Performance assessment method and device based on RBAC and medium

Abstract

The invention discloses a performance assessment method and device based on RBAC and a medium, and the method comprises the steps: obtaining organization structure change, personnel post adjustment and assessment rule updating events of an enterprise, generating a structured change log of the enterprise, and constructing a metadata version tree of the structured change log through a directed acyclic graph; performing metadata mapping conversion on the metadata version tree to construct a multi-dimensional permission topology; performing tensor mapping on the multi-dimensional permission topology through a tensor mapping algorithm to generate a multi-dimensional assessment data model; and carrying out weight optimization and real-time calculation on the changed employee assessment data through a multi-dimensional assessment data model so as to update the assessment score of the changed employee. By constructing the multi-dimensional permission topology, the problem that an existing assessment system is low in organization flexibility is solved, the problem that role assessment permissions of the existing assessment system are inconsistent is solved through the multi-dimensional assessment data model, and the accuracy and credibility of employee assessment are greatly improved.

Description

Technical Field

[0001] The present application relates to the field of computers, and in particular to a performance appraisal method, device, and medium based on RBAC. Background Art

[0002] Current enterprise assessment systems generally adopt a static architecture design and face multiple technical bottlenecks in matrix organizational management scenarios.

[0003] On the one hand, the permission model of static architectural design only supports tree-like organizational structures and cannot adapt to dynamic architectural changes such as department reorganization and cross-project team formation, resulting in inaccurate performance appraisals of changed employees and greatly reducing the organizational flexibility of the existing appraisal system. On the other hand, the dimensions of appraisal indicators are rigid and difficult to match changes in business scenarios, and there is a lack of fine-grained control over data access rights, resulting in the existing appraisal system being unable to meet enterprise needs in terms of role authority consistency and appraisal multidimensionality. Summary of the Invention

[0004] The embodiments of the present application provide a performance appraisal method, device, and medium based on RBAC, which are used to solve the problems of low flexibility and inconsistent role permissions in existing enterprise performance appraisal methods.

[0005] The embodiments of this application adopt the following technical solutions: On the one hand, an embodiment of the present application provides a performance appraisal method based on RBAC, which includes: obtaining the company's organizational structure change, personnel position adjustment and appraisal rule update events, generating the company's structured change log, and constructing a metadata version tree of the structured change log through a directed acyclic graph; performing metadata mapping conversion on the metadata version tree to construct a multi-dimensional permission topology; performing tensor mapping on the multi-dimensional permission topology through a tensor mapping algorithm to generate a multi-dimensional appraisal data model; and performing weight optimization and real-time calculation on the changed employee appraisal data through the multi-dimensional appraisal data model to update the appraisal score of the changed employee.

[0006] In one example, the metadata version tree is subjected to metadata mapping conversion to construct a multidimensional permission topology, specifically including: extracting the organizational unit snapshot and hierarchical relationship of the metadata version tree; mapping the organizational unit snapshot into a multidimensional role including contextual conditions, time windows and dynamic labels; constructing a multidimensional role inheritance graph based on the hierarchical relationship; detecting permission coverage conflicts in the multidimensional role inheritance graph through a graph theory algorithm; if there is a permission coverage conflict, prioritizing the role inheritance according to the preset priority to resolve the conflict, and associating and merging the multidimensional role with the multidimensional role inheritance graph to generate a multidimensional permission topology including a role-permission-inheritance relationship; if there is no permission coverage conflict, associating and merging the multidimensional role with the multidimensional role inheritance graph to generate a multidimensional permission topology including a role-permission-inheritance relationship.

[0007] In one example, the multidimensional permission topology is tensor mapped through a tensor mapping algorithm to generate a multidimensional assessment data model, specifically including: mapping the structural tensor of the multidimensional permission topology into the organizational dimension coordinates of the multidimensional assessment data model; extracting the permission timeliness attributes of the multidimensional permission topology, and determining them as the time window constraints and weight attenuation factors of the assessment indicators in the multidimensional assessment data model; converting the permission coverage conflict records of the multidimensional permission topology into the collaborative effectiveness scoring items of the multidimensional assessment data model; mapping the three-dimensional permission tensor space to the four-dimensional assessment tensor space through a tensor product operation, and integrating the organizational dimension coordinates, the time window constraints and weight attenuation factors of the assessment indicators, and the collaborative effectiveness scoring items to generate a multidimensional assessment data model.

