CN119941204A - A comprehensive management platform for enterprise human resources - Google Patents
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Abstract
本发明公开了一种企业人力资源综合管理平台，涉及数据管理技术领域，包括：资源管理体系确定模块，用于确定资源管理体系；管理逻辑链确定模块，用于确定管理逻辑链；任务管理策略确定模块，用于确定任务管理策略；敏感管理模式设定模块，用于设定针对敏感数据类的敏感管理模式；敏感管理策略确定模块，用于确定敏感管理策略；预管理任务管控模块，用于对预管理任务执行管控。解决了现有企业人力资源管理存在的难以应对复杂多变的企业管理需求、缺乏企业多应用系统的集成且敏感数据管理存在风险，进而导致企业人力资源管理不够精准和安全的技术问题，智能化实现人力资源信息的全面整合和高效利用，达到了提高企业管理的准确性、安全性的技术效果。
The present invention discloses an enterprise human resources comprehensive management platform, which relates to the field of data management technology, including: a resource management system determination module, used to determine the resource management system; a management logic chain determination module, used to determine the management logic chain; a task management strategy determination module, used to determine the task management strategy; a sensitive management mode setting module, used to set the sensitive management mode for sensitive data classes; a sensitive management strategy determination module, used to determine the sensitive management strategy; a pre-management task control module, used to control the pre-management task execution. It solves the technical problems of the existing enterprise human resources management that are difficult to cope with the complex and changeable enterprise management needs, lack of integration of multiple enterprise application systems, and risks in sensitive data management, which leads to the inaccurate and insecure enterprise human resources management. It realizes the comprehensive integration and efficient use of human resources information intelligently, and achieves the technical effect of improving the accuracy and security of enterprise management.
Description
Technical Field
The application relates to the technical field of data management, in particular to an enterprise human resource comprehensive management platform.
Background
In the current rapidly developed business environment, enterprise human resource management faces unprecedented challenges and opportunities, with the rapid development of information technology, especially the wide application of technologies such as cloud computing, big data, artificial intelligence and the like, a powerful technical support is provided for the construction of an enterprise human resource comprehensive management platform, but with the increasing of enterprise scale, the increasing of staff number and the aggravation of market competition, the traditional human resource management cannot meet the requirements of enterprises on comprehensive, efficient and precise management of human resource information, the traditional human resource management system is often isolated, the integration with other enterprise systems is lacking, the information island phenomenon is serious, the effective sharing and utilization of resources cannot be realized, the management mode is inflexible, the complex and changeable management requirements are difficult to deal with, and the management of sensitive data often faces the risks of data leakage and abuse.
Therefore, in the related technology of enterprise human resource management at present, there are technical problems that complex and changeable enterprise management needs are difficult to deal with, integration of enterprise multi-application systems is lacking, and sensitive data management is at risk, so that enterprise human resource management is not accurate and safe enough.
Disclosure of Invention
The application solves the technical problems that the existing enterprise manpower resource management is difficult to cope with complex and changeable enterprise management requirements, the integration of enterprise multi-application systems is lacking and the sensitive data management is at risk, thereby causing the insufficient precision and safety of enterprise manpower resource management, realizing the comprehensive integration and high-efficiency utilization of manpower resource information in an intelligent way, and achieving the technical effects of improving the accuracy and safety of enterprise management by adopting the technical means of multi-target logic fuzzy conversion, defuzzification, differential conversion, sensitive data identification and the like.
The application provides an enterprise human resource comprehensive management platform which comprises a resource management system determining module, a management logic chain determining module, a task management policy determining module and a sensitivity management policy determining module, wherein the resource management system determining module is used for integrating and systemizing a plurality of application systems aiming at human resource application systems, the resource management system is extensible, the management logic chain determining module is used for defining a management core point based on management types, performing multi-target logic fuzzy conversion and determining a management logic chain, the management logic chain corresponds to the management core point one by one, the management core point is used as a logic main body direction, the task management policy determining module is used for an intelligent management center to receive a pre-management task, traversing the management logic chain to match a target logic link, performing task subjective adjustment and defuzzification, determining a task management policy, the sensitivity management mode setting module is used for setting a sensitivity management mode aiming at sensitive data types, the sensitivity management mode is a differential conversion mode through balancing sensitivity and data value, the sensitivity management policy determining module is used for identifying the pre-management task, performing sensitivity data identification and sensitivity mode activation, and determining a sensitivity management policy, and the pre-management policy is used for responding to the intelligent management policy and performing the pre-management policy.
In a possible implementation manner, the resource management system determining module further performs the following processing that an extension mode of the resource management system comprises layout structure update and system newly-added update, saturation judgment is performed on the resource management system based on response time delay and concurrent processing state, if a preset saturation coefficient is met, a system optimization instruction is generated, and the resource management system is optimized based on the system optimization instruction.
In a possible implementation manner, the management logic chain determining module further performs the following processing of reading homologous management records, performing primary clustering based on management types to determine a first clustering result, traversing the first clustering result, mining management core points, performing secondary 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.
