Intelligent human resource planning method and system based on big data analysis

Abstract

The invention discloses a human resource intelligent planning method and system based on big data analysis, and relates to the technical field of human resource management, and the method comprises the steps: collecting the work performance data and eye movement tracking data of employees in a simulation training environment; skill labels of the employees are extracted by using a natural language processing technology, a personal skill map is constructed, and advantages and shortages of the employees are analyzed; evaluating the ability level of the employee according to the personal skill map and the work performance data; according to the skill label of the employee, combining the ability evaluation result of the employee, intelligently matching a proper post, and planning the occupational path of the employee; a personalized training plan is intelligently generated according to the skill weakness and post requirements of the employees; the comprehensive human resource intelligent planning method is constructed by integrating big data analysis and natural language processing technologies, and many problems in the prior art can be effectively solved.

Description

Technical Field

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

[0002] With the rapid development of big data analysis and artificial intelligence technology, intelligent analysis methods are gradually introduced into the field of human resource management. At present, traditional human resource management methods mainly rely on subjective evaluation or analysis based on limited data, which often has great limitations in dealing with complex employee ability assessment and job matching. In recent years, the application of big data and natural language processing technology has provided new possibilities for human resource management. However, the existing technology still has certain deficiencies in practical application. Existing evaluation methods often ignore the comprehensive analysis of employees' skills and cannot accurately identify employees' strengths and weaknesses. Although there are some intelligent job matching technologies, most of these technologies fail to combine employees' long-term career path planning, resulting in poor dynamics and foresight in job matching. At the same time, existing training programs often lack personalized design and cannot effectively solve the problem of employees' skill shortcomings. Summary of the invention

[0003] In view of the above existing problems, the present invention is proposed.

[0004] Therefore, the present invention provides a method and system for intelligent human resource planning based on big data analysis to solve the problems of incomplete employee skill assessment, inaccurate job matching and lack of personalized career planning in the prior art.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0006] In a first aspect, the present invention provides a method for intelligent human resource planning based on big data analysis, which includes collecting work performance data and eye tracking data of employees in a simulated training environment;

[0007] Use natural language processing technology to extract employees' skill tags and build personal skill maps to analyze employees' strengths and weaknesses;

[0008] Assess employees’ competency levels based on their personal skill profiles and work performance data;

[0009] Intelligently match employees with suitable positions and plan their career paths based on their skill tags and competency assessment results;

[0010] Intelligently generate personalized training plans based on employees’ skill shortcomings and job requirements.

[0011] As a preferred solution of the human resources intelligent planning method based on big data analysis described in the present invention, wherein: the work performance data includes the key performance indicators of the employee's reaction time, task completion and decision-making process in the simulated task;

[0012] The eye tracking data includes visual behavior data that tracks the employee's gaze point, gaze duration, and eye saccade frequency.

[0013] As a preferred solution of the human resources intelligent planning method based on big data analysis described in the present invention, wherein: using natural language processing technology to extract employee skill labels, and constructing a personal skill map, analyzing the strengths and weaknesses of employees, including the following steps,

[0014] Use natural language processing technology to analyze skill keywords in employee resumes and job descriptions, and map skill keywords to a skill classification system to form a skill tag set for employees;

[0015] Based on the employee's skill tag set, skill bar charts and stacked skill charts are used to visualize the employee's skill tags and levels to form a personal skill map;

[0016] According to industry standards, set a corresponding skill requirement baseline for each position;

[0017] Compare the employee's personal skill map with the skill demand baseline. If the employee's skill mastery level is significantly higher than the skill demand baseline, it will be identified as the employee's advantage skill;

[0018] Similarly, by comparing with job skill requirements, we can identify the skills in the employee's skill map that are lower than the job requirements, and then consider this skill to be the employee's shortcoming.

