have established special employee evaluation systems.

[0003] However, traditional employee evaluation methods, such as performance appraisals and questionnaires, rely on manual data collection and analysis, which is not only time-consuming and labor-intensive, but also easily interfered with by human factors and has a strong subjective problem, resulting in inaccurate and objective employee evaluation results. Summary of the invention

[0004] The present invention provides an employee evaluation method, device, equipment and storage medium to improve employee evaluation efficiency and improve the objectivity and accuracy of employee evaluation results.

[0005] According to one aspect of the present invention, there is provided an employee evaluation method, the method comprising:

[0006] Determine the target factors corresponding to the evaluation indicators;

[0007] According to the target factors corresponding to each evaluation indicator, the target data corresponding to each evaluation indicator is obtained from the original data of the target employee;

[0008] The target data corresponding to each evaluation indicator is input into the comprehensive evaluation model to obtain the target evaluation result of the target employee; wherein the comprehensive evaluation model is composed of sub-evaluation models corresponding to each evaluation indicator.

[0009] According to another aspect of the present invention, there is provided an employee evaluation device, the device comprising:

[0010] A target factor determination module is used to determine the target factor corresponding to the evaluation index;

[0011] The target data acquisition module is used to acquire the target data corresponding to each evaluation indicator from the original data of the target employee according to the target factor corresponding to each evaluation indicator;

[0012] The target evaluation result determination module is used to input the target data corresponding to each evaluation indicator into the comprehensive evaluation model to obtain the target evaluation result of the target employee; wherein the comprehensive evaluation model is composed of sub-evaluation models corresponding to each evaluation indicator.

[0013] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0014] at least one processor; and

[0015] a memory communicatively connected to at least one processor; wherein,

[0016] The memory stores a computer program that can be executed by at least one processor. The computer program is executed by at least one processor so that the at least one processor can execute the employee evaluation method of any embodiment of the present invention.

[0017] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the employee evaluation method of any embodiment of the present invention when executed.

[0018] According to another aspect of the present invention, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the employee evaluation method according to any embodiment of the present invention is implemented.

[0019] The technical solution of the embodiment of the present invention determines the target factor corresponding to the evaluation indicator; obtains the target data corresponding to each evaluation indicator from the original data of the target employee according to the target factor corresponding to each evaluation indicator; inputs the target data corresponding to each evaluation indicator into the comprehensive evaluation model to obtain the target evaluation result of the target employee; wherein the comprehensive evaluation model is composed of sub-evaluation models corresponding to each evaluation indicator. The above technical solution obtains the target data corresponding to each evaluation indicator from the original data of the target employee according to the target factor corresponding to each evaluation indicator, thereby improving the accuracy of the subsequent input data of the comprehensive evaluation model; thereafter, the target data corresponding to each evaluation indicator is automatically processed by the comprehensive evaluation model without human intervention, thereby avoiding the interference of human factors on the employee evaluation results, thereby improving the efficiency of employee evaluation and improving the objectivity and accuracy of employee evaluation results.

[0020] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0022] Figure 1 is a flow chart of an employee evaluation method provided according to Embodiment 1 of the present invention;

[0023] Figure 2 is a flow chart of an employee evaluation method provided according to Embodiment 2 of the present invention;

[0024] Figure 3 is a structural schematic diagram of an employee evaluation device provided according to Embodiment 3 of the present invention;

[0025] Figure 4 It is a schematic diagram of the structure of an electronic device for implementing the employee evaluation method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0026] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0027] It should be noted that the terms "target", "candidate", "first" and "second" in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0028] In addition, it should be noted that the original data of the target employees collected in the present invention, such as the personal information table of the target employees, is information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of the relevant data comply with the relevant laws, regulations and standards of the relevant countries and regions, take necessary confidentiality measures, do not violate public order and good customs, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0029] Embodiment 1

[0030] Figure 1This is a flow chart of an employee evaluation method provided in the first embodiment of the present invention. This embodiment is applicable to the situation of evaluating corporate employees. The method can be executed by an employee evaluation device, which can be implemented in the form of hardware and / or software and can be configured in an electronic device. Figure 1 As shown, the method includes:

[0031] S101. Determine the target factor corresponding to the evaluation index.