In a possible implementation manner, the management logic chain determining module further performs the following processing of traversing the second clustering result, extracting a first cluster, identifying a common logic point and an anisotropic logic point, constructing a bidirectional fuzzy conversion branch, performing fuzzy conversion on the anisotropic logic point to determine a fuzzy conversion logic point, and integrating the common logic point and the fuzzy conversion logic point in a positive sequence to generate a first management logic chain.
In a possible implementation manner, the sensitive management policy determining module further performs the following processing of identifying and extracting sensitive data, determining differential sag based on a data sensitivity level and an effective management characteristic, converting the sensitive data based on the differential sag, determining desensitized data, globally coordinating the desensitized data based on data correlation, and determining the sensitive management policy.
In a possible implementation manner, before the pre-management task management and control module executes the task management strategy and the sensitive management strategy, the management risk point is predicted by combining the homologous management record, wherein the risk type comprises objective risks and subjective risks, and the management strategy mapping mark is performed by identifying the risk type and the risk level based on the management risk point.
In a possible implementation manner, the enterprise human resource comprehensive management platform further performs the following processing of performing management policy analysis and concurrent management resource allocation if the enterprise human resource comprehensive management platform is a concurrent task, acquiring the management policy of the concurrent task, performing task collision judgment, positioning task collision nodes, and performing collision management on the task collision nodes by combining an avoidance principle if the task collision nodes are not empty.
In a possible implementation manner, the enterprise human resource comprehensive management platform further performs the following processing of identifying a management policy, determining a policy degree of freedom, wherein the policy degree of freedom comprises a management feature degree of freedom and a policy node degree of freedom, and performing task risk avoidance decision and collision avoidance decision based on the policy degree of freedom.
The enterprise human resource comprehensive management platform is designed to be used for integrating and systemizing a plurality of application systems aiming at human resource application systems, determining a resource management system, wherein the resource management system is extensible, defining management core points based on management types, performing multi-target logic fuzzy conversion, determining a management logic chain, wherein the management logic chain corresponds to the management core points one by one, the management core points are used as logic main body directions, an intelligent management center receives a pre-management task, traverses the management logic chain to match with target logic links, performs task subjective adjustment and defuzzification, determines a task management strategy, sets a sensitive management mode aiming at sensitive data types, the sensitive management mode is a differential conversion mode through balancing sensitivity and data value, identifies the pre-management task, performs sensitive data identification and sensitive mode activation, and determines a sensitive management strategy, and the task management strategy and the sensitive management strategy respond to the intelligent management center to perform management control on the pre-management task. The system solves the technical problems that the existing enterprise human resource management is difficult to deal with complex and changeable enterprise management requirements, the integration of enterprise multi-application systems is lacking, and the sensitive data management is at risk, so that the enterprise human resource management is not accurate and safe, the comprehensive integration and high-efficiency utilization of human resource information are intelligently realized, and the technical effects of improving the accuracy and safety of the enterprise management are achieved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the following will briefly describe the drawings of the embodiments of the present disclosure, and a flowchart is used in the present disclosure to illustrate operations performed by a platform according to embodiments of the present disclosure. It should be understood that the preceding or following operations are not necessarily performed in order precisely. Rather, the various steps may be processed in reverse order or simultaneously, as desired. Also, other operations may be added to or removed from these processes.
FIG. 1 is a schematic structural diagram of an enterprise human resources comprehensive management platform according to an embodiment of the present application;
fig. 2 is a schematic diagram of an execution process of a management logic chain determining module in an enterprise human resources integrated management platform according to an embodiment of the present application.
Reference numerals illustrate the resource management system determining module 10, the management logic chain determining module 20, the task management policy determining module 30, the sensitive management mode setting module 40, the sensitive management policy determining module 50, and the pre-management task management control module 60.
Detailed Description
The foregoing description is only an overview of the present application, and is intended to be implemented in accordance with the teachings of the present application in order that the same may be more clearly understood and to make the same and other objects, features and advantages of the present application more readily apparent.
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be described in further detail with reference to the accompanying drawings, and the described embodiments should not be construed as limiting the present application, and all other embodiments obtained by those skilled in the art without making any inventive effort are within the scope of the present application.
In the following description, reference is made to "some embodiments" which describe a subset of all possible embodiments, but it is to 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, the term "first\second" being referred to merely as distinguishing between similar objects and not representing a particular ordering for the objects. The terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, platform, article, or server that comprises a list of steps or elements is not necessarily limited to those steps or elements that are expressly listed or inherent to such process, article, or apparatus, but may include other steps or modules that are not expressly listed or inherent to such process, article, or apparatus, and unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. The terminology used herein is for the purpose of describing embodiments of the application only.