[0008] In one example, the multidimensional assessment data model is used to perform weight optimization and real-time calculation on the assessment data of the changed employee to update the assessment score of the changed employee, specifically including: extracting metadata feature parameters from the multidimensional assessment data model to initialize the basic weight matrix of the assessment indicators; inputting the historical assessment data of the changed employee into a pre-trained LSTM time series model to obtain the assessment weight distribution of the changed employee; obtaining the changed assessment data of the changed employee; and performing streaming calculation on the changed assessment data of the changed employee according to the assessment weight distribution of the changed employee through a streaming computing engine to generate a streaming calculation result to update the assessment score of the changed employee.

[0009] In one example, based on the weight distribution of the changed employee, the change assessment data of the changed employee is stream-calculated through a streaming computing engine to generate a streaming computing result to update the assessment score of the changed employee. The method also includes: visually transforming the streaming computing result to generate a visual permission topology map, an assessment heat map and a trend analysis panel map; the permission topology map displays the role inheritance relationship and conflict resolution path in real time; the assessment heat map maps the distribution of assessment results through color gradients; and the trend analysis panel map displays historical data fluctuations through comparison charts.

[0010] In one example, after visually transforming the streaming computing results to generate a visual permission topology map, assessment heat map, and trend analysis panel map, the method further includes: detecting abnormal fluctuations in indicators in the permission topology map, assessment heat map, and trend analysis panel map; when there are abnormal fluctuations in indicators, highlighting the abnormal map points and sending an alarm request to the client.

[0011] In one example, based on the weight distribution of the changed employee, the changed assessment data of the changed employee is stream-calculated through a streaming computing engine to generate a streaming computing result to update the assessment score of the changed employee. The method also includes: judging whether the assessment score is reasonable based on the assessment score of the changed employee and the feedback of the changed employee; when it is found that the assessment score is unreasonable, dynamically adjusting the assessment rules through a reinforcement learning algorithm, and regenerating the assessment score of the changed employee, and iterating in this way to make the assessment score of the changed employee reasonable.

[0012] In one example, after constructing the metadata version tree of the structured change log through a directed acyclic graph, the method also includes: verifying the integrity of the metadata version tree through a Merkle tree; when the metadata version tree is complete, adding a timestamp to the metadata version tree to trace back the organizational structure snapshot at any historical time point through the timestamp.

[0013] On the other hand, an embodiment of the present application provides an RBAC-based performance appraisal device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute any one of the above-mentioned RBAC-based performance appraisal methods.

[0014] On the other hand, an embodiment of the present application provides a non-volatile computer storage medium for RBAC-based performance appraisal, which stores computer-executable instructions capable of executing any of the above-mentioned RBAC-based performance appraisal methods.

[0015] At least one of the above technical solutions adopted in the embodiments of the present application can achieve the following beneficial effects: This application maps the metadata version tree into a multi-dimensional role and inheritance graph that includes contextual conditions, time windows, and dynamic labels. It uses graph theory algorithms to resolve permission conflicts and form a multi-dimensional permission topology of role-permission-inheritance relationships, so that the assessment system can flexibly adapt to complex organizational structures. The permission topology is converted into a four-dimensional assessment tensor space through a tensor mapping algorithm, integrating organizational dimension coordinates, time window constraints, weight attenuation factors, and collaboration effectiveness scoring items to build a multi-dimensional assessment model that is closer to the business scenario, solving the problem of dimensional solidification in traditional models. The streaming computing engine also processes change data in real time to ensure that the assessment scores can reflect the latest performance of employees in a timely manner and improve assessment accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solution of the present application, some embodiments of the present application will be described in detail below with reference to the accompanying drawings, in which: Figure 1A flowchart of a performance appraisal method based on RBAC provided in an embodiment of the present application; Figure 2 A system architecture diagram of a performance appraisal method based on RBAC provided in an embodiment of the present application; Figure 3 A role inheritance relationship diagram of a performance appraisal method based on RBAC provided in an embodiment of the present application; Figure 4 A core algorithm flow chart of a performance appraisal method based on RBAC provided in an embodiment of the present application; Figure 5 A data interaction sequence diagram of a performance appraisal method based on RBAC provided in an embodiment of the present application; Figure 6 A schematic diagram of the structure of a performance appraisal device based on RBAC provided in an embodiment of the present application. DETAILED DESCRIPTION

