A comprehensive enterprise human resources management platform

Abstract

The present invention discloses an enterprise human resources comprehensive management platform, which relates to the field of data management technology and includes: a resource management system determination module for determining a resource management system; a management logic chain determination module for determining a management logic chain; a task management strategy determination module for determining a task management strategy; a sensitive management mode setting module for setting a sensitive management mode for sensitive data classes; a sensitive management strategy determination module for determining a sensitive management strategy; and a pre-management task control module for executing and controlling pre-management tasks. The platform solves the technical problems of the existing enterprise human resources management, such as the difficulty in coping with complex and changing enterprise management needs, the lack of integration of multiple enterprise application systems, and the risks of sensitive data management, which lead to the inaccuracy and security of enterprise human resources management. The platform intelligently realizes the comprehensive integration and efficient use of human resources information, achieving the technical effect of improving the accuracy and security of enterprise management.

Description

Technical Field

[0001] The present application relates to the technical field related to data management, and specifically to an enterprise human resources comprehensive management platform. Background Art

[0002] In today's rapidly developing business environment, corporate human resource management faces unprecedented challenges and opportunities. The rapid development of information technology, especially the widespread application of technologies such as cloud computing, big data, and artificial intelligence, has provided strong technical support for the construction of corporate human resource comprehensive management platforms. However, with the increasing scale of enterprises, the increase in the number of employees, and the intensification of market competition, traditional human resource management can no longer meet the needs of enterprises for comprehensive, efficient, and accurate management of human resource information. Traditional human resource management systems are often isolated and lack integration with other enterprise systems, resulting in serious information islands, inability to effectively share and utilize resources, lack of flexibility in management methods, and difficulty in responding to complex and changing management needs. In addition, the management of sensitive data often faces the risk of data leakage and abuse.

[0003] Therefore, at the current stage, the technologies related to enterprise human resource management have difficulties in coping with complex and changing enterprise management needs, lack of integration of multiple enterprise application systems, and risks in sensitive data management, which in turn leads to technical problems such as inaccurate and insecure enterprise human resource management. Summary of the Invention

[0004] This application provides an enterprise human resources comprehensive management platform, which adopts multi-objective logical fuzzy conversion, defuzzification, differential conversion and sensitive data identification and other technical means to solve the technical problems of existing enterprise human resources management, such as difficulty in coping with complex and changing enterprise management needs, lack of integration of multiple enterprise application systems, and risks in sensitive data management, which in turn leads to inaccurate and insecure enterprise human resources management. It intelligently realizes the comprehensive integration and efficient use of human resources information, and achieves the technical effect of improving the accuracy and security of enterprise management.

[0005] The present application provides an enterprise human resources comprehensive management platform, which includes: a resource management system determination module, which is used to integrate and systematize multiple application systems for human resources application systems, determine a resource management system, and the resource management system is expandable; a management logic chain determination module, which is used to define management core points based on management types, perform multi-objective logical fuzzy conversion, and determine a management logic chain, wherein the management logic chain corresponds one-to-one with the management core point, and the management core point is the logical main direction; a task management strategy determination module, which is used for an intelligent management center to receive pre-management tasks, traverse the management logic chain to match target logic links, perform subjective adjustment and defuzzification of tasks, and determine task management strategies; a sensitive management mode setting module, which is used to set a sensitive management mode for sensitive data classes, wherein the sensitive management mode is a differential conversion mode that balances sensitivity and data value; a sensitive management strategy determination module, which is used to identify the pre-management tasks, perform sensitive data identification and sensitive mode activation, and determine the sensitive management strategy; a pre-management task control module, which is used for the task management strategy and the sensitive management strategy to respond to the intelligent management center and execute control on the pre-management tasks.

[0006] In a possible implementation, the resource management system determination module also performs the following processing: the expansion method of the resource management system includes layout structure update and system new update; based on the response delay and concurrent processing status, the resource management system is saturated, and if the preset saturation coefficient is met, a system optimization instruction is generated; based on the system optimization instruction, the resource management system is optimized.

[0007] In a possible implementation, the management logic chain determination module also performs the following processing: reading homologous management records, performing a clustering based on the management type, and determining a first clustering result; traversing the first clustering result, mining the management core points and performing a second clustering to determine the second clustering result; wherein each management type corresponds to at least one management core point; traversing the second clustering result, and mining the management logic chain.

