Human resource management method and system

Abstract

The invention discloses a human resource management method and system, and relates to the technical field of human resource management, and the method comprises the steps: obtaining task data from a company project end, collecting employee information, recording emotion information and performance information corresponding to the employee information, building a comprehensive scoring mechanism, outputting an employee score, and formulating a reward mechanism and a qualification mechanism. And obtaining reward information and task progress information, establishing task priority distribution, and outputting a task distribution model. By accurately analyzing task requirements, intelligently matching specialties, experiences, interests and skills of employees, comprehensively recording performance and emotions of the employees and realizing comprehensive scoring, the method is beneficial to preferentially selecting high-score employees, improving task completion quality, formulating an award and qualification mechanism, motivating enthusiasm of the employees, dynamically adjusting task priorities and optimizing resource allocation, and the task quality is improved. After the employees grow in the specific field, the new task priority is reasonably reduced, resources are balanced, and the overall working efficiency is improved.

Description

Technical Field

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

[0002] With the rapid development of modern enterprises, the effective management and allocation of human resources has become one of the key factors in improving corporate competitiveness. Traditional human resource management methods often rely on manual judgment and experience-based decision-making, which makes it difficult to accurately match tasks and employee capabilities, resulting in uneven resource allocation and inefficient task execution. Especially in project-driven enterprises, the diversity and complexity of project tasks place higher demands on employees' skills, experience and psychological state. Therefore, it is particularly important to develop a human resource management method that can automatically parse task requirements, accurately match employee information, record employee performance and dynamically adjust task priorities.

[0003] At present, the Chinese invention patent with application number 202310717851.7 discloses a human resources management system and management method. After entering the basic information of the archives, the warehouse manager can directly enter the archives into the warehouse without having to enter the information of the specific shelf location of the archives again after entering. This can greatly improve the efficiency of archive warehousing. At the same time, compared with manual warehousing and entry of warehousing information, this invention can analyze the images collected by the image acquisition unit to obtain the specific location where the warehouse manager places the archives. It is not easy to have incorrect warehousing information caused by errors in input by warehouse managers. The results are more accurate and reliable, and the warehousing efficiency is higher.

[0004] The above technologies find it difficult to parse task requirements, intelligently match employee information, comprehensively record employee performance, and dynamically adjust task priorities when issuing tasks, making it difficult to achieve effective management and optimal allocation of human resources. Summary of the invention

[0005] The technical problem solved by the present invention is that it is difficult for the existing technology to parse task requirements, intelligently match employee information, comprehensively record employee performance and dynamically adjust task priorities when issuing tasks, making it difficult to achieve effective management and optimal allocation of human resources.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: A human resource management method comprises the following steps: Step S1, obtaining task data from the company's project side and analyzing the personnel composition required by the task data; Step S2, collecting employee information and matching corresponding employee information according to the personnel composition required for the task; Step S3, recording the emotion information and performance information corresponding to the employee information, establishing a comprehensive scoring mechanism, inputting the emotion information and performance information into the comprehensive scoring mechanism, outputting the employee score, establishing a corresponding relationship between the employee score, task data and employee information, and outputting it as the employee task indicator; Step S4, formulate a reward mechanism and a qualification mechanism, input employee task indicators into the reward mechanism and the qualification mechanism, and obtain reward information and task progress information; Step S5, establish task priority allocation and output the task allocation model.

[0007] Preferably, the step S1 includes the following sub-steps: Step S101, obtaining task data from the company's project side, wherein the task data includes task objectives, task types, task cycles, and task requirements; Step S102, using natural language technology to analyze keywords of the task data and outputting them as task keyword data, wherein the task keyword data includes professional fields and corresponding technical requirements and experience requirements; Technical requirements are graded according to the length of experience, and the classification includes basic skills, professional skills and advanced skills. The length of experience corresponding to the basic skills is the first years of employment, the length of experience corresponding to the professional skills is the second years of employment, and the length of experience corresponding to the advanced skills is the third years of employment; Step S103, establishing a task requirement model according to step S101 and step S102, wherein the input data of the task requirement model are the task data and the employee information, and the output data of the task requirement model is the employee information matching the task data.

