Enterprise human resource integrated management platform

Abstract

The invention discloses an enterprise human resource integrated management platform, and relates to the technical field of data management, and the platform comprises a resource management system determination module which is used for determining a resource management system; the management logic chain determination module is used for determining a management logic chain; the task management strategy determination module is used for determining a task management strategy; the sensitive management mode setting module is used for setting a sensitive management mode for the sensitive data class; the sensitive management strategy determination module is used for determining a sensitive management strategy; and the pre-management task management and control module is used for executing management and control on the pre-management task. The technical problems that existing enterprise human resource management is difficult to cope with complex and changeable enterprise management requirements, integration of enterprise multi-application systems is lacked, sensitive data management has risks, and consequently enterprise human resource management is not accurate and safe enough are solved, comprehensive integration and efficient utilization of human resource information are intelligently achieved, and the enterprise human resource management efficiency is improved. The technical effect of improving the accuracy and safety of enterprise management is achieved.

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. With the rapid development of information technology, especially the widespread application of cloud computing, big data, artificial intelligence and other technologies, it provides 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 coping with 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, there are technical problems in enterprise human resource management related technologies, such as difficulty in coping with complex and changeable enterprise management needs, lack of integration of multiple enterprise application systems, and risks in sensitive data management, which in turn lead 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, 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 that are difficult to cope with complex and changeable 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, the platform comprising: a resource management system determination module, used for integrating and systematizing multiple application systems for human resources application systems, determining a resource management system, and the resource management system is extensible; a management logic chain determination module, used for defining management core points based on management types, performing multi-objective logical fuzzy conversion, and determining a management logic chain, wherein the management logic chain corresponds to the management core point one by one, and the management core point is the logical main direction; a task management strategy determination module, 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, used for setting a sensitive management mode for sensitive data classes, wherein the sensitive management mode is a differential conversion mode by balancing sensitivity and data value; a sensitive management strategy determination module, used for identifying the pre-management tasks, performing sensitive data identification and sensitive mode activation, and determining sensitive management strategies; a pre-management task control module, used for the task management strategy and the sensitive management strategy to respond to the intelligent management center and execute control over 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 addition 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 management core points and performing a second clustering to determine a 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 also performs the following processing: traverses the second clustering results, extracts the first clustering cluster, and identifies common logic points and anisotropic logic points; constructs a bidirectional fuzzy conversion branch, performs fuzzy conversion on the anisotropic logic points, and determines the fuzzy conversion logic points; and serializes and integrates 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, and determining differential relaxation based on the data sensitivity level and effective management characteristics; based on the differential relaxation, converting the sensitive data to determine desensitized data; based on data correlation, globally coordinating the desensitized data 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, predicting management risk points, where the risk types include objective risks and subjective risks; based on the management risk points, identifying risk types and risk levels, 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] It is intended to use a comprehensive enterprise human resources management platform proposed in this application to integrate and systematize multiple application systems for human resources application systems, determine a resource management system, and the resource management system is expandable; define management core points based on management types, perform multi-objective logical fuzzy conversion, and determine 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 the 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 existing in the human resource management of existing enterprises, 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 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] In order to more clearly illustrate the technical solution of the embodiment of the present disclosure, the accompanying drawings of the embodiment of the present disclosure are briefly introduced below. A flow chart is used in the present application to illustrate the operations performed by the platform according to the embodiment of the present application. It should be understood that the previous or following operations are not necessarily performed precisely in order. On the contrary, various steps can be processed in reverse order or simultaneously as needed. At the same time, other operations can also be added to these processes, or one or more operations can 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 the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the 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 describe a subset of all possible embodiments, but it is 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 to distinguish similar objects and do not represent a specific ordering of 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 inherent to these processes, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by technicians in the technical field of this application. The terms used herein are for the purpose of describing the embodiments of the present application only.

