Manpower management system based on large model and management method thereof

Abstract

The invention relates to a human management system and method based on a large model, and belongs to the field of human management, and the system comprises a data collection module which is used for collecting human resource data of an enterprise; the data processing module is used for cleaning, sorting and analyzing the collected data; the large model application module is used for carrying out deep learning on the processed data to realize intelligent management; the user interaction module provides a user-friendly interface, so that the user can conveniently inquire, modify and configure system parameters; and the feedback optimization module is used for continuously optimizing the system according to the feedback of the user and the use condition. The method has the advantages that the data processing capacity is improved, the system can process and analyze large-scale and diversified data sets including structured and unstructured data by using a large model, and the breadth and depth of data processing are improved.

Description

Technical Field

[0001] The invention relates to a human resource management system based on a large model, and belongs to the field of human resource management systems. Background Art

[0002] In modern enterprise management, human resource management is a core component, which is directly related to the operational efficiency of the enterprise and the job satisfaction of employees. However, the traditional human resource management system has the following disadvantages, which limit its application effect in modern enterprises:

[0003] Limited data processing capabilities: Traditional systems often rely on static data warehouses and limited data analysis tools, and are unable to process and analyze large-scale, diverse data sets, such as social media data, real-time behavioral data, etc.

[0004] Lack of prediction and adaptability: Existing systems generally lack predictive analysis capabilities and cannot predict future human resource needs based on historical and real-time data, nor can they flexibly adapt to market and business changes.

[0005] Insufficient personalized and refined management: Traditional human resource management lacks an in-depth understanding of employees’ individual needs and work performance, resulting in the inability to provide customized employee development plans and incentives.

[0006] Poor interactivity and user experience: Many existing systems have unfriendly interfaces and complex operations, which cause employees and managers to encounter obstacles during use, affecting user experience and management efficiency.

[0007] Slow update and response: Traditional systems are slow to respond to data updates and new policy implementations, and are unable to promptly reflect organizational structure changes, market trends, and regulatory updates.

[0008] Inadequate decision support: Existing technologies have limited capabilities in providing decision support and are unable to provide managers with data-driven insights and recommendations, leading to human bias and uncertainty in the decision-making process. Summary of the invention

[0009] In order to overcome the defects of the prior art, the present invention provides a human resource management system and a management method thereof based on a large model. The technical solution of the present invention is:

[0010] A human resource management system based on a large model, comprising:

[0011] Data collection module, used to collect human resource data of enterprises;

[0012] Data processing module, used to clean, organize and analyze the collected data;

[0013] Large model application module, used to conduct deep learning on processed data and realize intelligent management;

[0014] User interaction module, providing a user-friendly interface to facilitate users to query, modify and configure system parameters;

[0015] Feedback optimization module continuously optimizes the system based on user feedback and usage.

[0016] The data acquisition module comprises:

[0017] The employee information submodule is used to collect personal and professional information of employees;

[0018] The attendance data submodule is used to collect employees’ clock-in and clock-out times, leave records, and overtime records;

[0019] The work performance submodule is used to collect employees’ project completion status, work results, and superior evaluations;

[0020] The training and development submodule is used to collect employees’ training course participation and skills improvement information.

[0021] The data processing module comprises:

[0022] Data cleaning submodule, used to remove redundant, missing or erroneous information in the data;

[0023] The data integration submodule is used to integrate data from different sources into a unified data format;

[0024] The data analysis submodule is used to analyze the data and extract information and features.

[0025] The large model application module includes:

[0026] The intelligent shift scheduling submodule is used to automatically generate shift scheduling plans based on business needs and employee capabilities; the working time analysis submodule is used to conduct detailed analysis of employees' working time data;

[0027] Performance management submodule, used to automatically generate performance evaluation reports;

[0028] The cultural motivation submodule is used to recommend motivational programs and training courses.

[0029] The user interaction module comprises:

[0030] The information query submodule is used to provide query functions for employee information, attendance records, and performance evaluation;

[0031] The parameter configuration submodule is used by users to configure scheduling rules and performance evaluation standard parameters; the report generation submodule is used to generate various reports according to user needs.

[0032] The feedback optimization module includes:

[0033] The user feedback collection submodule collects user feedback through user surveys and online comments; the data analysis and evaluation submodule analyzes and evaluates the collected feedback to identify existing problems and areas for improvement;

[0034] The system optimization submodule optimizes and improves the system based on the analysis results.

