Human resource performance evaluation method and system

Abstract

The invention discloses a human resource performance evaluation method and system, and relates to the field of process management, and the human resource performance evaluation method comprises the steps: comprehensively collecting the basic personal information and abundant work data of employees, and then carrying out the precise processing of database construction, input, analysis, comparison and the like. Evaluating from multiple dimensions such as task completion, working efficiency, execution frequency, working benefit and anomaly detection according to a specific formula, and finally storing data through a data storage system with various convenient functions. Compared with the prior art, data collection is more complete, processing is more accurate, evaluation dimensions are more, anomaly detection is more effective, data storage is more convenient, employee performance can be evaluated more objectively and accurately, and human resource management decision is assisted.

Description

Technical Field

[0001] The present invention relates to the field of process management, and in particular to a human resource performance evaluation method and system. Background Art

[0002] Performance is the result and effect of an individual, team or organization in an activity. Effective companies will evaluate individuals, teams or organizations based on the results of performance. Performance can be big or small, good or bad, and there are certain standards for calculating performance. Some companies will reward or punish employees based on performance, so the management and calculation of performance is crucial.

[0003] For example, the Chinese patent announcement number is CN113011711B, which discloses a resource management system, which includes a detection device, a distribution device, an attendance device, a collection device, an adjustment device and a processor. The detection device is constructed to collect the personal information of the applicant; the distribution device is constructed to allocate the applicant based on the data of the adjustment device; the attendance device is constructed to examine the attendance of the employee or the audio; the collection device is constructed to examine the skills of the applicant or the employee; the adjustment device is constructed to adjust or correct the data of the employee or the recruiter. The present invention uses the cooperation of the detection mechanism and the sensing mechanism to update the human resources data, ensure that the different skills of each applicant or employee can be accurately detected, and provide a career planning roadmap for the applicant or the employee to make suggestions, so as to achieve the effect of intelligent analysis and planning.

[0004] The above patent can accurately detect the different skills of each applicant or employee through the detection mechanism and the sensing mechanism, but it does not solve the problem of how to evaluate the work skills of personnel in the work area based on the actual situation of personnel in the work area, so as to implement precise guidance and control of personnel with poor work skills to improve the management and disposal efficiency of the resource management platform. There are still many limitations in terms of comprehensiveness, objectivity, systematicness and data utilization efficiency. Summary of the invention

[0005] The purpose of the present invention is to provide a human resource performance evaluation method and system, which collects employee personal and work data, processes them, and evaluates them from dimensions such as task completion, work efficiency, execution frequency, and work benefits through specific formulas, combines them with anomaly detection, and finally stores the evaluated data for easy query and viewing, thereby completing a comprehensive and systematic performance evaluation.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A human resource performance evaluation method, characterized in that it comprises the following steps:

[0008] S1: Data collection, including personal information collection and work data collection; providing data support for subsequent evaluation by collecting various information.

[0009] S2: Data processing, including building an information database, data entry, data analysis and data comparison; organizing, entering, and deeply analyzing and comparing the collected data to make it more convenient for subsequent evaluation operations and ensure the accuracy and availability of the data.

[0010] S3: Data evaluation, including task completion, work efficiency, execution frequency, work benefit evaluation and anomaly detection. Through a series of specific formulas and methods, the processed data is evaluated in multiple dimensions to comprehensively measure the work performance of employees in different aspects.

[0011] S4: Data storage, including data query, data display and data storage. It is used to store the data of the evaluation process and results, and facilitate subsequent query, viewing and long-term storage and management of data.

[0012] Step S1: Data collection includes personal information collection and work data collection;

[0013] The personal information collected includes the basic personal information of the employee, age A, years of work experience Y, and education level E. The education level is quantified and different scores are set for different education levels;

[0014] The work data collection includes recording the total number of tasks completed by employees as n, and the detailed information of each task includes the task importance weight vector as n. where w t i represents the importance weight of the i-th task, and The quality score vector of task completion is set as where q ti represents the quality score of the completion of the i-th task, which can range from 0 to 100. The time vector used by employees to complete n tasks is The standard completion time vector of each task is The number of times an employee accepts a task in a period of time is m, and the number of times he completes a task is n. The direct economic benefit vector that records the employee's completion of the task is The cost vector invested in completing the task is

[0015] Step S2 data processing, specifically including building an information database, data entry, data analysis and data comparison; the specific steps are as follows:

[0016] S21: constructing an information database for storing personnel information and task information;

[0017] S22: Data entry, entering personnel information and task information;

[0018] S23: Data analysis, analyzing the data in the data entry module and the information database;

[0019] S24: Data comparison, comparing the data analyzed in the data analysis module.