[0032] Among them, evaluation indicators refer to indicators used to evaluate employees; optionally, the number of evaluation indicators can be predetermined according to actual business needs and the expert experience of technical personnel in this field. For example, there can be 5 evaluation indicators, namely work ability, work attitude, work performance, teamwork ability and team leader evaluation, so as to evaluate employees from multiple dimensions and improve the comprehensiveness of employee evaluation. It should be noted that each evaluation indicator corresponds to at least one target factor.

[0033] Specifically, the target factor corresponding to each evaluation index may be determined based on the expert experience of those skilled in the art.

[0034] S102. According to the target factors corresponding to the evaluation indicators, the target data corresponding to the evaluation indicators are obtained from the original data of the target employees.

[0035] The raw data refers to the unprocessed data closely related to the target employee; optionally, the raw data may include the target employee's personal information table, assessment information table, assessment details table, promotion information table and attendance and workload details table. The personal information table includes but is not limited to the following factors involved in employee evaluation: employee certification number, name, date of birth, education background, university graduated from, major studied, graduation time, software certification status, qualification, grade, personnel identification, job position, department, group, entry time and exit time; the assessment information table includes but is not limited to the following factors involved in employee evaluation: assessment year, assessment quarter, employee certification number, name, overall score and evaluation level; the assessment details table includes but is not limited to the following factors involved in employee evaluation: assessment year, assessment quarter, employee certification number, name and evaluation dimension; the promotion information table includes but is not limited to the following factors involved in employee evaluation: employee certification number, promotion category, planned completion time, whether recommended for promotion, final qualification and final promotion grade; the attendance and workload details table includes but is not limited to the following factors involved in employee evaluation: employee certification number, attendance year and month, attendance day, standard attendance day, personal leave, workload and acceptance workload. Target data refers to the data composed of the values ​​of the target factors corresponding to the evaluation indicators.

[0036] Specifically, data preprocessing and data derivation may be performed on the original data of the target employees to obtain new data; and according to the target factors corresponding to the evaluation indicators, the target data corresponding to the evaluation indicators are obtained from the new data.

[0037] More specifically, the target employee's employee certification number can be used as an index to obtain the target employee's original data from the preset human resource management database; the original data can be preprocessed using the preset data processing method to remove duplicate data, remove invalid data, fill in missing values, and unify data formats to obtain preprocessed data; the preprocessed data can be derived according to the preset data derivation rules to obtain new data. For example, according to the target employee's date of birth, the target employee's age can be obtained according to the following data derivation rules, namely:

[0038] age=date(report_month-birthday) / 365;

[0039] Among them, age represents the age of the target employee; report_month represents the current date; birthday represents the date of birth of the target employee.

[0040] Afterwards, for each target factor corresponding to each evaluation indicator, the variable name of the target factor is used as the index to obtain the value of the target factor from the new data, so that the value of each target factor corresponding to the evaluation indicator can be obtained, and then the target data corresponding to the evaluation indicator can be obtained.

[0041] For example, if there are three evaluation indicators, namely work ability, work attitude and work performance; the target factors corresponding to work ability are as follows: personnel identification, entry time, working time, education, software certification, number of promotions and acceptance workload; the target factors corresponding to work attitude are as follows: attendance days and attendance rate; for employees in development positions, the target factors corresponding to work performance are as follows: qualifications, grades, number of lines of code submitted, number of issues, number of projects participated in and workload in project management tools; for employees in testing positions, the target factors corresponding to work performance are as follows: qualifications, grades, number of cases submitted, number of projects participated in and workload in project management tools; for employees in other positions, the target factors corresponding to work performance are as follows: qualifications, grades, number of projects participated in and workload in project management tools. If the target employee's job position is a testing position, for each target factor corresponding to the evaluation indicator of work ability, the variable name of the target factor is used as the index to obtain the value of the target factor from the new data, so that the value of each target factor corresponding to the evaluation indicator of work ability can be obtained, and then the target data corresponding to the evaluation indicator of work ability can be obtained. Similarly, the target data corresponding to the evaluation indicator of work attitude and the target data corresponding to the evaluation indicator of work performance can be obtained from the new data.