The embodiment of the application provides an enterprise human resource comprehensive management platform, as shown in fig. 1, which comprises:
The resource management system determining module 10 is configured to integrate and systemize multiple application systems for human resource application systems, and determine a resource management system, where the resource management system is extensible. The system is used for integrating and structuring multiple application systems aiming at human resource application systems, and particularly integrates each independent human resource application system (such as recruitment, training, performance management and the like) onto a unified platform through technical means (such as API interfaces, middleware and the like) so as to realize sharing and circulation of information, each existing sub-application system needs to be evaluated and analyzed in the integration process to determine which can be integrated and integrated, a complete human resource management system is built on the basis of integrating each sub-application system, and comprises modules of compensation, performance, quality assessment, training and the like, and the association and cooperation modes among the modules are determined to form an organic whole to complete application system structuring; then, according to the enterprise strategy and the business development requirement, the target and direction of human resource management are defined, based on human resource management six modules (compensation, performance, quality assessment, training, etc.), a perfect management system is constructed, and detailed management system and flow are formulated to ensure the orderly progress of each management work, wherein the resource management system is expandable and may include technical expandability, functional expandability and data expandability, specifically, the technical expandability refers to selecting a technical architecture and development mode with good expandability, supporting the upgrade and expansion of the system, and reserving enough interfaces and expansion points to add new functions or modules in the future according to the requirement, the functional expandability refers to designing flexible management logic chains and management core points, supporting the requirement change under different management scenes, and simultaneously providing configurable parameters and options, the data expandability means to establish a perfect data management system, support data acquisition, storage, analysis and application, reserve data interfaces and expansion points, and support data exchange and sharing with other application systems.
The management logic chain determining module 20 defines a management core point based on a management type, performs multi-objective logic fuzzy conversion, and determines a management logic chain, wherein the management logic chain corresponds to the management core point one by one, and takes the management core point as a logic main body direction. The management type generally refers to a management mode or form formed based on different industries, different organization characteristics or different management targets, for example, production type management, production management, scientific research production management and the like, the management core point refers to the most critical or core element in each management type, and the management core point may include aspects of target setting, resource configuration, decision making, team construction, flow optimization and the like, and in real management, a plurality of targets related to each other or conflict often exist, and a solution for achieving balance or optimization between the targets is found by using multi-target decision theory. When the multiple targets have ambiguity (i.e. the target definition is not completely clear or is influenced by other factors), fuzzy conversion is needed, namely, fuzzy mathematic tools are used for quantifying the ambiguity of the targets, decisions are made based on the quantified results, then management logic chains are determined, the management logic chains refer to the complete process of starting from the management targets and through a plurality of management activities (such as planning, organization, leadership, control and the like), the management targets are finally realized, in the management logic chains, all links are mutually related and mutually influenced to form a tightly connected management logic chain, wherein the management logic chains are in one-to-one correspondence with the management core points, the management core points serve as starting points or key nodes of the logic chains, the direction and the key points of the whole logic chains are determined, for example, if the management core points are the efficiency of the organization is improved, the logic chains can be unfolded around the aspects of optimizing flow, improving employee efficiency, reducing operation cost and the like, specifically, in the management core points are taken as the direction of the logic main body, in the human resource management of an actual enterprise, all management core points are always guided, all management activities are ensured to be unfolded around the core points, and the management core points are continuously adjusted and the management activities are optimized to ensure that the management core points are realized to the maximum.
The task management policy determining module 30 is configured to receive a pre-management task from the intelligent management center, traverse the management logic chain to match with the target logic link, perform subjective adjustment and defuzzification of the task, and determine a task management policy. The intelligent management center is a digital management system integrating advanced technology and functions, realizes functions of data storage, scheduling, analysis processing and the like through an information technology, provides one-stop intelligent, efficient and convenient management service for enterprises, mainly comprises information management, decision support, flow management, work intellectualization, safety management and the like, specifically, the intelligent management center receives pre-management tasks such as various enterprise activities needing management, optimization or monitoring, then traverses defined management logic chains, and matches and identifies logic links related to the pre-management tasks according to the characteristics and the requirements of the tasks, namely, the intelligent management center performs detailed analysis on the characteristics, the requirements and the management targets of the tasks, finds out the most suitable and most matched target logic links of the pre-management tasks, and after the target logic links are determined, the intelligent management center performs management adjustment on the tasks according to specific conditions, and possibly comprises priority setting of the tasks, redistribution of the resources, optimization of the working flows and the like, so as to ensure that the tasks can better adapt to requirements of the logic links, specifically, the intelligent management center fully considers the capacity, the workload of staff, the work load, the factors of the staff and the factors of the management staff after adjustment can better execute the tasks. If ambiguity or uncertainty exists in the task, the intelligent management center refines and definitely processes the task by adopting a defuzzification method, such as based on a carding filtering method, a deep learning method, a variational Bayesian method and the like, and eliminates the ambiguous factors in the task, so that the task target is clearer, more specific and quantifiable, a final task management strategy is finally determined, key elements of a task execution plan, a resource allocation, a monitoring and evaluating mechanism and the like are defined, and the task is ensured to be carried out according to a preset plan.