[0020] Step S3: data evaluation, including task completion, work efficiency, execution frequency, work benefit evaluation and anomaly detection;

[0021] The task completion status S task , calculated by the following formula:

[0022]

[0023] Where n represents the total number of tasks completed by the employee, w ti is the task importance weight vector, and q ti It represents the quality score of the completion of the i-th task, ranging from 0 to 100. This formula multiplies the importance weight of each task by its completion quality score and then adds them together to obtain a comprehensive score on the dimension of task completion.

[0024] The work efficiency is first calculated by calculating the efficiency score e of each task. i :

[0025]

[0026] Among them, t i is the time vector that employees use to complete a task, T i represents the standard completion time vector of each task, k eff The efficiency adjustment coefficient is used to adjust the efficiency evaluation criteria according to the actual situation of the enterprise, and the value range is 0.5 to 2;

[0027] Then calculate the score S of the work efficiency dimension eff :

[0028]

[0029] Where n is the total number of tasks completed by the employee, e i Give each task an efficiency score e i ; By averaging the efficiency scores of each task, we get a comprehensive score on the work efficiency dimension.

[0030] The execution frequency S freq, calculated by the following formula:

[0031]

[0032] Among them, m is the number of times the task is accepted within a period of time, n is the number of times the task is completed, and F base The execution frequency benchmark value is set as the number of tasks that should be completed per month based on the business characteristics of the enterprise. freq is the execution frequency adjustment coefficient, which ranges from 0.8 to 1.2. The score on the execution frequency dimension is obtained by first calculating the ratio of the number of completed tasks to the number of accepted tasks, and then combining the gap with the execution frequency benchmark value and the adjustment coefficient.

[0033] The work benefit is calculated by the following steps:

[0034] First, calculate the benefit score r of each task i , the formula is:

[0035]

[0036] Then calculate the score S of the work effectiveness dimension bene , the formula is:

[0037]

[0038] Among them, b i represents the direct economic benefit brought by completing the i-th task, c i represents the cost of employees completing the i-th task, k bene It is the benefit adjustment coefficient, ranging from 1 to 3. It is used to adjust the benefit score calculation according to the company's emphasis on benefits, market environment and other factors. The larger the value, the more important the consideration of benefits is in the performance evaluation. By averaging the benefit scores of each task, the comprehensive score in the work benefit dimension is obtained.

[0039] The anomaly detection includes a preparation unit, a feature unit, an alignment unit and a log generation unit, wherein the preparation unit has a built-in rule library, the feature unit is used to collect personnel behavior and voice, and record the basic information of employees, age A, years of work Y, and educational level E, quantify the educational level, and set different scores corresponding to different educational levels; the alignment unit is used to compare the results collected by the feature unit with the built-in parameters of the preparation unit, and the specific steps are:

[0040] The feature unit collects information such as employee behavior and voice, and converts employee behavior and voice information into vector form by quantitative method, which are recorded as and

[0041] The alignment unit compares the results collected by the feature unit with the parameters built into the preparation unit and calculates the behavior comparison score S. B :

[0042]

[0043] in is the reference behavior vector set according to the rule base in the preparation unit, is the employee behavior vector;

[0044] Calculate the speech comparison score S V :

[0045]

[0046] in, is the reference speech vector set according to the rule base in the preparation unit, is the employee voice information vector, and the log generation unit records the relevant process and results of anomaly detection. If S B or S V If it is lower than the set threshold, it is recorded as an abnormal situation and the detailed information is recorded in the rule base.

[0047] Step S4 data storage specifically includes data query function, data display function and data storage function, wherein the data query function is used to query data, the data display function is used to display the data after the query is completed, and the data storage function is used to save the queried data in real time. The three functions of the data storage stage cooperate with each other. The data query function facilitates users to find specific data according to their needs, the data display function displays the queried data in an intuitive form, and the data storage function ensures that the queried data can be saved in real time, which is convenient for subsequent review or other analysis operations.

