Human resource service method based on big data analysis

Abstract

The invention relates to the technical field of human resource service, and particularly discloses a human resource service method based on big data analysis, which comprises a data acquisition layer, a data storage layer and a data analysis layer, the data acquisition layer comprises a human skill acquisition module, a human performance acquisition module, a training demand acquisition module, a salary competitiveness acquisition module, a human service state acquisition module and a human background state acquisition module. Whether the human resource recruitment process is scientific and reasonable and whether salary distribution is fair and effective can be accurately judged through the performance adaptation index and the remuneration development index; according to comparison between the performance adaptation index and a preset threshold value, the matching degree between the recruiters and the posts and the performance can be clarified, and then whether the recruitment process is optimized or not is determined; through the analysis of the emolument development index, the relationship between the emolument competitiveness and the employee training demand can be judged, the emolument strategy is reasonably adjusted or the training investment is increased, and the enterprise is assisted to make a scientific and accurate human resource management decision.

Description

Technical Field

[0001] The present invention relates to the technical field of human resource services, and in particular to a human resource service method based on big data analysis. Background Art

[0002] In today's highly digitalized and information-based business environment, human resource management, as a key link in business operations, plays a vital role in the survival and development of enterprises. Effective human resource management can rationally allocate talent resources, improve employee performance, and enhance the core competitiveness of enterprises. With the continuous development and widespread application of big data technology, human resource service methods based on big data analysis have gradually become an important means to improve the level of human resource management. Such systems are designed to integrate and analyze a large amount of human resource-related data to achieve more scientific and accurate human resource decisions, covering recruitment, training, performance evaluation, salary management and other aspects.

[0003] Traditional human resource management methods often rely on experience and subjective judgment, and lack systematic analysis of large amounts of data. In the recruitment process, it is usually difficult to accurately assess the match between job seekers and positions based only on resume screening and simple interviews, resulting in the recruited personnel not being able to meet the job requirements, affecting work performance and corporate development. In terms of performance evaluation, qualitative evaluation is mostly used, lacking objective and quantitative indicators, making the evaluation results inaccurate and unfair, and unable to provide targeted guidance for employee development. In terms of salary management, there is a lack of comprehensive understanding and analysis of market salary data, and salary distribution may be unreasonable, making it difficult to attract and retain outstanding talents. In addition, there are many deficiencies in training needs analysis and human resource cost control, which make it impossible to achieve efficient use of human resources and sustainable development of enterprises. Summary of the invention

[0004] In view of the above situation, in order to overcome the defects of the prior art, the present invention provides a human resources service method based on big data analysis to solve the above-mentioned technical defects.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: A human resources service method based on big data analysis, comprising the following steps:

[0006] Step 1: Collect and acquire various big data required for human resource services through various collection modules in the data collection layer, and store them in the collection data repository in the data storage layer;

[0007] Step 2: Use the talent-performance matching analysis module in the data analysis layer to conduct a comprehensive analysis of the skill matching coefficient and performance completion coefficient of human resources to obtain the talent-performance matching index RP of human resources corresponding to each service cycle. i ;

[0008] Step 3: Match the talent-performance matching index RP of human resources to each service cycle i Compare and analyze with the preset talent-performance adaptation index threshold. If the talent-performance adaptation index RP i If the value is greater than the preset talent-performance adaptation index threshold, it means that the recruited personnel are highly matched with the position and have good performance, and there is no need to optimize the recruitment process; otherwise, it means that there is a problem in the recruitment process and unsuitable personnel are recruited, and the recruitment process needs to be optimized;

[0009] Step 4: The pay development analysis module conducts a comprehensive analysis of the training demand coefficient and pay competitiveness coefficient of human resources to obtain the pay development index TS of human resources in each service cycle. i ;

[0010] Step 5: Correspond human resources to the ability to develop index TS in each service cycle i Compare and analyze with the preset reward development index threshold. If the reward development index TS i If the value is greater than the preset threshold of the ability-to-pay development index, it means that there is a great demand for employee training and the company's salary competitiveness is strong; otherwise, it means that the salary is not competitive and it is necessary to consider adjusting the salary strategy;

[0011] Step 4: According to the talent-performance matching index RP of human resources in each service cycle i TS i The value of the human resource recruitment process and human resource salary distribution are analyzed to determine whether they are in line with the plan. Finally, the human resource service cost is analyzed through the human resource cost analysis module to obtain the cost-effectiveness coefficient CB of human resources in each service cycle. i .

