Residential accumulation fund human resource performance management system and method based on diversified incentive mechanism
By introducing diversified incentive mechanisms and advanced data processing technology into the housing provident fund human resources performance management system, the problems of poor incentive effects and lack of personalized incentives in traditional systems are solved, and more comprehensive and personalized employee incentives are achieved, which improves employee enthusiasm and overall performance.
Patent Information
- Application Number
- CN202510057452.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-01-14
- Publication Date
- 2025-05-09
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
The traditional human resources performance management system focuses on a single material reward, neglecting the needs of employees' personal growth, career development, job satisfaction, etc., resulting in poor incentive results and low employee motivation. Especially in the field of housing provident fund management, due to the lack of incentive mechanisms combined with housing provident fund payment, employees are insufficient to improve their enthusiasm for paying housing provident fund and improve their work performance.
Provide a housing provident fund human resources performance management system based on diversified incentive mechanisms, including user interface modules, performance data acquisition modules, diversified incentive mechanism construction modules, performance evaluation and allocation modules, feedback and adjustment modules, and data security and privacy protection modules. The system automatically collects employee performance data, designs incentive mechanisms that include various incentive methods such as material rewards, career development opportunities, training improvement, honor recognition, etc., and conducts performance evaluation and incentive allocation through a combination of quantitative and qualitative methods, and dynamically adjusts the incentive mechanism to ensure its continuous optimization and effectiveness.
The system not only considers material rewards, but also integrates various incentive methods such as career development opportunities, training improvement, honor commendation, etc., which fully meets the employees' needs and effectively improves the employees' enthusiasm and overall performance. By automatically collecting and processing performance data, the efficiency and accuracy of data processing are improved, and through advanced algorithms and machine learning technology, the personalization and differentiation of incentive mechanisms are achieved, and the accuracy and robustness of performance evaluation are improved.
Smart Images
Figure CN119963148A_ABST
Abstract
Description
Technical Field
[0001] The invention belongs to the technical field of human resource performance management, and in particular to a housing provident fund human resource performance management system and method based on a diversified incentive mechanism. Background Art
[0002] Traditional human resource performance management systems often focus on a single material reward, ignoring the needs of employees in terms of personal growth, career development, job satisfaction, etc., resulting in poor incentive effects and low employee enthusiasm. Especially in the field of housing provident fund management, due to the lack of an incentive mechanism combined with housing provident fund payment, employees lack the motivation to increase their enthusiasm for housing provident fund payment and improve their work performance.
[0003] To this end, technical personnel in this field have proposed a housing provident fund human resources performance management system and method based on a diversified incentive mechanism, which can combine the characteristics of housing provident fund management and integrate a human resources performance management system with diversified incentive means to stimulate employee potential and improve the overall performance of the organization. Summary of the invention
[0004] In order to solve the above technical problems, the present invention provides a housing provident fund human resources performance management system and method based on a diversified incentive mechanism to solve the problem that the human resources performance management system in the prior art often focuses on a single material reward, ignoring the needs of employees in various aspects such as personal growth, career development, and job satisfaction, resulting in poor incentive effects and low employee enthusiasm. Especially in the field of housing provident fund management, due to the lack of an incentive mechanism combined with the housing provident fund payment situation, employees are not motivated enough to improve their enthusiasm for housing provident fund payment and work performance.
[0005] Housing provident fund human resources performance management system and method based on diversified incentive mechanism, including:
[0006] User interface module for displaying performance data and receiving user input and feedback;
[0007] The performance data collection module is integrated with the housing provident fund management system to automatically collect employees' housing provident fund payment status, work performance, attendance rate and other performance-related data;
[0008] The diversified incentive mechanism building module designs an incentive mechanism including material rewards, career development opportunities, training and promotion, honorary recognition and other incentive methods based on the collected performance data, combined with the personal development needs of employees and organizational goals;
[0009] The performance evaluation and allocation module uses a combination of quantitative and qualitative methods to objectively evaluate employee performance and automatically allocate corresponding incentives based on the evaluation results;
[0010] Feedback and adjustment module, which collects employees’ feedback on the incentive mechanism, regularly analyzes the incentive effect, and dynamically adjusts the incentive mechanism based on the analysis results to ensure its continuous optimization and effectiveness;
[0011] The data security and privacy protection module ensures that the storage, processing and transmission of all performance data and employee personal information comply with relevant laws and regulations and protect employee privacy.