The sensitivity management mode setting module 40 is configured to set a sensitivity management mode for a sensitive data class, where the sensitivity management mode is a differential conversion mode that balances sensitivity and data value. Setting a sensitive management mode aiming at sensitive data, specifically adopting a differential conversion mode for balancing sensitivity and data value, wherein the mode aims at ensuring that the value utilization of the data is maximized while protecting the sensitivity of the data, specifically, the sensitive management mode is a special data management method, and is designed and implemented especially aiming at the sensitive data, and the core idea is to realize effective management and utilization of the sensitive data by balancing the sensitivity and the data value, wherein the differential conversion mode is a key component of the sensitive management mode and provides scientific basis for management and utilization of the sensitive data by differential conversion of the sensitivity and the data value. Firstly, carrying out quantization analysis on the sensitivity degree of data, such as evaluating confidentiality, integrity, availability and the like of the data, and then carrying out quantization analysis on the potential value of the data, such as commercial value, scientific research value, social value and the like of the data, so as to be helpful for knowing the possible risk brought by data leakage and the importance and utilization potential of the data, wherein a differential conversion mode is to carry out quantization comparison and conversion on the sensitivity and the data value, namely finding a balance point, so that the value utilization of the data is not excessively limited while the sensitivity of the data is protected, for example, a certain conversion rule and a certain threshold value are set, the sensitivity and the data value are converted into comparable values, so that the balance of the sensitivity and the data value is realized, and a more specific and more accurate management strategy is formulated according to the values.
The sensitive management policy determining module 50 is configured to identify the pre-management task, perform sensitive data identification and sensitive mode activation, and determine a sensitive management policy. The method comprises the steps of identifying a pre-management task, namely comprehensively analyzing and understanding content, targets and the like of the pre-management task, wherein the method comprises the steps of collecting, sorting and analyzing task related data to clear specific requirements and potential risks of the task, then carrying out sensitive data identification and sensitive mode activation, wherein the sensitive data identification is a key link in the pre-management task, detecting the existence of sensitive information through deep analysis of data, using special tools or technologies, judging the sensitivity of the data according to specific regulations and standards of organizations or industries, discovering and managing data which can have great influence on the organizations or individuals, the sensitive data comprises but not limited to personal identity information (such as names, identification card numbers, addresses, telephones and the like), protected health information, business, intellectual property and the like, and the sensitive mode activation comprises the steps of setting specific access rights, encrypting the sensitive data, implementing data desensitization measures, establishing a real-time monitoring and alarm system and the like according to the sensitive level and potential risks of the data after the sensitive data is identified, wherein the sensitive mode activation needs to ensure that the sensitive data is protected, the sensitive data is not influenced at the same time, the sensitive data can be normally responded by the sensitive data and the normal operation policy, the sensitive data can be established and the normal operation policy and the sensitive data can be responded by the normal operation policy and the normal data is established by the specific policy and the sensitive data. In summary, the pre-management task is identified, the sensitive data identification and the sensitive mode activation are performed, and the sensitive management strategy is determined, so that the safety and the compliance use of the data are ensured.
And the pre-management task management and control module 60 is used for responding to the intelligent management center by the task management strategy and the sensitive management strategy and performing management and control on the pre-management task. The intelligent management center monitors, schedules, analyzes and controls the pre-management task in real time according to the task management strategy and the requirement of the sensitive management strategy to ensure the smooth execution of the task and the safety management of the sensitive data, specifically, the intelligent management center monitors the execution of the pre-management task in real time, collects the data and information in the task execution process, schedules and controls the task according to the requirement of the task management strategy to ensure the task to be carried out according to a preset plan and step, analyzes the data generated in the task execution process, discovers problems and optimizes the problems to improve the task execution efficiency and quality, the intelligent management center identifies the sensitive data related in the task execution process according to the requirement of the sensitive management strategy to ensure the accuracy and the integrity of the sensitive data, and after the sensitive data is identified, the intelligent management center activates the corresponding sensitive management mode according to the sensitive level and the potential risk of the sensitive data, such as setting access authority, encrypting the sensitive data and the like, and finally carries out real-time monitoring on the sensitive data to ensure the safety and compliance use of the sensitive data in the task execution process.
According to the enterprise human resource comprehensive management platform provided by the embodiment of the invention, the technical problems that complex and changeable enterprise management requirements are difficult to deal with, integration of enterprise multi-application systems is lacked, and sensitive data management is risky in the existing enterprise human resource management are solved, so that the enterprise human resource management is not accurate and safe are further caused, comprehensive integration and efficient utilization of human resource information are realized intelligently, and the technical effects of improving the accuracy and safety of enterprise management are achieved. The enterprise human resource comprehensive management platform comprises a resource management system determining module 10, a management logic chain determining module 20, a task management strategy determining module 30, a sensitive management mode setting module 40, a sensitive management strategy determining module 50 and a pre-management task management and control module 60.
Next, the specific configuration of the resource management hierarchy determination module 10 will be described in detail. The resource management system determination module 10 may further include that the extension mode of the resource management system includes a layout structure update and a system newly added update. The system newly-added update refers to adding new resources, functions or modules on the basis of the existing resource management system so as to expand the coverage range of the management system and improve the management capability, such as introducing new technologies, equipment, software or personnel, developing new management strategies and methods, enriching the content of the resource management system and improving the resource utilization rate and management efficiency of the organization.
