Human resource post digital evaluation system based on big data and AI

Abstract

The invention discloses a human resource post digital evaluation system based on big data and AI, and belongs to the technical field of post evaluation, and the system comprises a data collection module which is used for obtaining a plurality of pieces of data information related to post personnel evaluation from big data and enterprises; the analysis and evaluation module is used for performing corresponding analysis according to the acquired data information so as to evaluate corresponding posts; and the decision module performs corresponding decision processing according to the evaluation result. According to the invention, during personnel recruitment through the post personnel recruitment evaluation unit, each job seeker can be evaluated according to the resume information of the job seekers, so that the job seekers can be subjected to primary screening and secondary screening, a large number of resumes can be processed at the same time, and the job seekers meeting specific conditions can be quickly identified and screened out; the recruitment cycle can be greatly shortened, the overall recruitment efficiency is improved, and the workload of a human resource department can be remarkably reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of job evaluation, and specifically relates to a digital evaluation system for human resources jobs based on big data and AI. Background Art

[0002] In human resource management, job evaluation is a vital task. Job evaluation, also known as position assessment or job evaluation, refers to the quantitative and qualitative analysis of various positions within an enterprise through a series of scientific methods to determine the relative value of each position. The results of job evaluation are usually expressed as a job value hierarchy system, which provides a decision-making basis for internal salary management, employee promotion, career planning, etc., thereby achieving sustainable development of the enterprise.

[0003] The existing method of evaluating positions is mainly through manual operation, which is highly subjective and inefficient. For example, in job performance rewards, the performance of job personnel is mostly based on the personal impression and qualitative evaluation of superiors, which is easily affected by the evaluator's personal bias, emotional factors, etc., resulting in inaccurate evaluation results; for example, in the recruitment process, due to the large number of job applicants, manual screening is required before interviews, which requires a lot of manpower and time for screening, resulting in low efficiency and easy missing of applicants who meet the requirements of the company. Summary of the invention

[0004] The purpose of the present invention is to provide a digital evaluation system for human resources positions based on big data and AI to solve the problems faced in the above-mentioned background technology.

[0005] The purpose of the present invention can be achieved through the following technical solutions:

[0006] A digital evaluation system for human resources positions based on big data and AI, the evaluation system comprising:

[0007] A data collection module, which is used to obtain multiple data information related to job personnel evaluation from big data and enterprises;

[0008] An analysis and evaluation module, which is used to perform corresponding analysis based on the acquired data information, thereby evaluating the corresponding position;

[0009] A decision-making module, wherein the decision-making module performs corresponding decision processing according to the evaluation result;

[0010] The analysis and evaluation module includes a post personnel recruitment evaluation unit and a post personnel performance evaluation unit. The post personnel recruitment evaluation unit is used to analyze the relevant information obtained to obtain the matching value of each job seeker, and screen the job seekers according to the matching value;

[0011] The post personnel performance evaluation unit is used to analyze the acquired relevant information, thereby digitally evaluating the performance of the post personnel.

[0012] Furthermore, the data information includes recruitment information and work information, wherein the recruitment information includes enterprise job requirement information and job application resume information, and the work information includes job personnel attendance information, customer feedback information, work completion information, work text information, and voice information, etc.

[0013] Furthermore, the working method of the post personnel recruitment and evaluation unit is:

[0014] Perform an initial screening of job seekers, and formulate multiple job characteristics related to the job according to the company's job recruitment needs, thereby forming a job characteristic collection A, and each job characteristic has a corresponding matching value ;

[0015] Obtain the resume information of all job seekers from the job search website, perform feature extraction based on the resume information, obtain the resume features of the job seekers for each position, and form a resume feature collection B;

[0016] When resume feature set B contains all the features in job feature set A, the job seeker for this job will be retained, otherwise the job seeker for this job will be screened out.

[0017] Furthermore, the working method of the post personnel recruitment and evaluation unit also includes:

[0018] Rescreen job seekers, divide each job feature into multiple matching value levels based on the recruitment information of relevant positions in big data and the company's historical recruitment information, and build an AI training model. Input the resume feature set B of job seekers after the initial screening into the AI ​​training model to obtain the matching value of each job feature of the job seekers ;

[0019] By formula Obtain the matching value of job seekers for each position , then match the value Matches the preset threshold For comparison, when If the applicant is selected, the applicant will be retained; otherwise, the applicant will be screened out.