[0012] Preferably, in the performance data collection module, the collected performance-related data such as the employees' housing provident fund payment status, work performance, attendance rate, etc. are processed, and a variational autoencoder is used for dimensionality reduction, feature extraction and generation of new data samples to improve the generalization ability of the performance evaluation model.
[0013] Preferably, the diversified incentive mechanism construction module further includes a sub-module for designing differentiated housing provident fund subsidies or loan preferential policies as incentives based on the employees' housing provident fund payment years, amount and growth. The sub-module introduces a particle swarm optimization algorithm, which searches for the optimal solution by simulating the movement of particles in the search space.
[0014] Preferably, the performance evaluation and allocation module adopts a machine learning algorithm to analyze historical performance data, predict future performance trends, and formulate personalized performance improvement plans and incentive programs for employees. The machine learning algorithm is used to automatically learn and capture the complex relationship between performance indicators to improve the accuracy and robustness of performance evaluation;
[0015] At the same time, the fuzzy comprehensive evaluation method is introduced and combined with the neural network model to deal with the fuzziness and uncertainty in performance indicators and improve the comprehensiveness and accuracy of the evaluation.
[0016] Preferably, in the feedback and adjustment module, the incentive mechanism is dynamically adjusted according to the analysis results by introducing a reinforcement learning algorithm. By using the reinforcement learning algorithm, the system can adaptively adjust the incentive mechanism according to the employee's feedback and performance data to achieve dynamic optimization and personalized incentives.
[0017] Preferably, a Bayesian network is introduced in the feedback and adjustment module, and the Bayesian network is used to represent the dependency relationship between variables. By constructing the Bayesian network, reasoning and prediction can be performed.
[0018] Preferably, the feedback and adjustment module also includes an employee satisfaction survey function, which collects employees' satisfaction with the incentive mechanism and improvement suggestions through regular surveys, thereby promoting the democratization and transparency of the incentive mechanism.
[0019] The housing provident fund human resources performance management method based on a diversified incentive mechanism uses the housing provident fund human resources performance management system based on a diversified incentive mechanism, including:
[0020] Step 1: Collect employee performance-related data through the performance data collection module;
[0021] Step 2: Use the diversified incentive mechanism building module to design a diversified incentive mechanism based on organizational strategy and employee needs;
[0022] Step 3: Evaluate employee performance through the performance evaluation and allocation module, and allocate incentives based on the evaluation results;
[0023] Step 4: Implement incentive measures and display incentive results to employees through the user interface module;
[0024] Step 5: Collect feedback through the feedback and adjustment module, analyze the incentive effect, and continuously optimize the incentive mechanism.
[0025] A processor is configured to execute the housing provident fund human resources performance management system based on the diversified incentive mechanism.
[0026] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the above-mentioned housing provident fund human resources performance management system based on a diversified incentive mechanism.
[0027] Compared with the prior art, the present invention has the following beneficial effects:
[0028] 1. The present invention constructs a housing provident fund human resources performance management system based on a diversified incentive mechanism, which not only takes into account material rewards, but also incorporates various incentive methods such as career development opportunities, training and promotion, and honorary recognition, thereby more comprehensively meeting the needs of employees in terms of personal growth, career development, job satisfaction, etc., and effectively improving the enthusiasm and overall performance of employees.
[0029] 2. The present invention realizes the automatic collection and processing of employee performance-related data through the integration of the performance data collection module and the housing provident fund management system, thereby improving the efficiency and accuracy of data processing; at the same time, it adopts technologies such as variational autoencoders for dimensionality reduction, feature extraction and generation of new data samples, further improving the generalization ability of the performance evaluation model.
[0030] 3. The present invention introduces advanced algorithms such as particle swarm optimization algorithm in the diversified incentive mechanism construction module, which can design differentiated housing provident fund subsidies or loan preferential policies as incentives according to the employees' housing provident fund payment years, amount and growth, thereby realizing the personalization and differentiation of the incentive mechanism.
[0031] 4. In the performance evaluation and allocation module, the present invention adopts a method combining machine learning algorithm and fuzzy comprehensive evaluation method, which can automatically learn and capture the complex relationship between performance indicators, improve the accuracy and robustness of performance evaluation; at the same time, by predicting future performance trends, personalized performance improvement plans and incentive programs are formulated for employees, which is conducive to the personal growth of employees and the improvement of the overall performance of the organization.