[0019] As a preferred solution of the human resources intelligent planning method based on big data analysis described in the present invention, wherein: evaluating the ability level of employees according to personal skill maps and work performance data includes the following steps:

[0020] Combine personal skill maps with employee performance data in a simulated training environment to set employee work capability levels;

[0021] The grade division algorithm of the gradient boosting model is used to score the employee's work ability. The expression is:

[0022]

[0023] Among them, e represents the skills of employees, w e represents the weight of the skill, I(s ie ) represents the employee's skill mastery index, f represents the assessment index, v fIndicates the importance of the assessment indicator, c if Indicates the quantitative value of the employee's completion of the assessment indicators, F i Indicates the overall rating of the employee;

[0024] After obtaining the comprehensive scores of employees, calculate the mean and standard deviation of the comprehensive scores, set the mean as the lowest grading limit, and set the standard deviation as the highest grading limit;

[0025] When the employee's comprehensive score is greater than or equal to the highest rating limit, the employee's work ability is excellent;

[0026] When the employee's overall score is less than the highest grade limit and greater than or equal to the lowest grade limit, the employee's work ability is good;

[0027] When an employee's comprehensive score is less than the minimum grading limit, the employee's work ability is poor.

[0028] As a preferred solution of the human resources intelligent planning method based on big data analysis described in the present invention, wherein: according to the employee's skill label, combined with the employee's ability assessment result, intelligent matching of suitable positions includes the following steps:

[0029] Based on the assessment results, job requirements for skill levels are formulated according to employees’ skill tags;

[0030] Using the intelligent matching algorithm, the matching degree between employees and positions is calculated according to the position requirements. The expression is:

[0031]

[0032] Among them, f m (i, k) represents the matching degree between employees and positions, i represents the employee's serial number, k represents the serial number of the position, j represents the index variable of the different skills possessed by the employee, and a ij Indicates the degree of employees' mastery of skills, b j Indicates the weight of skills to positions;

[0033] Arrange the matching degrees of all positions in descending order and select the one with the highest matching degree as the suitable position for the employee.

[0034] As a preferred solution of the human resources intelligent planning method based on big data analysis described in the present invention, wherein: planning the career path of employees according to the matching positions includes the following steps:

[0035] By comparing the employee's personal skill map with the skill requirements of the target position, identify the employee's strengths and weaknesses in the skills required for the current position and generate a skill gap analysis report;

[0036] Develop specific short-term career development goals for employees based on job requirements and gap analysis, including improving weak skills and strengthening strong skills;

[0037] Based on the job requirements of the employee's current position and other positions within the company, plan the employee's future promotion path by analyzing the match with the employee's skill map;

[0038] Generate a skills-specific enhancement plan based on short-term and long-term career development goals;

[0039] Dynamically adjust employees' career path planning based on their performance evaluation and feedback.

[0040] As a preferred solution of the human resources intelligent planning method based on big data analysis described in the present invention, wherein: according to the job requirements and career paths of employees, a personalized training plan is intelligently generated, including the following steps:

[0041] Collect employees’ job requirements through questionnaire surveys;

[0042] According to the job requirements, the matching degree between employees and their jobs, and their skills shortcomings, we use 3D modeling and animation technology in VR technology to design realistic virtual scenes, and let employees take the lead in operating and experiencing the virtual scenes;

[0043] Develop a learning outline with topics, subtopics, learning objectives, and estimated training time based on employee experience;

[0044] Design training courses according to the learning outline, provide course background and explain the operation process and steps of the training courses to employees, and generate personalized training plans.

[0045] In a second aspect, the present invention provides a human resource intelligent planning system based on big data analysis, including: a data collection module responsible for collecting employee work performance data and eye tracking data in a simulated training environment;

[0046] The technical analysis module is responsible for extracting employees' skill tags using natural language processing technology, building personal skill maps, and analyzing employees' strengths and weaknesses;

[0047] The competency assessment module is responsible for assessing employees’ competency levels based on their personal skill profiles and work performance data;

[0048] The intelligent matching module is responsible for intelligently matching employees with suitable positions and planning their career paths based on their skill tags and competency assessment results.