[0035] A management method for a human resource management system based on a large model comprises the following steps:

[0036] Step 1: Data collection, using the data collection module to collect the company's human resources data;

[0037] Step 2: Data processing, using the data processing module to clean, organize and analyze the collected data;

[0038] Step 3: Big model application: Use the big model application module to conduct deep learning on the processed data to achieve intelligent management;

[0039] Step 4: User interaction: Provide a user-friendly interface through the user interaction module to facilitate users to query, modify and configure system parameters;

[0040] Step 5: Feedback optimization: Use the feedback optimization module to continuously optimize the system based on user feedback and usage.

[0041] The advantages of the present invention are:

[0042] 1. Improve data processing capabilities: Using large models, the system can process and analyze large-scale, diverse data sets, including structured and unstructured data, improving the breadth and depth of data processing.

[0043] 2. Enhanced prediction and adaptability: Ability to predict future human resource needs based on historical and real-time data, flexibly adapt to market changes and business development, and provide forward-looking decision-making support for enterprises.

[0044] 3. Realize personalized and refined management: Through in-depth analysis of employees' personalized needs and work performance, the system can provide customized employee development plans and incentives to improve employee satisfaction and work efficiency.

[0045] 4. Optimize interactivity and user experience: The interface is friendly and the operation is simple, providing an intuitive user interaction experience, reducing the user's learning cost and improving management efficiency.

[0046] 5. Accelerate updating and response speed: Able to update data in real time and quickly respond to the implementation of new policies, promptly reflect organizational structure changes, market trends and regulatory updates, and maintain the timeliness of information. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is a schematic diagram of the main structure of the present invention.

[0048] Figure 2 yes Figure 1 Structural block diagram of the data acquisition module.

[0049] Figure 3 yes Figure 1 Structural block diagram of the data processing module.

[0050] Figure 4 yes Figure 1 Structural block diagram of the Zhongda model application module.

[0051] Figure 5 yes Figure 1 Structural block diagram of the user interaction module.

[0052] Figure 6 yes Figure 1 Structural block diagram of the feedback optimization module. DETAILED DESCRIPTION

[0053] The present invention will be further described below in conjunction with specific embodiments, and the advantages and features of the present invention will become clearer as the description proceeds. However, these embodiments are exemplary only and do not constitute any limitation to the scope of the present invention. It should be understood by those skilled in the art that the details and forms of the technical solution of the present invention may be modified or replaced without departing from the spirit and scope of the present invention, but these modifications and replacements all fall within the scope of protection of the present invention.

[0054] See also Figures 1 to 6 The present invention relates to a human resource management system based on a large model, comprising:

[0055] The data collection module 1 is used to collect the human resources data of the enterprise; the data sources include the internal information systems of the enterprise: such as ERP, CRM, etc., which provide work-related data such as employee personal information and attendance data.

[0056] Information submitted by individual employees: such as leave applications, overtime applications, etc., providing employees' personal needs and wishes.

[0057] Third-party platforms: such as social networks and recruitment websites, which provide external information such as employees’ social data and average industry salary levels.

[0058] Data processing module 2, used for cleaning, collating and analyzing the collected data;

[0059] Large model application module 3, used to conduct deep learning on processed data and realize intelligent management;

[0060] User interaction module 4, providing a user-friendly interface to facilitate users to query, modify and configure system parameters;

[0061] Feedback optimization module 5 continuously optimizes the system based on user feedback and usage.

[0062] The data acquisition module 1 comprises:

[0063] The employee information submodule 11 is used to collect personal and professional information of employees;

[0064] Attendance data submodule 12, used to collect employees' clock-in and clock-out times, leave records, and overtime records;

[0065] Work performance submodule 13 is used to collect employees’ project completion status, work results and superior evaluation;

[0066] The training and development submodule 14 is used to collect information on employees’ participation in training courses and skill improvement.

[0067] The data processing module 2 comprises:

[0068] The data cleaning submodule 21 is used to remove redundant, missing or erroneous information in the data;

[0069] The data integration submodule 22 is used to integrate data from different sources to form a unified data format;

[0070] The data analysis submodule 23 is used to analyze the data and extract information and features.