[0032] 5. The present invention introduces technologies such as reinforcement learning algorithms and Bayesian networks in the feedback and adjustment module, which can adaptively adjust the incentive mechanism according to employee feedback and performance data to achieve dynamic optimization and personalized incentives; at the same time, through the employee satisfaction survey function, it promotes the democratization and transparency of the incentive mechanism and enhances employees' sense of belonging and loyalty. BRIEF DESCRIPTION OF THE DRAWINGS
[0033] Figure 1 It is a framework diagram of the housing provident fund human resources performance management system based on a diversified incentive mechanism of the present invention;
[0034] Figure 2 The present invention is a flow chart of the housing provident fund human resources performance management method based on a diversified incentive mechanism. DETAILED DESCRIPTION
[0035] The following embodiments of the present invention are described in further detail in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.
[0036] Embodiment: The present invention provides a housing provident fund human resources performance management system based on a diversified incentive mechanism, such as Figure 1 As shown, it includes a user interface module, a performance data collection module, a diversified incentive mechanism construction module, a performance evaluation and allocation module, a feedback and adjustment module and a data security and privacy protection module, and the performance data collection module, the diversified incentive mechanism construction module, the performance evaluation and allocation module, and the feedback and adjustment module are electrically connected in sequence:
[0037] User interface module for displaying performance data and receiving user input and feedback;
[0038] The performance data collection module is integrated with the housing provident fund management system to automatically collect employees' housing provident fund payment status, work performance, attendance rate and other performance-related data;
[0039] The diversified incentive mechanism building module designs an incentive mechanism including material rewards, career development opportunities, training and promotion, honorary recognition and other incentive methods based on the collected performance data, combined with the personal development needs of employees and organizational goals;
[0040] The performance evaluation and allocation module uses a combination of quantitative and qualitative methods to objectively evaluate employee performance and automatically allocate corresponding incentives based on the evaluation results;
[0041] Feedback and adjustment module, which collects employees’ feedback on the incentive mechanism, regularly analyzes the incentive effect, and dynamically adjusts the incentive mechanism based on the analysis results to ensure its continuous optimization and effectiveness;
[0042] The data security and privacy protection module ensures that the storage, processing and transmission of all performance data and employee personal information comply with relevant laws and regulations and protect employee privacy.
[0043] From the above, we can see that by integrating multiple modules, we have achieved the automatic collection of performance data, the construction of diversified incentive mechanisms, objective and fair performance evaluation and distribution, dynamic adjustment of incentive mechanisms, and data security and privacy protection, thereby more comprehensively meeting the needs of employees in terms of personal growth, career development, job satisfaction, etc., effectively improving the enthusiasm and overall performance of employees, and ensuring the compliance of data processing and the protection of employee privacy.
[0044] Furthermore, in the performance data collection module, the collected performance-related data such as the housing provident fund payment, work performance, attendance rate, etc. of the employees are processed, and a variational autoencoder is used for dimensionality reduction, feature extraction and generation of new data samples to improve the generalization ability of the performance evaluation model. The formula involved in the variational autoencoder is as follows:
[0045] Encoding process: z~N(μ(x),σ 2 (x)), where μ(x) and σ 2 (x) is learned by neural network;
[0046] Decoding process: x′~pθ(xz), where pθ is the generative model.
[0047] From the above, we can see that by using variational autoencoders to process employees' housing provident fund payment, work performance, attendance rate and other performance-related data, we can achieve data dimensionality reduction, feature extraction and generation of new data samples, which not only improves the generalization ability of the performance evaluation model, but also enhances the model's processing efficiency and accuracy for complex performance data, providing a more solid data foundation for the construction of subsequent incentive mechanisms and performance evaluation.
[0048] Furthermore, the diversified incentive mechanism construction module further includes a submodule for designing differentiated housing provident fund subsidies or loan preferential policies as incentives according to the employees' housing provident fund payment years, amount and growth. The submodule introduces a particle swarm optimization algorithm, which simulates the movement of particles in the search space to find the optimal solution. The formula of the particle swarm optimization algorithm is as follows:
[0049] v i+1 =w·v i +c1·r1·(p i -x i )+c2·r2·(p g -x i );
[0050] x i+1 =x i +v i+1 ;
[0051] Where v is the velocity, x is the position, w is the inertia weight, c1 and c2 are learning factors, r1 and r2 are random numbers, and p i is the optimal position of the individual, p g is the global optimal position.