[0012] Furthermore, the data collection layer includes human skill collection module, human performance collection module, training demand collection module, salary competitiveness collection module, human service status collection module, and human background status collection module; the data storage layer includes a collection data repository and a human resource preset module; the data analysis layer includes a talent-performance adaptation analysis module, a capability-remuneration development analysis module, a human resource recruitment optimization module, and a human resource cost analysis module.

[0013] Furthermore, the human skill collection module extracts the skill keywords and work experience keywords in the resumes of each applicant by using natural language processing technology to obtain a skill keyword set JS j and work experience keyword set WS g , obtain the job skill requirement keyword set from the human resources preset module Keyword collection of job experience requirements Use natural language processing technology to collect JS skills keywords j and job skill requirements keyword set Calculate the matching value PJ. If the keywords are the same, score 1 point. If they are different, score 0 point. Similarly, calculate the work experience keyword WS. g Keyword collection of job experience requirements The matching value PW.

[0014] Furthermore, the human performance collection module obtains the task completion performance score JP, behavior performance score BP and overall evaluation score QP of each applicant after joining the company through the enterprise project management system.

[0015] Furthermore, the training demand collection module obtains the employee's current skill level CS, the employee's personal skill development demand PS, and the enterprise project skill demand SS through the employee skill testing system.

[0016] Furthermore, the salary competitiveness acquisition module obtains the average salary level CI of the same industry, the salary level CC of the enterprise, and the employee salary satisfaction score XS from the salary research organization database.

[0017] Furthermore, the human background status collection module sets the number of personnel background investigation indicators according to the enterprise's job requirements, obtains the scores of each indicator of each applicant, and sums them up to obtain an abnormal score A, where each indicator is normal and has a score of 0, and each indicator is abnormal and has a score of 1.

[0018] Furthermore, the talent-performance matching analysis module performs a comprehensive calculation through the skill matching coefficient PP and the performance completion coefficient PX to obtain the talent-performance matching index RP of the human resources corresponding to each service cycle. i , the talent-performance matching index RP of human resources corresponding to each service cycle i Compare and analyze with the preset talent-performance adaptation index threshold. If the talent-performance adaptation index RP i If the value is greater than the preset talent-performance adaptation index threshold, it means that the recruited personnel are highly matched with the position and have good performance, and there is no need to optimize the recruitment process; otherwise, it means that there is a problem in the personnel recruitment process and personnel who are not suitable for the position are recruited, and the recruitment process needs to be optimized.

[0019] Furthermore, the pay development analysis module performs a comprehensive analysis through the training demand coefficient PN and the salary competitiveness coefficient CJ to obtain the pay development index TS of human resources corresponding to each service cycle. i , the human resources correspond to the energy development index TS in each service cycle i Compare and analyze with the preset reward development index threshold. If the reward development index TS iIf the value is greater than the preset threshold of the energy-remuneration development index, it means that there is a large demand for employee training and the company's salary competitiveness is strong; otherwise, it means that the salary lacks competitiveness, resulting in low employee training enthusiasm, and it is necessary to consider adjusting the salary strategy to improve salary competitiveness.

[0020] Furthermore, the human resource recruitment optimization module optimizes talent recruitment from three aspects: applicant matching screening, applicant waiting experience, and comprehensive background investigation of applicants. The specific optimization methods are as follows:

[0021] Natural language processing technology for skill keyword collection JS j and job skill requirements keyword set The matching value PJ and work experience keyword WS g Keyword collection of job experience requirements The matching values ​​PW are summed to obtain the matching degree M of the candidate's application keywords;

[0022] Obtain the personnel application keyword screening threshold L from the human resources preset module, and enter the next round of screening for personnel whose application keyword matching degree M≥L; conversely, personnel whose application keyword matching degree M<L are initially screened out;

[0023] By dividing the waiting time TD of the personnel in the recruitment service process by the industry average waiting time The time difference coefficient C is obtained. If C>1, it means that the waiting time of the applicant is longer than the industry average. At the same time, according to the formula The experience optimization coefficient E is calculated and compared with the preset experience optimization coefficient threshold. If the value of the experience optimization coefficient E is less than the preset experience optimization coefficient threshold, it indicates that there is a problem with the applicant's experience.