[0017] To make the objectives, technical solutions, and advantages of this application more clear, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0018] Some embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0019] Figure 1 This is a flow chart of a performance appraisal method based on RBAC provided in an embodiment of the present application. This method can be applied to different business areas. Certain input parameters or intermediate results in this process can be manually adjusted to help improve accuracy.

[0020] The analysis method involved in the embodiments of the present application can be implemented by a terminal device or a server, and the present application does not impose any special restrictions on this. For ease of understanding and description, the following embodiments are described in detail using a controller as an example.

[0021] Based on this, Figure 1 The process in may include the following steps: S101: Obtaining the company's organizational structure change, personnel position adjustment, and assessment rule update events, generating the company's structured change log, and constructing a metadata version tree of the structured change log through a directed acyclic graph.

[0022] In some embodiments of this application, performance appraisals require access to various enterprise and employee data. This application connects to the enterprise's existing IT system through an API gateway, monitoring in real time organizational structure change events such as department mergers, splits, and temporary team creation; personnel changes such as employee position adjustments, part-time appointments, and project team joining and leaving; and business rule updates such as assessment cycle adjustments and indicator weight revisions. A structured change log is then generated, containing the operation type and impact scope, providing the original input for subsequent metadata management.

[0023] Furthermore, a versioned metadata management system is built based on the change log, and a metadata version tree is established using a directed acyclic graph to convert each change into an incremental version record; and the version integrity is verified through the Merkle tree. When the metadata version tree is complete, a timestamp is added to the metadata version tree, and it supports backtracking to the organizational structure snapshot at any historical time point through the timestamp; and when cross-departmental collaboration needs are detected, a virtual organizational unit containing multi-dimensional attributes is automatically created to provide the basic data structure for the multi-dimensional permission topology.

[0024] An incremental storage strategy is used, recording only the delta of each change. This approach significantly reduces storage overhead, much like saving only the modified portion of a document, rather than the entire document, effectively conserving storage space. The version rollback feature, implemented by constructing a version tree using a directed acyclic graph, supports restoring metadata snapshots at any point in time, allowing enterprises to trace historical data when needed and understand the evolution of organizational structures, personnel, and business rules.

[0025] S102: Perform metadata mapping conversion on the metadata version tree to construct a multi-dimensional permission topology.

[0026] In some embodiments of the present application, after generating a metadata version tree, an organizational unit snapshot and hierarchical relationship of the metadata version tree are extracted, and then the organizational unit snapshot is mapped to a multi-dimensional role including context conditions, time windows, and dynamic tags; at the same time, a multi-dimensional role inheritance graph is constructed based on the hierarchical relationship; the multi-dimensional role inheritance graph includes the priority and timeliness of role inheritance presets; Furthermore, a graph theory algorithm is used to detect permission coverage conflicts in a multidimensional role inheritance graph. If a permission coverage conflict exists, priority is sorted according to the preset priority of role inheritance, the conflict is automatically resolved, and the multidimensional role is associated and merged with the multidimensional role inheritance graph to generate a multidimensional permission topology including role-permission-inheritance relationships; if there is no permission coverage conflict, the multidimensional role is associated and merged with the multidimensional role inheritance graph to generate a multidimensional permission topology including role-permission-inheritance relationships.

[0027] This application maps the metadata version tree into a multi-dimensional role and inheritance graph that includes contextual conditions, time windows, and dynamic tags. It resolves permission conflicts through graph theory algorithms and forms a multi-dimensional permission topology of role-permission-inheritance relationships, enabling the assessment system to flexibly adapt to complex organizational structures.