[0052] From the above, we can see that by introducing the particle swarm optimization algorithm, differentiated housing provident fund subsidies or loan preferential policies can be designed according to the employees' housing provident fund payment years, amount and growth, which can more accurately match the personal needs of employees and organizational goals, and realize the personalization and differentiation of incentive mechanisms; the application of this algorithm not only improves the scientificity and rationality of the incentive mechanism design, but also finds the optimal solution by simulating the movement of particles in the search space, ensuring the optimality and effectiveness of the incentive policy, thereby further stimulating the work enthusiasm and creativity of employees.
[0053] Furthermore, the performance evaluation and allocation module adopts a machine learning algorithm to analyze historical performance data, predict future performance trends, and formulate personalized performance improvement plans and incentive plans for employees. The machine learning algorithm is used to automatically learn and capture the complex relationship between performance indicators to improve the accuracy and robustness of performance evaluation. The formula of the machine learning algorithm is as follows:
[0054] Y = f(W·X+b);
[0055] Among them, Y is the predicted performance, W is the weight matrix, X is the input feature (such as work performance, attendance rate, housing provident fund payment, etc.), b is the bias term, and f is the activation function;
[0056] At the same time, the fuzzy comprehensive evaluation method is introduced and combined with the neural network model to deal with the fuzziness and uncertainty in the performance indicators and improve the comprehensiveness and accuracy of the evaluation. The formula of the fuzzy comprehensive evaluation method is as follows:
[0057]
[0058] Among them, S is the comprehensive evaluation score, w i is the weight of the ith indicator, r i is the fuzzy evaluation score of the i-th indicator.
[0059] From the above, we can see that in the performance evaluation and allocation module, by adopting a method that combines machine learning algorithms and fuzzy comprehensive evaluation methods, it is possible to automatically learn and capture the complex relationship between performance indicators, accurately predict future performance trends, and effectively deal with the ambiguity and uncertainty in performance indicators; this method not only improves the accuracy and robustness of performance evaluation, but also develops personalized performance improvement plans and incentive programs for employees, which is conducive to the personal growth of employees and the improvement of the overall performance of the organization; at the same time, by introducing a neural network model, the comprehensiveness and accuracy of the evaluation are further enhanced, providing strong support for the optimization and adjustment of the incentive mechanism.
[0060] Furthermore, in the feedback and adjustment module, the incentive mechanism is dynamically adjusted according to the analysis results by introducing a reinforcement learning algorithm. By using the reinforcement learning algorithm, the system can adaptively adjust the incentive mechanism according to the employee's feedback and performance data to achieve dynamic optimization and personalized incentives. The reinforcement learning algorithm formula is as follows:
[0061] Q(s,a)←Q(s,a)+α·[r+γ·max a′ Q(s′,a′)-Q(s,a)];
[0062] Among them, Q(s,a) is the state-action value function, α is the learning rate, r is the immediate reward, γ is the discount factor, and s′ is the next state.
[0063] From the above, we can see that by introducing the reinforcement learning algorithm, the system can adaptively adjust the incentive mechanism according to employee feedback and performance data, realize dynamic optimization and personalized incentives of the incentive mechanism; the reinforcement learning algorithm enables the system to continuously learn and optimize the decision-making process, ensuring that the incentive mechanism is always highly consistent with the actual performance of employees and organizational goals; this method not only improves the flexibility and adaptability of the incentive mechanism, but also ensures the long-term effectiveness of the incentive policy and the continuous motivation of employees through continuous learning and optimization, and promotes the steady improvement of organizational performance.
[0064] Furthermore, in the feedback and adjustment module, a Bayesian network is introduced, and the Bayesian network is used to represent the dependency relationship between variables. By constructing a Bayesian network, reasoning and prediction can be performed. The theorem of the Bayesian network is as follows:
[0065]
[0066] Among them, P(A|B) is given B Under the conditions A probability.