[0008] Preferably, step S2 includes the following sub-steps: Step S201, collecting employee information, the employee information includes employee name, interests and skills, employee major, employee major corresponding experience time and employee current idle time; Step S202: input employee information and task data into the task requirement model to obtain employee information matching the task data.

[0009] Preferably, the matching logic of the task requirement model is: Screening employee majors, obtaining employee majors corresponding to professional fields, dividing employee majors into basic skills, professional skills and advanced skills according to the classification and the length of experience corresponding to the employee majors, assigning values ​​to the basic skills, professional skills and advanced skills, and outputting them as employee professional level values, wherein the assigned values ​​increase step by step; Assign weights to professional fields, and output the weights of professional fields; Calculate the skill matching degree, the mathematical expression of the skill matching degree is: ; in, For skill matching, The professional level of employees, Weight for professional field; Calculate the experience matching degree, the mathematical expression of the experience matching degree is: ; in, is the experience matching degree, For the duration of experience, For experience needs; Calculate the load matching degree, the mathematical expression of the load matching degree is: ; in, is the load matching degree, is the employee's current idle time. is the task cycle; Define the weight distribution corresponding to skill matching, experience matching and load matching, and the weight distribution is: The corresponding weight of skill matching is 50%; The corresponding weight of experience matching is 30%; The corresponding weight of load matching is 20%; Calculate the comprehensive matching degree, the mathematical expression of which is: ; in, For the comprehensive matching degree, is the weight corresponding to the skill matching degree, is the weight corresponding to the experience matching degree, is the weight corresponding to the load matching degree; Compare the interest skills and professional fields, obtain the number of interest skills matching the professional fields, and output as interest skill quantity data; Sort the comprehensive matching degrees in descending order, obtain the comprehensive matching degrees in the first percentile, establish the first correspondence between the comprehensive matching degrees, the quantity data of interests and skills, and the employee information, and output them as a screening group; The filter group is sorted in descending order according to the interest and skill quantity data, and the employee information corresponding to the interest and skill quantity data in the top second percentile is obtained and output.

[0010] Preferably, step S3 includes the following sub-steps: Step S301, recording the emotion information and performance information corresponding to the employee information, wherein the emotion information includes the overall satisfaction score, stress score and collaboration satisfaction score, and the performance information includes the subjective score and objective score, and establishing a comprehensive scoring mechanism; Step S302, input the emotion information and performance information into the comprehensive scoring mechanism and output the employee score; Step S303, establishing a corresponding relationship between employee scores, task data and employee information, and outputting them as employee task indicators.

[0011] Preferably, the logic of the comprehensive scoring mechanism is: Collect the work completion time and calculate the time completion rate. The mathematical expression of the time completion rate is: ; in, is the time completion rate, The time for completion of the work; Calculate the sum of the overall satisfaction score, stress score, and collaboration satisfaction score, and output the overall sentiment score; According to the objective score, weights are assigned to the overall emotion score, subjective score, objective score and time completion rate; If the objective scoring value is greater than the preset first objective scoring value threshold, the corresponding weight distribution is: The weight corresponding to the objective scoring score is 40%; The weight corresponding to the overall sentiment score is 20%; The weight corresponding to the subjective scoring score is 20%; The weight corresponding to the time completion rate is 20%; If the objective scoring value is less than the preset first objective scoring value threshold, the corresponding weight distribution is: The weight corresponding to the objective scoring score is 40%; The weight corresponding to the overall sentiment score is 20%; The weight corresponding to the subjective scoring score is 10%; The weight corresponding to the time completion rate is 20%; The comprehensive score is calculated based on the weight distribution and the output is the employee score.

[0012] Preferably, step S4 includes the following sub-steps: Step S401, formulate a reward mechanism and a qualification mechanism, and input employee task indicators into the reward mechanism and the qualification mechanism; Step S402, obtaining reward information and task progress information.

[0013] Preferably, the logic of the reward mechanism is: If the employee score is higher than the preset employee score threshold, the task bonus is calculated. The mathematical expression of the task bonus is: ; in, For the task bonus, The preset bonus base value; Output the task bonus as reward information; The logic of the qualification mechanism is: Establish a correspondence between employee scores and task keyword data, output as current task progress data, add employee scores to employee scores in corresponding past task progress data to obtain total employee scores, establish a correspondence between total employee scores, task keyword data and employee information, and output as task progress information.