[0024] Furthermore, the human resource cost analysis module calculates the enterprise's recruitment cost ZC, training cost PC, salary cost XC, economic benefits EB created by employees, economic benefits EP of production, and benefits EI of employees' innovation achievements according to the formula Calculate the cost-effectiveness coefficient CB of human resources corresponding to each service cycle i .

[0025] Beneficial effects of the present invention:

[0026] 1. In the present invention, multi-dimensional data such as human skills, performance, and training needs are comprehensively collected through multiple modules of the data collection layer to provide a solid data foundation for subsequent analysis. At the data analysis layer, with the help of the talent-performance adaptation analysis module, the ability-reward development analysis module, etc., the various coefficients are comprehensively analyzed to obtain key indexes, such as the talent-performance adaptation index and the ability-reward development index. These indexes can accurately determine whether the human resource recruitment process is scientific and reasonable, and whether the salary distribution is fair and effective; based on the comparison between the talent-performance adaptation index and the preset threshold, the matching degree and performance of the recruited personnel and the position can be clarified, and then it can be determined whether to optimize the recruitment process; through the analysis of the ability-reward development index, the relationship between salary competitiveness and employee training needs can be determined, and the salary strategy can be reasonably adjusted or the training investment can be increased, helping enterprises make scientific and accurate human resource management decisions.

[0027] 2. Calculate the cost-effectiveness coefficient by counting the costs of recruitment, training, salary, etc., as well as the economic benefits created by employees. Comparing this coefficient with the preset threshold can clearly determine the company's status in terms of human resource input and output. If the cost-effectiveness coefficient is high, it means that human resource services are effective in cost control and benefit improvement; if the coefficient is low, it can prompt the company to review various cost expenditures and find out whether there is a waste of resources or insufficient investment, so as to flexibly adjust the human resource budget and resource allocation and improve resource utilization efficiency. At the same time, the human resource recruitment optimization module optimizes talent recruitment from aspects such as application matching screening, waiting experience, background investigation, etc., further improves the quality and efficiency of recruitment, realizes the efficient use of human resources, enhances the competitiveness of enterprises in talent management, and promotes the sustainable development of enterprises. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] The present invention will be further described below in conjunction with the accompanying drawings.

[0029] Figure 1 This is a flow chart of a human resources service method based on big data analysis according to an embodiment of the present invention;

[0030] Figure 2 This is a principle block diagram of the data acquisition layer, data storage layer, and data analysis layer of an embodiment of the present invention;

[0031] Figure 3 This is a principle block diagram of the data acquisition layer of an embodiment of the present invention;

[0032] Figure 4 This is a principle block diagram of the data storage layer of an embodiment of the present invention;

[0033] Figure 5 This is a principle block diagram of the data analysis layer according to an embodiment of the present invention. DETAILED DESCRIPTION

[0034] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work also fall within the scope of protection of the present invention.

[0035] As shown in the present invention and claims, unless the context clearly indicates an exception, the words "a", "an", "an" and / or "the" do not refer to the singular, but also include the plural. Generally speaking, the terms "include" and "comprise" only indicate the inclusion of the steps and elements that have been clearly identified, and these steps and elements do not constitute an exclusive list, and the method or device may also include other steps or elements.

[0036] Although the present invention has made various references to certain modules in the system according to an embodiment of the present invention, any number of different modules can be used and run on a user terminal and / or a server. The modules are only illustrative, and different aspects of the system and method can use different modules.

[0037] The present invention uses a flow chart to illustrate the operations performed by the system according to an embodiment of the present invention. It should be understood that the preceding or following operations are not necessarily performed precisely in order. On the contrary, various steps may be processed in reverse order or simultaneously as required. At the same time, other operations may also be added to these processes, or one or more operations may be removed from these processes.

[0038] Below, the exemplary embodiments according to the present invention will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments of the present invention, and it should be understood that the present invention is not limited to the exemplary embodiments described here.