[0067] From the above, we can see that by introducing the Bayesian network, the system can intuitively represent the dependency relationship between variables, and perform reasoning and prediction by constructing the Bayesian network; this method helps the system to deeply understand the complex relationship between employee feedback and incentive effects, and provide a scientific basis for the dynamic adjustment of the incentive mechanism; the application of the Bayesian network not only improves the transparency and interpretability of the decision-making process, but also enhances the system's ability to predict potential problems, making the adjustment of the incentive mechanism more accurate and timely; this helps to maintain employees' trust and satisfaction with the incentive mechanism, and further promotes the continuous optimization and improvement of organizational performance.
[0068] Furthermore, the feedback and adjustment module also includes an employee satisfaction survey function, which collects employees' satisfaction with the incentive mechanism and improvement suggestions through regular surveys, thereby promoting the democratization and transparency of the incentive mechanism.
[0069] From the above, we can see that by introducing the employee satisfaction survey function, the system can regularly collect employees' satisfaction with the incentive mechanism and improvement suggestions, thereby effectively promoting the democratization and transparency of the incentive mechanism. This function not only enhances employees' sense of participation and belonging, but also makes the incentive mechanism closer to employees' actual needs and expectations; by continuously collecting and analyzing employee feedback, the system can promptly discover and solve problems in the incentive mechanism to ensure its continuous optimization and effectiveness; this two-way communication mechanism not only improves the pertinence and practicality of the incentive policy, but also enhances employees' trust and loyalty to the organization, laying a solid foundation for the long-term improvement of organizational performance.
[0070] Housing provident fund human resources performance management method based on diversified incentive mechanism, such as Figure 2 As shown, the housing provident fund human resources performance management system based on the above-mentioned diversified incentive mechanism includes:
[0071] Step 1: Collect employee performance-related data through the performance data collection module;
[0072] Step 2: Use the diversified incentive mechanism building module to design a diversified incentive mechanism based on organizational strategy and employee needs;
[0073] Step 3: Evaluate employee performance through the performance evaluation and allocation module, and allocate incentives based on the evaluation results;
[0074] Step 4: Implement incentive measures and display incentive results to employees through the user interface module;
[0075] Step 5: Collect feedback through the feedback and adjustment module, analyze the incentive effect, and continuously optimize the incentive mechanism.
[0076] Working Principle: Through the integration of multiple modules such as user interface, performance data collection, diversified incentive mechanism construction, performance evaluation and allocation, feedback and adjustment, data security and privacy protection, the system can automatically collect, process and analyze performance data, design diversified incentive measures in combination with employees' personal development needs and organizational goals, allocate incentives through a combination of quantitative and qualitative evaluation methods, and dynamically adjust the incentive mechanism based on employee feedback and performance data to ensure the continuous optimization and effectiveness of the system while protecting employee privacy and data security.
[0077] Furthermore, the housing provident fund human resources performance management system based on a diversified incentive mechanism of the embodiment is compared with the traditional human resources performance management system (comparative ratio), and the following table is obtained:
[0078]
[0079]
[0080] As can be seen from the above table, the table shows in detail the advantages and improvements of the housing provident fund human resources performance management system based on a diversified incentive mechanism compared with the traditional system in many aspects, highlighting its significant role in improving employee incentive effects, optimizing performance management processes, and enhancing employee participation and sense of belonging.
[0081] The embodiment of the present application provides an electronic device, which is applicable to the above-mentioned housing provident fund human resources performance management system based on a diversified incentive mechanism, including:
[0082] Memory, used to protect computer programs and data;
[0083] Processor, used to run system programs.
[0084] An embodiment of the present application provides a computer storage medium, which is applicable to the above-mentioned housing provident fund human resources performance management system based on a diversified incentive mechanism, and performs hierarchical confidentiality management on the above-mentioned system and data in accordance with confidentiality management requirements.
[0085] Those skilled in the art will appreciate that the embodiments of the present application may be provided as a system or a computer program product. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.
[0086] The present application is described with reference to the flowcharts and / or block diagrams of the devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.
[0087] 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.
[0088] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.
[0089] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.
[0090] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.
[0091] Computer readable media include permanent and non-permanent, removable and non-removable media, and can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include transitory media such as modulated data signals and carrier waves.
[0092] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, commodity or device including the elements.