The resource management system determining module 10 further includes performing saturation determination on the resource management system based on the response delay and the concurrent processing state, and if the saturation coefficient satisfies the preset saturation coefficient, generating a system optimization instruction. With the internal redundancy of data processing quantity, application increment and the like, management energy efficiency is low, optimization adjustment is needed immediately, namely saturation judgment is conducted based on response time delay and concurrent processing state, whether a resource management system reaches the processing capacity limit of the resource management system is evaluated, specifically, the response time delay refers to time required by the system to respond to a request, response speed and processing capacity of the system are known through monitoring the response time delay, when the response time delay exceeds a set threshold value, the system is close to or reaches the saturation state, the concurrent processing state refers to the capacity of the system for simultaneously processing a plurality of tasks or requests, the load condition and the processing capacity of the system are known through monitoring the concurrent processing state, when the concurrent processing state reaches or exceeds the design capacity of the system, the system is close to or reaches the saturation state, the preset saturation coefficient is a threshold value set according to the organization requirement and the system performance, when the response time delay and the concurrent processing state reach or exceed the threshold value, the system is judged to be saturated, and a system optimization instruction is generated.
The resource management hierarchy determination module 10 further includes optimizing the resource management hierarchy based on the hierarchy optimization instructions. Optimizing a resource management system based on a system optimization instruction may comprise resource reallocation, flow optimization, technology upgrading, resource expansion, adjustment management strategy and the like, wherein the resource reallocation is to reallocate resources according to the load condition and performance requirement of a system, ensure that critical tasks or requests are supported by enough resources, the flow optimization is to inspect and improve the existing flow, eliminate bottlenecks and waste, improve flow efficiency and response speed, the technology upgrading is to guide new technology, equipment or software, improve the processing capacity and efficiency of the system, the resource expansion is to consider adding new resources such as servers, storage equipment or personnel and the like if the system resources are insufficient, and the adjustment management strategy is to adjust the management strategy and method according to actual conditions so as to adapt to the development requirement of an organization and the change of market environment.
Next, the specific configuration of the management logic chain determination module 20 will be described in detail. As shown in fig. 2, the management logic chain determining module 20 may further include reading the homology management record, performing clustering once based on the management type, and determining a first clustering result. The relevant management records are read from a certain data source (such as a database, a file and the like) and comprise historical management records and application record data (such as detailed information of various management activities) of the same line, then the read management records are subjected to preliminary clustering through a clustering algorithm (such as K-means, hierarchical clustering and the like) or a simple classification method (such as classification based on rules), the management records are divided into different groups, each group represents one management type, and a first clustering result is obtained.
The management logic chain determining module 20 further includes traversing the first clustering result, mining management core points, and performing secondary clustering to determine a second clustering result, where each management type corresponds to at least one management core point. After the first clustering result is obtained, each management type (i.e. each cluster) is subjected to deeper analysis, specifically, in each management type, key management elements or points of interest are identified, for example, in human resource management, management core points may include employee information management, risk management and the like, secondary clustering is performed based on the management core points, similar or related management core points are clustered together to form a more subdivided category or sub-category, and a second clustering result is obtained, so that the internal structure and complexity of management activities can be better reflected, and each management type corresponds to at least one management core point. And traversing the second aggregation result, and mining the management logic chain. After the second clustering result is obtained, the logical relationship (management logic chain) among the clusters is mined, namely, the logical relationship and the dependency relationship among different steps, links or elements in each management activity are mined, and a complete management flow is constructed.
Next, the specific configuration of the management logic chain determination module 20 will be described in further detail. The management logic chain determination module 20 may further include traversing the second clustering result, extracting a first cluster, and identifying common logical points and anisotropic logical points. Traversing each cluster in the second clustering result, extracting a first cluster, namely any one of a plurality of clusters, and identifying common logical points and anisotropic logical points of the first cluster, wherein the common logical points are common management logical points with commonality in the first cluster, and the anisotropic logical points are management logical points with differences or specificities in the first cluster, namely a plurality of approximate logical relations are contained in one cluster, and the differentiated logical points exist.
The management logic chain determining module 20 further includes constructing a bidirectional fuzzy conversion branch, performing fuzzy conversion on the anisotropic logic point, and determining a fuzzy conversion logic point. For anisotropic logic points, because they may have different manifestations or processing manners under different circumstances, in order to improve universality of a finally determined logic, fuzzy conversion is performed on multiple mappings corresponding to the logic points, and a bidirectional fuzzy conversion branch refers to a model capable of converting the anisotropic logic points into a unified format or representation form according to different circumstances or conditions, and generally includes a set of conversion rules and conversion functions for mapping the original logic points to a certain position in a fuzzy space, then performing inverse conversion according to needs, and converting the anisotropic logic points into fuzzy conversion logic points by applying the bidirectional fuzzy conversion branch.