[0049] The personalized training plan generation module is responsible for intelligently generating personalized training plans based on employees' skill shortcomings and job requirements.

[0050] In a third aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of the human resources intelligent planning method based on big data analysis as described in the first aspect of the present invention is implemented.

[0051] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the human resources intelligent planning method based on big data analysis as described in the first aspect of the present invention.

[0052] The beneficial effects of the present invention are as follows: by integrating big data analysis and natural language processing technology, a comprehensive human resource intelligent planning method is constructed, which can effectively solve many problems in the prior art. By using natural language processing technology to extract skill labels from employee resumes and job descriptions, and through the visual display of skill maps, the strengths and weaknesses of employees can be accurately identified. By combining the work performance data and eye tracking data in the simulation tasks, the actual work ability of employees is further evaluated to ensure the objectivity and accuracy of the evaluation results. In addition, the gradient boosting machine model is used to grade and score the work ability of employees, and the matching degree between employees and positions is calculated through an intelligent matching algorithm to ensure that employees can be matched to the most suitable positions. Finally, by generating personalized training plans, employees are helped to improve their shortcomings in a targeted manner and further achieve career advancement and development. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of 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.

[0054] Figure 1 This is a flow chart of the human resources intelligent planning method based on big data analysis in Example 1. DETAILED DESCRIPTION

[0055] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0056] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0057] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0058] Example 1, reference Figure 1 , which is the first embodiment of the present invention, and provides a method for intelligent human resource planning based on big data analysis, comprising the following steps:

[0059] S1. Collect employee performance data and eye tracking data in a simulated training environment, including the following steps:

[0060] Job performance data included key performance indicators of employees’ reaction time, task completion, and decision-making processes in simulated tasks;

[0061] Eye tracking data includes visual behavior data that tracks employees’ gaze points, gaze duration, and eye saccade frequency.

[0062] It should be noted that by collecting work performance data and eye tracking data, a multi-dimensional assessment of employees' work capabilities is achieved. The key performance indicators of employees' reaction time, task completion and decision-making process in simulated tasks can objectively reflect their efficiency and ability in real work scenarios, while eye tracking data (fixation point, fixation duration and eye saccade frequency, etc.) can further analyze employees' attention allocation and information processing methods. The role of this step is to integrate employees' behavioral data and eye movement data to form a comprehensive work performance analysis, which can not only help evaluate employees' current capabilities, but also identify their potential thinking patterns and cognitive loads when performing tasks. Ultimately, this step provides accurate data support for subsequent skill assessment and job matching, ensuring the objectivity and accuracy of the assessment results.

[0063] S2. Use natural language processing technology to extract employees’ skill labels and build personal skill maps to analyze employees’ strengths and weaknesses, including the following steps:

[0064] Use natural language processing technology to analyze the skill keywords in employee resumes and job descriptions, and map the skill keywords to a skill classification system to form a set of employee skill tags. Each tag can be used to perform a preliminary skill level classification based on the employee's work experience and project description.

[0065] Based on the skill tag set of employees, skill bar charts and stacked skill charts are used to visualize the skill tags and levels of employees, forming a personal skill map. The skill bar chart shows the mastery of each skill. For example, an employee's "programming skills" may be displayed as 80%, while "communication skills" may be 60%. The stacked skill chart classifies and displays skills by category, making it easier to identify the strengths and weaknesses of a certain skill category. For example, the skills under the "technical skills" category may be divided into programming, database design, system architecture, etc., showing their mastery status respectively;

[0066] According to industry standards, set a corresponding skill requirement baseline for each position;

[0067] Compare the employee's personal skill map with the skill requirement baseline. If the employee's skill mastery is significantly higher than the skill requirement baseline, it will be identified as the employee's advantage skill. Furthermore, in the skill map, different colors or marks are used to distinguish the employee's advantage skills. For example, if an employee scores 85% on "programming skills" and the job requirement is 70%, this skill will be marked as an advantage skill.