[0008] In a possible implementation, the management logic chain determination module further performs the following processing: traversing the second clustering results, extracting the first cluster cluster, and identifying common logic points and anisotropic logic points; constructing a bidirectional fuzzy conversion branch, performing fuzzy conversion on the anisotropic logic points, and determining the fuzzy conversion logic points; and positively serializing and integrating the common logic points and the fuzzy conversion logic points to generate a first management logic chain.

[0009] In a possible implementation, the sensitive management policy determination module also performs the following processing: identifying and extracting sensitive data, determining differential relaxation based on the data sensitivity level and effective management characteristics; converting the sensitive data based on the differential relaxation to determine desensitized data; and globally coordinating the desensitized data based on data correlation to determine the sensitive management policy.

[0010] In a possible implementation, before the pre-management task control module is executed, the following processing is also performed: identifying the task management strategy and the sensitive management strategy, combining the homologous management records, and predicting management risk points, where the risk types include objective risks and subjective risks; based on the management risk points, identifying the risk type and risk level, and performing management strategy mapping marking.

[0011] In a possible implementation, the enterprise human resources comprehensive management platform also performs the following processing: if it is a concurrent task, perform management strategy analysis and concurrent management resource allocation; obtain the management strategy of the concurrent task, perform task collision judgment, and locate the task collision node; if the task collision node is not empty, combine the avoidance principle to perform collision management on the task collision node.

[0012] In a possible implementation, the enterprise human resources comprehensive management platform also performs the following processing: identifying management strategies and determining strategy degrees of freedom, wherein the strategy degrees of freedom include management feature degrees of freedom and strategy node degrees of freedom; and making task risk avoidance decisions and collision avoidance decisions based on the strategy degrees of freedom.

[0013] The present application proposes an enterprise human resources comprehensive management platform, which integrates and systematizes multiple application systems for human resources application systems, determines a resource management system, and the resource management system is expandable; defines management core points based on management types, performs multi-objective logical fuzzy conversion, and determines a management logic chain, which corresponds one-to-one to the management core points, with the management core points as the logical main direction; the intelligent management center receives pre-management tasks, traverses the management logic chain to match the target logic link, performs subjective adjustment and defuzzification of tasks, and determines task management strategies; sets a sensitive management mode for sensitive data classes, which is a differential conversion mode that balances sensitivity and data value; identifies pre-management tasks, performs sensitive data identification and sensitive mode activation, and determines sensitive management strategies; the task management strategies and the sensitive management strategies respond to the intelligent management center to control the execution of the pre-management tasks. It solves the technical problems of existing enterprise human resource management, such as difficulty in coping with complex and changing enterprise management needs, lack of integration of multiple enterprise application systems, and risks in sensitive data management, which in turn lead to inaccurate and insecure enterprise human resource management. It intelligently realizes the comprehensive integration and efficient use of human resource information, achieving the technical effect of improving the accuracy and security of enterprise management. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] To more clearly illustrate the technical solutions of the embodiments of the present disclosure, the accompanying drawings of the embodiments of the present disclosure are briefly introduced below. Flowcharts are used in this application to illustrate the operations performed by the platform according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in precise order. On the contrary, various steps may be processed in reverse order or simultaneously as needed. Furthermore, other operations may be added to these processes, or one or more operations may be removed from these processes.

[0015] Figure 1 A schematic diagram of the structure of an enterprise human resources comprehensive management platform provided in an embodiment of the present application;

[0016] Figure 2 A schematic diagram of the execution process of a management logic chain determination module in an enterprise human resources comprehensive management platform provided in an embodiment of the present application.

[0017] Explanation of the accompanying drawings: resource management system determination module 10, management logic chain determination module 20, task management strategy determination module 30, sensitive management mode setting module 40, sensitive management strategy determination module 50, pre-management task control module 60. DETAILED DESCRIPTION

[0018] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0019] In order to make the purpose, technical solutions and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limiting this application. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0020] In the following description, reference is made to “some embodiments”, which describes a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict, and the terms “first\second” involved are merely used to distinguish similar objects and do not represent a specific ordering of the objects. The terms “including” and “having” and any variations are intended to cover non-exclusive inclusions. For example, a process, platform, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or modules that are not clearly listed or that are inherent to these processes, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs. The terms used herein are for the purpose of describing the embodiments of this application only.