The management logic chain determining module 20 further includes a positive serialization integrating the common logical point and the fuzzy conversion logical point to generate a first management logic chain. According to the actual sequence or logic relation of the management flow, the common logic points and the fuzzy conversion logic points are ordered, each step in the logic chain is ensured to be arranged according to the actual occurrence sequence of the common logic points and the fuzzy conversion logic points in the management flow, the ordered logic points are connected according to the positive sequence (namely the logic sequence) to form a continuous management logic chain, and a series of key steps and decision processes from management start to end are clearly reflected.
Next, the specific configuration of the sensitive management policy determination module 50 will be described in detail. The sensitivity management policy determination module 50 may further include identifying and extracting sensitive data, determining differential sag based on data sensitivity level and effective management features. Identifying and extracting sensitive data (e.g., personal identity information, financial information, business secrets, etc.), data sensitivity level refers to a level that is classified by the sensitivity level of the data, differential sag may refer to privacy protection strength determined according to the sensitivity level of the sensitive data and effective management features (e.g., use of data, storage time, access rights, etc.), the higher the sensitivity level, the more stringent the effective management features, and the less differential sag may be required, meaning higher privacy protection requirements for the data.
The sensitive management policy determination module 50 further includes converting the sensitive data based on the differential sag to determine desensitized data. According to the determined differential sag, 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 Laplace mechanism), data generalization (such as replacing age range with age range), data anonymization (such as replacing real name with anonymous identifier), and the like, and finally the desensitized data is determined.
The sensitive management policy determination module 50 further includes globally reconciling the desensitized data based on the data correlation to determine the sensitive management policy. When determining the sensitive management strategy, the data with high correlation needs to take stricter protection measures to prevent sensitive information from being inferred through the correlation between the data, global coordination specifically refers to comprehensive consideration of a plurality of data sources or data sets to ensure the consistency of privacy protection level of the whole data ecosystem, finally determine the sensitive management strategy, and define privacy protection requirements of all links such as collection, storage, use, sharing and destruction of the data, and corresponding responsibility and punishment measures.
Next, the specific configuration of the pre-management task management module 60 will be described in detail. The pre-management task management module 60 may further identify the task management policy and the sensitive management policy, and predict management risk points in combination with the homologous management records, where the risk type includes objective risk and subjective risk. And identifying a task management strategy and a sensitive management strategy, and carrying out prediction of management risk points by combining homologous management records, namely identifying potential management risk points by analyzing the records and combining the current task management strategy and the sensitive management strategy, wherein the risk points possibly comprise task delay, insufficient resources, data leakage, privacy infringement and the like, and the risk types comprise subjective risks and objective risks.
The pre-management task management module 60 further includes identifying a risk type and a risk level based on the management risk point, and performing management policy mapping marking. Analyzing the obtained management risk points, identifying risk types and risk grades, specifically, objective risks including uncontrollable factors such as technical faults, subjective risks including human misoperation, decision errors and the like, classifying the management point risks into different grades according to the possibility and influence degree of the risks, such as general risks, larger risks, major risks, extra-large risks and the like, mapping corresponding management strategies onto corresponding risk points according to the identified risk types and risk grades, and carrying out clear marking on each risk point and management strategy mapping, wherein the risk types, the risk grades, countermeasures, responsible persons, expiration dates and the like can be included so as to facilitate tracking and management.
Next, a detailed description will be continued of a specific configuration of an enterprise human resources integrated management platform. The enterprise human resource comprehensive management platform further comprises management strategy analysis and concurrency management resource allocation if the enterprise human resource comprehensive management platform is a concurrency task. If the tasks are concurrent tasks, management policy analysis and concurrent management resource allocation are performed, specifically, the management policy analysis refers to analyzing the target, priority, dependency relationship and the like of each concurrent task, determining proper execution sequence and resource allocation, and the concurrent management resource allocation refers to allocating hardware resources such as a processor, a memory, storage and the like according to the requirements and the priority of the tasks, so that fairness and efficiency of resource allocation are ensured, and excessive allocation or insufficient resource is avoided. And acquiring a management strategy of the concurrent task, judging task collision, and positioning task collision nodes. The management strategy of each concurrent task is obtained from the task management system, and comprises the steps of executing sequence, dependency relationship and the like, checking whether the concurrent task has resource conflict, time conflict or dependency relationship conflict, namely detecting potential collision points such as time overlapping, shared resource competition and the like, and if collision is detected, positioning a task collision node, namely precisely positioning a specific node or time period such as task step, resource access point or time window when the collision occurs. And if the task collision node is not empty, carrying out collision management on the task collision node by combining with an avoidance principle. The task collision node is not empty, which means that in the process of executing concurrent tasks, the task collision or collision point actually existing is detected through a task collision judging step, the avoidance principle is used for guiding how to adjust the task execution plan when the collision occurs, and may include a priority principle (high priority task priority execution), a resource priority principle (task with most urgent resource demand is met first), a time priority principle (task with most strict time limit is met first), and the like, and the task collision node is subjected to collision management, which means that the collision node is adjusted according to the avoidance principle, for example, resources are redistributed, the task execution sequence is adjusted, tasks are delayed or split, and the effect after the collision management is monitored, so that the adjusted task plan can be successfully executed.