[0068] Similarly, by comparing with the job skill requirements, we can identify the skills in the employee skill map that are lower than the job requirements, and consider this skill as the employee's shortcoming. Furthermore, in the skill map, different colors or marks are used to distinguish the employee's shortcomings. For example, if an employee scores 50% on "communication skills" and the job requirement is 60%, this skill will be marked as a shortcoming.

[0069] It should be noted that by using natural language processing technology to extract skill labels and construct personal skill maps, a structured analysis of employee skills is achieved. This step extracts skill keywords from employee resumes and job descriptions and maps them to the skill classification system. It can not only automatically generate a set of skill labels, but also visualize the mastery of each skill through skill bar charts and stacked skill charts. The purpose of this step is to visualize the skill strengths and weaknesses of employees through graphical means, so as to quickly identify employees' areas of expertise and skills that need to be improved. By comparing employees' skill maps with job skill baselines, the classification and qualitative analysis of employee skills are further strengthened. Ultimately, this step effectively improves the accuracy and efficiency of skill assessment and ensures the accuracy of subsequent job matching and career planning.

[0070] S3. Evaluate the employee's ability level based on their personal skill map and work performance data, including the following steps:

[0071] Combine personal skill maps with employee performance data in a simulated training environment to set employee work capability levels;

[0072] The grade division algorithm of the gradient boosting model is used to score the employee's work ability. The expression is:

[0073]

[0074] Among them, e represents the skills of employees, w e Indicates the weight of the skill, which is used to reflect the importance of the skill. ie ) represents the employee's skill mastery index, f represents the assessment index, v f Indicates the importance of the assessment indicator, used to evaluate employee performance, c if Indicates the quantitative value of the employee's completion of the assessment indicators, F i Indicates the overall rating of the employee;

[0075] It should be noted that the basis for designing the scoring formula is to comprehensively consider the employees' skill mastery and work performance, combine the importance of each skill and assessment indicator, use the weighted average method to accurately evaluate the employees' overall work ability, and achieve quantitative and differentiated evaluation of their abilities.

[0076] After obtaining the comprehensive scores of employees, calculate the mean and standard deviation of the comprehensive scores, set the mean as the lowest grading limit, and set the standard deviation as the highest grading limit;

[0077] It should be noted that the mean and standard deviation are selected as the minimum and maximum grading limits because they can effectively reflect the distribution of employees' comprehensive scores and ensure that the scoring limits are statistically reasonable, thereby more accurately distinguishing employees with different ability levels.

[0078] When the employee's comprehensive score is greater than or equal to the highest rating limit, the employee's work ability is excellent;

[0079] When the employee's overall score is less than the highest grade limit and greater than or equal to the lowest grade limit, the employee's work ability is good;

[0080] When an employee's comprehensive score is less than the minimum grading limit, the employee's work ability is poor.

[0081] It should be noted that by combining personal skill maps and work performance data, the level of employee work ability is set, and a comprehensive score of employee ability is achieved. This step uses the gradient boosting machine model to weight the employee's skills and performance, ensuring the refinement and data-driven nature of the evaluation process. By calculating the mean and standard deviation of the comprehensive score, different grading boundaries of employee work ability can be set, making the final evaluation results more discriminatory. The role of this step is to quantitatively evaluate employee capabilities through an accurate algorithm model, ensuring the objectivity and rigor of the scoring, and providing a reliable basis for subsequent job matching and career planning. Finally, this step achieves the beneficial effect of accurately positioning the employee's ability level by comprehensively evaluating the employee's skills and performance.