[0039] Embodiment 1:

[0040] See also Figure 1-Figure 5 As shown, the human resource service method based on big data analysis includes the following steps:

[0041] Step 1: Collect and acquire various big data required for human resource services through various collection modules in the data collection layer, and store them in the collection data repository in the data storage layer;

[0042] Step 2: Use the talent-performance matching analysis module in the data analysis layer to conduct a comprehensive analysis of the skill matching coefficient and performance completion coefficient of human resources to obtain the talent-performance matching index RP of human resources corresponding to each service cycle. i ;

[0043] Step 3: Match the talent-performance matching index RP of human resources to each service cycle i Compare and analyze with the preset talent-performance adaptation index threshold. If the talent-performance adaptation index RP i If the value is greater than the preset talent-performance adaptation index threshold, it means that the recruited personnel are highly matched with the position and have good performance, and there is no need to optimize the recruitment process; otherwise, it means that there is a problem in the recruitment process and unsuitable personnel are recruited, and the recruitment process needs to be optimized;

[0044] Step 4: The pay development analysis module conducts a comprehensive analysis of the training demand coefficient and pay competitiveness coefficient of human resources to obtain the pay development index TS of human resources in each service cycle. i ;

[0045] Step 5: Correspond human resources to the ability to develop index TS in each service cycle i Compare and analyze with the preset reward development index threshold. If the reward development index TS i If the value is greater than the preset threshold of the ability-to-pay development index, it means that there is a great demand for employee training and the company's salary competitiveness is strong; otherwise, it means that the salary is not competitive and it is necessary to consider adjusting the salary strategy;

[0046] Step 4: According to the talent-performance matching index RP of human resources in each service cycle i TS i The value of the human resource recruitment process and human resource salary distribution are analyzed to determine whether they are in line with the plan. Finally, the human resource service cost is analyzed through the human resource cost analysis module to obtain the cost-effectiveness coefficient CB of human resources in each service cycle. i .

[0047] The data collection layer includes human skill collection module, human performance collection module, training demand collection module, salary competitiveness collection module, human service status collection module, and human background status collection module; the data storage layer includes the collection data repository and the human resource preset module; the data analysis layer includes the talent-performance adaptation analysis module, the ability-remuneration development analysis module, the human resource recruitment optimization module, and the human resource cost analysis module;

[0048] The various data required for human resource services are collected and acquired through the various collection modules in the data collection layer, and stored in the collection data repository in the data storage layer. The talent-performance adaptation analysis module in the data analysis layer conducts a comprehensive analysis on the skill matching coefficient and performance completion coefficient of human resources to obtain the talent-performance adaptation index RP of human resources corresponding to each service cycle. iThe pay development analysis module conducts a comprehensive analysis of the training demand coefficient and pay competitiveness coefficient of human resources to obtain the pay development index TS of human resources corresponding to each service cycle. i ; According to the talent-performance matching index RP of human resources in each service cycle i TS i The value of the human resource recruitment process and human resource salary distribution are analyzed to determine whether they are in line with the plan. Finally, the human resource service cost is further analyzed through the human resource cost analysis module to obtain the cost-effectiveness coefficient CB of human resources in each service cycle. i , according to the cost-effectiveness coefficient CB of human resources in each service cycle i Determine whether there is any waste of resources or insufficient investment so as to adjust the human resource budget and resource allocation to improve cost-effectiveness.

[0049] It should be noted that i represents the number of each service cycle, i=1, 2, ..., n, and n represents the total number of service cycle numbers.

[0050] Specifically, the data collection layer comprehensively collects data on human skills, performance, training needs, etc., and stores them in the data storage layer, providing a rich data basis for subsequent analysis. The data analysis layer uses the talent-performance matching analysis module, the ability-reward development analysis module, etc. to comprehensively analyze various coefficients to obtain key indexes, so as to accurately judge whether the human resource recruitment process and salary distribution are reasonable. Finally, with the help of the human resource cost analysis module, the cost-effectiveness coefficient is obtained to judge the resource utilization situation, so as to flexibly adjust the human resource budget and resource allocation. The overall solution can improve the scientificity, accuracy and efficiency of human resource management, help enterprises optimize human resource allocation, improve human resource service levels, enhance the competitiveness of enterprises in talent management, and achieve efficient use of human resources and sustainable development of enterprises.