[0048] The data query function data query module includes an information input unit, an information query unit and an information screening unit, wherein the information input unit is used to input query data, including a text input unit and a voice input unit, the text input unit is used to input text information, the voice input unit is used to input voice information, the information query unit is used to query information, and the information screening unit is used to screen the queried data. The information input unit provides a variety of input methods. The text input unit allows users to input text information for query through a keyboard, etc., and the voice input unit facilitates users to query directly through voice commands. The information query unit is responsible for performing query operations based on the input information, and the information screening unit further filters the queried results so that users can obtain more accurate results.

[0049] The data storage function includes storage in a cloud database and a mobile hard disk. Storing data in a cloud database and a mobile hard disk can not only take advantage of the convenience of a cloud database, such as remote access and data backup, but also enable local storage through a mobile hard disk, so that data can be obtained even when the network is disconnected, thus ensuring data security and accessibility.

[0050] A human resource performance evaluation system comprises a server and a processor, wherein the server stores a system program, and is characterized in that the processor implements the steps of the method described in any one of claims 1 to 8 when executing the computer program.

[0051] The system is equipped with a data storage system for storing data evaluated by the data evaluation system. It has a data query module, a data display module and a data storage module, which is convenient for HR to retrieve and evaluate.

[0052] First, the data collection stage is carried out. On the one hand, basic personal information of employees is collected, such as age, years of work experience, quantified educational level, etc.; on the other hand, work data is recorded in detail, covering the total number of completed tasks, the importance weight vector of each task, the quality score vector, the time taken to complete the vector, the standard completion time vector, the number of times tasks are accepted and completed, as well as the direct economic benefit vector brought by completing the tasks and the cost vector invested, etc.

[0053] Next comes the data processing stage. First, an information database is built to store personnel and task information. Then, this information is accurately entered. The data analysis module is used to perform multi-faceted analysis on the entered data and the data in the database. Finally, the data comparison module is used to compare the analyzed data to ensure that the data is accurate and can truly reflect the work situation of employees.

[0054] Then comes the data evaluation stage, in which the task completion status dimension scores are calculated separately through specific formulas, that is, they are obtained by multiplying and adding the task weight and the quality score; the work efficiency dimension score is obtained by first calculating the efficiency score of a single task (determined by comparing the task completion time with the standard time and the efficiency adjustment coefficient), and then averaging it; the execution frequency dimension score is calculated by combining the ratio of the number of completed and accepted tasks with the execution frequency benchmark value and the adjustment coefficient; the work efficiency dimension score is obtained by first calculating the efficiency score of each task (taking into account the economic benefit, cost and benefit adjustment coefficient), and then averaging it. There is also anomaly detection, which collects employee behavior and voice through the feature unit and converts them into vector form. The alignment unit compares it with the built-in parameters of the preparation unit to calculate the comparison score, and the log generation unit records abnormal situations.

[0055] The last stage is the data storage stage, which has data query, display and storage functions. The data query module contains a variety of input and screening methods to facilitate data search. The data display module intuitively displays the query results. The data storage module uses cloud databases and mobile hard drives to save data in real time. The whole process realizes the complete process from data collection to processing, evaluation and storage, so as to comprehensively and systematically evaluate human resource performance.

[0056] Compared with the prior art, the present invention has the following beneficial effects:

[0057] It not only covers traditional basic personal information of employees, such as age and years of work experience, but also quantifies educational level so that it can be better incorporated into the evaluation system. At the same time, it collects work data in detail, including various detailed information related to tasks, such as task weight, quality score, completion time, economic benefits and costs. Compared with existing technologies, it can obtain richer and more comprehensive data, providing a more solid foundation for accurate evaluation.

[0058] By building an information database for unified storage, and then going through data entry, in-depth data analysis, and data comparison, we can ensure the accuracy and consistency of data during the processing process. This can better tap into the value of data and make subsequent evaluations more reliable.

[0059] Comprehensively evaluate employee performance from multiple dimensions, including task completion, work efficiency, execution frequency, work benefit evaluation, and anomaly detection. It can more comprehensively and three-dimensionally measure employee performance in different aspects, thereby obtaining more objective and accurate performance evaluation results.