[0082] S4. According to the employee's skill tag and the employee's ability assessment results, intelligently match the appropriate position and plan the employee's career path, including the following steps:

[0083] S4.1. Based on the assessment results, formulate job requirements for skill levels according to the employee’s skill tags;

[0084] Using the intelligent matching algorithm, the matching degree between employees and positions is calculated according to the position requirements. The expression is:

[0085]

[0086] Among them, f m (i, k) represents the matching degree between employees and positions. The higher the matching score, the higher the matching degree between employees and positions. The lower the matching score, the lower the matching degree between employees and positions. i represents the employee's serial number, k represents the serial number of the position, and j represents the index variable of the different skills possessed by the employee, which is used to traverse all the skills mastered by the employee. ij Indicates the degree of employees' mastery of skills, b j Indicates the weight of the skill to the position, reflecting the importance of the skill to the position. The higher the weight, the greater the impact of the skill on the position, and the lower the weight, the smaller the impact of the skill on the position;

[0087] It should be noted that the basis for the design of the matching formula is to measure the deviation between the employee's skill mastery and the job skill requirements, and combine the importance of each skill to the job, and use an exponential function to nonlinearly scale the difference, so as to accurately calculate the matching degree between employees and jobs, ensuring that the matching results are more differentiated and reasonable.

[0088] Arrange the matching degrees of all positions in descending order and select the one with the highest matching degree as the suitable position for the employee.

[0089] S4.2. By comparing the employee's personal skill map with the skill requirements of the target position, identify the employee's strengths and weaknesses in the skills required for the current position and generate a skills gap analysis report;

[0090] Develop specific short-term career development goals for employees based on job requirements and gap analysis, including improving weak skills and strengthening strong skills;

[0091] Specifically, set a clear time frame and key performance indicators for each short-term goal. The weak skill goal is to improve communication skills from the current level 5 to level 7 within 6 months, participate in at least 3 cross-departmental project meetings and host one. The strong skill goal is to further improve system architecture design skills within 3 months and participate in the company's architecture upgrade project;

[0092] Based on the job requirements of the employee's current position and other positions within the company, plan the employee's future promotion path by analyzing the match with the employee's skill map;

[0093] Specifically, job promotions, for example, from mid-level software engineer to senior software engineer to technical manager. If an employee excels in multiple skill areas, planning may include lateral job transfers, such as from software development to product management.

[0094] Generate a skills-specific enhancement plan based on short-term and long-term career development goals;

[0095] Dynamically adjust career path planning based on employee performance evaluation and feedback.

[0096] It should be noted that the matching degree between employees and positions is calculated through the intelligent matching algorithm, so as to achieve the optimal combination of employees and positions. Based on the personal skill map and comprehensive score, this step uses the matching formula to accurately compare the employee's skill mastery with the position requirements, and arranges the matching degree of the positions in descending order, so as to ensure that employees can match the most suitable position for their career development. The role of this step is to realize the automation and intelligence of position matching in a data-driven way, avoiding the subjective problem in traditional job allocation. At the same time, by identifying the strong skills and weak skills required by employees in the target positions, a skill gap analysis report is generated, and short-term career development goals are set for employees. This process not only improves the accuracy of job matching, but also provides directional guidance for employees' long-term career development. Finally, this step achieves the beneficial effect of intelligent job allocation and provides data support for the planning of employees' career paths.

[0097] S5. Intelligently generate personalized training plans based on employees’ skill shortcomings and job requirements, including the following steps:

[0098] Collect employees’ job requirements through questionnaire surveys;

[0099] According to the job requirements, the matching degree between employees and their jobs, and their skills shortcomings, we use 3D modeling and animation technology in VR technology to design realistic virtual scenes, and let employees take the lead in operating and experiencing the virtual scenes;

[0100] Develop a learning outline with topics, subtopics, learning objectives, and estimated training time based on employee experience;

[0101] Design training courses according to the learning outline, provide course background and explain the operation process and steps of the training courses to employees, and generate personalized training plans.

[0102] It should be noted that by combining the job requirements, skill shortcomings and matching degree of employees, VR technology is used to design virtual scenes to achieve the generation of personalized training plans. This step collects employees' job requirements through questionnaires, and then uses VR technology to simulate realistic work scenes for employees, and formulates learning outlines and training courses based on operational experience. The purpose of this step is to provide an immersive training experience through virtual reality technology, so that employees can improve their skills in a highly realistic environment, especially to strengthen their shortcomings. In the end, this step achieved the beneficial effect of generating personalized training plans based on the specific needs of employees, which not only improved the pertinence of training, but also enhanced the learning effect of employees and the efficiency of skill improvement.