[0042] It can be understood that by performing data preprocessing and data derivation on the original data of the target employees, new data is obtained, duplicate data and invalid data in the original data are eliminated, missing values ​​or blanks in the original data are filled, and the data format of the original data is unified, thereby improving the data quality of the new data, improving the integrity and availability of the new data, and further improving the accuracy of the target data corresponding to each evaluation indicator.

[0043] S103. Input the target data corresponding to each evaluation indicator into the comprehensive evaluation model to obtain the target evaluation result of the target employee; wherein the comprehensive evaluation model is composed of sub-evaluation models corresponding to each evaluation indicator.

[0044] Among them, the comprehensive evaluation model refers to the model used to comprehensively evaluate the target employee. It should be noted that the sub-evaluation model corresponding to each evaluation indicator refers to a pre-trained scorecard model based on a logistic regression model. The target evaluation result refers to the comprehensive evaluation result of the target employee. It should be noted that the target evaluation result includes the sub-evaluation result of the target employee on each scoring indicator.

[0045] Specifically, the target data corresponding to each evaluation indicator is input into the sub-evaluation model corresponding to each evaluation indicator in the comprehensive evaluation model to obtain the sub-evaluation results of the target employee on each evaluation indicator, thereby obtaining the target evaluation result composed of the sub-evaluation results of the target employee on each evaluation indicator.

[0046] Optionally, after obtaining the target evaluation results of the target employees, the target evaluation results can also be displayed visually to help enterprise managers understand the comprehensive capabilities of the target employees more intuitively and clearly, and more easily identify the strengths and weaknesses of the target employees, thereby facilitating enterprise managers to formulate targeted personal development plans for the target employees, providing strong support for the decision-making of enterprise managers and promoting the career growth of target employees.

[0047] Specifically, after obtaining the target evaluation results of the target employees, the target evaluation results can be visualized in the form of a radar chart.

[0048] The technical solution of the embodiment of the present invention determines the target factor corresponding to the evaluation indicator; obtains the target data corresponding to each evaluation indicator from the original data of the target employee according to the target factor corresponding to each evaluation indicator; inputs the target data corresponding to each evaluation indicator into the comprehensive evaluation model to obtain the target evaluation result of the target employee; wherein the comprehensive evaluation model is composed of sub-evaluation models corresponding to each evaluation indicator. The above technical solution obtains the target data corresponding to each evaluation indicator from the original data of the target employee according to the target factor corresponding to each evaluation indicator, thereby improving the accuracy of the subsequent input data of the comprehensive evaluation model; thereafter, the target data corresponding to each evaluation indicator is automatically processed by the comprehensive evaluation model without human intervention, thereby avoiding the interference of human factors on the employee evaluation results, thereby improving the efficiency of employee evaluation and improving the objectivity and accuracy of employee evaluation results.

[0049] Embodiment 2

[0050] Figure 2 This is a flowchart of an employee evaluation method provided in Example 2 of the present invention. Based on the above Example 1, this example further optimizes "determining the target factor corresponding to the evaluation index" and "inputting the target data corresponding to each evaluation index into the comprehensive evaluation model to obtain the target evaluation result of the target employee", and provides an optional implementation plan. It should be noted that for the parts not described in detail in the examples of the present invention, reference can be made to the relevant descriptions of other examples. Figure 2 As shown, the method includes:

[0051] S201. Obtain a sample factor set corresponding to an evaluation index.

[0052] Among them, sample factors refer to factors related to evaluation indicators. Sample factor sets refer to data sets composed of sample factors. For example, for the evaluation indicator of work ability, the corresponding sample factor set may include the following sample factors: personnel identification, education, entry time, working time, promotion category, number of promotions, software certification status, final qualification, acceptance workload, cumulative workload, cumulative attendance days, cumulative standard attendance days, cumulative acceptance workload, cumulative number of recommendations, cumulative number of projects participated in, cumulative number of test case submissions, cumulative test tool online time, and cumulative number of project code submissions.