[0014] Preferably, the step S5 establishes task priority allocation, and the logic of outputting the task allocation model is: Obtain task keyword data for the new task, output it as updated task keyword data, compare the updated task keyword data with the task keyword data in the task progress information, find the total employee score corresponding to the task keyword data that is the same as the updated task keyword data, and if the total employee score is higher than the preset total employee score threshold, output a priority reduction signal and establish a corresponding relationship between the priority reduction signal and the employee information, and output it as a priority adjustment signal.

[0015] A human resource management system, comprising a task analysis module, an information collection module, a task scoring module, a mechanism establishment module and a priority adjustment module; The task analysis module is used to obtain task data from the company's project end and analyze the personnel composition required for the task data; The information collection module is used to collect employee information and match the corresponding employee information according to the personnel composition required by the task; The task scoring module is used to record the emotion information and performance information corresponding to the employee information, establish a comprehensive scoring mechanism, input the emotion information and performance information into the comprehensive scoring mechanism, output the employee score, establish the corresponding relationship between the employee score, task data and employee information, and output it as the employee task indicator; The mechanism establishment module is used to formulate a reward mechanism and a qualified mechanism, input employee task indicators into the reward mechanism and the qualified mechanism, and obtain reward information and task progress information; The priority adjustment module is used to establish task priority allocation and output a task allocation model.

[0016] Beneficial effects of the present invention: The present invention accurately analyzes task requirements, intelligently matches employees' expertise, experience, interests and skills, comprehensively records employee performance and emotions, and achieves comprehensive scoring, which helps to give priority to high-scoring employees and improve the quality of task completion. At the same time, it formulates a reward and qualification mechanism to stimulate employee enthusiasm, dynamically adjust task priorities, optimize resource allocation, and ensure that after employees grow in specific fields, their new task priorities are reasonably lowered, resources are balanced, and overall work efficiency is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A flowchart of a method for managing human resources provided by one embodiment of the present invention; Figure 2 A basic flow chart of a human resources management system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0018] 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 drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, but not all of the embodiments.

[0019] Example 1, reference Figure 1 , provides a human resource management method, comprising the following steps: Step S1, obtain task data from the company's project side and analyze the personnel composition required by the task data.

[0020] Step S2: Collect employee information and match corresponding employee information according to the personnel composition required by the task.

[0021] Step S3, record the emotion information and performance information corresponding to the employee information, establish a comprehensive scoring mechanism, input the emotion information and performance information into the comprehensive scoring mechanism, output the employee score, establish the corresponding relationship between the employee score, task data and employee information, and output it as the employee task indicator.

[0022] Step S4, formulate a reward mechanism and a qualification mechanism, input employee task indicators into the reward mechanism and the qualification mechanism, and obtain reward information and task progress information.

[0023] Step S5, establish task priority allocation and output the task allocation model.

[0024] Step S1 includes the following sub-steps: Step S101, obtaining task data from the company's project side, wherein the task data includes task objectives, task types, task cycles and task requirements.

[0025] Step S101 successfully obtains the task data including the task objective, task type, task cycle and task requirements, providing a basis for subsequent analysis.

[0026] Step S102: Analyze keywords of task data using natural language technology and output them as task keyword data, wherein the task keyword data includes professional fields and corresponding technical requirements and experience requirements.

[0027] The technical requirements are graded according to the length of experience, and the grades include basic skills, professional skills and advanced skills. The length of experience corresponding to the basic skills is the first year of experience, the length of experience corresponding to the professional skills is the second year of experience, and the length of experience corresponding to the advanced skills is the third year of experience.

[0028] Step S102 uses natural language technology to accurately extract task keywords, including professional fields and technical and experience requirements, and scientifically classifies technical requirements according to the length of experience, providing a detailed basis for task matching.

[0029] Step S103, establishing a task requirement model according to step S101 and step S102, wherein the input data of the task requirement model are the task data and the employee information, and the output data of the task requirement model is the employee information matching the task data.

[0030] In step S103, a task requirement model is successfully established based on the task data and employee information. The model can output employee information that is highly matched with the task data, thereby improving the efficiency and accuracy of task allocation.

[0031] Step S1 obtains and analyzes the task data on the company's project side, parses the task keywords with natural language technology, and grades the technical requirements according to the length of experience. Finally, a task requirement model is established to achieve accurate matching of tasks and employee information.