[0103] This embodiment also provides a human resource intelligent planning system based on big data analysis, including: a data collection module responsible for collecting employee work performance data and eye tracking data in a simulated training environment;

[0104] The technical analysis module is responsible for extracting employees' skill tags using natural language processing technology, building personal skill maps, and analyzing employees' strengths and weaknesses;

[0105] The competency assessment module is responsible for assessing employees’ competency levels based on their personal skill profiles and work performance data;

[0106] The intelligent matching module is responsible for intelligently matching employees with suitable positions and planning their career paths based on their skill tags and competency assessment results.

[0107] The personalized training plan generation module is responsible for intelligently generating personalized training plans based on employees' skill shortcomings and job requirements.

[0108] This embodiment also provides a computer device, which is suitable for the case of a human resources intelligent planning method based on big data analysis, including: a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the human resources intelligent planning method based on big data analysis proposed in the above embodiment.

[0109] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0110] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method for realizing intelligent human resource planning based on big data analysis as proposed in the above embodiment is implemented; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, referred to as EPROM), programmable read-only memory (Programmable Red-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0111] In summary, the present invention has constructed a comprehensive human resource intelligent planning method by integrating big data analysis and natural language processing technology, which can effectively solve many problems in the prior art. Natural language processing technology is used to extract skill labels from employee resumes and job descriptions, and through the visual display of skill maps, the strengths and weaknesses of employees can be accurately identified. By combining the work performance data and eye tracking data in the simulation task, the actual work ability of employees is further evaluated to ensure the objectivity and accuracy of the evaluation results. In addition, the gradient boosting machine model is used to grade and score the work ability of employees, and the matching degree between employees and positions is calculated through an intelligent matching algorithm to ensure that employees can match the most suitable positions. Finally, by generating personalized training plans, employees can be helped to improve their shortcomings in a targeted manner and further achieve career advancement and development.

[0112] Example 2, referring to Table 1, is the second example of the present invention. To further verify the technical solution of the present invention, experimental simulation data of a human resource intelligent planning method based on big data analysis is provided.

[0113] In this experiment, two groups of employees were selected to participate in the experiment, namely the experimental group and the control group, each group containing 50 employees. The main purpose of the experiment is to evaluate the effectiveness of the human resources intelligent planning method based on big data analysis in employee competency assessment, job matching and career path planning. The experimental group adopted the method of the present invention, including collecting employees' work performance data and eye tracking data in a simulated training environment, extracting employees' skill labels using natural language processing technology, constructing a personal skill map, and evaluating employees' ability levels based on the map and work performance data. Next, the intelligent matching algorithm recommends suitable positions based on employees' skill labels and competency assessment results, and finally generates a personalized training plan for each employee.

[0114] The control group used a traditional manual evaluation and job assignment method. Employees in the control group filled out self-assessment forms manually, and supervisors assigned jobs based on employees' work experience and subjective impressions. The training plan was formulated by the human resources department based on job requirements, and no big data analysis or intelligent algorithms were used. One month before the experiment, all employees received the same initial training to ensure a consistent starting point between the two groups. The experiment lasted for six months, during which each employee's key work performance data (such as reaction time, task completion, decision-making process, etc.) and eye tracking data (fixation point, fixation duration, eye saccade frequency, etc.) were recorded for subsequent comparative analysis. At the same time, employees' skill labels and work performance results were processed and evaluated using natural language processing technology and graph analysis tools.

[0115] The details are shown in Table 1 below:

[0116] Table 1 Experimental record table

[0117]

[0118] By comparing the data of the experimental group and the control group in Table 1, it can be clearly seen that the human resource intelligent planning method of the present invention shows significant advantages in many aspects. First, in terms of work reaction time, the average reaction time of the experimental group is 1.25 seconds, while the average reaction time of the control group is 2.48 seconds, and the reaction time of the experimental group is about half of that of the control group. This shows that by collecting employees' work performance data in a simulated environment and combining it with eye tracking data, it is possible to more effectively improve employees' reaction speed in actual work.