[0020] in, The matching value of the i-th job feature proposed for the enterprise job recruitment, and , is the total number of job characteristics.

[0021] Furthermore, the working method of the post personnel performance evaluation unit is:

[0022] Obtain the staff's attendance information, customer feedback information, work completion information, work text information, and voice information for the past year;

[0023] Based on attendance information and customer feedback, AI technology is used to obtain attendance scores for employees at each position. And customer maintenance score ;

[0024] Based on the work completion information of the post employees, the efficiency score of the post employees is obtained ;

[0025] Based on the work text information and voice information of the post employees, the emotional score of the post employees is obtained ;

[0026] By formula Obtain performance scores for personnel in each position ;

[0027] in, All are preset coefficients.

[0028] Furthermore, the efficiency score The acquisition method is:

[0029] Obtain the performance of all employees in the company within a year, divide it by month, and obtain the monthly performance of each employee ;

[0030] By formula Obtain the efficiency score of employees in each position every month , and then through the formula Get the efficiency score of each employee in each position ;

[0031] in, as well as is the weight coefficient, is the number of positions per month, and , is the preset average, which is obtained based on the historical performance of each month. Score the efficiency of the employee in the jth position in January. Assigns a value to the rating parameter.

[0032] Furthermore, the emotion score The acquisition method is:

[0033] Based on the work text information and voice information of the employees in the past year, keywords are extracted through AI technology, and the keywords are divided into positive emotions and negative emotions, and the number of times positive emotions and negative emotions appear is obtained. , , through the formula Get the emotional score of employees in the position ;in Assigns a value to the rating parameter.

[0034] Furthermore, the working method of the decision module is:

[0035] When screening job seekers, if a screening instruction is generated, the job seekers will be removed, otherwise they will be retained;

[0036] When conducting performance evaluation of personnel in each position, obtain the performance scores of personnel in each position, sort them in order from large to small according to the performance scores, and reward them in order of sorting.

[0037] Beneficial effects of the present invention:

[0038] The present invention can evaluate each job seeker according to their resume information when conducting personnel recruitment through the job recruitment evaluation unit, thereby conducting primary and secondary screening of the job seekers. In this way, a large number of resumes can be processed simultaneously while job seekers who meet specific conditions can be quickly identified and screened, which can greatly shorten the recruitment cycle, improve overall recruitment efficiency, and significantly reduce the workload of the human resources department.

[0039] The present invention uses a job performance evaluation unit to conduct a comprehensive analysis based on the job personnel's attendance score, customer maintenance score, efficiency score, and emotion score, so as to more accurately evaluate the performance of each job personnel and eliminate the influence of subjective factors, thereby establishing a fairer incentive mechanism, which can not only more comprehensively evaluate the performance of job personnel, but also improve the enthusiasm and satisfaction of job personnel.

[0040] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0042] Figure 1 It is a system block diagram of the present invention. DETAILED DESCRIPTION

[0043] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions 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 are within the scope of protection of the present invention.

[0044] In one embodiment, a human resources position digital evaluation system based on big data and AI is disclosed. Figure 1 As shown, the evaluation system includes:

[0045] Data collection module: The data collection module is used to obtain multiple data information related to job evaluation from big data and enterprises. The data information includes recruitment information and work information. Recruitment information includes enterprise job demand information and job resume information. Work information includes job personnel attendance information, customer feedback information, work completion information, work text information, and voice information;

[0046] Analysis and evaluation module: The analysis and evaluation module is used to perform corresponding analysis based on the acquired data information, so as to evaluate the corresponding positions;

[0047] Decision-making module, which makes corresponding decisions based on the evaluation results;

[0048] The analysis and evaluation module includes a post recruitment evaluation unit and a post performance evaluation unit. The post recruitment evaluation unit is used to analyze the relevant information obtained to obtain the matching value of job seekers for each post, and screen the job seekers according to the matching value.

[0049] The post personnel performance evaluation unit is used to analyze the relevant information obtained, thereby digitally evaluating the performance of post personnel.