[0021] The present application embodiment provides a comprehensive enterprise human resources management platform, 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 resource application system and determine the resource management system, and the resource management system is expandable. It is used to integrate and systematize multiple application systems for the human resource application system. Specifically, it refers to integrating various independent human resource application systems (such as recruitment, training, performance management, etc.) into a unified platform through technical means (such as API interface, middleware, etc.) 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 integrating the various sub-application systems, a complete human resource management system is constructed, including modules such as salary, performance, quality assessment, and training. The relationship and collaboration between the modules 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 directions of human resource management are clarified, based on the six modules of human resource 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 point based on the management type, performs multi-objective logic fuzzy conversion, and determines the management logic chain. The management logic chain corresponds to the management core point one by one, and the management core point is used as the logical main direction. Management type usually refers to the management method or form formed based on different industries, different organizational characteristics or different management goals, such as production management, production and operation management, scientific research and production management, etc. The management core point refers to the most critical or core element in each management type, which may include goal setting, resource allocation, decision making, team building, process optimization and other aspects. In real management, there are often multiple interrelated or conflicting goals. Use multi-objective decision theory to find a solution to achieve a balance or optimization between these goals. When multiple goals are ambiguous (i.e., the goal definition is not completely clear or is affected by many other factors), fuzzy conversion is required, that is, using fuzzy mathematical tools to quantify the fuzziness of the goal, and making decisions based on these quantified results, and then determining the management logic chain. The management logic chain refers to the complete process of starting from the management goal, going through multiple management activities (such as planning, organization, leadership, control, etc.), and finally achieving the management goal. In the management logic chain, each link is interrelated and influences each other to form a closely connected management logic chain. Among them, 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 the efficiency of the organization, then the logic chain may revolve around how to optimize the process, improve employee efficiency, and reduce operating costs. Specifically, taking the management core point as the logical main direction means that in actual enterprise human resource management, it is always guided by the management core point to ensure that all management activities revolve around this core point, and through continuous adjustment and optimization of management activities, to ensure that the management core point is realized 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 realizes data storage, scheduling, analysis and processing through information technology, and provides enterprises with one-stop intelligent, efficient and convenient management services, mainly including information management, decision support, process management, work intelligence and safety management. Specifically, the intelligent management center receives pre-management tasks, such as various enterprise activities that need to be managed, optimized or monitored, and then traverses the defined management logic chain to match and identify the logical links related to the pre-management tasks according to the characteristics and needs of the tasks. That is, the intelligent management center conducts a detailed analysis of the task characteristics, needs and management goals to find the most suitable and matching target logical link for the pre-management task. After determining the target logical link, the intelligent management center makes supervisory adjustments to the tasks according to the specific circumstances, which may include task priority setting, resource reallocation, workflow optimization, etc., to ensure that the tasks can better adapt to the requirements of the logical link. Specifically, in the process of supervisory adjustments, 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 Bayes 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 the 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 the sensitive management mode for sensitive data classes, and 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, and specifically adopts a differential conversion mode that balances sensitivity and data value. This mode aims to ensure that while protecting data sensitivity, the value of data is maximized. Specifically, the sensitive management mode is a special data management method, which is specially designed and implemented 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, a scientific basis is provided for the management and utilization of sensitive data. First, quantify the sensitivity of the data, such as evaluating the confidentiality, integrity and availability of the data, and then quantify 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 caused by data leakage and the importance and utilization potential of the data. The differential conversion model is to quantify and 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 the 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 and objectives 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 techniques to detect the presence 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 information, etc. Protected health information, business secrets, intellectual property, etc.; Sensitive mode activation means that after sensitive data is identified, the corresponding management strategy or mode is activated according to the sensitivity level and potential risks of the data, which may include setting specific access rights, encrypting sensitive data, implementing data desensitization measures, establishing real-time monitoring and alarm systems, etc. The activation of sensitive mode needs to ensure that while protecting the sensitivity of data, it does not affect the normal use of data and the normal operation of the organization; according to the identification results of sensitive data and the activation of sensitive mode, specific management measures and strategies are formulated, which may include formulating data use policies, strengthening employee training, establishing data leakage emergency response mechanisms, and conducting regular data security audits. In summary, identify pre-management tasks, perform sensitive data identification and sensitive mode activation, and determine sensitive management strategies to ensure the security and compliance of data use.