[0060] A special anomaly detection module is set up, which collects information such as employee behavior and voice through the feature unit and converts it into a comparable vector form, compares it with the built-in parameters of the preparation unit in detail, and the log generation unit records the abnormal situation. It can detect abnormal performance of employees in the work process more promptly and keenly, which helps to intervene and solve potential problems in advance.

[0061] The data storage system has complete functions such as data query, display and storage. The data query module provides multiple input methods and filtering functions to facilitate the quick location of required data. The data display module can intuitively present the query results. The data storage module uses cloud databases and mobile hard disks to ensure real-time storage and accessibility of data. Data storage and subsequent retrieval and viewing are more convenient and efficient, which makes it easier for the human resources department to grasp the performance of employees at any time and make management decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 A flow chart of a human resource performance evaluation method of the present invention

[0063] Figure 2This is a diagram of a human resource performance evaluation system of the present invention. DETAILED DESCRIPTION

[0064] The technical solutions in the embodiments of the present invention will be fully described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0065] like Figure 1-2 As shown, a human resource performance evaluation method is characterized by comprising the following steps:

[0066] S1: Data collection, including personal information collection and work data collection;

[0067] S2: Data processing, including building information database, data entry, data analysis and data comparison;

[0068] S3: Data evaluation, including task completion, work efficiency, execution frequency, work benefit evaluation and anomaly detection,

[0069] S4: Data storage, specifically including data query, data display and data storage.

[0070] Step S1: Data collection includes personal information collection and work data collection;

[0071] The personal information collected includes the basic personal information of the employee, age A, years of work experience Y, and education level E. The education level is quantified and different scores are set for different education levels;

[0072] The work data collection includes recording the total number of tasks completed by employees as n, and the detailed information of each task includes the task importance weight vector as n. where w t i represents the importance weight of the i-th task, and The quality score vector of task completion is set as where q ti represents the quality score of the completion of the i-th task, which can range from 0 to 100. The time vector used by employees to complete n tasks is The standard completion time vector of each task is The number of times an employee accepts a task in a period of time is m, and the number of times he completes a task is n. The direct economic benefit vector that records the employee's completion of the task is The cost vector invested in completing the task is

[0073] Step S2 data processing, specifically including building an information database, data entry, data analysis and data comparison; the specific steps are as follows:

[0074] S21: constructing an information database for storing personnel information and task information;

[0075] S22: Data entry, entering personnel information and task information;

[0076] S23: Data analysis, analyzing the data in the data entry module and the information database;

[0077] S24: Data comparison, comparing the data analyzed in the data analysis module.

[0078] Step S3: data evaluation, including task completion, work efficiency, execution frequency, work benefit evaluation and anomaly detection;

[0079] The task completion status S task , calculated by the following formula:

[0080]

[0081] Where n represents the total number of tasks completed by the employee, w ti is the task importance weight vector, and q ti Indicates the quality score of the i-th task completion, ranging from 0 to 100;

[0082] The work efficiency is first calculated by calculating the efficiency score e of each task. i :

[0083]

[0084] Among them, t i is the time vector that employees use to complete a task, T i represents the standard completion time vector of each task, k eff The efficiency adjustment coefficient is used to adjust the efficiency evaluation criteria according to the actual situation of the enterprise, and the value range is 0.5 to 2;

[0085] Then calculate the score S of the work efficiency dimension eff :

[0086]

[0087] Where n is the total number of tasks completed by the employee, e i Give each task an efficiency score e i ; By averaging the efficiency scores of each task, we get a comprehensive score on the work efficiency dimension;

[0088] The execution frequency S freq , calculated by the following formula:

[0089]

[0090] Among them, m is the number of times the task is accepted within a period of time, n is the number of times the task is completed, and F base The execution frequency benchmark value is set as the number of tasks that should be completed per month based on the business characteristics of the enterprise. freq The frequency adjustment coefficient is in the range of 0.8 to 1.2. The work efficiency is calculated by the following steps:

[0091] First, calculate the benefit score r of each task i , the formula is:

[0092]

[0093] Then calculate the score S of the work effectiveness dimension bene , the formula is:

[0094]

[0095] Among them, b i represents the direct economic benefit brought by completing the i-th task, c i represents the cost of employees completing the i-th task, k bene It is the benefit adjustment coefficient, and its value range is 1 to 3.