[0053] Specifically, for each evaluation indicator, based on the expert experience of technical personnel in this field, factors related to the evaluation indicator can be extracted from the factors involved in employee evaluation as sample factors, thereby obtaining a sample factor set composed of sample factors, that is, obtaining a sample factor set corresponding to the evaluation indicator.

[0054] S202: Perform correlation analysis on the sample factors in the sample factor set to obtain correlation coefficients between any two sample factors.

[0055] Among them, the correlation coefficient refers to a statistical indicator used to measure the degree of linear correlation between two sample factors.

[0056] Specifically, for each evaluation index, a correlation function, such as a correlation function, may be used to perform a correlation analysis on sample factors in a sample factor set corresponding to the evaluation index to obtain correlation coefficients between any two sample factors.

[0057] S203: Determine a candidate factor set from the sample factor set according to the correlation coefficients between any two sample factors.

[0058] The candidate factor set refers to a data set determined from the sample factor set. Specifically, for each evaluation indicator, according to the correlation coefficient between the two sample factors in the sample factor set corresponding to the evaluation indicator, the sample factor pairs with correlation coefficients greater than the coefficient threshold are screened out from the sample factor set corresponding to the evaluation indicator, and the sample factors in the screened sample factor pairs are used as candidate factors, thereby obtaining a candidate factor set composed of candidate factors. The coefficient threshold can be pre-set according to actual business needs or the experience of those skilled in the art. For example, the coefficient threshold can be 0.7, and the embodiment of the present invention does not specifically limit it.

[0059] Exemplarily, taking the evaluation indicator of work ability as an example, if the coefficient threshold is 0.7, for the evaluation indicator of work ability, according to the correlation coefficient between the two sample factors in the corresponding sample factor set, sample factor pairs with correlation coefficients greater than 0.7 can be screened out from the sample factor set corresponding to the evaluation indicator of work ability, and the sample factors in the screened sample factor pairs are used as candidate factors, thereby obtaining the candidate factor set corresponding to the evaluation indicator of work ability; wherein the candidate factor set includes but is not limited to the following candidate factors: personnel identification, academic qualifications, entry time, working time, number of promotions, software certification status, acceptance workload, cumulative number of recommendations, cumulative workload, cumulative attendance days, cumulative standard attendance days and cumulative acceptance workload.

[0060] It can be understood that by performing correlation analysis on the sample factors in the sample factor set corresponding to the evaluation index, a candidate factor set is determined from the sample factor set, and sample factors in the sample factor set with low correlation with the evaluation index are eliminated.

[0061] S204: Determine the information value of each candidate factor in the candidate factor set.

[0062] Among them, the information value (IV) is used to evaluate the predictive ability of each candidate factor in the candidate factor set.

[0063] Specifically, for each candidate factor in the candidate factor set, the information value of the candidate factor is determined according to the variable type of the candidate factor, so as to improve the accuracy of the information value of each candidate factor in the candidate factor set.

[0064] More specifically, for each candidate factor in the candidate factor set corresponding to each evaluation index, if the candidate factor is a discrete variable, the candidate factor is converted into a dummy variable with a value of 0 or 1, see the discrete variable conversion method shown in Table 1.

[0065] It should be noted that personnel_sign in Table 1 represents personnel identification, and its original value is 1, 2 or 3; the personnel identification is now converted into the following three dummy variables: is_group_leader, is_group_core and is_group_member; among which is_group_leader indicates whether it is a group leader; is_group_core indicates whether it is a backbone; is_group_member indicates whether it is a group member. final_promote_grade in Table 1 represents the final promotion grade, and its original value is 1, 2, 3 or 4; the final promotion grade is now converted into the following four dummy variables: is_grade_1, is_grade_2, is_grade_3 and is_grade_4; among which is_grade_1 indicates whether it is the first grade; is_grade_2 indicates whether it is the second grade; is_grade_3 indicates whether it is the third grade; is_grade_4 indicates whether it is the fourth grade.