[0071] The large model application module 3 includes:

[0072] Intelligent shift scheduling module 31, used to automatically generate shift scheduling plans based on business needs and employee capabilities;

[0073] The working time analysis submodule 32 is used to conduct detailed analysis of the working time data of employees;

[0074] Performance management submodule 33, for automatically generating performance evaluation reports;

[0075] The cultural motivation submodule 34 is used to recommend motivational programs and training courses.

[0076] The user interaction module 4 includes:

[0077] The information query submodule 41 is used to provide query functions for employee information, attendance records, and performance evaluation;

[0078] The parameter configuration submodule 42 is used for the user to configure the scheduling rules and performance evaluation standard parameters; the report generation submodule 43 is used to generate various reports according to user needs.

[0079] The feedback optimization module 5 includes:

[0080] The user feedback collection submodule 51 collects user feedback through user surveys and online comments; the data analysis and evaluation submodule 52 analyzes and evaluates the collected feedback to identify existing problems and improvement points;

[0081] The system optimization submodule 53 optimizes and improves the system according to the analysis results.

[0082] The present invention also relates to a management method of a human resource management system based on a large model, comprising the following steps:

[0083] Step 1: Data collection, using the data collection module to collect the enterprise's human resources data; Step 2: Data processing, using the data processing module to clean, organize and analyze the collected data;

[0084] Step 3: Big model application: Use the big model application module to conduct deep learning on the processed data to achieve intelligent management;

[0085] Step 4: User interaction: Provide a user-friendly interface through the user interaction module to facilitate users to query, modify and configure system parameters;

[0086] Step 5: Feedback optimization: Use the feedback optimization module to continuously optimize the system based on user feedback and usage.

[0087] The specific description of the present invention is as follows:

[0088] 1. Intelligent Scheduling

[0089] 1. Overview of Intelligent Scheduling

[0090] Intelligent scheduling is one of the core functional modules of the human resource management system combined with the big model of the present invention. It realizes the intelligent scheduling and optimization of human resources by introducing advanced big data processing and artificial intelligence technology, thereby improving the operational efficiency and management level of the enterprise.

[0091] 2. Combination of intelligent scheduling and big models

[0092] The combination of intelligent scheduling and big models is mainly reflected in the following aspects:

[0093] Data input: The system will first collect and integrate various human resource data of the enterprise, including but not limited to employees’ personal information, skill levels, work time preferences, historical scheduling records, etc. These data will be used as input information for the big model.

[0094] Big model training: The system will train the big model using the collected human resources data. During the training process, the big model will learn how to automatically generate the optimal scheduling plan based on the company's business needs, employee work capabilities and scheduling rules.

[0095] Algorithm application: In the process of intelligent scheduling, the big model will use a variety of algorithms and formulas for calculation. These algorithms and formulas include but are not limited to formula configuration based on business and multiple conditions, dynamic adjustment algorithms, business prediction algorithms, etc. Through the application of these algorithms and formulas, the big model can achieve refined management and intelligent scheduling of human resources.

[0096] Result output: After calculation and optimization of the big model, the system will eventually generate an optimal scheduling plan. This plan will comprehensively consider the business needs of the enterprise, the work ability of employees and scheduling rules to ensure the rationality and efficiency of the scheduling.

[0097] 3. Steps to implement intelligent scheduling

[0098] The implementation steps of intelligent scheduling can be divided into the following stages:

[0099] Data preparation: First, the system needs to collect and organize the company's human resources data, including employees' personal information, skill levels, working time preferences, etc. This data will be used for subsequent large model training and scheduling calculations.

[0100] Model training: The system will train the big model using the collected human resources data. During the training process, the big model will learn how to generate a reasonable scheduling plan based on the actual needs of the enterprise.

[0101] Shift calculation: During the shift calculation stage, the system will use a large model and corresponding algorithms to calculate according to the business needs of the enterprise, the work ability of employees and the shift rules. During the calculation process, the system will comprehensively consider multiple factors, including the employee's skill level, work time preference, shift rules, etc., to ensure the rationality and efficiency of the shift scheduling.

[0102] Result optimization: After the shift calculation is completed, the system will optimize the generated shift plan. During the optimization process, the system will consider a variety of factors, including the work saturation of employees, the balance of the shift plan, etc., to ensure the optimality of the shift plan.