[0021] The embodiment of the present application provides a comprehensive human resources management platform for an enterprise, such as Figure 1 As shown, the platform includes:

[0022] The resource management system determination module 10 is used to integrate and systematize multiple application systems for the human resources application system, and to determine the resource management system, which is extensible. It is used to integrate and systematize multiple application systems for the human resources application system, specifically referring to the use of technical means (such as API interfaces, middleware, etc.) to integrate various independent human resources application systems (such as recruitment, training, performance management, etc.) into a unified platform to achieve information sharing and circulation. During the integration process, it is necessary to evaluate and analyze the existing sub-application systems to determine which ones can be integrated. On the basis of the integration of various sub-application systems, a complete human resources management system is constructed, including modules such as salary, performance, quality assessment, and training. The relationship and collaboration between each module are determined to form an organic whole and complete the systematization of the application system; then, according to the corporate strategy and business development needs, the goals and direction of human resources management are clarified, based on the six modules of human resources management (salary, performance, quality assessment, training, etc.) , build a complete management system, and formulate detailed management systems and processes to ensure the orderly progress of various management tasks. Among them, the resource management system is extensible, which may include technical scalability, functional scalability, and data scalability. Specifically, technical scalability refers to the selection of a technical architecture and development model with good scalability to support system upgrades and expansions, and reserve sufficient interfaces and extension points to allow new functions or modules to be added as needed in the future; functional scalability refers to the design of flexible management logic chains and management core points to support changes in requirements under different management scenarios, while providing configurable parameters and options to meet the personalized needs of different enterprises; data scalability refers to the establishment of a complete data management system to support data collection, storage, analysis and application, and reserve data interfaces and extension points to support data exchange and sharing with other application systems.

[0023] The management logic chain determination module 20 defines the management core points based on the management type, performs multi-objective logical fuzzy conversion, and determines the management logic chain. The management logic chain corresponds one-to-one with the management core points, and the management core points serve as the logical main direction. Management types generally refer to management methods or forms formed based on different industries, different organizational characteristics, or different management goals. For example, production-oriented management, production and operation-oriented management, scientific research and production and operation-oriented management, etc. The management core points refer to the most critical or core elements of each management type, which may include goal setting, resource allocation, decision making, team building, process optimization, etc. In real-world management, there are often multiple interrelated or conflicting goals. Multi-objective decision-making theory is used to find solutions that achieve a balance or optimization between these goals. When multiple objectives are ambiguous (i.e., the objectives are not fully defined or are affected by multiple other factors), fuzzy transformation is required, that is, using fuzzy mathematical tools to quantify the fuzziness of the objectives and make decisions based on these quantified results. Then, the management logic chain is determined. The management logic chain refers to the complete process from the management objective to multiple management activities (such as planning, organizing, leading, controlling, etc.) to the ultimate realization of the management objective. In the management logic chain, each link is interrelated and influences each other, forming a closely connected management logic chain. The one-to-one correspondence between the management logic chain and the management core point means that the management core point, as the starting point or key node of the logic chain, determines the direction and focus of the entire logic chain. For example, if the management core point is to improve organizational efficiency, then the logic chain may revolve around how to optimize processes, improve employee efficiency, reduce operating costs, etc. Specifically, taking the management core point as the logical main direction means that in actual enterprise human resource management, the management core point is always the guide, ensuring that all management activities revolve around this core point. By continuously adjusting and optimizing management activities, the management core point is ensured to be achieved to the greatest extent.

[0024] The task management strategy determination module 30 is used for the intelligent management center to receive the pre-management task, traverse the management logic chain to match the target logic link, perform subjective adjustment and defuzzification of the task, and determine the task management strategy. The Intelligent Management Center is a digital management system that integrates advanced technologies and functions. It uses information technology to implement functions such as data storage, scheduling, analysis and processing, providing enterprises with one-stop intelligent, efficient, and convenient management services. These services mainly include information management, decision support, process management, work intelligence, and security management. Specifically, the Intelligent Management Center receives pre-management tasks, such as various enterprise activities that need to be managed, optimized, or monitored. The Intelligent Management Center then traverses the defined management logic chain and matches and identifies the logical links related to the pre-management tasks based on the characteristics and requirements of the tasks. That is, the Intelligent Management Center carefully analyzes the characteristics, requirements, and management goals of the tasks to find the target logical link that is most suitable and matches the pre-management tasks. After determining the target logical link, the Intelligent Management Center adjusts the tasks based on the specific situation. This may include setting task priorities, reallocating resources, and optimizing workflows to ensure that the tasks can better meet the requirements of the logical chain. Specifically, during the adjustment process, the Intelligent Management Center fully considers factors such as employee capabilities, workload, and teamwork to ensure that the adjusted tasks can be better executed. If there is ambiguity or uncertainty in the task, the intelligent management center uses defuzzification, such as refinement and clarification based on combing filtering, deep learning, variational Bayesian and other methods to eliminate unclear factors in the task, make the task objectives clearer, more specific and quantifiable, and finally determine the final task management strategy, clarify key elements such as the task execution plan, resource allocation, monitoring and evaluation mechanism, to ensure that the task can be carried out according to the predetermined plan.