[0066] Table 1 Discrete variable transformation method

[0067]

[0068] Afterwards, for each dummy variable corresponding to the candidate factor, the weight of evidence (WOE) of the dummy variable is determined according to the good sample ratio and bad sample ratio of the dummy variable through the following weight of evidence calculation formula:

[0069]

[0070] Among them, WOE i represents the weight of evidence of the i-th dummy variable corresponding to the candidate factor; Indicates the bad sample ratio of the i-th dummy variable corresponding to the candidate factor; Bad i Indicates the number of bad samples of the i-th dummy variable corresponding to the candidate factor; Bad T Represents the total number of bad samples of the candidate factor; Good represents the good sample ratio of the i-th dummy variable corresponding to the candidate factor; i Good represents the number of good samples of the i-th dummy variable corresponding to the candidate factor; T Indicates the total number of good samples for the candidate factor.

[0071] Afterwards, according to the good sample ratio, bad sample ratio and evidence weight of the dummy variable, the information value of the dummy variable is determined by the following information value calculation formula;

[0072]

[0073] Among them, IV i represents the information value of the i-th dummy variable corresponding to the candidate factor. Then, the information value of each dummy variable corresponding to the candidate factor is summed to obtain the information value of the candidate factor, that is:

[0074]

[0075] Among them, IV0 represents the information value of the candidate factor; n represents the total number of dummy variables corresponding to the candidate factor.

[0076] For example, for a candidate factor A in the candidate factor set corresponding to a certain evaluation index, if the candidate factor A is converted into three dummy variables, namely dummy variable 1, dummy variable 2 and dummy variable 3; the number of samples of candidate factor A is 100, the total number of good samples is 70, and the total number of bad samples is 30; the number of good samples of dummy variable 1 is 20, and the number of bad samples is 10; the number of good samples of dummy variable 2 is 20, and the number of bad samples is 10; the number of good samples of dummy variable 3 is 30, and the number of bad samples is 10. It can be seen that the proportion of good samples of dummy variable 1 is The proportion of bad samples is The weight of evidence is The value of information is The good sample ratio of dummy variable 2 is The proportion of bad samples is The weight of evidence is The value of information is The good sample ratio of dummy variable 3 is The proportion of bad samples is The weight of evidence is The value of information is Thus, the information value of candidate factor A can be obtained, that is, IV0=IV1+IV2+IV3.

[0077] If the candidate factor is a continuous variable, the candidate factor can be divided into boxes according to the data distribution of the candidate factor and the actual business needs to obtain multiple data boxes; for each data box, the evidence weight of the data box is determined according to the good sample ratio and the bad sample ratio of the data box through the above-mentioned evidence weight calculation formula; the information value of the data box is determined according to the good sample ratio, bad sample ratio and evidence weight of the data box through the above-mentioned information value calculation formula; the information value of each data box is summed up to obtain the information value of the candidate factor.

[0078] It should be noted that for each candidate factor in the candidate factor set corresponding to each evaluation indicator, the number of samples of the candidate factor can be determined according to actual business needs, and the embodiment of the present invention does not specifically limit it. In the process of determining the information value of the candidate factor, good samples and bad samples are manually specified.

[0079] S205. Determine the target factor corresponding to the evaluation index from the candidate factor set according to the information value of each candidate factor.

[0080] Specifically, for each candidate factor set corresponding to an evaluation indicator, the candidate factor whose information value satisfies the value condition can be used as the target factor corresponding to the evaluation indicator according to the information value of each candidate factor in the candidate factor set. The value condition can be determined according to actual business needs. For example, the value condition can be that the information value is greater than 0.1 and less than or equal to 0.5. For another example, the value condition can be that the information value is greater than 0.1. The present invention does not specifically limit this.

[0081] It can be understood that for each evaluation indicator, the target factor corresponding to the evaluation indicator is determined from the candidate factor set corresponding to the evaluation indicator according to the information value of each candidate factor in the candidate factor set corresponding to the evaluation indicator, so that the target factors corresponding to the evaluation indicator are all factors with strong correlation with the evaluation indicator and good predictive ability, thereby improving the accuracy of subsequent target evaluation results.

[0082] S206. According to the target factors corresponding to the evaluation indicators, the target data corresponding to the evaluation indicators are obtained from the original data of the target employees.