[0119] In terms of task completion, the experimental group completed more than 90% of the tasks, while the control group completed about 80%. This further proves that using natural language processing technology to extract employee skill tags and combining work performance data for ability assessment can help employees better perform their tasks and improve work efficiency.

[0120] The difference in job matching is even more obvious, with the experimental group matching above 89%, while the control group matching is between 58% and 63%. The experimental group uses big data analysis and intelligent matching algorithms to accurately match the most suitable jobs based on employees' personal skill maps and ability assessment results, significantly improving the fit between employees and jobs. In contrast, the control group uses manual evaluation and job allocation, which makes it difficult to accurately match employees' skills with job requirements, resulting in a lower matching degree.

[0121] In terms of the number of skill shortcomings, employees in the experimental group had only 1-2 skill shortcomings on average, while each employee in the control group had more than 5 shortcomings. This shows that analysis based on the skill map can effectively identify and reduce employees' skill shortcomings and help them improve key skills in a targeted manner.

[0122] In terms of training effect improvement rate, the average improvement rate of the experimental group was 79.33%, while that of the control group was only 41.33%. This shows that through the personalized training plan, the working ability of the experimental group employees has been significantly improved, and the skill shortcomings have been made up in a short period of time. However, due to the lack of personalized design, the training plan of the control group cannot effectively improve the overall ability of employees.

[0123] Finally, the accuracy of career path planning also reflects the advantages of the experimental group. The accuracy of career path planning in the experimental group is between 80% and 88%, while that in the control group is only 55% to 61%. This shows that through the matching and dynamic adjustment of intelligent algorithms, the career development path of employees in the experimental group is clearer, in line with the direction of employee ability development, and ensures that employees' long-term career planning is more reasonable and forward-looking.

[0124] In summary, the experimental group performed significantly better than the control group in key indicators such as work reaction time, task completion, job matching, number of skill shortcomings, training effect improvement rate and career path planning accuracy, which fully demonstrated the innovation and practicality of the method of the present invention.

[0125] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A human resources intelligent planning method based on big data analysis, characterized by: include, Collect employee performance data and eye-tracking data in a simulated training environment; Use natural language processing technology to extract employees' skill tags and build personal skill maps to analyze employees' strengths and weaknesses; Assess employees’ competency levels based on their personal skill profiles and work performance data; Intelligently match employees with suitable positions and plan their career paths based on their skill tags and competency assessment results; Intelligently generate personalized training plans based on employees’ skill shortcomings and job requirements.

2. The method for intelligent human resource planning based on big data analysis according to claim 1, characterized in that: The work performance data includes key performance indicators of employees’ reaction time, task completion and decision-making process in simulated tasks; The eye tracking data includes visual behavior data that tracks the employee's gaze point, gaze duration, and eye saccade frequency.

3. The method for intelligent human resource planning based on big data analysis according to claim 2, characterized in that: Use natural language processing technology to extract employee skill tags and build a personal skill map to analyze employees' strengths and weaknesses, including the following steps: Use natural language processing technology to analyze skill keywords in employee resumes and job descriptions, and map skill keywords to a skill classification system to form a skill tag set for employees; Based on the employee's skill tag set, skill bar charts and stacked skill charts are used to visualize the employee's skill tags and levels to form a personal skill map; According to industry standards, set a corresponding skill requirement baseline for each position; Compare the employee's personal skill map with the skill demand baseline. If the employee's skill mastery level is significantly higher than the skill demand baseline, it will be identified as the employee's advantage skill; Similarly, by comparing with job skill requirements, we can identify the skills in the employee's skill map that are lower than the job requirements, and then consider this skill as the employee's shortcoming.