[0103] Result output: Finally, the system will output the generated shift scheduling plan to the user. The user can adjust and modify the shift scheduling plan according to actual needs to meet the actual needs of the enterprise.

[0104] 4. Algorithms and formulas in intelligent scheduling

[0105] In the process of intelligent scheduling, the big model will use a variety of algorithms and formulas for calculation. These algorithms and formulas include but are not limited to:

[0106] Formula configuration based on business and multiple conditions: This algorithm allows users to configure multiple scheduling conditions and formulas based on the actual needs of the enterprise. These conditions and formulas can include employee work time preferences, skill levels, business needs, etc. The system will automatically generate a scheduling plan that meets the needs based on the conditions and formulas configured by the user.

[0107] Dynamic adjustment algorithm: Dynamic adjustment algorithm is used to adjust the scheduling plan in real time according to the actual operation of the enterprise and the actual needs of employees. This algorithm can cope with emergencies or sudden changes in business needs, ensuring the rationality and efficiency of scheduling.

[0108] Business prediction algorithm:

[0109] Business forecasting algorithms are used to predict future business needs and generate reasonable scheduling plans based on the forecast results. This algorithm can make forecasts based on historical data, market trends and other information to help companies plan human resources in advance.

[0110] Business forecasting algorithms may include a variety of mathematical models and statistical methods, such as time series analysis, regression analysis, etc. These models and methods predict future business needs based on information such as historical data and market trends. The specific formula may vary depending on the forecasting model, but usually includes some mathematical operations and statistical methods.

[0111] For example, in time series analysis, exponential smoothing can be used to predict future business demand. The formula for exponential smoothing is as follows:

[0112] V(t)=α*X(t)+(1-α)*V(t-1);

[0113] Among them, V(t) is the predicted value at the current moment, X(t) is the actual value at the current moment, V(t-1) is the predicted value at the previous moment, and α is the smoothing coefficient (0≤α≤1).

[0114] Complex overtime and support management algorithms:

[0115] Complex overtime and support management algorithms are used to handle complex overtime and support management issues. This algorithm ensures that employees receive timely support when needed, while avoiding unnecessary overtime and waste of human resources.

[0116] The algorithm includes some rule judgments and logical operations to determine when overtime or support is needed, as well as the specific method and time of overtime or support. The specific formula may vary depending on the application scenario, but usually includes some conditional judgments and mathematical operations.

[0117] For example, when calculating overtime pay, you might use the following formula:

[0118] Overtime pay = overtime hours * overtime rate;

[0119] Among them, the overtime hours are calculated based on the employees' actual overtime hours, and the overtime pay rate is determined based on the company's regulations and the employees' salary levels.

[0120] Man-hour analysis and cost analysis algorithms:

[0121] The working time analysis and cost analysis algorithms are used to conduct detailed analysis and statistics on employees’ working hours and costs. This algorithm can help companies understand employees’ work saturation, efficiency, and human resource costs, so as to formulate more reasonable human resource management strategies.

[0122] The time analysis and cost analysis algorithms may include a variety of mathematical operations and statistical methods, such as average calculation, variance analysis, cost-benefit analysis, etc. These methods and operations will analyze and calculate based on the employee's time data and the company's cost data.

[0123] For example, when calculating the average hours worked by an employee, you can use the following formula:

[0124] Average working hours = total working hours / number of employees

[0125] Among them, total working hours is the sum of the working hours of all employees, and the number of employees is the number of employees involved in the calculation.

[0126] Working hours analysis and optimization

[0127] 1. The importance of work time analysis and optimization

[0128] Working time analysis and optimization is one of the key functions in the human resource management system. It helps enterprises understand the working status, work efficiency and cost consumption of employees through detailed statistics and analysis of employees' working hours, and then puts forward optimization suggestions to achieve the purpose of reducing costs and increasing efficiency, improving employee satisfaction and enterprise operating efficiency.

[0129] 2. Logic of work time analysis and optimization

[0130] Data collection:

[0131] The system first collects employees' working time data from various business systems and data sources through the data collection module, including attendance records, overtime records, vacation records, etc.

[0132] The data must be complete, accurate and timely to facilitate subsequent analysis and optimization.

[0133] Data cleaning:

[0134] Clean the collected data to remove duplicate, invalid and abnormal data.