[0083] S207. For each evaluation indicator, the target data corresponding to the evaluation indicator is input into the first calculation layer of the sub-evaluation model corresponding to the evaluation indicator to obtain the evaluation value of the target employee on the evaluation indicator; wherein the first calculation layer is composed of a trained logistic regression model.

[0084] The definition of the logistic regression model is as follows:

[0085]

[0086] Among them, θ T X=θ0+θ1x1+θ2x2+…+θ n x n ; θ0 represents the bias term; θ1, θ2, …, θ n Represent the input features x1, x2,…, x n The weight value of p(X) represents the input feature X = {x1, x2, ..., x n}, the probability of an event occurring.

[0087] Optionally, taking the evaluation index of work ability as an example, the training process of the logistic regression model is as follows: select a number of sample employees, for example, select 207,362 sample employees; according to the target factors corresponding to the evaluation index of work ability: personnel identification, entry time, working time, education level, software certification status, number of promotions and acceptance workload, obtain the following characteristics of each sample employee: personnel identification, entry time, working time, education level, software certification status, number of promotions and acceptance workload, and obtain the evaluation value of each sample employee on the evaluation index of work ability; bring the characteristics of each sample employee and the evaluation value on the evaluation index of work ability into the logistic regression model to obtain a number of equations, for example, if there are 207,362 sample employees, 207,362 equations can be obtained; by solving these equations, θ0, θ1, θ2, …, θ in the logistic regression model can be obtained. n The specific value of is used to obtain the trained logistic regression model.

[0088] Specifically, for each evaluation indicator, the target data corresponding to the evaluation indicator is input into the first calculation layer of the sub-evaluation model corresponding to the evaluation indicator, and the evaluation value of the target employee on the evaluation indicator is obtained after being processed by the first calculation layer.

[0089] S208: Input the evaluation value of the target employee on the evaluation indicator into the second calculation layer of the sub-evaluation model corresponding to the evaluation indicator to obtain the sub-evaluation result of the target employee on the evaluation indicator.

[0090] Specifically, the evaluation value of the target employee on the evaluation indicator is input into the second calculation layer of the sub-evaluation model corresponding to the evaluation indicator, and the sub-evaluation result of the target employee on the evaluation indicator is obtained through the following scoring formula in the second calculation layer:

[0091]

[0092] Wherein, Score represents the sub-evaluation result of the target employee on the evaluation indicator; A and B are constant terms, which can be determined according to the evaluation indicator and the experience of technical personnel in this field, or can be determined through a large number of experiments based on the evaluation indicator, and the embodiment of the present invention does not make specific limitations thereto; p represents the evaluation value of the target employee on the evaluation indicator.

[0093] S209. Obtain the target evaluation result of the target employee according to the sub-evaluation results of the target employee on each evaluation indicator.

[0094] Specifically, according to the sub-evaluation results of the target employee on each evaluation indicator, a target evaluation result composed of the sub-evaluation results of the target employee on each evaluation indicator is obtained.

[0095] The technical solution of the embodiment of the present invention is as follows: obtaining a sample factor set corresponding to the evaluation index; performing correlation analysis on the sample factors in the sample factor set to obtain the correlation coefficients between the two sample factors; determining a candidate factor set from the sample factor set according to the correlation coefficients between the two sample factors; determining the information value of each candidate factor in the candidate factor set; determining the target factor corresponding to the evaluation index from the candidate factor set according to the information value of each candidate factor; obtaining the target data corresponding to each evaluation index from the original data of the target employee according to the target factor corresponding to each evaluation index; for each evaluation index, inputting the target data corresponding to the evaluation index into the first calculation layer of the sub-evaluation model corresponding to the evaluation index to obtain the evaluation value of the target employee on the evaluation index; wherein the first calculation layer is composed of a trained logistic regression model; inputting the evaluation value of the target employee on the evaluation index into the second calculation layer of the sub-evaluation model corresponding to the evaluation index to obtain the sub-evaluation result of the target employee on the evaluation index. The above technical scheme determines the target factors corresponding to the evaluation indicators by performing correlation analysis on the sample factors in the sample factor set corresponding to the evaluation indicators and determining the information value of the sample factors, thereby reducing the subjectivity of the target factor determination and improving the objectivity and accuracy of the target factor determination; then, according to the target factors corresponding to each evaluation indicator, the target data corresponding to each evaluation indicator is obtained from the original data of the target employee, thereby improving the accuracy of the target data corresponding to each evaluation indicator; then, for each evaluation indicator, the target data corresponding to the evaluation indicator is automatically processed through the first calculation layer and the second calculation layer in the sub-evaluation model corresponding to the evaluation indicator in the comprehensive evaluation model, without the need for human intervention, thereby avoiding the interference of human factors on the employee evaluation results, thereby improving the employee evaluation efficiency and improving the objectivity and accuracy of the employee evaluation results.