[0096] The anomaly detection includes a preparation unit, a feature unit, an alignment unit and a log generation unit, wherein the preparation unit has a built-in rule library, the feature unit is used to collect personnel behavior and voice, and record the basic information of employees, age A, years of work Y, and educational level E, quantify the educational level, and set different scores corresponding to different educational levels; the alignment unit is used to compare the results collected by the feature unit with the built-in parameters of the preparation unit, and the specific steps are:

[0097] The feature unit collects information such as employee behavior and voice, and converts employee behavior and voice information into vector form by quantitative method, which are recorded as and

[0098] The alignment unit compares the results collected by the feature unit with the parameters built into the preparation unit and calculates the behavior comparison score S. B :

[0099]

[0100] in is the reference behavior vector set according to the rule base in the preparation unit, is the employee behavior vector;

[0101] Calculate the speech comparison score S V :

[0102]

[0103] in, is the reference speech vector set according to the rule base in the preparation unit, is the employee voice information vector, and the log generation unit records the relevant process and results of anomaly detection. If S B or S V If it is lower than the set threshold, it is recorded as an abnormal situation and the detailed information is recorded in the rule base.

[0104] Step S4 data storage specifically includes data query function, data display function and data storage function, wherein the data query function is used to query data, the data display function is used to display the data after the query is completed, and the data storage function is used to save the queried data in real time.

[0105] The data query function data query module includes an information input unit, an information query unit and an information screening unit, wherein the information input unit is used to input query data, including a text input unit and a voice input unit, the text input unit is used to input text information, the voice input unit is used to input voice information, the information query unit is used to query information, and the information screening unit is used to screen the queried data.

[0106] The data storage function includes storage in a cloud database and a mobile hard disk.

[0107] A human resource performance evaluation system includes a server and a processor, wherein the server stores a system program, and is characterized in that the processor implements the steps of the method when executing the computer program.

[0108] The system is equipped with a data storage system for storing data evaluated by the data evaluation system. It has a data query module, a data display module and a data storage module, which is convenient for HR to retrieve and evaluate.

[0109] The specific implementation is as follows:

[0110] S1: Data collection, specifically including personal information collection and work data collection; Step S1 data collection includes personal information collection and work data collection;

[0111] The personal information collected includes the basic personal information of the employee, age A, years of work experience Y, and education level E. The education level is quantified and different scores are set for different education levels;

[0112] The work data collection includes recording the total number of tasks completed by employees as n, and the detailed information of each task includes the task importance weight vector as n. where w t i represents the importance weight of the i-th task, and The quality score vector of task completion is set as where q ti represents the quality score of the completion of the i-th task, which can range from 0 to 100. The time vector used by employees to complete n tasks is The standard completion time vector of each task is The number of times an employee accepts a task in a period of time is m, and the number of times he completes a task is n. The direct economic benefit vector that records the employee's completion of the task is The cost vector invested in completing the task is

[0113] S2: Data processing, including building an information database, data entry, data analysis and data comparison; including building an information database, data entry, data analysis and data comparison; the specific steps are as follows:

[0114] S21: constructing an information database for storing personnel information and task information;

[0115] S22: Data entry, entering personnel information and task information;

[0116] S23: Data analysis, analyzing the data in the data entry module and the information database;

[0117] S24: Data comparison, comparing the data analyzed in the data analysis module.

[0118] S3: Data evaluation, including task completion, work efficiency, execution frequency, work benefit evaluation and anomaly detection, including task completion, work efficiency, execution frequency, work benefit evaluation and anomaly detection;

[0119] The task completion status S task , calculated by the following formula:

[0120]

[0121] Where n represents the total number of tasks completed by the employee, w ti is the task importance weight vector, and q ti Indicates the quality score of the i-th task completion, ranging from 0 to 100;

[0122] The work efficiency is first calculated by calculating the efficiency score e of each task. i :

[0123]

[0124] Among them, t i is the time vector that employees use to complete a task, T i represents the standard completion time vector of each task, k eff The efficiency adjustment coefficient is used to adjust the efficiency evaluation criteria according to the actual situation of the enterprise, and the value range is 0.5 to 2;