[0032] Step S2 includes the following sub-steps: Step S201, collecting employee information, the employee information includes employee name, interests and skills, employee major, employee major corresponding experience time and employee current idle time.

[0033] Step S201 successfully collects comprehensive employee information including employee name, interests and skills, major, experience duration and current idle time, providing a detailed data basis for subsequent task matching.

[0034] Step S202: input employee information and task data into the task requirement model to obtain employee information matching the task data.

[0035] The matching logic of the task requirement model is: Filter employee majors, obtain employee majors corresponding to professional fields, divide employee majors into basic skills, professional skills and advanced skills according to the classification and the experience duration corresponding to the employee majors, assign values ​​to the basic skills, professional skills and advanced skills, and output them as employee professional level values, where the assigned values ​​increase step by step.

[0036] Assign weights to professional fields and output the professional field weights.

[0037] Calculate the skill matching degree, the mathematical expression of the skill matching degree is: ; in, For skill matching, The professional level of employees, Weight for professional field.

[0038] Calculate the experience matching degree, the mathematical expression of the experience matching degree is: ; in, is the experience matching degree, For the duration of experience, For experience requirements.

[0039] Calculate the load matching degree, the mathematical expression of the load matching degree is: ; in, is the load matching degree, is the employee's current idle time. The task cycle.

[0040] Define the weight distribution corresponding to skill matching, experience matching and load matching, and the weight distribution is: The corresponding weight of skill matching is 50%.

[0041] The corresponding weight of experience matching is 30%.

[0042] The corresponding weight of load matching is 20%.

[0043] Calculate the comprehensive matching degree, the mathematical expression of which is: ; in, For the comprehensive matching degree, is the weight corresponding to the skill matching degree, is the weight corresponding to the experience matching degree, is the weight corresponding to the load matching degree.

[0044] Compare interest skills with professional fields, obtain the number of interest skills that match professional fields, and output as interest skill quantity data.

[0045] The comprehensive matching degrees are sorted in descending order to obtain the comprehensive matching degrees in the first percentile, and the first corresponding relationship between the comprehensive matching degrees, the quantity data of interests and skills and the employee information is established, and the output is a screening group.

[0046] The filter group is sorted in descending order according to the interest and skill quantity data, and the employee information corresponding to the interest and skill quantity data in the top second percentile is obtained and output.

[0047] Step S202 inputs employee information and task data into the task requirement model. First, skill levels are divided and assigned according to employee expertise and experience, ensuring accurate assessment of employee professional levels. Next, professional fields are weighted and skill matching, experience matching and load matching are calculated. Through reasonable weight distribution, the matching degree between employees and tasks is comprehensively assessed. The matching between interest skills and professional fields is also considered, and the quantity data of interest skills is added as a basis for matching.

[0048] Finally, through sorting and screening, the model outputs the information of employees who are in the top certain percentage with high comprehensive matching degree and a large number of interests and skills, achieving accurate and efficient matching between employees and tasks.

[0049] Step S2 collects employee information and inputs it and task data into the task requirement model to achieve accurate matching between employees and tasks, taking into account employees' professional level, experience, current workload, and interests and skills, ensuring the efficiency and rationality of task allocation.

[0050] Step S3 includes the following sub-steps: Step S301, recording the emotion information and performance information corresponding to the employee information, wherein the emotion information includes the overall satisfaction score, the stress score and the collaboration satisfaction score, and the performance information includes the subjective score and the objective score, and establishing a comprehensive scoring mechanism.

[0051] Step S301 successfully collected employees' emotional information and performance information, including overall satisfaction, stress, collaboration satisfaction, and subjective and objective scores, providing a comprehensive and objective data basis for the establishment of a comprehensive scoring mechanism. At the same time, by integrating this information, a preliminary comprehensive scoring framework was established.

[0052] Step S302, input the emotion information and performance information into the comprehensive scoring mechanism, and output the employee score.

[0053] The logic of the comprehensive scoring mechanism is: Collect the work completion time and calculate the time completion rate. The mathematical expression of the time completion rate is: ; in, is the time completion rate, The time for completing the work.

[0054] The sum of the overall satisfaction score, stress score, and collaboration satisfaction score is calculated and output as the overall sentiment score.