[0050] Through the above technical solution, this application can evaluate each job applicant based on their resume information through the job applicant recruitment evaluation unit during recruitment, thereby conducting primary and secondary screening of job applicants. In this way, a large number of resumes can be processed simultaneously while quickly identifying and screening out job applicants that meet specific conditions, which can greatly shorten the recruitment cycle, improve overall recruitment efficiency, and significantly reduce the workload of the human resources department. At the same time, through the job performance evaluation unit, comprehensive analysis and processing based on the attendance score, customer maintenance score, efficiency score, and emotional score of the job applicants can be performed to more accurately evaluate the performance of each job applicant, eliminate the influence of subjective factors, and thus establish a fairer incentive mechanism, which can not only more comprehensively evaluate the performance of job applicants, but also improve the enthusiasm and satisfaction of job applicants.

[0051] The working method of the post recruitment evaluation unit is: firstly, the job seekers are screened, and according to the company's job recruitment needs, multiple job characteristics related to the job are formulated to form a job characteristic set A, and each job characteristic has a corresponding matching value. ;

[0052] Obtain the resume information of all job seekers from the job search website, perform feature extraction based on the resume information, obtain the resume features of the job seekers for each position, and form a resume feature collection B;

[0053] When resume feature set B contains all the features in job feature set A, the job seeker for this job will be retained, otherwise the job seeker for this job will be screened out;

[0054] Then, job seekers are screened again. According to the recruitment information of relevant positions in the big data and the historical recruitment information of the company, the characteristics of each position are divided into multiple matching value levels, and an AI training model is constructed. The resume feature set B of the job seekers after the initial screening is input into the AI ​​training model to obtain the matching values ​​of each job characteristic of the job seekers. ;

[0055] By formula Obtain the matching value of job seekers for each position , then match the value Matches the preset threshold For comparison, when If the applicant is selected, the applicant will be retained; otherwise, the applicant will be screened out.

[0056] in, The matching value of the i-th job feature proposed for the enterprise job recruitment, and , is the total number of job characteristics.

[0057] Through the above technical solution, this embodiment mainly provides a specific method for the job recruitment evaluation unit to screen personnel. First, the job seekers are screened initially. According to the job recruitment needs of the enterprise, multiple job characteristics related to the job are formulated to form a job characteristic set A, and each job characteristic has a corresponding matching value. , obtain the resume information of all job seekers from the job search website, extract features based on the resume information, obtain the resume features of each job seeker, and form a resume feature collection B; compare the job feature collection A with the resume feature collection B, and when the resume feature collection B contains all the features in the job feature collection A, it means that the job seeker meets the application requirements of the company, and the job seeker is retained, otherwise it means that it does not meet the requirements, and the job seeker is screened out; in this way, it is possible to quickly find people who meet the needs of the company from a large number of job seekers, narrow the resume search scope, and facilitate subsequent processing; then screen the job seekers again, and divide the characteristics of each position into multiple matching value levels according to the recruitment information of relevant positions in the big data and the historical recruitment information of the company, and build an AI training model, and input the resume feature collection B of the job seekers after the initial screening into the AI ​​training model to obtain the matching values ​​of each job feature of the job seekers , through the formula Obtain the matching value of job seekers for each position Obviously, when the matching value is smaller, it means that the job characteristics of the job seeker are more consistent with the job characteristics of the enterprise's needs. Therefore, in order to screen out relatively suitable and high-quality job seekers, the matching value is Matches the preset threshold Compare and set the matching threshold According to experience, when If the applicant meets the requirements, the applicant will be retained, otherwise the applicant will be screened out. In this way, the resumes can be further shortened to accurately match the suitable applicants. For example, the initial screening is based on the applicant's basic information, such as education, age, work experience, etc. The secondary screening can consider more complex factors, such as skills, achievements, social skills, etc., so as to achieve accurate matching, reduce recruitment costs, and improve the accuracy and effectiveness of recruitment.

[0058] Through the above technical solution, this application can evaluate each job seeker based on their resume information through the job recruitment evaluation unit during personnel recruitment, thereby performing initial and secondary screening of the job seekers. In this way, a large number of resumes can be processed at the same time while job seekers who meet specific conditions can be quickly identified and screened, which can greatly shorten the recruitment cycle, improve overall recruitment efficiency, and significantly reduce the workload of the human resources department.