[0125] Then calculate the score S of the work efficiency dimension eff :

[0126]

[0127] Where n is the total number of tasks completed by the employee, e i Give each task an efficiency score e i ; By averaging the efficiency scores of each task, we get a comprehensive score on the work efficiency dimension;

[0128] The execution frequency S freq , calculated by the following formula:

[0129]

[0130] Among them, m is the number of times the task is accepted within a period of time, n is the number of times the task is completed, and F base The execution frequency benchmark value is set as the number of tasks that should be completed per month based on the business characteristics of the enterprise. freq The frequency adjustment coefficient is in the range of 0.8 to 1.2. The work efficiency is calculated by the following steps:

[0131] First, calculate the benefit score r of each task i , the formula is:

[0132]

[0133] Then calculate the score S of the work effectiveness dimension bene , the formula is:

[0134]

[0135] Among them, b i represents the direct economic benefit brought by completing the i-th task, c i represents the cost of employees completing the i-th task, kbene is the benefit adjustment coefficient, and its value range is 1 to 3. The anomaly detection includes a preparation unit, a feature unit, an alignment unit and a log generation unit, wherein the preparation unit has a built-in rule base, the feature unit is used to collect personnel behavior and voice, and record the basic information of employees, age A, years of work Y, and educational level E, quantify the educational level, and set different scores corresponding to different educational levels; the alignment unit is used to compare the results collected by the feature unit with the built-in parameters of the preparation unit, and the specific steps are:

[0136] The feature unit collects information such as employee behavior and voice, and converts employee behavior and voice information into vector form by quantitative method, which are recorded as and

[0137] The alignment unit compares the results collected by the feature unit with the parameters built into the preparation unit and calculates the behavior comparison score S. B :

[0138]

[0139] in is the reference behavior vector set according to the rule base in the preparation unit, is the employee behavior vector;

[0140] Calculate the speech comparison score S V :

[0141]

[0142] in, is the reference speech vector set according to the rule base in the preparation unit, is the employee voice information vector, and the log generation unit records the relevant process and results of anomaly detection. If S B or S V If it is lower than the set threshold, it is recorded as an abnormal situation and the detailed information is recorded in the rule base.

[0143] S4: Data storage, specifically including data query, data display and data storage. Among them, the data query function is used to query data, the data display function is used to display the data after the query is completed, and the data storage function is used to save the queried data in real time. The data query function data query module includes an information input unit, an information query unit and an information screening unit, wherein the information input unit is used to input query data, including a text input unit and a voice input unit, the text input unit is used to input text information, the voice input unit is used to input voice information, the information query unit is used to query information, and the information screening unit is used to screen the queried data. The data storage function includes storage in a cloud database and a mobile hard disk.

[0144] A human resource performance evaluation system includes a server and a processor, wherein the server stores a system program, and is characterized in that the processor implements the steps of the method when executing the computer program.

[0145] The system is equipped with a data storage system for storing data evaluated by the data evaluation system. It has a data query module, a data display module and a data storage module, which is convenient for HR to retrieve and evaluate.

Claims

1. A human resource performance evaluation method, characterized in that: The following steps are involved: S1: Data collection, including personal information collection and work data collection; S2: Data processing, including building information database, data entry, data analysis and data comparison; S3: Data evaluation, including task completion, work efficiency, execution frequency, work benefit evaluation and anomaly detection, S4: Data storage, specifically including data query, data display and data storage.

2. A human resource performance evaluation method according to claim 1, characterized in that: Step S1: Data collection includes personal information collection and work data collection; The personal information collected includes the basic personal information of the employee, age A, years of work experience Y, and education level E. The education level is quantified and different scores are set for different education levels; The work data collection includes recording the total number of tasks completed by employees as n, and the detailed information of each task includes the task importance weight vector as n. where w t i represents the importance weight of the i-th task, and The quality score vector of task completion is set as where q ti represents the quality score of the completion of the i-th task, which can range from 0 to 100. The time vector used by employees to complete n tasks is The standard completion time vector of each task is The number of times an employee accepts tasks in a period of time is m, and the number of times he completes tasks is n. The direct economic benefit vector brought by the employee completing tasks is The cost vector invested in completing the task is 3. A human resource performance evaluation method according to claim 1, characterized in that: Step S2: data processing, including building an information database, data entry, data analysis and data comparison; The specific steps are as follows: S21: constructing an information database for storing personnel information and task information; S22: Data entry, entering personnel information and task information; S23: Data analysis, analyzing the data in the data entry module and the information database; S24: Data comparison, comparing the data analyzed in the data analysis module.