[0051] Furthermore, the human skill collection module uses natural language processing technology to extract the skill keywords and work experience keywords in the resumes of each applicant to obtain the skill keyword set JS j and work experience keyword set WS g , obtain the job skill requirement keyword set from the human resources preset module Keyword collection of job experience requirements Use natural language processing technology to collect JS skills keywords j and job skill requirements keyword set Calculate the matching value PJ. If the keywords are the same, score 1 point. If they are different, score 0 point. Similarly, calculate the work experience keyword WS. g Keyword collection of job experience requirements The matching value PW;

[0052] Specifically, for the skill keyword set JS j and job skill requirements keyword set The matching value PJ is based on the formula Calculated, for the work experience keyword set WS g Keyword collection of job experience requirements The matching value PW is based on the formula Calculated, where j represents the number of each skill keyword in the skill keyword set, j=1, 2, ..., m, m represents the total number of skill keyword numbers, g represents the number of each work experience keyword in the work experience keyword set, g=1, 2, ..., h, h represents the total number of work experience keyword numbers.

[0053] The human performance collection module obtains the task completion performance score JP, behavioral performance score BP and overall evaluation score QP of each candidate after joining the company through the enterprise project management system;

[0054] Specifically, the performance score JP of each candidate after joining the company is calculated based on the formula It is calculated by: T represents the task completion time, D represents the task difficulty coefficient, Q represents the task completion quality assessment, the behavioral performance score BP is obtained by standardizing the communication frequency and feedback timeliness, and then multiplying the standardized values, and the overall evaluation score QP is obtained by weighting the superior evaluation score, colleague evaluation score and subordinate evaluation score respectively and then summing them up.

[0055] The training demand collection module obtains the employee's current skill level CS, the employee's personal skill development needs PS, and the enterprise project skill needs SS through the employee skill testing system;

[0056] Specifically, employees' current skill level CS, employees' personal skill development needs PS, and enterprise project skill needs SS are all quantified on a scale of 0-10. The value range of employees' current skill level CS, employees' personal skill development needs PS, and enterprise project skill needs SS is 0-10.

[0057] The salary competitiveness collection module obtains the average salary level CI of the same industry, the salary level CC of the enterprise, and the employee salary satisfaction score XS through the database of the salary research organization;

[0058] Specifically, the employee salary satisfaction score XS takes a value of 0-1 after standardization.

[0059] Human service status collection module, real-time collection of personnel's waiting time TD and industry average waiting time during the service application process Staff feedback satisfaction score SF and feedback timeliness score ST;

[0060] The human background status collection module sets the number of personnel background investigation indicators according to the company's job requirements, obtains the scores of each indicator of each applicant, and sums them up to obtain the abnormal score A, where each normal indicator is 0 points and an abnormal indicator is 1 point.

[0061] Furthermore, the talent-performance matching analysis module uses the skill matching coefficient PP and the performance completion coefficient PX to perform a comprehensive calculation to obtain the talent-performance matching index RP of human resources corresponding to each service cycle. i ;

[0062] Specifically, through the natural language processing technology, the skill keyword set JS j and job skill requirements keyword set The matching value PJ and work experience keyword WS g Keyword collection of job experience requirements The matching value PW is comprehensively calculated to obtain the skill matching coefficient PP; where PP = PJ×0.6+PW×0.4.

[0063] The performance completion coefficient PX is obtained by weighted calculation of each applicant's task completion performance score JP, behavioral performance score BP and overall evaluation score QP after joining the company; where PX = JP×0.5+BP×0.3+QP×0.2.

[0064] According to the formula Calculate the talent-performance matching index RP of human resources corresponding to each service cycle i , where w1 and w2 represent the weights of the skill matching coefficient PP and the performance completion coefficient PX respectively. Specifically, w1=0.4 and w2=0.6.

[0065] Matching human resources to the talent-performance matching index RP within each service cycle i Compare and analyze with the preset talent-performance adaptation index threshold. If the talent-performance adaptation index RP i If the value is greater than the preset talent-performance adaptation index threshold, it means that the recruited personnel are highly matched with the position and have good performance, and there is no need to optimize the recruitment process; otherwise, it means that there is a problem in the personnel recruitment process and personnel who are not suitable for the position are recruited, and the recruitment process needs to be optimized.