[0059] The working method of the post personnel performance evaluation unit is: obtaining the attendance information, customer feedback information, work completion information, work text information and voice information of the post personnel within one year;

[0060] Based on attendance information and customer feedback, AI technology is used to obtain attendance scores for employees at each position. And customer maintenance score ;

[0061] Based on the work completion information of the post employees, the efficiency score of the post employees is obtained ;

[0062] Based on the work text information and voice information of the post employees, the emotional score of the post employees is obtained ;

[0063] By formula Obtain performance scores for personnel in each position ;

[0064] in, All are preset coefficients;

[0065] Among them, the efficiency score The acquisition method is: obtain the performance of all personnel in the company within a year, divide it by month, and obtain the monthly performance of each personnel in each position ;

[0066] By formula Obtain the efficiency score of employees in each position every month , and then through the formula Get the efficiency score of each employee in each position ;

[0067] in, as well as is the weight coefficient, is the number of positions per month, and , is the preset average, which is obtained based on the historical performance of each month. Score the efficiency of the employee in the jth position in January. Assigns a value to the rating parameter.

[0068] Among them, the sentiment score The acquisition method is: based on the work text information and voice information of the post employees within one year, the keywords are extracted through AI technology, and the keywords are divided into positive emotions and negative emotions, and the number of occurrences of positive emotions and negative emotions is obtained. , , through the formula Get the emotional score of employees in the position ;in Assigns a value to the rating parameter.

[0069] Through the above technical solution, this embodiment mainly provides a working method of the post personnel performance evaluation unit, firstly obtaining the post personnel's attendance information, customer feedback information, work completion information, work text information and voice information within one year;

[0070] According to the attendance information and customer feedback information, AI is used to process it, and the attendance score of the employee in the position is simulated according to the attendance rate and feedback. And customer maintenance score , when the score is higher, the attendance rate is higher. The better the feedback is; then according to the work completion information of the post employees, the efficiency score of the post employees is obtained. When scoring work efficiency, due to the influence of time and other factors, the work completion status in different time periods is different. For example, in months with more holidays, the work completion is less, and in months with fewer holidays, the work completion is relatively more. If it is only evaluated based on a fixed reward weight, it is not accurate enough. Therefore, the performance of all positions in the company in a year is obtained, divided by month, and the monthly performance of each position is obtained. , and then by the formula Obtain the efficiency score of employees in each position every month , and then through the formula Get the efficiency score of each employee in each position In this way, dynamic reward weights are set according to the total workload completed each month, so that the work efficiency of post personnel can be evaluated more accurately and fairly; then, based on the work text information and voice information of post employees in a year, keywords are extracted through AI technology, and divided into positive emotions and negative emotions according to keywords, and the number of times positive emotions and negative emotions appear is obtained. , , through the formula Get the emotional score of employees in the position , emotional scoring can be used to understand the performance of employees at work. Generally, the greater the positive emotions, the better the performance. At the same time, emotional scoring can also be used to discover the potential interpersonal relationships and status of employees, so as to make timely interventions and reduce talent loss. Finally, through the formula Combine attendance score, customer maintenance score, efficiency score and emotion score for comprehensive analysis to obtain the performance score of each position Generally speaking, when performance rating The larger the value, the better the employee's performance and the higher the performance evaluation. In this way, comprehensive analysis and processing based on the attendance score, customer maintenance score, efficiency score and emotion score of the post personnel can be performed more accurately to evaluate the performance of each post personnel, eliminate the influence of subjective factors, and thus establish a fairer incentive mechanism, which can not only more comprehensively evaluate the performance of post personnel, but also improve the enthusiasm and satisfaction of post personnel.

[0071] It should be noted that the preset coefficient , weight coefficient as well as , set the scoring parameters as well as They can all be formulated based on historical data and empirical data.

[0072] The working method of the decision-making module is: when screening job seekers, if a screening instruction is generated, the job seekers will be selected, otherwise they will be retained;

[0073] When conducting performance evaluation of personnel in each position, obtain the performance scores of personnel in each position, sort them in order from large to small according to the performance scores, and reward them in order of sorting.

[0074] Through the above technical scheme, this embodiment mainly provides a working method of the decision-making module, which is that when screening job seekers for a position, when an instruction to screen out job seekers is generated, the job seeker is screened out, otherwise it is retained; when the performance evaluation of the position personnel is carried out, the performance score of each position personnel is obtained, and they are sorted in order from large to small according to the performance score, and rewards are given in order of the sorting method, and the rewards are given according to the reward mechanism of each enterprise.

[0075] The above contents are merely examples and explanations of the concept of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the claims, they should all fall within the protection scope of the present invention.