[0096] Embodiment 3

[0097] Figure 3 This is a schematic diagram of the structure of an employee evaluation device provided in Embodiment 3 of the present invention. This embodiment is applicable to the situation of evaluating corporate employees. The device can be implemented in the form of hardware and / or software and can be configured in an electronic device. Figure 3 As shown, the device comprises:

[0098] The target factor determination module 301 is used to determine the target factor corresponding to the evaluation index;

[0099] The target data acquisition module 302 is used to acquire the target data corresponding to each evaluation indicator from the original data of the target employee according to the target factor corresponding to each evaluation indicator;

[0100] The target evaluation result determination module 303 is used to input the target data corresponding to each evaluation indicator into the comprehensive evaluation model to obtain the target evaluation result of the target employee; wherein the comprehensive evaluation model is composed of sub-evaluation models corresponding to each evaluation indicator.

[0101] The technical solution of the embodiment of the present invention determines the target factor corresponding to the evaluation indicator; obtains the target data corresponding to each evaluation indicator from the original data of the target employee according to the target factor corresponding to each evaluation indicator; inputs the target data corresponding to each evaluation indicator into the comprehensive evaluation model to obtain the target evaluation result of the target employee; wherein the comprehensive evaluation model is composed of sub-evaluation models corresponding to each evaluation indicator. The above technical solution obtains the target data corresponding to each evaluation indicator from the original data of the target employee according to the target factor corresponding to each evaluation indicator, thereby improving the accuracy of the subsequent input data of the comprehensive evaluation model; thereafter, the target data corresponding to each evaluation indicator is automatically processed by the comprehensive evaluation model without human intervention, thereby avoiding the interference of human factors on the employee evaluation results, thereby improving the efficiency of employee evaluation and improving the objectivity and accuracy of employee evaluation results.

[0102] Optionally, the target factor determination module 301 includes:

[0103] A sample factor set acquisition unit, used to acquire a sample factor set corresponding to the evaluation index;

[0104] A correlation coefficient determination unit is used to perform correlation analysis on sample factors in the sample factor set to obtain correlation coefficients between any two sample factors;

[0105] A candidate factor set determination unit, used to determine a candidate factor set from a sample factor set according to correlation coefficients between two sample factors;

[0106] An information value determination unit, used to determine the information value of each candidate factor in the candidate factor set;

[0107] The target factor determination unit is used to determine the target factor corresponding to the evaluation index from the candidate factor set according to the information value of each candidate factor.

[0108] Optionally, the information value determination unit is used to:

[0109] For each candidate factor in the candidate factor set, the information value of the candidate factor is determined according to the variable type of the candidate factor.

[0110] Optionally, the target data acquisition module 302 is specifically used to:

[0111] Perform data preprocessing and data derivation on the original data of the target employees to obtain new data;

[0112] According to the target factors corresponding to each evaluation index, the target data corresponding to each evaluation index is obtained from the new data.

[0113] Optionally, the target evaluation result determination module 303 is specifically used to:

[0114] For each evaluation indicator, the target data corresponding to the evaluation indicator is input into the first calculation layer of the sub-evaluation model corresponding to the evaluation indicator to obtain the evaluation value of the target employee on the evaluation indicator; wherein the first calculation layer is composed of a trained logistic regression model;

[0115] Inputting the evaluation value of the target employee on the evaluation indicator into the second calculation layer of the sub-evaluation model corresponding to the evaluation indicator to obtain the sub-evaluation result of the target employee on the evaluation indicator;

[0116] According to the sub-evaluation results of the target employee on each evaluation indicator, the target evaluation result of the target employee is obtained.