[0027] The pre-management task control module 60 is used for the task management strategy and the sensitive management strategy to respond to 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, analyzes the data generated during the task execution process, finds problems and optimizes them to improve the efficiency and quality of task execution; the intelligent management center identifies the sensitive data involved in the task execution process according to the requirements of the sensitive management strategy 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, an enterprise human resources comprehensive management platform is used to solve the technical problems of the existing enterprise human resources management, such as difficulty in coping with complex and changeable enterprise management needs, lack of integration of multiple enterprise application systems, and risks in sensitive data management, which leads to the inaccuracy and insecurity of 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. An enterprise human resources comprehensive 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 manage and utilize resources more efficiently, 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 resource utilization and management efficiency of the organization.

[0030] The resource management system determination module 10 also includes, based on the response delay and concurrent processing state, making a saturation determination on the resource management system, and generating a system optimization instruction if the preset saturation coefficient is met. With the internal redundancy such as the amount of data to be processed and the addition of new applications, the management efficiency is low, and it is necessary to make an optimization adjustment immediately, that is, to make a saturation determination based on the response delay and the concurrent processing state, and to evaluate whether the resource management system has reached its processing capacity limit. Specifically, the response delay refers to the time required for the system to respond to a request. By monitoring the response delay, the response speed and processing capacity of the system are understood. When the response delay exceeds the set threshold, it means that the system has approached or reached a saturation state; the concurrent processing state refers to the system's ability to process multiple tasks or requests at the same time. By monitoring the concurrent processing state, the system's load and processing capacity are understood. When the concurrent processing state reaches or exceeds the system's design capacity, it means that the system has approached or reached a saturation state; the preset saturation coefficient is a threshold set according to organizational needs and system performance. When the response delay and the concurrent processing state reach or exceed this threshold, it is determined that the system is 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 upgrade, resource expansion, and management strategy adjustment. Specifically, resource reallocation refers to reallocating resources according to the system load and performance requirements to ensure that key 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 upgrade refers to introducing new technologies, equipment, or software to improve the system's processing power and efficiency; expanding resources means that if system resources are insufficient, you can consider adding new resources, such as servers, storage devices, or personnel; adjusting management strategies refers to adjusting management strategies and methods according to 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. Read relevant management records from a data source (such as a database, file, etc.), including historical management records and peer application record data (such as detailed information of various management activities), and then perform preliminary clustering on the read management records through a clustering algorithm (such as K-means, hierarchical clustering, etc.) or a simple classification method (such as rule-based classification), divide the management records into different groups, each group represents a management type, and obtain a first clustering result.

[0033] The management logic chain determination module 20 also includes traversing the first clustering result, mining the management core points and performing secondary clustering to determine the second clustering result; wherein each management type corresponds to at least one management core point. After obtaining the first clustering result, 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, the management core points may include employee information management, risk management, etc. Based on these management core points, secondary clustering is performed to cluster similar or related management core points together to form more detailed categories or subcategories, and obtain the 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 result and mining the management logic chain. After obtaining the second clustering result, the logical relationship (management logic chain) between these clusters is mined, that is, the logical relationship and dependency relationship between different steps, links or elements in each management activity is mined to build 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 clustering cluster, and identifying common logic points and anisotropic logic points. Traverse each clustering cluster in the second clustering result, extract the first clustering cluster, that is, any one of the multiple clustering clusters, identify its common logic points and anisotropic logic points, specifically, the common logic point refers to the management logic point that is prevalent and common in the first clustering cluster; the anisotropic logic point refers to the management logic point that shows differences or particularities in the first clustering cluster, that is, a point cluster contains multiple approximate logical relationships, among which there are differentiated logic points.