[0055] The overall sentiment score, subjective score, objective score and time completion rate are weighted according to the objective score.

[0056] If the objective scoring value is greater than the preset first objective scoring value threshold, the corresponding weight distribution is: The weight corresponding to the objective scoring score is 40%.

[0057] The weight corresponding to the overall sentiment score is 20%.

[0058] The weight corresponding to the subjective rating score is 20%.

[0059] The weight corresponding to the time completion rate is 20%.

[0060] If the objective scoring value is less than the preset first objective scoring value threshold, the corresponding weight distribution is: The weight corresponding to the objective scoring score is 40%.

[0061] The weight corresponding to the overall sentiment score is 20%.

[0062] The weight corresponding to the subjective rating score is 10%.

[0063] The weight corresponding to the time completion rate is 20%.

[0064] The comprehensive score is calculated based on the weight distribution and the output is the employee score.

[0065] Step S302 inputs the emotional information and performance information into the comprehensive scoring mechanism. Through scientific weight distribution and calculation logic, an objective and fair employee score is obtained, which not only reflects the employee's work results, but also takes into account his or her emotional state and subjective effort level, providing strong support for subsequent performance evaluation.

[0066] Step S303, establishing a corresponding relationship between employee scores, task data and employee information, and outputting them as employee task indicators.

[0067] Step S303 establishes the corresponding relationship between employee scores, task data and employee information, namely, employee task indicators, which not only helps to understand the performance of employees in different tasks, but also provides a basis for decision-making for task allocation and personnel scheduling. By comparing the task indicators of different employees, high-performance employees and potential development targets can be identified more accurately, providing strong support for the company's talent management and training. At the same time, it also lays a solid foundation for the company to optimize task allocation and improve overall work efficiency.

[0068] Step S3 establishes a comprehensive scoring mechanism by recording and analyzing employees' emotional information and performance information, and calculates employee scores based on this. Ultimately, a corresponding relationship between employee scores, task data, and employee information is established, providing a scientific basis for employee performance evaluation and task allocation.

[0069] Step S4 includes the following sub-steps: Step S401, formulate a reward mechanism and a qualification mechanism, and input employee task indicators into the reward mechanism and the qualification mechanism.

[0070] The logic of the reward mechanism is: If the employee score is higher than the preset employee score threshold, the task bonus is calculated. The mathematical expression of the task bonus is: ; in, For the task bonus, The preset bonus base value.

[0071] Output the task bonus as reward information.

[0072] The logic of the eligibility mechanism is: Establish a correspondence between employee scores and task keyword data, output as current task progress data, add employee scores to employee scores in corresponding past task progress data to obtain total employee scores, establish a correspondence between total employee scores, task keyword data and employee information, and output as task progress information.

[0073] Step S401 successfully formulated the reward mechanism and the qualification mechanism, and used the employee task indicators as input to ensure the fairness and accuracy of the mechanism. The reward mechanism can stimulate the enthusiasm and creativity of employees and promote employees to continuously improve their personal abilities and work efficiency; while the qualification mechanism can ensure that employees meet the established task requirements and ensure the smooth progress of the overall project, laying a solid foundation for subsequent reward distribution and task progress tracking.

[0074] Step S402, obtaining reward information and task progress information.

[0075] Step S402 successfully obtains reward information for high-performance employees through the reward mechanism. This information can not only motivate employees in a timely manner, but also form a good competitive atmosphere within the team and promote the improvement of overall performance. At the same time, the qualification mechanism also outputs detailed task progress information. This information reflects the actual performance of employees in the task execution process, which helps management to discover problems and adjust strategies in a timely manner to ensure that projects are completed on time and with high quality. In addition, task progress information can also provide important reference for subsequent performance evaluation, employee training, etc.

[0076] Step S4 effectively motivates employees to improve work efficiency and quality by formulating and implementing reward mechanisms and qualification mechanisms, while accurately tracking task progress and providing valuable decision-making support for corporate management.

[0077] Step S5 establishes task priority allocation, and the logic of the output task allocation model is: Obtain task keyword data for the new task, output it as updated task keyword data, compare the updated task keyword data with the task keyword data in the task progress information, find the total employee score corresponding to the task keyword data that is the same as the updated task keyword data, and if the total employee score is higher than the preset total employee score threshold, output a priority reduction signal and establish a corresponding relationship between the priority reduction signal and the employee information, and output it as a priority adjustment signal.