[0025] The sensitive management mode setting module 40 is used to set a sensitive management mode for sensitive data classes. The sensitive management mode is a differential conversion mode that balances sensitivity and data value. The sensitive management mode for sensitive data classes is set by specifically adopting a differential conversion mode that balances sensitivity and data value. This mode aims to maximize the value of data while protecting data sensitivity. Specifically, the sensitive management mode is a special data management method designed and implemented specifically for sensitive data classes. The core idea is to achieve effective management and utilization of sensitive data by balancing sensitivity and data value. Among them, the differential conversion mode is a key component of the sensitive management mode. Through the differential conversion of sensitivity and data value, it provides a scientific basis for the management and utilization of sensitive data. First, conduct a quantitative analysis of the sensitivity of the data, such as evaluating the confidentiality, integrity and availability of the data, and then conduct a quantitative analysis of the potential value of the data, such as the commercial value, scientific research value and social value of the data. This helps to understand the risks that may be brought about by data leakage and the importance and utilization potential of the data. The differential conversion model is to quantitatively compare and convert sensitivity and data value, that is, to find a balance point so that while protecting data sensitivity, it will not excessively restrict the value utilization of data. For example, by setting certain conversion rules and thresholds, sensitivity and data value can be converted into comparable values, thereby achieving a balance between sensitivity and data value, and formulating more specific and accurate management strategies based on these values.

[0026] The sensitive management strategy determination module 50 is used to identify the pre-management task, perform sensitive data identification and sensitive mode activation, and determine the sensitive management strategy. Identifying the pre-management task refers to a comprehensive analysis and understanding of the content, objectives, etc. of the pre-management task, including the collection, organization and analysis of task-related data to clarify the specific requirements and potential risks of the task, and then perform sensitive data identification and sensitive mode activation. Specifically, sensitive data identification is a key link in the pre-management task. Through in-depth analysis of the data, the use of specialized tools or technologies to detect the existence of sensitive information, and the determination of the sensitivity of the data according to the specific regulations and standards of the organization or industry, data that may have a significant impact on the organization or individual is discovered and managed. Sensitive data includes but is not limited to personal identity information (such as name, ID number, address, telephone number, etc.), protected Protected health information, business secrets, intellectual property, etc.; Sensitive mode activation refers to the activation of corresponding management policies or modes after identifying sensitive data, based on the data's sensitivity level and potential risks. This may include setting specific access permissions, encrypting sensitive data, implementing data desensitization measures, and establishing real-time monitoring and alarm systems. Sensitive mode activation must ensure that while protecting data sensitivity, it does not affect the normal use of data and the normal operation of the organization. Based on the identification results of sensitive data and the activation of sensitive modes, specific management measures and strategies are formulated. These may include formulating data usage policies, strengthening employee training, establishing data breach emergency response mechanisms, and conducting regular data security audits. In summary, identifying pre-management tasks, performing sensitive data identification and sensitive mode activation, and determining sensitive management strategies ensure the secure and compliant use of data.

[0027] The pre-management task control module 60 is used to respond to the task management strategy and the sensitive management strategy in the intelligent management center and control the pre-management task execution. During the task execution process, the intelligent management center monitors, schedules, analyzes and controls the pre-management task in real time according to the requirements of the task management strategy and the sensitive management strategy to ensure the smooth execution of the task and the safe management of sensitive data. Specifically, the intelligent management center monitors the execution of the pre-management task in real time and collects data and information during the task execution process. According to the requirements of the task management strategy, the intelligent management center schedules and controls the task to ensure that the task is carried out according to the predetermined plan and steps. The intelligent management center analyzes the data generated during the task execution process, finds problems and optimizes them to improve the efficiency and quality of task execution. According to the requirements of the sensitive management strategy, the intelligent management center identifies the sensitive data involved in the task execution process to ensure the accuracy and integrity of the sensitive data. After identifying the sensitive data, the intelligent management center activates the corresponding sensitive management mode according to the sensitivity level and potential risks of the sensitive data, such as setting access rights, encrypting sensitive data, etc. Finally, the sensitive data is monitored in real time to ensure the safe and compliant use of sensitive data during the task execution process.