[0117] Optionally, the device further comprises:

[0118] The target evaluation result display module is used to visualize the target evaluation results after obtaining the target employee's target evaluation results.

[0119] The employee evaluation device provided in the embodiment of the present invention can execute the employee evaluation method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing each employee evaluation method.

[0120] According to an embodiment of the present invention, the present invention also provides an electronic device, a readable storage medium and a computer program product.

[0121] Embodiment 4

[0122] Figure 4 A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0123] like Figure 4As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, ROM 12 and RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0124] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0125] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the employee evaluation method.

[0126] In some embodiments, the employee evaluation method may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the employee evaluation method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to execute the employee evaluation method in any other appropriate manner (e.g., by means of firmware).

[0127] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0128] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0129] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0130] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0131] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0132] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0133] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0134] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for evaluating employees, characterized in that: include: Determine the target factors corresponding to the evaluation indicators; According to the target factors corresponding to each evaluation indicator, the target data corresponding to each evaluation indicator is obtained from the original data of the target employee; The target data corresponding to each evaluation indicator is input into the comprehensive evaluation model to obtain the target evaluation result of the target employee; wherein the comprehensive evaluation model is composed of sub-evaluation models corresponding to each evaluation indicator.

2. The method according to claim 1, characterized in that The target factor corresponding to the evaluation index is determined, including: Obtain the sample factor set corresponding to the evaluation index; Performing correlation analysis on the sample factors in the sample factor set to obtain correlation coefficients between any two sample factors; Determining a candidate factor set from the sample factor set according to the correlation coefficients between the two sample factors; Determining the information value of each candidate factor in the candidate factor set; According to the information value of each candidate factor, a target factor corresponding to the evaluation index is determined from the candidate factor set.

3. The method according to claim 2, characterized in that Determining the information value of each candidate factor in the candidate factor set includes: For each candidate factor in the candidate factor set, the information value of the candidate factor is determined according to the variable type of the candidate factor.

4. The method according to claim 1, characterized in that: The step of obtaining target data from the original data of the target employee according to the target factors corresponding to the evaluation indicators includes: Perform data preprocessing and data derivation on the original data of the target employees to obtain new data; According to the target factors corresponding to the evaluation indicators, the target data corresponding to the evaluation indicators are obtained from the new data.

5. The method according to claim 1, characterized in that The target data corresponding to each evaluation indicator is input into the comprehensive evaluation model to obtain the target evaluation result of the target employee, including: For each evaluation indicator, the target data corresponding to the evaluation indicator is input into the first calculation layer of the sub-evaluation model corresponding to the evaluation indicator to obtain the evaluation value of the target employee on the evaluation indicator; wherein the first calculation layer is composed of a trained logistic regression model; Inputting the evaluation value of the target employee on the evaluation indicator into the second calculation layer of the sub-evaluation model corresponding to the evaluation indicator to obtain the sub-evaluation result of the target employee on the evaluation indicator; According to the sub-evaluation results of the target employee on each evaluation indicator, the target evaluation result of the target employee is obtained.

6. The method according to claim 1, characterized in that After obtaining the target evaluation result of the target employee, the method further includes: The target evaluation results are visually displayed.

7. An employee evaluation device, characterized in that: include: A target factor determination module is used to determine the target factor corresponding to the evaluation index; The target data acquisition module is used to acquire the target data corresponding to each evaluation indicator from the original data of the target employee according to the target factor corresponding to each evaluation indicator; The target evaluation result determination module is used to input the target data corresponding to each evaluation indicator into the comprehensive evaluation model to obtain the target evaluation result of the target employee; wherein the comprehensive evaluation model is composed of sub-evaluation models corresponding to each evaluation indicator.

8. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the employee evaluation method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the employee evaluation method according to any one of claims 1 to 6 when executed.

10. A computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the employee evaluation method according to any one of claims 1 to 6 is implemented.

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