[0135] The purpose of data cleaning is to ensure the accuracy and reliability of analysis results.

[0136] Working hours statistics:

[0137] Using big model technology, the cleaned data is used to perform working hour statistics, including normal working hours, overtime hours, vacation hours, etc.

[0138] The statistical results need to be displayed in an intuitive manner, such as bar charts, line charts, etc., so that the company can quickly understand the distribution of employees' working hours.

[0139] Working hours analysis:

[0140] Conduct in-depth analysis of the statistical results, including analysis of labor time utilization, labor time efficiency, labor time cost, etc.

[0141] Utilize the predictive power of large models to predict future work hour demands, providing a basis for the company’s labor planning and scheduling management.

[0142] By comparing the working time data of different departments, different positions and different time periods, we can discover the differences and patterns in working time consumption, and provide a basis for optimizing working time allocation and improving work efficiency.

[0143] Optimization suggestions:

[0144] Based on the analysis results, optimization suggestions are put forward, such as adjusting the scheduling plan, optimizing the work process, improving employee skills, etc.

[0145] Optimization suggestions must be targeted and operational, and be able to effectively help companies reduce labor costs, improve work efficiency and employee satisfaction.

[0146] 3. Steps for implementing work time analysis and optimization

[0147] System Configuration:

[0148] Configure relevant parameters of work time analysis in the system, such as work time statistics rules, work time cost calculation methods, etc.

[0149] According to the actual situation of the enterprise, choose appropriate working time analysis method and optimization strategy.

[0150] Data import:

[0151] Import the collected working time data into the system to ensure the integrity and accuracy of the data.

[0152] The system automatically cleans and pre-processes the data to prepare for subsequent analysis. Working hours statistics and analysis:

[0153] The system automatically performs work time statistics and analysis, and generates work time analysis reports and visual charts.

[0154] The report should include analysis results on labor time utilization, labor time efficiency, labor time cost, etc., as well as optimization suggestions and improvement measures.

[0155] Optimization plan formulation:

[0156] Based on the analysis results and optimization suggestions, formulate specific optimization plans and implementation plans.

[0157] The optimization plan should comprehensively consider the actual situation of the enterprise, the needs and interests of employees, and the requirements of laws and regulations.

[0158] Program implementation and monitoring:

[0159] Carry out implementation work according to the formulated optimization plan and implementation plan.

[0160] Monitor and evaluate the implementation process to ensure the effectiveness and sustainability of the optimization plan.

[0161] Provide feedback and adjustments based on the implementation results, and continuously optimize and perfect the working time analysis and optimization functions.

[0162] Continuous optimization and iteration:

[0163] As the business of the enterprise develops and the needs of employees change, the working time analysis and optimization functions are continuously optimized to improve the accuracy of working time analysis and the effectiveness of optimization suggestions.

[0164] Regularly upgrade and iterate the system to ensure its stability and advancement.

[0165] Algorithms and formulas for work time optimization

[0166] 1) Calculation of standard working hours

[0167] Standard working hours refer to the total time required to complete a certain process under normal operating conditions. The calculation formula is: Standard working hours = normal working hours × (1 + tolerance). Among them, the tolerance is the extra time ratio set according to the process characteristics and operating environment, which is used to take into account factors such as employee rest, equipment maintenance, and operation interruption.

[0168] 2) Attendance calculation

[0169] The attendance rate reflects the degree of actual attendance of employees during the prescribed working hours. The calculation formula is: Attendance rate (%) = attendance working days (working hours) ÷ institutional working days (working hours) × 100%.

[0170] 3) Overtime intensity ratio

[0171] The overtime intensity ratio is used to measure the frequency of employees' overtime work. The calculation formula is: Overtime intensity ratio (%) = number of overtime hours in the month ÷ total working hours in the month × 100%.

[0172] 4) Staff attendance and absenteeism

[0173] The attendance rate refers to the ratio of the number of employees present on the day to the total number of employees in the enterprise. The calculation formula is: Attendance rate (%) = Number of employees present on the day ÷ Total number of employees in the enterprise on the day × 100%.

[0174] The staff absenteeism rate refers to the ratio of the number of absent employees on that day to the total number of employees in the enterprise. The calculation formula is: Staff absenteeism rate (%) = number of absent employees on that day ÷ total number of employees in the enterprise on that day × 100%.