[0028] According to an embodiment of the present invention, a comprehensive enterprise human resources management platform is designed to address technical issues with existing enterprise human resources management, such as difficulty in coping with complex and ever-changing enterprise management needs, lack of integration of multiple enterprise application systems, and risks in sensitive data management, which in turn leads to inaccurate and insecure enterprise human resources management. The platform intelligently implements comprehensive integration and efficient utilization of human resources information, achieving the technical effect of improving the accuracy and security of enterprise management. The comprehensive enterprise human resources management platform includes: a resource management system determination module 10, a management logic chain determination module 20, a task management strategy determination module 30, a sensitive management mode setting module 40, a sensitive management strategy determination module 50, and a pre-management task control module 60.

[0029] Below, the specific configuration of the resource management system determination module 10 will be described in detail. The resource management system determination module 10 may further include: the expansion method of the resource management system includes layout structure update and system addition update. Layout structure update is to re-plan and design the internal structure and layout of the existing resource management system to more efficiently manage and utilize resources, such as reallocating resources, optimizing processes, improving organizational structure, etc., to improve management efficiency and response speed, so that the resource management system can better adapt to the development needs of the organization and changes in the market environment; system addition update refers to adding new resources, functions or modules on the basis of the existing resource management system to expand the coverage of the management system and enhance management capabilities, such as introducing new technologies, equipment, software or personnel, and developing new management strategies and methods to enrich the content of the resource management system and improve the organization's resource utilization and management efficiency.

[0030] The resource management system determination module 10 further includes determining saturation of the resource management system based on response latency and concurrent processing status, and generating system optimization instructions if a preset saturation coefficient is met. With increasing internal redundancy such as the amount of processed data and newly added applications, management efficiency is low, requiring immediate optimization and adjustment. This involves determining saturation based on response latency and concurrent processing status to assess whether the resource management system has reached its processing capacity limit. Specifically, response latency refers to the time required for the system to respond to a request. By monitoring the response latency, the system's response speed and processing capacity are understood. When the response latency exceeds a set threshold, it means that the system has approached or reached saturation. Concurrent processing status refers to the system's ability to simultaneously process multiple tasks or requests. By monitoring the concurrent processing status, the system's load and processing capacity are understood. When the concurrent processing status reaches or exceeds the system's design capacity, it means that the system has approached or reached saturation. The preset saturation coefficient is a threshold set based on organizational needs and system performance. When the response latency and concurrent processing status reach or exceed this threshold, the system is determined to be saturated and a system optimization instruction is generated.

[0031] The resource management system determination module 10 also includes optimizing the resource management system based on the system optimization instructions. Optimizing the resource management system based on the system optimization instructions may include resource reallocation, process optimization, technology upgrades, resource expansion, and management strategy adjustment. Specifically, resource reallocation refers to reallocating resources based on the system's load and performance requirements to ensure that critical tasks or requests receive sufficient resource support; process optimization refers to reviewing and improving existing processes, eliminating bottlenecks and waste, and improving process efficiency and response speed; technology upgrades refer to introducing new technologies, equipment, or software to enhance the system's processing power and efficiency; expanding resources means considering adding new resources, such as servers, storage devices, or personnel, if system resources are insufficient; and adjusting management strategies refers to adjusting management strategies and methods based on actual conditions to adapt to the organization's development needs and changes in the market environment.

[0032] The specific configuration of the management logic chain determination module 20 will be described in detail below. Figure 2 As shown, the management logic chain determination module 20 may further include: reading homologous management records, performing a clustering based on the management type, and determining a first clustering result. Relevant management records are read from a data source (such as a database, file, etc.), including historical management records and peer application record data (such as detailed information on various management activities), and then a clustering algorithm (such as K-means, hierarchical clustering, etc.) or a simple classification method (such as rule-based classification) is used to perform preliminary clustering on the read management records, dividing the management records into different groups, each group representing a management type, to obtain a first clustering result.