[0035] The management logic chain determination module 20 also includes constructing a bidirectional fuzzy conversion branch, performing fuzzy conversion on the anisotropic logic point, and determining the fuzzy conversion logic point. For anisotropic logic points, since they may have different forms of expression or processing methods in different situations, in order to improve the universality of a logic finally determined, multiple mappings corresponding to these logic points are fuzzy converted. The bidirectional fuzzy conversion branch refers to a model that can convert anisotropic logic points into a unified format or representation according to different situations or conditions, and usually includes a set of conversion rules and conversion functions, which are used to map the original logic point to a position in the fuzzy space, and then perform reverse conversion as needed. By applying the bidirectional fuzzy conversion branch, the anisotropic logic point is converted into a fuzzy conversion logic point.

[0036] The management logic chain determination module 20 also includes positive serialization integration of the common logic points and the fuzzy conversion logic points to generate a first management logic chain. According to the actual order or logical relationship of the management process, the common logic points and the fuzzy conversion logic points are sorted to ensure that each step in the logic chain is arranged according to the actual order of their occurrence in the management process, and the sorted logic points are connected in a positive sequence (i.e., logical order) to form a continuous management logic chain, which clearly reflects a 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 according to the sensitivity of the data, and the differential relaxation may refer to the privacy protection strength determined according to 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 strategy determination module 50 also includes, based on the differential relaxation, converting the sensitive data to determine the desensitized data. According to the determined differential relaxation, the sensitive data is converted or desensitized to reduce the sensitivity of the data while maintaining the availability and accuracy of the data. For example, the conversion method may include adding random noise (such as Laplace mechanism), data generalization (such as replacing the age range with an age group), data anonymization (such as replacing the real name with an anonymous identifier), etc., and finally determining the desensitized data.

[0039] The sensitive management strategy determination module 50 also includes, based on the data correlation, global coordination of the desensitized data to determine the sensitive management strategy. When determining the sensitive management strategy, the correlation between the data needs to be considered. Data with high correlation needs to take 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 that the privacy protection level of the entire data ecosystem is consistent, and finally determine the sensitive management strategy, clarify the privacy protection requirements of 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 task management strategies and sensitive management strategies, combine the same-source management records, and predict management risk points, that is, by analyzing these records, combined with the current task management strategies and sensitive management strategies, 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, based on the management risk point, identifying the risk type and risk level, and marking the management strategy mapping. The obtained management risk point is analyzed to identify the risk type and risk level. Specifically, objective risks include uncontrollable factors such as technical failures, and subjective risks include human operation errors, decision-making errors, etc. According to the possibility and impact of the risk, the management point risk is divided into different levels, such as general risk, greater risk, major risk, and extremely large risk; according to the identified risk type and risk level, the corresponding management strategy is mapped to the corresponding risk point, and each risk point and management strategy mapping is clearly marked, which may include risk type, risk level, response measures, responsible person, deadline and other information for easy tracking and management.