[0093] The embodiments of the present invention are provided for the purpose of illustration and description. Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations of the present invention. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present invention.
Claims
1. The housing provident fund human resources performance management system based on a diversified incentive mechanism is characterized by: include: User interface module for displaying performance data and receiving user input and feedback; The performance data collection module is integrated with the housing provident fund management system to automatically collect data related to employees’ housing provident fund payment, work performance, and attendance rate; The diversified incentive mechanism building module designs an incentive mechanism including material rewards, career development opportunities, training and promotion, and honorary recognition based on the collected performance data, combined with the personal development needs of employees and organizational goals; The performance evaluation and allocation module uses a combination of quantitative and qualitative methods to objectively evaluate employee performance and automatically allocate corresponding incentives based on the evaluation results; Feedback and adjustment module, which collects employees’ feedback on the incentive mechanism, regularly analyzes the incentive effect, and dynamically adjusts the incentive mechanism based on the analysis results to ensure its continuous optimization and effectiveness; The data security and privacy protection module ensures that the storage, processing and transmission of all performance data and employee personal information comply with relevant laws and regulations and protect employee privacy.
2. The housing provident fund human resources performance management system based on a diversified incentive mechanism as claimed in claim 1, characterized in that: In the performance data collection module, the collected data on employees’ housing provident fund payment, work performance, and attendance rate are processed, and a variational autoencoder is used for dimensionality reduction, feature extraction, and generation of new data samples to improve the generalization ability of the performance evaluation model.
3. The housing provident fund human resources performance management system based on a diversified incentive mechanism as claimed in claim 1, characterized in that: The diversified incentive mechanism building module further includes a sub-module for designing differentiated housing provident fund subsidies or loan preferential policies as incentives based on the employees' housing provident fund payment years, amount and growth. The sub-module introduces a particle swarm optimization algorithm, which searches for the optimal solution by simulating the movement of particles in the search space.
4. The housing provident fund human resources performance management system based on a diversified incentive mechanism as claimed in claim 1, characterized in that: The performance evaluation and allocation module uses a machine learning algorithm to analyze historical performance data, predict future performance trends, and develop personalized performance improvement plans and incentive programs for employees. The machine learning algorithm is used to automatically learn and capture the complex relationships between performance indicators; At the same time, the fuzzy comprehensive evaluation method is introduced and combined with the neural network model to deal with the fuzziness and uncertainty in performance indicators.
5. The housing provident fund human resources performance management system based on a diversified incentive mechanism as claimed in claim 1, characterized in that: In the feedback and adjustment module, the incentive mechanism is dynamically adjusted according to the analysis results by introducing a reinforcement learning algorithm. By using the reinforcement learning algorithm, the system can adaptively adjust the incentive mechanism according to the employee's feedback and performance data.
6. The housing provident fund human resources performance management system based on a diversified incentive mechanism as claimed in claim 1, characterized in that: In the feedback and adjustment module, a Bayesian network is introduced, and the Bayesian network is used to represent the dependency relationship between variables. By constructing the Bayesian network, reasoning and prediction can be performed.
7. The housing provident fund human resources performance management system based on a diversified incentive mechanism as claimed in claim 1, characterized in that: The feedback and adjustment module also includes an employee satisfaction survey function, which collects employees' satisfaction with the incentive mechanism and improvement suggestions through regular surveys, thereby promoting the democratization and transparency of the incentive mechanism.
8. The housing provident fund human resources performance management method based on a diversified incentive mechanism is characterized by: The housing provident fund human resources performance management system based on the diversified incentive mechanism described in any one of claims 1 to 7 comprises: Step 1: Collect employee-related data through the performance data collection module; Step 2: Use the diversified incentive mechanism building module to design a diversified incentive mechanism based on organizational strategy and employee needs; Step 3: Evaluate employee performance through the performance evaluation and allocation module, and allocate incentives based on the evaluation results; Step 4: Implement incentive measures and display incentive results to employees through the user interface module; Step 5: Collect feedback through the feedback and adjustment module, analyze the incentive effect, and continuously optimize the incentive mechanism.
9. A processor, characterized in that: The invention is configured to execute a housing provident fund human resources performance management system based on a diversified incentive mechanism according to any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed by a processor, a housing provident fund human resources performance management system based on a diversified incentive mechanism as described in any one of claims 1 to 7 is implemented.