[0033] The management logic chain determination module 20 also includes traversing the first clustering results, mining management core points and performing secondary clustering to determine the second clustering results; wherein, each management type corresponds to at least one management core point. After obtaining the first clustering results, a more in-depth analysis is performed on each management type (i.e., each cluster). Specifically, within each management type, key management elements or concerns are identified. For example, in human resource management, management core points may include employee information management, risk management, etc., and secondary clustering is performed based on these management core points to cluster similar or related management core points together to form more detailed categories or subcategories to obtain a second clustering result, which can better reflect the internal structure and complexity of management activities. Each management type corresponds to at least one management core point. It also includes traversing the second clustering results and mining the management logic chain. After obtaining the second clustering results, the logical relationship between these clusters (management logic chain) is mined, that is, the logical relationship and dependency relationship between different steps, links or elements in each management activity is mined to construct a complete management process.

[0034] The specific configuration of the management logic chain determination module 20 will be described in detail below. The management logic chain determination module 20 may further include: traversing the second clustering result, extracting the first cluster, and identifying common logic points and anisotropic logic points. Traversing each cluster in the second clustering result, extracting the first cluster, that is, any one of the multiple clusters, and identifying its common logic points and anisotropic logic points. Specifically, common logic points refer to management logic points that are prevalent and have commonalities in the first cluster; anisotropic logic points refer to management logic points that exhibit differences or particularities in the first cluster, that is, a point cluster contains multiple similar logical relationships, among which there are differentiated logic points.

[0035] The management logic chain determination module 20 further includes constructing a bidirectional fuzzy conversion branch to perform fuzzy conversion on the anisotropic logic points and determine the fuzzy conversion logic points. Since anisotropic logic points may have different representations or processing methods in different contexts, fuzzy conversion is performed on multiple mappings corresponding to these logic points to improve the universality of the final determined logic. The bidirectional fuzzy conversion branch is a model that can convert anisotropic logic points into a unified format or representation based on different contexts or conditions. It typically includes a set of conversion rules and conversion functions, which are used to map the original logic points to a location in the fuzzy space and then perform reverse conversion as needed. By applying the bidirectional fuzzy conversion branch, the anisotropic logic points are converted into fuzzy conversion logic points.

[0036] The management logic chain determination module 20 further includes a positive sequence integration of the common logic points and the fuzzy transition logic points to generate a first management logic chain. The common logic points and fuzzy transition logic points are sorted according to the actual sequence or logical relationship of the management process, ensuring that each step in the logic chain is arranged in the order in which they actually occur in the management process. The sorted logic points are then connected in a positive sequence (i.e., logical order) to form a continuous management logic chain that clearly reflects the series of key steps and decision-making processes from the beginning to the end of management.

[0037] Below, the specific configuration of the sensitive management policy determination module 50 will be described in detail. The sensitive management policy determination module 50 may further include: identifying and extracting sensitive data, and determining differential relaxation based on the data sensitivity level and effective management features. Identify and extract sensitive data (such as personal identity information, financial information, business secrets, etc.). The data sensitivity level refers to the level divided by the sensitivity of the data. The differential relaxation may refer to the privacy protection strength determined based on the sensitivity level of the sensitive data and the effective management features (such as the purpose of the data, storage time, access rights, etc.). The higher the sensitivity level and the stricter the effective management features, the smaller the required differential relaxation may be, which means that the privacy protection requirements for the data are higher.

[0038] The sensitive management policy determination module 50 further includes converting the sensitive data based on the differential relaxation to determine desensitized data. Based on the determined differential relaxation, the sensitive data is converted or desensitized to reduce the sensitivity of the data while maintaining the usability and accuracy of the data. For example, the conversion method may include adding random noise (such as the Laplace mechanism), data generalization (such as replacing age ranges with age groups), data anonymization (such as replacing real names with anonymous identifiers), etc., to ultimately determine the desensitized data.

[0039] The sensitive management policy determination module 50 also includes, based on data relevance, global coordination of the desensitized data to determine the sensitive management policy. When determining the sensitive management policy, the relevance between the data needs to be considered. Data with high relevance requires more stringent protection measures to prevent sensitive information from being inferred through the association between the data. Global coordination specifically refers to the comprehensive consideration of multiple data sources or data sets to ensure a consistent level of privacy protection across the entire data ecosystem. Ultimately, the sensitive management policy is determined to clarify the privacy protection requirements for each link, such as data collection, storage, use, sharing, and destruction, as well as the corresponding responsibilities and penalties.