Claims

1. A digital evaluation system for human resources positions based on big data and AI, characterized in that: The evaluation system includes: A data collection module, which is used to obtain multiple data information related to job personnel evaluation from big data and enterprises; An analysis and evaluation module, which is used to perform corresponding analysis based on the acquired data information, thereby evaluating the corresponding position; A decision-making module, wherein the decision-making module performs corresponding decision processing according to the evaluation result; The analysis and evaluation module includes a post personnel recruitment evaluation unit and a post personnel performance evaluation unit. The post personnel recruitment evaluation unit is used to analyze the relevant information obtained to obtain the matching value of each job seeker, and screen the job seekers according to the matching value; The post personnel performance evaluation unit is used to analyze the acquired relevant information, thereby digitally evaluating the performance of the post personnel.

2. According to the human resources position digital evaluation system based on big data and AI according to claim 1, it is characterized in that: The data information includes recruitment information and work information, wherein the recruitment information includes enterprise job demand information and job application resume information, and the work information includes job personnel's attendance information, customer feedback information, work completion information, work text information and voice information.

3. According to claim 2, a digital evaluation system for human resources positions based on big data and AI is characterized in that: The working method of the post personnel recruitment and evaluation unit is: Perform an initial screening of job seekers, and formulate multiple job characteristics related to the job according to the company's job recruitment needs, thereby forming a job characteristic collection A, and each job characteristic has a corresponding matching value ; Obtain the resume information of all job seekers from the job search website, perform feature extraction based on the resume information, obtain the resume features of the job seekers for each position, and form a resume feature collection B; When resume feature set B contains all the features in job feature set A, the job seeker for this job will be retained, otherwise the job seeker for this job will be screened out.

4. According to claim 3, a digital evaluation system for human resources positions based on big data and AI is characterized in that: The post personnel recruitment and evaluation unit working method also includes: Rescreen job seekers, divide each job feature into multiple matching value levels based on the recruitment information of relevant positions in big data and the company's historical recruitment information, and build an AI training model. Input the resume feature set B of job seekers after the initial screening into the AI ​​training model to obtain the matching value of each job feature of the job seekers ; By formula Obtain the matching value of job seekers for each position , then match the value Matches the preset threshold For comparison, when If the applicant is selected, the applicant will be retained; otherwise, the applicant will be screened out. in, The matching value of the i-th job feature proposed for the enterprise job recruitment, and , is the total number of job characteristics.

5. According to claim 2, a digital evaluation system for human resources positions based on big data and AI is characterized in that: The working method of the post personnel performance evaluation unit is: Obtain the staff's attendance information, customer feedback information, work completion information, work text information, and voice information for the past year; Based on attendance information and customer feedback, AI technology is used to obtain attendance scores for employees at each position. And customer maintenance score ; Based on the work completion information of the post employees, the efficiency score of the post employees is obtained ; Based on the work text information and voice information of the post employees, the emotional score of the post employees is obtained ; By formula Obtain performance scores for personnel in each position ; in, All are preset coefficients.

6. According to claim 5, a digital evaluation system for human resources positions based on big data and AI is characterized in that: The efficiency score The acquisition method is: Obtain the performance of all employees in the company within a year, divide it by month, and obtain the monthly performance of each employee ; By formula Obtain the efficiency score of employees in each position every month , and then through the formula Get the efficiency score of each employee in each position ; in, as well as is the weight coefficient, is the number of positions per month, and , is the preset average, which is obtained based on the historical performance of each month. Score the efficiency of the employee in the jth position in January. Assigns a value to the rating parameter.

7. According to claim 5, a digital evaluation system for human resources positions based on big data and AI is characterized in that: The sentiment score The acquisition method is: Based on the work text information and voice information of the employees in the past year, keywords are extracted through AI technology, and the keywords are divided into positive emotions and negative emotions, and the number of times positive emotions and negative emotions appear is obtained. , , through the formula Get the emotional score of employees in the position ;in Assigns a value to the rating parameter.

8. According to claim 1, a digital evaluation system for human resources positions based on big data and AI is characterized in that: The working method of the decision module is: When screening job seekers, if a screening instruction is generated, the job seekers will be removed, otherwise they will be retained; When conducting performance evaluation of personnel in each position, obtain the performance scores of personnel in each position, sort them in order from large to small according to the performance scores, and reward them in order of sorting.

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