[0028] S103: Perform tensor mapping on the multi-dimensional authority topology through a tensor mapping algorithm to generate a multi-dimensional assessment data model.

[0029] In some embodiments of the present application, after a multidimensional permission topology is generated, the structural tensor of the multidimensional permission topology is mapped to the organizational dimension coordinates of a multidimensional assessment data model, and then the permission timeliness attributes of the multidimensional permission topology are extracted and determined as the time window constraints and weight attenuation factors of the assessment indicators in the multidimensional assessment data model, and the permission coverage conflict records of the multidimensional permission topology are converted into collaborative effectiveness scoring items of the multidimensional assessment data model. Furthermore, the three-dimensional permission tensor space is mapped to the four-dimensional assessment tensor space through a tensor product operation, and the organizational dimension coordinates, the time window constraints and weight attenuation factors of the assessment indicators, and the collaborative effectiveness scoring items are integrated to generate a multidimensional assessment data model.

[0030] Through the tensor mapping algorithm, the permission topology is converted into a four-dimensional assessment tensor space, integrating the organizational dimension coordinates, time window constraints, weight attenuation factors and collaboration effectiveness scoring items to build a multi-dimensional assessment model that is closer to the business scenario, solving the problem of dimensional rigidity in traditional models.

[0031] S104: Using the multi-dimensional assessment data model, weight optimization and real-time calculation are performed on the assessment data of the changed employee to update the assessment score of the changed employee.

[0032] In some embodiments of the present application, after constructing a multidimensional assessment data model, metadata feature parameters are extracted from the multidimensional assessment data model to initialize the basic weight matrix of the assessment indicators, and then the historical assessment data of the changed employee is input into the pre-trained LSTM time series model to obtain the assessment weight distribution of the changed employee; then the change assessment data of the changed employee is obtained, and according to the assessment weight distribution of the changed employee, the change assessment data of the changed employee is stream-calculated through the streaming computing engine to generate a streaming computing result to update the assessment score of the changed employee. It should be noted that the multidimensional assessment data model includes an LSTM time series model and a streaming computing engine.

[0033] Furthermore, the streaming computing results are transformed into visualizations to generate visual permission topology maps, assessment heat maps, and trend analysis panel maps; the permission topology map displays role inheritance relationships and conflict resolution paths in real time; the assessment heat map maps the distribution of assessment results through color gradients; and the trend analysis panel map displays historical data fluctuations through comparison charts.

[0034] It also detects abnormal fluctuations in indicators in the permission topology map, assessment heat map, and trend analysis panel map. When there are abnormal fluctuations in indicators, the abnormal map points are highlighted and an alarm request is sent to the client.

[0035] At the same time, after updating the assessment score of the changed employee, whether the assessment score is reasonable is judged based on the assessment score of the changed employee and the feedback of the changed employee; when it is found that the assessment score is unreasonable, the assessment rules are dynamically adjusted through the reinforcement learning algorithm, and the assessment score of the changed employee is regenerated, and this iteration is repeated to make the assessment score of the changed employee reasonable.

[0036] The streaming computing engine boasts powerful real-time processing capabilities. Within 500ms of metadata changes, the assessment data of affected personnel can be rapidly updated, enabling instant assessment results and providing timely data support for corporate decision-making. Furthermore, the LSTM model predicts trend deviations in assessment indicators, effectively detecting anomalies and identifying potential issues in advance.

[0037] It provides multi-view display function. The permission topology diagram dynamically displays the role inheritance relationship and conflict resolution path, helping administrators to intuitively understand the permission structure and conflict handling process; the assessment heat map displays the distribution characteristics of assessment results by dimension, clearly presenting the assessment situation under different dimensions; the trend analysis panel compares historical data for the same period, making it convenient for users to analyze the changing trends of assessment data.

[0038] It should be noted that although the embodiments of this application are based on Figure 1 Steps S101 to S104 are described in sequence, but this does not mean that steps S101 to S104 must be performed in a strict order. Figure 1 The order shown in FIG1 is to introduce and explain step S101 to step S104 in order to facilitate those skilled in the art to understand the technical solution of the embodiment of the present application. In other words, in the embodiment of the present application, the order between step S101 to step S104 can be appropriately adjusted according to actual needs.