[0078] Step S5 realizes dynamic adjustment of task priority by intelligently analyzing the task keyword data of the new task and the task keyword data in the existing task progress information. When it is found that the total score of the employee related to the new task is higher than the preset threshold, the system can automatically output a priority reduction signal and establish a corresponding employee information correspondence to form a priority adjustment signal. This mechanism helps to allocate resources reasonably, avoid over-reliance on high-performance employees, ensure the balance and efficiency of task allocation, and promote collaboration and growth among employees.

[0079] This method uses natural language processing technology to automatically analyze keywords in task data, extract professional fields, technical requirements and experience requirements, and establish a task requirement model, which helps enterprises to accurately understand task requirements and quickly identify the professional skills and experience levels of required personnel, thereby optimizing personnel composition and improving task execution efficiency. Employee information, including professional, experience and interest skills, is collected and intelligently matched through the task requirement model. This method not only takes into account employees' professional skills and experience, but also their interest skills, which helps to improve employees' enthusiasm and task completion quality. At the same time, through comprehensive matching calculation, employees who best meet task requirements can be screened out to improve task matching, record employees' emotional information and performance information in tasks, including overall satisfaction, stress, collaboration satisfaction, and subjective and objective scores, and establish a comprehensive scoring mechanism. This helps enterprises to fully understand employees' performance and psychological state in tasks, and provide a basis for subsequent task allocation and reward mechanisms. At the same time, the comprehensive scoring mechanism can also motivate employees to improve their work performance and overall work efficiency. According to the comprehensive scores of employees in past tasks, employees with high scores are given priority to undertake new tasks. This helps ensure that tasks are completed by the most capable and experienced employees, improves the quality of task completion and customer satisfaction, and rewards outstanding employees and recognizes qualified employees by developing reward and qualification mechanisms. This helps to stimulate employee enthusiasm and creativity, improve the overall work atmosphere and team cohesion, and dynamically adjust task priorities based on the total employee scores in specific task areas. When an employee completes enough tasks in a certain area and performs well, lower their priority in new tasks to give other employees the opportunity to participate and grow. At the same time, appropriately increasing the priority of other related tasks helps balance resource allocation and improve overall task execution efficiency.

[0080] Example 2, reference Figure 2 , provides a human resource management system, including a task analysis module, an information collection module, a task scoring module, a mechanism establishment module and a priority adjustment module; The task analysis module is used to obtain task data from the company's project side and analyze the personnel composition required for the task data.

[0081] The task analysis module can efficiently obtain task data from the company's project side and deeply analyze this data to determine the personnel composition required to complete the task. Through accurate task analysis, it helps ensure that the project team has the right skills and expertise, thereby improving the efficiency and quality of task execution.

[0082] The information collection module is used to collect employee information and match the corresponding employee information according to the personnel composition required by the task.

[0083] The information collection module is responsible for collecting comprehensive employee information and matching it with the personnel requirements provided by the task analysis module. It can quickly identify and screen employees who meet the requirements of specific tasks, thereby optimizing the allocation of human resources. Through this process, enterprises can use talent resources more effectively and reduce unnecessary waste.

[0084] The task scoring module is used to record the emotional information and performance information corresponding to the employee information, establish a comprehensive scoring mechanism, input the emotional information and performance information into the comprehensive scoring mechanism, output the employee score, establish the corresponding relationship between the employee score, task data and employee information, and output it as the employee task indicator.

[0085] The task scoring module establishes a comprehensive scoring mechanism by recording employees' emotional information and performance information. This mechanism can comprehensively evaluate employees' performance and ensure the fairness and accuracy of the evaluation. By inputting emotional information and performance information into the comprehensive scoring mechanism, it can output employee scores and establish a corresponding relationship between employee scores, task data and employee information to form employee task indicators. This provides companies with valuable employee performance evaluation data, which helps motivate employees to improve their work performance.

[0086] The mechanism establishment module is used to formulate reward mechanisms and qualification mechanisms, input employee task indicators into the reward mechanisms and qualification mechanisms, and obtain reward information and task progress information.