[0042] Below, the specific configuration of an enterprise human resources comprehensive management platform will be described in detail. An enterprise human resources comprehensive management platform further includes: if it is a concurrent task, management strategy analysis and concurrent management resource allocation are performed. If it is a concurrent task, management strategy analysis and concurrent management resource allocation are performed. Specifically, management strategy analysis refers to analyzing the goals, priorities, dependencies, etc. of each concurrent task to determine the appropriate execution order and resource configuration. Concurrent management resource allocation refers to allocating hardware resources such as processors, memory, and storage according to the needs and priorities of the task to ensure the fairness and efficiency of resource allocation and avoid excessive or insufficient resource allocation. It also includes obtaining the management strategy of the concurrent task, performing task collision determination, and locating the task collision node. The management strategy of each concurrent task is obtained from the task management system, including the execution order, dependencies, etc., and the concurrent tasks are checked for resource conflicts, time conflicts, or dependency conflicts, that is, potential collision points are detected, such as time overlap, shared resource competition, etc. If a collision is detected, the task collision node is located, that is, the specific node or time period where the collision occurs is accurately located, such as task steps, resource access points, or time windows. It also includes that if the task collision node is not empty, collision management is performed on the task collision node in combination with the avoidance principle. Task collision node is not empty means that during the execution of concurrent tasks, the actual existing 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. Collision management of task collision nodes refers to 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 of 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 degrees of freedom can be divided into two main parts: management feature degrees of freedom and strategy node degrees of freedom. Specifically, management feature degrees of freedom refer 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 management strategies and the speed of the organization's response to changes. For example, a flexible management strategy may allow adjustments in resource allocation, task priority, decision-making process, etc. as needed. A higher degree of management feature freedom can better cope with uncertainty and improve the adaptability and competitiveness of the organization. Strategy node degrees of freedom refer to the flexibility and adjustability of the organization during the strategy implementation process, especially at key nodes (such as decision points, resource allocation points, etc.). It is manifested in whether the organization can flexibly adjust the strategy at a specific node according to the actual situation, such as changing decisions, reallocating resources, or adjusting task priorities. A higher degree of strategy node freedom can make the organization more flexible in the strategy implementation process, 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 strategy freedom. After identifying and determining the management strategy freedom (including management feature freedom and strategy node freedom), these freedoms are used to make and execute decisions to reduce the risks and collisions that may be encountered during the execution of the task. Specifically, the mission risk avoidance decision refers to a comprehensive assessment of the risks that may be encountered during the execution of the task, including resource risks, technical risks, market risks, etc. According to the risk assessment results, the specific goals of risk avoidance are clarified, such as reducing the possibility of risk occurrence, reducing the losses caused by risks, etc., and then adjusting and optimizing the risk avoidance strategy according to the strategy freedom, making mission risk avoidance decisions, such as changing the order of task execution, adjusting the decision-making process, etc., to avoid potential risks; at the same time, the strategy 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 the present application makes various references to certain modules in the platform according to the embodiments of the present application, any number of different modules may 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 implemented; 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 implementations do not constitute a limitation on the protection scope of this application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of this application should be included in the protection scope of this application.

Claims

1. An enterprise human resources comprehensive management platform, characterized in that: 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, which is extensible; A management logic chain determination module is used to define a management core point based on the management type, perform multi-objective logic fuzzy conversion, and determine a management logic chain, wherein the management logic chain corresponds to the management core point one by one, and the management core point is used as the logical main direction; The task management strategy determination module 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; A sensitive management mode setting module, used to set a sensitive management mode for sensitive data, wherein the sensitive management mode is a differential conversion mode by balancing sensitivity and data value; A sensitive management strategy determination module, used to identify the pre-management task, perform sensitive data identification and sensitive mode activation, and determine the sensitive management strategy; The pre-management task control module is used for the task management strategy and the sensitive management strategy to respond to the intelligent management center and perform control on the pre-management task.

2. The enterprise human resources integrated management platform as claimed in 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 as claimed in claim 1, characterized in that: The management logic chain determination module performs the following steps: Read the homologous management records, perform clustering based on the management type, and determine the first clustering result; Traversing the first clustering results, mining the 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; The second clustering result is traversed to mine the management logic chain.

4. The enterprise human resources integrated management platform as claimed in claim 3, characterized in that: The management logic chain determination module performs the following steps: Traversing the second clustering results, extracting the first clustering 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 the fuzzy conversion logic point; The common logic point and the fuzzy conversion logic point are integrated by serialization to generate a first management logic chain.

5. The enterprise human resources integrated management platform as claimed in claim 1, characterized in that: The sensitive management policy determination module performs the following steps: Identify and extract sensitive data, and determine differential relaxation 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.

6. The enterprise human resources integrated management platform as claimed in claim 3, characterized in that: Before the pre-management task control module is executed, it also includes: Identify the task management strategy and the sensitive management strategy, combine the same-source management records, and predict management risk points, wherein 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.

7. The enterprise human resources integrated management platform as claimed in claim 1, characterized in that: The enterprise human resources comprehensive management platform also 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.

8. The enterprise human resources integrated management platform as claimed in claim 1, characterized in that: The enterprise human resources comprehensive management platform also includes: Identify the management strategy and determine the strategy freedom, wherein the strategy freedom includes management feature freedom and strategy node freedom; Based on the strategy degrees of freedom, mission risk avoidance decisions and collision avoidance decisions are made.

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