[0039] pass Figure 1This application maps the metadata version tree into a multi-dimensional role inheritance graph that includes contextual conditions, time windows, and dynamic labels. It resolves permission conflicts through graph theory algorithms and forms a multi-dimensional permission topology of role-permission-inheritance relationships, so that the assessment system can flexibly adapt to complex organizational structures. The permission topology is converted into a four-dimensional assessment tensor space through a tensor mapping algorithm, integrating organizational dimension coordinates, time window constraints, weight attenuation factors, and collaboration effectiveness scoring items to build a multi-dimensional assessment model that is closer to the business scenario, solving the problem of dimensional solidification in traditional models. The streaming computing engine is used to process change data in real time to ensure that the assessment scores can reflect the latest performance of employees in a timely manner and improve the accuracy of the assessment.

[0040] Figure 2 This is a system architecture diagram of a performance appraisal method based on RBAC provided in an embodiment of the present application.

[0041] exist Figure 2 In the figure, the system framework of this application is shown, including the data source layer, dynamic metadata layer, extended RBAC layer, matrix assessment layer, and decision-making layer.

[0042] Figure 3 A role inheritance relationship diagram of a performance appraisal method based on RBAC provided in an embodiment of the present application.

[0043] exist Figure 3 In the figure, the inheritance relationship of the roles in this application is displayed. For example, 0.8 of the weight of role A is inherited to role B, 1.0 of the weight of role B is inherited to role C, and there is a path conflict between role C and role A.

[0044] Figure 4 This is a flowchart of the core algorithm of a performance appraisal method based on RBAC provided in an embodiment of the present application.

[0045] exist Figure 4 The figure shows the process of this application from event capture to returning real-time results (assessment scores), which requires key steps and core algorithms such as permission topology reconstruction and conflict detection.

[0046] Figure 5 A data interaction sequence diagram of a performance appraisal method based on RBAC provided in an embodiment of the present application.

[0047] exist Figure 5 The data interaction sequence of this application is shown in Figure 1, which passes through the user, metadata layer, RBAC layer, assessment engine, and visualization service in sequence, and finally returns the result to the user.

[0048] Figure 6 A schematic diagram of the structure of a performance evaluation device based on RBAC provided in an embodiment of the present application includes: at least one processor; and, a memory communicatively connected to at least one processor; wherein, The memory stores instructions that can be executed by at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute any one of the above-mentioned RBAC-based performance evaluation methods.

[0049] Some embodiments of the present application provide a non-volatile computer storage medium for RBAC-based performance appraisal, which stores computer-executable instructions capable of executing any one of the above-mentioned RBAC-based performance appraisal methods.

[0050] The various embodiments in this application are described in a progressive manner. Similar portions between the various embodiments can be referred to in conjunction with each other. Each embodiment focuses on the differences between the other embodiments. In particular, the device and medium embodiments are generally similar to the method embodiments, so their descriptions are relatively simple. For relevant portions, refer to the descriptions of the method embodiments.

[0051] The devices and media provided in the embodiments of the present application correspond one-to-one to the methods. Therefore, the devices and media also have similar beneficial technical effects to their corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the devices and media will not be repeated here.

[0052] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0053] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0054] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0055] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The steps for the function specified in one or more boxes.

[0056] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0057] Memory may include non-permanent storage in a computer-readable medium, random access memory (RAM), and non-volatile memory such as read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

[0058] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can be implemented using any method or technology for information storage. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change RAM (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices, or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media, such as modulated data signals and carrier waves.

[0059] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0060] The above are merely embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modifications, equivalent replacements, improvements, etc. made within the technical principles of the present application should fall within the scope of protection of the present application.

Claims

1. A performance appraisal method based on RBAC, characterized in that: The method comprises: Obtaining the company's organizational structure changes, personnel position adjustments, and assessment rule update events, generating the company's structured change log, and constructing a metadata version tree of the structured change log through a directed acyclic graph; Performing metadata mapping conversion on the metadata version tree to construct a multi-dimensional permission topology; Performing tensor mapping on the multi-dimensional authority topology through a tensor mapping algorithm to generate a multi-dimensional assessment data model; The multi-dimensional assessment data model is used to perform weight optimization and real-time calculation on the assessment data of the changed employees to update the assessment scores of the changed employees.