[0087] The mechanism establishment module is responsible for formulating reward mechanisms and qualification mechanisms, which are essential for motivating employees and ensuring the smooth completion of tasks. By inputting employee task indicators into the reward mechanism and qualification mechanism, reward information and task progress information can be automatically generated. This not only helps maintain employee work motivation, but also ensures that projects are completed on time and with quality.

[0088] The priority adjustment module is used to establish task priority allocation and output the task allocation model.

[0089] The priority adjustment module is responsible for establishing task priority allocation and outputting a task allocation model. Through this model, enterprises can flexibly adjust the priority of tasks to ensure that key tasks are given priority. This helps to optimize workflows and improve overall operational efficiency. At the same time, the task allocation model can also ensure that each employee is clear about their responsibilities and tasks, thereby enhancing team collaboration and execution.

[0090] It should be understood by those skilled in the art that the embodiments of the present invention may be provided as methods, systems or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware aspects. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program codes. Among them, the storage medium may 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 Read-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic memory, flash memory, magnetic disk or optical disk. These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0091] 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 resource management method, characterized in that: The steps include: Step S1, obtaining task data from the company's project side and analyzing the personnel composition required by the task data; Step S2, collecting employee information and matching corresponding employee information according to the personnel composition required for the task; Step S3, recording the emotion information and performance information corresponding to the employee information, establishing a comprehensive scoring mechanism, inputting the emotion information and performance information into the comprehensive scoring mechanism, outputting the employee score, establishing a corresponding relationship between the employee score, task data and employee information, and outputting it as the employee task indicator; Step S4, formulate a reward mechanism and a qualification mechanism, input employee task indicators into the reward mechanism and the qualification mechanism, and obtain reward information and task progress information; Step S5, establish task priority allocation and output the task allocation model.

2. A human resource management method as claimed in claim 1, characterized in that: The step S1 includes the following sub-steps: Step S101, obtaining task data from the company's project side, wherein the task data includes task objectives, task types, task cycles, and task requirements; Step S102, using natural language technology to analyze keywords of the task data and outputting them as task keyword data, wherein the task keyword data includes professional fields and corresponding technical requirements and experience requirements; Technical requirements are graded according to the length of experience, and the classification includes basic skills, professional skills and advanced skills. The length of experience corresponding to the basic skills is the first years of employment, the length of experience corresponding to the professional skills is the second years of employment, and the length of experience corresponding to the advanced skills is the third years of employment; Step S103, establishing a task requirement model according to step S101 and step S102, wherein the input data of the task requirement model are the task data and the employee information, and the output data of the task requirement model is the employee information matching the task data.

3. A human resource management method as claimed in claim 2, characterized in that: The step S2 includes the following sub-steps: Step S201, collecting employee information, the employee information includes employee name, interests and skills, employee major, employee major corresponding experience time and employee current idle time; Step S202: input employee information and task data into the task requirement model to obtain employee information matching the task data.

4. A human resource management method as claimed in claim 3, characterized in that: The matching logic of the task requirement model is: Screening employee majors, obtaining employee majors corresponding to professional fields, dividing employee majors into basic skills, professional skills and advanced skills according to the classification and the length of experience corresponding to the employee majors, assigning values ​​to the basic skills, professional skills and advanced skills, and outputting them as employee professional level values, wherein the assigned values ​​increase step by step; Assign weights to professional fields, and output the weights of professional fields; Calculate the skill matching degree, the mathematical expression of the skill matching degree is: ； in, For skill matching, The professional level of employees, Weight for professional field; Calculate the experience matching degree, the mathematical expression of the experience matching degree is: ； in, is the experience matching degree, For the duration of experience, For experience needs; Calculate the load matching degree, the mathematical expression of the load matching degree is: ； in, is the load matching degree, is the employee's current idle time. is the task cycle; Define the weight distribution corresponding to skill matching, experience matching and load matching, and the weight distribution is: The corresponding weight of skill matching is 50%; The corresponding weight of experience matching is 30%; The corresponding weight of load matching is 20%; Calculate the comprehensive matching degree, the mathematical expression of which is: ； in, For the comprehensive matching degree, is the weight corresponding to the skill matching degree, is the weight corresponding to the experience matching degree, is the weight corresponding to the load matching degree; Compare the interest skills and professional fields, obtain the number of interest skills matching the professional fields, and output as interest skill quantity data; Sort the comprehensive matching degrees in descending order, obtain the comprehensive matching degrees in the first percentile, establish the first correspondence between the comprehensive matching degrees, the quantity data of interests and skills, and the employee information, and output them as a screening group; The filter group is sorted in descending order according to the interest and skill quantity data, and the employee information corresponding to the interest and skill quantity data in the top second percentile is obtained and output.