[0066] The ability and remuneration development analysis module conducts a comprehensive analysis of the training demand coefficient PN and the salary competitiveness coefficient CJ to obtain the ability and remuneration development index TS of human resources corresponding to each service cyclei ;

[0067] Specifically, the training demand coefficient PN is calculated according to the formula PN = (SS-CS) × 0.6 + PS × 0.4;

[0068] The salary competitiveness coefficient CJ is based on the formula Calculated;

[0069] According to the formula Calculate the human resources' ability to develop index TS in each service cycle i , where α1 and α2 represent the weights of the training demand coefficient PN and the salary competitiveness coefficient CJ respectively. Specifically, α1 = 0.5, α2 = 0.5.

[0070] Corresponding human resources to the reward development index TS in each service cycle i Compare and analyze with the preset reward development index threshold. If the reward development index TS i If the value is greater than the preset threshold of the ability-to-reward development index, it means that there is a large demand for employee training and the company's salary competitiveness is strong. At this time, you can increase training investment to further improve employee capabilities. Otherwise, it means that the salary lacks competitiveness, resulting in low employee training enthusiasm. It is necessary to consider adjusting the salary strategy to improve salary competitiveness.

[0071] The human resources recruitment optimization module optimizes talent recruitment from three aspects: applicant matching and screening, applicant waiting experience, and comprehensive background investigation of applicants. The specific optimization methods are as follows:

[0072] Natural language processing technology for skill keyword collection JS j and job skill requirements keyword set The matching value PJ and work experience keyword WS g Keyword collection of job experience requirements The matching values ​​PW are summed to obtain the matching degree M of the candidate's application keywords;

[0073] Obtain the personnel application keyword screening threshold L from the human resources preset module, and enter the next round of screening for personnel whose application keyword matching degree M≥L; conversely, personnel whose application keyword matching degree M<L are initially screened out;

[0074] By dividing the waiting time TD of the personnel in the recruitment service process by the industry average waiting time The time difference coefficient C is obtained. If C>1, it means that the waiting time of the applicant is longer than the industry average. At the same time, according to the formula Calculate the experience optimization coefficient E, and compare the experience optimization coefficient E with the preset experience optimization coefficient threshold. If the value of the experience optimization coefficient E is less than the preset experience optimization coefficient threshold, it means that there are problems with the applicant's experience, and improvements need to be made in shortening the waiting time and improving the timeliness of feedback;

[0075] Finally, the applicant's abnormal score A is compared with the preset abnormal threshold. If the abnormal score A ≥ the preset abnormal threshold, it means that the applicant's background has risks and needs further evaluation. Otherwise, the background check is relatively passed.

[0076] The human resource cost analysis module calculates the company's recruitment cost ZC, training cost PC, salary cost XC, economic benefits created by employees EB, economic benefits of production EP, and benefits of employees' innovation results EI according to the formula Calculate the cost-effectiveness coefficient CB of human resources corresponding to each service cycle i ;

[0077] Corresponding human resources to the cost-effectiveness coefficient CB in each service cycle i Compared with the preset cost-effectiveness coefficient threshold, if the cost-effectiveness coefficient CB i If it is greater than the preset cost-effectiveness coefficient threshold, it means that the human resource investment has obtained a good return and the human resource service has performed well in cost control and efficiency improvement. Otherwise, it means that the company needs to examine whether the various human resource cost expenditures are reasonable and whether there is any waste of resources or insufficient investment.

[0078] In a specific embodiment, the present invention comprehensively collects multi-dimensional data such as human skills, performance, and training needs through multiple modules of the data collection layer, providing a solid data foundation for subsequent analysis. At the data analysis layer, with the help of the talent-performance adaptation analysis module, the ability-reward development analysis module, etc., a comprehensive analysis of various coefficients is performed to obtain key indexes, such as the talent-performance adaptation index and the ability-reward development index. These indexes can accurately determine whether the human resource recruitment process is scientific and reasonable, and whether the salary distribution is fair and effective; based on the comparison between the talent-performance adaptation index and the preset threshold, the matching degree and performance of the recruited personnel with the position can be clarified, and then it can be determined whether to optimize the recruitment process; through the analysis of the ability-reward development index, the relationship between salary competitiveness and employee training needs can be determined, and the salary strategy can be reasonably adjusted or the training investment can be increased, helping enterprises make scientific and accurate human resource management decisions.