Next, a detailed description will be continued of a specific configuration of an enterprise human resources integrated management platform. The enterprise human resource comprehensive management platform further comprises the steps of identifying management strategies and determining strategy degrees of freedom, wherein the strategy degrees of freedom comprise management characteristic degrees of freedom and strategy node degrees of freedom. The management policy is analyzed and identified, the degree of freedom of the policy in the execution process is definitely divided into two main parts, the degree of freedom of the management feature and the degree of freedom of the policy node, specifically, the degree of freedom of the management feature refers to the capability or range of the management policy in the design and execution process according to the change of the internal and external environments of an organization, the degree of freedom of the management policy depends on the flexibility and adaptability of the management policy and the response speed of the organization to the change, for example, a flexible management policy may allow the adjustment according to the requirements in aspects of resource allocation, task priority, decision process and the like, the higher degree of freedom of the management feature can better cope with uncertainty and improve the adaptability and competitiveness of the organization, and the degree of freedom of the policy node refers to the flexibility and adjustability of the organization in the policy execution process, especially in key nodes (such as decision points, resource allocation points and the like), the organization can flexibly adjust the policy according to actual conditions, such as changing decisions, reallocating resources or adjusting task priority and the like, and the higher degree of freedom of the node can enable the policy to be better implemented in the policy execution process to change the environment flexibly and better.
And performing task risk avoidance decisions and collision avoidance decisions based on the policy degrees of freedom. After the degrees of freedom (including the degrees of freedom of the management features and the degrees of freedom of the policy nodes) of the management policy are identified and determined, the degrees of freedom are utilized to make and execute decisions so as to reduce risks and collisions possibly encountered in the task execution process, specifically, the task risk avoidance decision refers to comprehensively evaluating risks possibly encountered in the task execution process, including resource risks, technical risks, market risks and the like, according to the risk evaluation result, specific targets of risk avoidance, such as reducing the possibility of risk occurrence, reducing loss caused by the risks and the like, are determined, then the risk avoidance decision is adjusted and optimized according to the policy degrees of freedom so as to avoid the potential risks, for example, the task execution sequence is changed, the decision process is adjusted and the like, and meanwhile, the policy degree of freedom adjustment policy is utilized, such as optimizing task scheduling, enhancing resource coordination, changing task priority, adjusting resource allocation and the like so as to reduce the occurrence of collisions.
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 the server, and the included units and modules are merely divided according to functional logic, but are not limited to the above-described division, so long as the corresponding functions can be implemented, and in addition, specific names of the functional units are only for convenience of distinguishing from each other, and are not intended to limit the scope of protection of the present application.
The above embodiments do not limit the scope of the present application. It will be apparent to those skilled in the art that various modifications, combinations, and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application should be included in the scope of the present application.
Claims (8)
1. An enterprise human resources integrated management platform, which is characterized in that the platform comprises:
The resource management system determining module is used for integrating and systemizing a plurality of application systems aiming at the human resource application system to determine a resource management system, wherein the resource management system is expandable;
the management logic chain determining module is used for defining a management core point based on a management type, carrying out multi-target logic fuzzy conversion, and determining a management logic chain, wherein the management logic chain corresponds to the management core point one by one, and takes the management core point as a logic main body direction;
The task management strategy determining module is used for receiving a pre-management task by the intelligent management center, traversing the management logic chain to match with a target logic link, performing subjective adjustment and defuzzification of the task, and determining a task management strategy;
The sensitive management mode setting module is used for setting a sensitive management mode aiming at sensitive data types, wherein the sensitive management mode is a differential conversion mode for balancing sensitivity and data value;
the sensitive management strategy determining module is used for identifying the pre-management task, carrying out sensitive data identification and sensitive mode activation and determining a sensitive management strategy;
and the pre-management task management and control module is used for responding to the intelligent management center by the task management strategy and the sensitive management strategy and executing management and control on the pre-management task.
2. The integrated management platform for human resources of an enterprise of claim 1, wherein the resource management hierarchy determination module performs the steps comprising:
the expansion mode of the resource management system comprises layout structure update and system newly-added update;
based on the response time delay and the concurrent processing state, carrying out saturation judgment on the resource management system, and if the preset saturation coefficient is met, generating a system optimization instruction;
and optimizing the resource management system based on the system optimization instruction.
3. The integrated management platform for human resources of an enterprise as claimed in claim 1, wherein the management logic chain determination module performs the steps comprising:
Reading a homologous management record, carrying out primary clustering based on the management type, and determining a first clustering result;
traversing the first clustering result, mining management core points, performing secondary clustering, and determining a second clustering result, wherein each management type corresponds to at least one management core point;
Traversing the second aggregation result, and mining the management logic chain.
4. The enterprise human resources comprehensive management platform of claim 3, wherein the management logic chain determination module performs the steps comprising:
traversing the second clustering result, extracting a first cluster, and identifying a common logical point and an anisotropic logical point;
constructing a bidirectional fuzzy conversion branch, performing fuzzy conversion on the anisotropic logic points, and determining fuzzy conversion logic points;
and integrating the common logic point and the fuzzy conversion logic point in a positive sequence way to generate a first management logic chain.