[0040] Below, the specific configuration of the pre-management task control module 60 will be described in detail. The pre-management task control module 60 may further include: identifying the task management strategy and the sensitive management strategy, combining the same-source management records, and predicting management risk points, wherein the risk types include objective risks and subjective risks. Identify the task management strategy and the sensitive management strategy, combine the same-source management records, and predict management risk points, that is, by analyzing these records, combined with the current task management strategy and sensitive management strategy, identify potential management risk points, risk points may include task delays, insufficient resources, data leakage, privacy infringement, etc., and risk types include subjective risks and objective risks.

[0041] The pre-management task control module 60 also includes identifying risk types and risk levels based on the management risk points, and marking management strategy mappings. The obtained management risk points are analyzed to identify risk types and risk levels. Specifically, objective risks include uncontrollable factors such as technical failures, and subjective risks include human operational errors and decision-making errors. Management point risks are divided into different levels based on the likelihood and impact of the risk, such as general risk, major risk, significant risk, and extremely significant risk. Based on the identified risk type and risk level, the corresponding management strategy is mapped to the corresponding risk point. Each risk point and management strategy mapping is clearly marked, which may include information such as risk type, risk level, response measures, responsible person, and deadline, for easy tracking and management.

[0042] The specific configuration of a comprehensive enterprise human resources management platform will be described in detail below. The comprehensive enterprise human resources management platform further includes: For concurrent tasks, management policy analysis and concurrent management resource allocation are performed. Specifically, management policy analysis refers to analyzing the goals, priorities, and dependencies of each concurrent task to determine the appropriate execution order and resource allocation. Concurrent management resource allocation refers to allocating hardware resources such as processors, memory, and storage based on task requirements and priorities to ensure fairness and efficiency in resource allocation and avoid over- or under-allocation of resources. The platform also includes obtaining the management policy of the concurrent tasks, performing task collision determination, and locating task collision nodes. The management policy of each concurrent task, including the execution order and dependencies, is obtained from the task management system. The concurrent tasks are checked for resource, time, or dependency conflicts, i.e., potential collision points are detected, such as time overlap or shared resource contention. If a collision is detected, the task collision node is located, i.e., the specific node or time period where the collision occurred is precisely located, such as a task step, resource access point, or time window. It also includes, if the task collision node is not empty, combining the avoidance principle, performing collision management on the task collision node. Task collision node is not empty means that in the process of concurrent task execution, the actual task conflict or collision point is detected through the task collision determination step. The avoidance principle is used to guide how to adjust the task execution plan when a collision occurs, which may include the priority principle (high priority tasks are executed first), the resource priority principle (the tasks with the most urgent resource requirements are met first), the time priority principle (the tasks with the strictest time constraints are met first), etc., and performing collision management on task collision nodes means adjusting the collision nodes according to the avoidance principle, such as reallocating resources, adjusting the order of task execution, delaying or splitting tasks, etc., monitoring the effect after collision management, and ensuring that the adjusted task plan can be executed smoothly.

[0043] The specific configuration of an enterprise human resources integrated management platform will be described in detail below. An enterprise human resources integrated management platform further includes: identifying management strategies, determining strategy freedom, and the strategy freedom includes management feature freedom and strategy node freedom. Analyze and identify management strategies and clarify the degrees of freedom these strategies have during implementation. Strategy freedom can be divided into two main components: management feature freedom and strategy node freedom. Specifically, management feature freedom refers to the ability or scope of management strategies to flexibly adjust and change according to changes in the internal and external environment of the organization during design and implementation. Its level depends on the flexibility and adaptability of the management strategy and the organization's response speed to changes. For example, a flexible management strategy may allow adjustments in resource allocation, task priorities, decision-making processes, etc. as needed. A higher level of management feature freedom can better cope with uncertainty and improve the organization's adaptability and competitiveness. Strategy node freedom refers to the flexibility and adjustability of the organization during strategy implementation, especially at key nodes (such as decision points and resource allocation points). It is manifested in whether the organization can flexibly adjust the strategy at specific nodes according to actual conditions, such as changing decisions, reallocating resources, or adjusting task priorities. A higher level of strategy node freedom can make the organization more flexible during strategy implementation, better adapt to environmental changes, and ensure the effective implementation of the strategy.