[0079] At the same time, the cost-effectiveness coefficient is calculated by counting the costs of recruitment, training, salary, etc., as well as the economic benefits created by employees. By comparing this coefficient with the preset threshold, the company's human resource input and output can be clearly judged. If the cost-effectiveness coefficient is high, it means that human resource services are effective in cost control and benefit improvement; if the coefficient is low, it can prompt the company to review various cost expenditures and find out whether there is a waste of resources or insufficient investment, so as to flexibly adjust the human resource budget and resource allocation and improve resource utilization efficiency. At the same time, the human resource recruitment optimization module optimizes talent recruitment from aspects such as application matching screening, waiting experience, background investigation, etc., further improves the quality and efficiency of recruitment, realizes the efficient use of human resources, enhances the competitiveness of enterprises in talent management, and promotes the sustainable development of enterprises.

[0080] The above formulas are all dimensionless and numerical calculations. The formula is a formula that is obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The size of the coefficient is to quantify each parameter to obtain a specific value. Regarding the size of the coefficient, it is acceptable as long as it does not affect the proportional relationship between the parameter and the quantified value.

[0081] In addition, those skilled in the art will appreciate that various aspects of the present invention may be illustrated and described by a number of patentable categories or situations, including any new and useful process, machine, product or combination of substances, or any new and useful improvements thereto. Accordingly, various aspects of the present invention may be performed entirely by hardware, entirely by software (including firmware, resident software, microcode, etc.), or by a combination of hardware and software. The above hardware or software may all be referred to as "data blocks", "modules", "engines", "units", "components" or "systems". In addition, various aspects of the present invention may be represented as a computer product located in one or more computer-readable media, which includes computer-readable program code.

[0082] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the invention belongs. It should also be understood that terms such as those defined in common dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant technology and should not be interpreted in an idealized or extremely formal sense, unless explicitly defined as such herein.

[0083] The above is an explanation of the present invention and should not be considered as a limitation thereof. Although several exemplary embodiments of the present invention have been described, it will be readily appreciated by those skilled in the art that many modifications may be made to the exemplary embodiments without departing from the novel teachings and advantages of the present invention. Therefore, all such modifications are intended to be included within the scope of the present invention as defined in the claims. It should be understood that the above is an explanation of the present invention and should not be considered as being limited to the specific embodiments disclosed, and modifications to the disclosed embodiments and other embodiments are intended to be included within the scope of the appended claims. The present invention is defined by the claims and their equivalents.

Claims

1. A human resource service method based on big data analysis, characterized in that: The following steps are involved: Step 1: Collect and acquire various big data required for human resource services through various collection modules in the data collection layer, and store them in the collection data repository in the data storage layer; Step 2: Use the talent-performance matching analysis module in the data analysis layer to conduct a comprehensive analysis of the skill matching coefficient and performance completion coefficient of human resources to obtain the talent-performance matching index RP of human resources corresponding to each service cycle. i ; Step 3: Match the talent-performance matching index RP of human resources to each service cycle i Compare and analyze with the preset talent-performance adaptation index threshold. If the talent-performance adaptation index RP i If the value is greater than the preset talent-performance adaptation index threshold, it means that the recruited personnel are highly matched with the position and have good performance, and there is no need to optimize the recruitment process; otherwise, it means that there is a problem in the recruitment process and unsuitable personnel are recruited, and the recruitment process needs to be optimized; Step 4: The pay development analysis module conducts a comprehensive analysis of the training demand coefficient and pay competitiveness coefficient of human resources to obtain the pay development index TS of human resources in each service cycle. i ; Step 5: Correspond human resources to the ability to develop index TS in each service cycle i Compare and analyze with the preset reward development index threshold. If the reward development index TS i If the value is greater than the preset threshold of the ability-to-pay development index, it means that there is a great demand for employee training and the company's salary competitiveness is strong; otherwise, it means that the salary is not competitive and it is necessary to consider adjusting the salary strategy; Step 6: According to the talent-performance matching index RP of human resources in each service cycle i TS i The value of the human resource recruitment process and human resource salary distribution are analyzed to determine whether they are in line with the plan. Finally, the human resource service cost is analyzed through the human resource cost analysis module to obtain the cost-effectiveness coefficient CB of human resources in each service cycle. i .