5. The integrated management platform for human resources of an enterprise as claimed in claim 1, wherein the sensitive management policy determination module performs the steps comprising:
Identifying and extracting sensitive data, and determining differential looseness based on data sensitivity level and effective management characteristics;
converting the sensitive data based on the differential sag to determine desensitized data;
and carrying out global coordination on the desensitization data based on the data correlation, and determining the sensitive management strategy.
6. The integrated management platform for human resources of an enterprise as set forth in claim 3, wherein the pre-management task management module further comprises, prior to execution:
identifying the task management strategy and the sensitive management strategy, and predicting management risk points by combining the homologous management records, wherein the risk type comprises objective risks and subjective risks;
And identifying the risk type and the risk grade based on the management risk points, and carrying out management strategy mapping marking.
7. The integrated management platform for human resources of an enterprise of claim 1, further comprising:
If the task is the concurrent task, carrying out management strategy analysis and concurrent management resource allocation;
acquiring a management strategy of the concurrent task, judging task collision, and positioning task collision nodes;
and if the task collision node is not empty, carrying out collision management on the task collision node by combining with an avoidance principle.
8. The integrated management platform for human resources of an enterprise of claim 1, further comprising:
identifying a management policy, and determining a policy degree of freedom, wherein the policy degree of freedom comprises a management characteristic degree of freedom and a policy node degree of freedom;
and carrying out task risk avoidance decisions and collision avoidance decisions based on the policy degrees of freedom.
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Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20100114672A1 (en) * | 2008-11-05 | 2010-05-06 | Oracle International Corporation | Employee Talent Review Management Module |
| US7783666B1 (en) * | 2007-09-26 | 2010-08-24 | Netapp, Inc. | Controlling access to storage resources by using access pattern based quotas |
| US20110153362A1 (en) * | 2009-12-17 | 2011-06-23 | Valin David A | Method and mechanism for identifying protecting, requesting, assisting and managing information |
| US20160162478A1 (en) * | 2014-11-25 | 2016-06-09 | Lionbridge Techologies, Inc. | Information technology platform for language translation and task management |
| CN117744157A (en) * | 2024-01-12 | 2024-03-22 | 上海零数众合信息科技有限公司 | Intelligent contract management method and system based on information confusion desensitization |
| CN118154233A (en) * | 2024-01-31 | 2024-06-07 | 北京极致车网科技有限公司 | Automobile merchant intelligent risk identification system based on merchant state self-monitoring |
| CN118487879A (en) * | 2024-07-16 | 2024-08-13 | 北京联讯星烨科技有限公司 | Full-process intrusion risk detection method and system for enterprise service platform |
| CN118657389A (en) * | 2024-08-21 | 2024-09-17 | 山西大数据产业发展有限公司 | Intelligent management and control method and system based on enterprise-wide risk analysis |
| CN118945033A (en) * | 2024-10-12 | 2024-11-12 | 深圳恒通未来科技有限公司 | A management method, system and terminal device for communication transmission platform |
-
2025
- 2025-01-06 CN CN202510017673.6A patent/CN119941204B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7783666B1 (en) * | 2007-09-26 | 2010-08-24 | Netapp, Inc. | Controlling access to storage resources by using access pattern based quotas |
| US20100114672A1 (en) * | 2008-11-05 | 2010-05-06 | Oracle International Corporation | Employee Talent Review Management Module |
| US20110153362A1 (en) * | 2009-12-17 | 2011-06-23 | Valin David A | Method and mechanism for identifying protecting, requesting, assisting and managing information |
| US20160162478A1 (en) * | 2014-11-25 | 2016-06-09 | Lionbridge Techologies, Inc. | Information technology platform for language translation and task management |
| CN117744157A (en) * | 2024-01-12 | 2024-03-22 | 上海零数众合信息科技有限公司 | Intelligent contract management method and system based on information confusion desensitization |
| CN118154233A (en) * | 2024-01-31 | 2024-06-07 | 北京极致车网科技有限公司 | Automobile merchant intelligent risk identification system based on merchant state self-monitoring |
| CN118487879A (en) * | 2024-07-16 | 2024-08-13 | 北京联讯星烨科技有限公司 | Full-process intrusion risk detection method and system for enterprise service platform |
| CN118657389A (en) * | 2024-08-21 | 2024-09-17 | 山西大数据产业发展有限公司 | Intelligent management and control method and system based on enterprise-wide risk analysis |
| CN118945033A (en) * | 2024-10-12 | 2024-11-12 | 深圳恒通未来科技有限公司 | A management method, system and terminal device for communication transmission platform |
Non-Patent Citations (2)
| Title |
|---|
| 于彦臻: "事业单位人力资源信息化建设与实践探索", 环渤海经济瞭望, 3 September 2024 (2024-09-03), pages 8 - 11 * |
| 陶俊: "YL公司绩效管理体系优化研究", 中国优秀硕士学位论文全文数据库, 15 May 2024 (2024-05-15) * |
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|---|---|
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