4. The method for intelligent human resource planning based on big data analysis according to claim 3, characterized in that: Based on personal skill maps and work performance data, assess the ability level of employees, including the following steps: Combine personal skill maps with employee performance data in a simulated training environment to set employee work capability levels; The grade division algorithm of the gradient boosting model is used to score the employee's work ability. The expression is: Among them, e represents the skills of employees, w e represents the weight of the skill, I(s ie ) represents the employee's skill mastery index, f represents the assessment index, v f Indicates the importance of the assessment indicator, c if Indicates the quantitative value of the employee's completion of the assessment indicators, F i Indicates the overall rating of the employee; After obtaining the comprehensive scores of employees, calculate the mean and standard deviation of the comprehensive scores, set the mean as the lowest grading limit, and set the standard deviation as the highest grading limit; When the employee's comprehensive score is greater than or equal to the highest rating limit, the employee's work ability is excellent; When the employee's overall score is less than the highest grade limit and greater than or equal to the lowest grade limit, the employee's work ability is good; When an employee's comprehensive score is less than the minimum grading limit, the employee's work ability is poor.

5. The method for intelligent human resource planning based on big data analysis according to claim 4, characterized in that: According to the employee's skill tag and the employee's ability assessment results, intelligent matching of suitable positions is carried out, including the following steps: Based on the assessment results, job requirements for skill levels are formulated according to employees’ skill tags; Using the intelligent matching algorithm, the matching degree between employees and positions is calculated according to the position requirements. The expression is: Among them, f m (i, k) represents the matching degree between employees and positions, i represents the employee's serial number, k represents the serial number of the position, j represents the index variable of the different skills possessed by the employee, and a ij Indicates the degree of employees' mastery of skills, b j Indicates the weight of skills to positions; Arrange the matching degrees of all positions in descending order and select the one with the highest matching degree as the suitable position for the employee.

6. The method for intelligent human resource planning based on big data analysis according to claim 5, characterized in that: According to the matching positions, plan the career path of employees, including the following steps: By comparing the employee's personal skill map with the skill requirements of the target position, identify the employee's strengths and weaknesses in the skills required for the current position and generate a skill gap analysis report; Develop specific short-term career development goals for employees based on job requirements and gap analysis, including improving weak skills and strengthening strong skills; Based on the job requirements of the employee's current position and other positions within the company, plan the employee's future promotion path by analyzing the match with the employee's skill map; Generate a skills-specific enhancement plan based on short-term and long-term career development goals; Dynamically adjust employees' career path planning based on their performance evaluation and feedback.

7. The method for intelligent human resource planning based on big data analysis according to claim 6, characterized in that: According to the employee's job requirements and career path, intelligently generate personalized training plans, including the following steps: Collect employees’ job requirements through questionnaire surveys; According to the job requirements, the matching degree between employees and their jobs, and their skills shortcomings, we use 3D modeling and animation technology in VR technology to design realistic virtual scenes, and let employees take the lead in operating and experiencing the virtual scenes; Develop a learning outline with topics, subtopics, learning objectives, and estimated training time based on employee experience; Design training courses according to the learning outline, provide course background and explain the operation process and steps of the training courses to employees, and generate personalized training plans.

8. A human resources intelligent planning system based on big data analysis, based on the human resources intelligent planning method based on big data analysis according to any one of claims 1 to 7, characterized in that: include, The data collection module is responsible for collecting employees’ work performance data and eye tracking data in a simulated training environment; The technical analysis module is responsible for extracting employees' skill tags using natural language processing technology, building personal skill maps, and analyzing employees' strengths and weaknesses; The competency assessment module is responsible for assessing employees’ competency levels based on their personal skill profiles and work performance data; The intelligent matching module is responsible for intelligently matching employees with suitable positions and planning their career paths based on their skill tags and competency assessment results. The personalized training plan generation module is responsible for intelligently generating personalized training plans based on employees' skill shortcomings and job requirements.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the human resources intelligent planning method based on big data analysis described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the human resources intelligent planning method based on big data analysis described in any one of claims 1 to 7 are implemented.

Images