5. A human resource management method as claimed in claim 4, characterized in that: The step S3 includes the following sub-steps: Step S301, recording the emotion information and performance information corresponding to the employee information, wherein the emotion information includes the overall satisfaction score, stress score and collaboration satisfaction score, and the performance information includes the subjective score and objective score, and establishing a comprehensive scoring mechanism; Step S302, input the emotion information and performance information into the comprehensive scoring mechanism and output the employee score; Step S303, establishing a corresponding relationship between employee scores, task data and employee information, and outputting them as employee task indicators.

6. A human resource management method as claimed in claim 5, characterized in that: The logic of the comprehensive scoring mechanism is: Collect the work completion time and calculate the time completion rate. The mathematical expression of the time completion rate is: ； in, is the time completion rate, The time for completion of the work; Calculate the sum of the overall satisfaction score, stress score, and collaboration satisfaction score, and output the overall sentiment score; According to the objective score, weights are assigned to the overall emotion score, subjective score, objective score and time completion rate; If the objective scoring value is greater than the preset first objective scoring value threshold, the corresponding weight distribution is: The weight corresponding to the objective scoring score is 40%; The weight corresponding to the overall sentiment score is 20%; The weight corresponding to the subjective scoring score is 20%; The weight corresponding to the time completion rate is 20%; If the objective scoring value is less than the preset first objective scoring value threshold, the corresponding weight distribution is: The weight corresponding to the objective scoring score is 40%; The weight corresponding to the overall sentiment score is 20%; The weight corresponding to the subjective score is 10%; The weight corresponding to the time completion rate is 20%; The comprehensive score is calculated based on the weight distribution and the output is the employee score.

7. A human resource management method as claimed in claim 6, characterized in that: The step S4 includes the following sub-steps: Step S401, formulate a reward mechanism and a qualification mechanism, and input employee task indicators into the reward mechanism and the qualification mechanism; Step S402, obtaining reward information and task progress information.

8. A human resource management method as claimed in claim 7, characterized in that: The logic of the reward mechanism is: If the employee score is higher than the preset employee score threshold, the task bonus is calculated. The mathematical expression of the task bonus is: ； in, For the task bonus, The preset bonus base value; Output the task bonus as reward information; The logic of the qualification mechanism is: Establish a correspondence between employee scores and task keyword data, output as current task progress data, add employee scores to employee scores in corresponding past task progress data to obtain total employee scores, establish a correspondence between total employee scores, task keyword data and employee information, and output as task progress information.

9. A human resource management method as claimed in claim 8, characterized in that: The step S5 establishes task priority allocation, and the logic of outputting the task allocation model is: Obtain task keyword data for the new task, output it as updated task keyword data, compare the updated task keyword data with the task keyword data in the task progress information, find the total employee score corresponding to the task keyword data that is the same as the updated task keyword data, and if the total employee score is higher than the preset total employee score threshold, output a priority reduction signal and establish a corresponding relationship between the priority reduction signal and the employee information, and output it as a priority adjustment signal.

10. A human resource management system, applied to a human resource management method as claimed in any one of claims 1 to 9, characterized in that: It includes task analysis module, information collection module, task scoring module, mechanism establishment module and priority adjustment module; The task analysis module is used to obtain task data from the company's project end and analyze the personnel composition required for the task data; The information collection module is used to collect employee information and match the corresponding employee information according to the personnel composition required by the task; The task scoring module is used to record the emotion information and performance information corresponding to the employee information, establish a comprehensive scoring mechanism, input the emotion information and performance information into the comprehensive scoring mechanism, output the employee score, establish the corresponding relationship between the employee score, task data and employee information, and output it as the employee task indicator; The mechanism establishment module is used to formulate a reward mechanism and a qualified mechanism, input employee task indicators into the reward mechanism and the qualified mechanism, and obtain reward information and task progress information; The priority adjustment module is used to establish task priority allocation and output a task allocation model.

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