[0175] Performance Management

[0176] 1. Performance management logic

[0177] Data collection and preprocessing

[0178] Data source: The system collects employee performance data from multiple channels, including but not limited to work results, project participation, team collaboration, customer feedback, etc.

[0179] Data preprocessing: Clean, organize and analyze the collected data, remove outliers and duplicate data, and ensure the accuracy and completeness of the data.

[0180] Large model construction and training

[0181] Model selection: Choose a large model suitable for processing complex data relationships, such as a deep learning model or a machine learning model.

[0182] Feature engineering: Extract key features from preprocessed data based on business needs, such as working hours, task completion, team collaboration frequency, etc.

[0183] Model training: Using historical performance data and extracted features, the model is trained to accurately predict employee performance.

[0184] Performance Evaluation

[0185] Real-time evaluation: The system uses the trained large model to evaluate the performance of employees based on the data collected in real time and generate performance scores and rankings.

[0186] Multi-dimensional analysis: In addition to the overall performance score, the system also provides multi-dimensional analysis functions, such as department performance comparison, job performance comparison, etc., to help companies understand the performance of employees more comprehensively.

[0187] Performance feedback and optimization

[0188] Feedback generation: Based on the performance evaluation results, the system automatically generates a performance feedback report, including the employee's strengths, weaknesses and improvement suggestions.

[0189] Optimization suggestions: The system makes targeted optimization suggestions based on employees' performance data and feedback reports, such as training needs, promotion opportunities, etc.

[0190] Continuous optimization: Based on employee feedback and usage, the system is continuously optimized to improve the accuracy and practicality of performance management.

[0191] 2. Implementation steps

[0192] Data collection and integration:

[0193] Integrate with the company's existing ERP, CRM and other systems to achieve automatic data collection and integration. Develop special data collection tools to collect real-time data of employees during work. Large model construction and training:

[0194] Choose a suitable large model framework, such as TensorFlow, PyTorch, etc.

[0195] Design model structure and algorithm based on business needs and data characteristics.

[0196] Use historical data to train the model and continuously adjust model parameters to improve the model's prediction accuracy.

[0197] Performance evaluation system development:

[0198] Develop the front-end interface of the performance evaluation system to provide a user-friendly operation experience.

[0199] Implement back-end data processing and model calling functions to ensure that the system can evaluate employee performance in real time.

[0200] System testing and optimization:

[0201] Conduct comprehensive tests on the system, including functional testing, performance testing, security testing, etc. Optimize and improve the system based on the test results to ensure system stability and reliability. System deployment and training:

[0202] Deploy the system to the company's server to ensure that the system can run normally.

[0203] Provide system usage training to corporate employees to ensure that they can use the system proficiently.

[0204] Cultural motivation and training development

[0205] 1. Logic

[0206] Cultural Incentives:

[0207] Objective: Create a positive corporate culture and inspire employees' enthusiasm and creativity. Logic: Through big model technology, collect and analyze employees' behavioral data, work performance, feedback, etc. to understand their values ​​and needs. Based on this information, customize personalized cultural incentive programs, such as honorary recognition, team building activities, career development opportunities, etc., to meet employees' expectations and enhance their sense of belonging and loyalty.

[0208] Training and Development:

[0209] Objective: To provide efficient and personalized training and development opportunities to enhance employees' professional skills and overall quality.

[0210] Logic: Use big model technology to conduct a comprehensive analysis of employees' learning needs, skill levels, career development plans, etc. According to the analysis results, formulate personalized training plans, including course content, training methods, time arrangements, etc. At the same time, through the intelligent recommendation and feedback mechanism of the big model, continuously optimize the training effect to ensure that employees can acquire the required skills and knowledge.

[0211] 2. Implementation steps

[0212] Needs Analysis and Goal Setting:

[0213] Collect and analyze employees' cultural incentives and training development needs, and clarify the system's goals and functional requirements.

[0214] Determine the application scenarios and key indicators of big model technology, such as employee satisfaction, training effectiveness, etc.

[0215] Introduction and integration of large model technology:

[0216] Choose a suitable large-model technology platform, such as NLP (natural language processing), machine learning, etc.

[0217] Integrate big model technology into the human resource management system to realize functions such as data collection, analysis, and recommendation.

[0218] Personalized cultural incentive program design:

[0219] Design personalized cultural incentive plans based on the analysis results of big model technology.