4. A human resource performance evaluation method according to claim 1, characterized in that: Step S3: data evaluation, including task completion, work efficiency, execution frequency, work benefit evaluation and anomaly detection; The task completion status S task , calculated by the following formula: Where n represents the total number of tasks completed by the employee, w ti is the task importance weight vector, and q ti Indicates the quality score of the i-th task completion, ranging from 0 to 100; The work efficiency is first calculated by calculating the efficiency score e of each task. i : Among them, t i is the time vector that employees use to complete a task, T i represents the standard completion time vector of each task, k eff The efficiency adjustment coefficient is used to adjust the efficiency evaluation criteria according to the actual situation of the enterprise, and the value range is 0.5 to 2; Then calculate the score S of the work efficiency dimension eff : Where n is the total number of tasks completed by the employee, e i Give each task an efficiency score e i ; By averaging the efficiency scores of each task, we get a comprehensive score on the work efficiency dimension; The execution frequency S freq , calculated by the following formula: Among them, m is the number of times the task is accepted within a period of time, n is the number of times the task is completed, and F base The execution frequency benchmark value is set as the number of tasks that should be completed per month based on the business characteristics of the enterprise. freq The frequency adjustment coefficient is in the range of 0.8 to 1.

2. The work efficiency is calculated by the following steps: First, calculate the benefit score r of each task i , the formula is: Then calculate the score S of the work effectiveness dimension bene , the formula is: Among them, b i represents the direct economic benefit brought by completing the i-th task, c i represents the cost of employees completing the i-th task, k bene It is the benefit adjustment coefficient, and its value range is 1 to 3.

5. A human resource performance evaluation method according to claim 4, characterized in that: The anomaly detection includes a preparation unit, a feature unit, an alignment unit and a log generation unit, wherein the preparation unit has a built-in rule library, the feature unit is used to collect personnel behavior and voice, and record the basic information of employees, age A, years of work Y, and educational level E, quantify the educational level, and set different scores corresponding to different educational levels; the alignment unit is used to compare the results collected by the feature unit with the built-in parameters of the preparation unit, and the specific steps are: The feature unit collects information such as employee behavior and voice, and converts employee behavior and voice information into vector form by quantitative method, which are recorded as and The alignment unit compares the results collected by the feature unit with the parameters built into the preparation unit and calculates the behavior comparison score S. B : in is the reference behavior vector set according to the rule base in the preparation unit, is the employee behavior vector; Calculate the speech comparison score S V : in, is the reference speech vector set according to the rule base in the preparation unit, is the employee voice information vector, and the log generation unit records the relevant process and results of anomaly detection. If S B or S V If it is lower than the set threshold, it is recorded as an abnormal situation and the detailed information is recorded in the rule base.

6. A human resource performance evaluation method according to claim 1, characterized in that: Step S4 data storage specifically includes data query function, data display function and data storage function, wherein the data query function is used to query data, the data display function is used to display the data after the query is completed, and the data storage function is used to save the queried data in real time.

7. A human resource performance evaluation method according to claim 5, characterized in that: The data query function data query module includes an information input unit, an information query unit and an information screening unit, wherein the information input unit is used to input query data, including a text input unit and a voice input unit, the text input unit is used to input text information, the voice input unit is used to input voice information, the information query unit is used to query information, and the information screening unit is used to screen the queried data.

8. A human resource performance evaluation method according to claim 5, characterized in that: The data storage function includes storage in a cloud database and a mobile hard disk.

9. A human resource performance evaluation system, comprising a server and a processor, wherein the server stores a system program, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 8 are implemented.

10. A human resource performance evaluation system according to claim 9, characterized in that: The system is equipped with a data storage system for storing data evaluated by the data evaluation system. It has a data query module, a data display module and a data storage module, which is convenient for HR to retrieve and evaluate.

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