[0044] It also includes making mission risk avoidance decisions and collision avoidance decisions based on the policy freedom. After identifying and determining the management policy freedom (including management feature freedom and policy node freedom), these freedoms are used to formulate and execute decisions to reduce the risks and collisions that may be encountered during mission execution. Specifically, mission risk avoidance decisions refer to a comprehensive assessment of the risks that may be encountered during mission execution, including resource risks, technical risks, market risks, etc. Based on the risk assessment results, the specific goals of risk avoidance are clarified, such as reducing the possibility of risk occurrence and mitigating the losses caused by risks. Then, the risk avoidance strategy is adjusted and optimized according to the policy freedom to make mission risk avoidance decisions, such as changing the order of task execution and adjusting the decision-making process, to avoid potential risks. At the same time, the policy freedom is used to adjust the strategy, such as optimizing task scheduling, enhancing resource coordination, changing task priorities, adjusting resource allocation, etc., to reduce the occurrence of collisions.

[0045] Although this application makes various references to certain modules in the platform according to the embodiments of this application, any number of different modules can be used and run on the user terminal and / or server, and the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other and are not used to limit the scope of protection of the present invention.

[0046] The above specific embodiments do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application shall be included within the scope of protection of this application.

Claims

1. An enterprise human resources comprehensive management platform, characterized by: The platform includes: A resource management system determination module is used to integrate and systematize multiple application systems for the human resources application system and determine a resource management system that is scalable; A management logic chain determination module is used to define a management core point based on the management type, perform multi-objective logical fuzzy conversion, and determine a management logic chain. The management logic chain corresponds to the management core point one by one, with the management core point as the logical main direction; The task management strategy determination module is used for the intelligent management center to receive pre-management tasks, traverse the management logic chain to match the target logic link, perform subjective adjustment and defuzzification of tasks, and determine the task management strategy; A sensitive management mode setting module is used to set a sensitive management mode for sensitive data. The sensitive management mode adopts a differential conversion mode that balances sensitivity and data value. A sensitive management policy determination module is used to identify the pre-management task, perform sensitive data identification and sensitive mode activation, and determine the sensitive management policy; A pre-management task control module, configured to control the pre-management task in response to the task management strategy and the sensitive management strategy of the intelligent management center; The sensitive management policy determination module performs the following steps: Identify and extract sensitive data, and determine differential slack based on data sensitivity level and effective management characteristics; Based on the differential relaxation, the sensitive data is converted to determine desensitized data; Based on data relevance, the desensitized data is globally coordinated to determine the sensitivity management strategy.

2. The enterprise human resources integrated management platform according to claim 1, characterized in that: The resource management system determines the module, and the steps performed include: The expansion methods of the resource management system include layout structure update and system addition update; Based on the response delay and concurrent processing status, the resource management system is saturated, and if a preset saturation coefficient is met, a system optimization instruction is generated; Based on the system optimization instruction, the resource management system is optimized.

3. The enterprise human resources integrated management platform according to claim 1, characterized in that: The management logic chain determination module performs the following steps: Read the same-source management records, perform clustering based on the management type, and determine the first clustering result; Traversing the first clustering results, mining management core points and performing secondary clustering to determine a second clustering result; wherein each management type corresponds to at least one management core point; The second clustering result is traversed to mine the management logic chain.

4. The enterprise human resources integrated management platform according to claim 3, characterized in that: The management logic chain determination module performs the following steps: Traversing the second clustering results, extracting the first clusters, and identifying common logic points and anisotropic logic points; Constructing a bidirectional fuzzy conversion branch, performing fuzzy conversion on the anisotropic logic point, and determining a fuzzy conversion logic point; The common logic points and the fuzzy conversion logic points are integrated in a positive sequence to generate a first management logic chain.

5. The enterprise human resources integrated management platform according to claim 3, characterized in that: Before the pre-management task control module is executed, the following steps are also included: Identify the task management strategy and the sensitive management strategy, combine the same-source management records, and predict management risk points, where the risk types include objective risks and subjective risks; Based on the management risk points, the risk type and risk level are identified, and management strategy mapping marking is performed.

6. The enterprise human resources integrated management platform according to claim 1, characterized in that: The enterprise human resources comprehensive management platform further includes: If it is a concurrent task, conduct management strategy analysis and concurrent management resource allocation; Obtaining the management strategy of the concurrent tasks, performing task collision determination, and locating task collision nodes; If the task collision node is not empty, collision management is performed on the task collision node in combination with the avoidance principle.

7. The enterprise human resources integrated management platform according to claim 1, characterized in that: The enterprise human resources comprehensive management platform further includes: Identify the management policy and determine the policy freedom, wherein the policy freedom includes the management feature freedom and the policy node freedom; Based on the strategic degrees of freedom, mission risk avoidance decisions and collision avoidance decisions are made.

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