2. The human resources service method based on big data analysis according to claim 1 is characterized by: The data collection layer includes a human skill collection module, a human performance collection module, a training demand collection module, a salary competitiveness collection module, a human service status collection module, and a human background status collection module; the data storage layer includes a collection data repository and a human resource preset module; the data analysis layer includes a talent-performance adaptation analysis module, a capability-salary development analysis module, a human resource recruitment optimization module, and a human resource cost analysis module.

3. The human resources service method based on big data analysis according to claim 2 is characterized by: The human skill collection module extracts the skill keywords and work experience keywords in the resumes of each applicant by using natural language processing technology to obtain the skill keyword set JS j and work experience keyword set WS g , get the job skill requirement keyword set JS from the human resources preset module j Keyword set WS for job experience requirements g , using natural language processing technology to collect skill keywords JS j And job skill requirements keyword collection JS j Calculate the matching value PJ. If the keywords are the same, score 1 point. If they are different, score 0 point. Similarly, calculate the work experience keyword WS. g Keyword set WS for job experience requirements g The matching value PW.

4. The human resources service method based on big data analysis according to claim 3 is characterized by: The human performance collection module obtains the task completion performance score JP, behavioral performance score BP and overall evaluation score QP of each candidate after joining the company through the enterprise project management system.

5. The human resources service method based on big data analysis according to claim 4 is characterized by: The training demand collection module obtains the employee's current skill level CS, the employee's personal skill development demand PS and the enterprise project skill demand SS through the employee skill testing system.

6. The human resources service method based on big data analysis according to claim 5 is characterized by: The salary competitiveness acquisition module obtains the average salary level CI of the same industry, the salary level CC of the enterprise and the employee salary satisfaction score XS through the database of the salary research organization.

7. The human resources service method based on big data analysis according to claim 6 is characterized by: The human background status collection module sets the number of personnel background investigation indicators according to the enterprise's job requirements, obtains the scores of each indicator of each applicant, and sums them up to obtain an abnormal score A, where each indicator is normal and has a score of 0, and each indicator is abnormal and has a score of 1.

8. The human resources service method based on big data analysis according to claim 7 is characterized by: The talent-performance matching analysis module performs a comprehensive calculation through the skill matching coefficient PP and the performance completion coefficient PX to obtain the talent-performance matching index RP of human resources corresponding to each service cycle. i The pay development analysis module performs a comprehensive analysis through the training demand coefficient PN and the salary competitiveness coefficient CJ to obtain the pay development index TS of human resources corresponding to each service cycle. i .

9. The human resources service method based on big data analysis according to claim 8 is characterized by: The human resource recruitment optimization module optimizes talent recruitment from three aspects: applicant matching screening, applicant waiting experience, and comprehensive background investigation of applicants. The specific optimization methods are as follows: Natural language processing technology for skill keyword collection JS j And job skill requirements keyword collection JS j The matching value PJ and work experience keyword WS g Keyword set WS for job experience requirements g The matching values ​​PW are summed to obtain the matching degree M of the candidate's application keywords; Obtain the personnel application keyword screening threshold L from the human resources preset module, and enter the next round of screening for personnel whose application keyword matching degree M≥L; conversely, personnel whose application keyword matching degree M<L are initially screened out; Divide the waiting time TD of the personnel in the recruitment service process by the industry average waiting time The time difference coefficient C is obtained. If C>1, it means that the waiting time of the applicant is longer than the industry average. At the same time, according to the formula The experience optimization coefficient E is calculated and compared with the preset experience optimization coefficient threshold. If the value of the experience optimization coefficient E is less than the preset experience optimization coefficient threshold, it indicates that there is a problem with the applicant's experience.

10. The human resources service method based on big data analysis according to claim 1 is characterized by: The human resource cost analysis module calculates the enterprise's recruitment cost ZC, training cost PC, salary cost XC, economic benefits EB created by employees, economic benefits EP of production, and benefits EI of employees' innovation achievements according to the formula Calculate the cost-effectiveness coefficient CB of human resources corresponding to each service cycle i .

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