[0220] Implement honorary awards, team-building activities, etc. to create a positive corporate culture atmosphere.

[0221] Personalized training plan development and implementation:

[0222] Develop personalized training plans based on the analysis results of large model technology.

[0223] Organize training courses, online learning resources, etc., and provide diversified training methods.

[0224] Optimize training content and effects through the intelligent recommendation and feedback mechanism of the big model.

[0225] Effect evaluation and optimization:

[0226] Regularly evaluate the effectiveness of cultural incentives and training development.

[0227] Adjust and optimize system functions and implementation plans based on the evaluation results.

[0228] 3. Combination with large models

[0229] Data Collection and Analysis:

[0230] Utilize the data collection function of big model technology to collect data such as employees' work performance, learning needs, feedback, etc.

[0231] Through the analytical capabilities of big model technology, these data can be deeply mined and analyzed to understand the values ​​and needs of employees.

[0232] Intelligent recommendation and feedback:

[0233] Based on the analysis results of big model technology, provide employees with personalized cultural incentives and training development suggestions.

[0234] Through the intelligent recommendation mechanism of the big model, employees are provided with training courses and learning resources that meet their needs and interests.

[0235] Utilize the feedback mechanism of the big model to collect employees’ feedback on training content and effectiveness, and continuously optimize the training program.

[0236] Real-time monitoring and optimization:

[0237] Understand employees’ cultural incentives and training development trends through the real-time monitoring capabilities of big model technology.

[0238] Based on real-time monitoring results, timely adjust and optimize system functions and implementation plans to ensure system stability and effectiveness.

[0239] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A human resource management system based on a large model, characterized in that: include: Data collection module, used to collect human resource data of enterprises; Data processing module, used to clean, organize and analyze the collected data; Large model application module, used to conduct deep learning on processed data and realize intelligent management; User interaction module, providing a user-friendly interface to facilitate users to query, modify and configure system parameters; Feedback optimization module continuously optimizes the system based on user feedback and usage.

2. The human resource management system based on a large model according to claim 1 is characterized in that: The data acquisition module comprises: The employee information submodule is used to collect personal and professional information of employees; The attendance data submodule is used to collect employees’ clock-in and clock-out times, leave records, and overtime records; The work performance submodule is used to collect employees’ project completion status, work results, and superior evaluations; The training and development submodule is used to collect employees’ training course participation and skills improvement information.

3. The human resource management system based on a large model according to claim 1 or 2, characterized in that: The data processing module comprises: Data cleaning submodule, used to remove redundant, missing or erroneous information in the data; The data integration submodule is used to integrate data from different sources into a unified data format; The data analysis submodule is used to analyze the data and extract information and features.

4. The human resource management system based on a large model according to claim 3 is characterized in that: The large model application module includes: Intelligent scheduling module, used to automatically generate scheduling plans based on business needs and employee capabilities; The working time analysis submodule is used to conduct detailed analysis of employees’ working time data; Performance management submodule, used to automatically generate performance evaluation reports; The cultural motivation submodule is used to recommend motivational programs and training courses.

5. The human resource management system based on a large model according to claim 4 is characterized in that: The user interaction module comprises: The information query submodule is used to provide query functions for employee information, attendance records, and performance evaluation; The parameter configuration submodule is used by users to configure scheduling rules and performance evaluation standard parameters; The report generation submodule is used to generate various reports according to user needs.

6. The human resource management system based on a large model according to claim 5 is characterized in that: The feedback optimization module includes: User feedback collection submodule collects user feedback through user surveys and online reviews; The data analysis and evaluation submodule analyzes and evaluates the collected feedback to identify existing problems and areas for improvement; The system optimization submodule optimizes and improves the system based on the analysis results.

7. A management method for a human resource management system based on a large model, characterized in that: The following steps are involved: Step 1: Data collection, using the data collection module to collect the company's human resources data; Step 2: Data processing, using the data processing module to clean, organize and analyze the collected data; Step 3: Big model application: Use the big model application module to conduct deep learning on the processed data to achieve intelligent management; Step 4: User interaction: Provide a user-friendly interface through the user interaction module to facilitate users to query, modify and configure system parameters; Step 5: Feedback optimization: Use the feedback optimization module to continuously optimize the system based on user feedback and usage.

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