2. The method according to claim 1, characterized in that The metadata mapping conversion of the metadata version tree to construct a multi-dimensional permission topology specifically includes: Extracting an organizational unit snapshot and hierarchical relationships of the metadata version tree; Mapping the organizational unit snapshot to a multi-dimensional role including context conditions, time windows, and dynamic tags; Constructing a multi-dimensional role inheritance graph based on the hierarchical relationship; the multi-dimensional role inheritance graph includes the priority and timeliness of role inheritance presets; Detecting permission coverage conflicts in the multidimensional role inheritance graph using a graph theory algorithm; If there is a permission coverage conflict, the priority is sorted according to the preset priority of role inheritance to resolve the conflict, and the multi-dimensional role is associated and merged with the multi-dimensional role inheritance graph to generate a multi-dimensional permission topology including role-permission-inheritance relationship; If there is no permission coverage conflict, the multi-dimensional role is associated and merged with the multi-dimensional role inheritance graph to generate a multi-dimensional permission topology including a role-permission-inheritance relationship.

3. The method according to claim 1, characterized in that The multi-dimensional authority topology is subjected to tensor mapping by a tensor mapping algorithm to generate a multi-dimensional assessment data model, specifically including: Map the structural tensor of the multi-dimensional authority topology to the organizational dimension coordinates of the multi-dimensional assessment data model; Extract the permission timeliness attributes of the multi-dimensional permission topology and determine them as the time window constraints and weight attenuation factors of the assessment indicators in the multi-dimensional assessment data model; Convert the permission coverage conflict records of the multi-dimensional permission topology into the collaboration effectiveness scoring items of the multi-dimensional assessment data model; The three-dimensional authority tensor space is mapped to the four-dimensional assessment tensor space through tensor product operations, and the organizational dimension coordinates, time window constraints and weight attenuation factors of assessment indicators, and collaboration effectiveness scoring items are integrated to generate a multi-dimensional assessment data model.

4. The method according to claim 1, wherein The multi-dimensional assessment data model is used to perform weight optimization and real-time calculation on the assessment data of the changed employee to update the assessment score of the changed employee, specifically including: Extract metadata feature parameters from the multidimensional assessment data model to initialize the basic weight matrix of assessment indicators; Input the historical assessment data of the changed employees into the pre-trained LSTM time series model to obtain the assessment weight distribution of the changed employees; Obtain change assessment data for changed employees; Based on the assessment weight distribution of the changed employee, the stream computing engine is used to perform stream computing on the changed employee's assessment data to generate a stream computing result to update the assessment score of the changed employee.

5. The method according to claim 4, characterized in that After performing streaming calculation on the changed employee's assessment data based on the weight distribution of the changed employee by a streaming calculation engine to generate a streaming calculation result to update the assessment score of the changed employee, the method further includes: The streaming computing results are transformed into visualizations to generate visualizations of permission topology maps, assessment heat maps, and trend analysis panel maps; the permission topology map displays role inheritance relationships and conflict resolution paths in real time; the assessment heat map maps the distribution of assessment results through color gradients; and the trend analysis panel map displays historical data fluctuations through comparison charts.

6. The method according to claim 5, characterized in that After visually converting the stream computing results to generate a visual permission topology map, an assessment heat map, and a trend analysis panel map, the method further includes: Detect abnormal fluctuations in indicators in the authority topology map, assessment heat map, and trend analysis panel map; When there are abnormal fluctuations in indicators, the abnormal graph points are highlighted and an alarm request is sent to the client.

7. The method according to claim 4, characterized in that After performing streaming calculation on the changed employee's assessment data based on the weight distribution of the changed employee through the streaming calculation engine to generate a streaming calculation result to update the assessment score of the changed employee, the method further includes: Based on the assessment scores of the changed employees and their feedback, determine whether the assessment scores are reasonable; When it is found that the assessment score is unreasonable, the assessment rules are dynamically adjusted through the reinforcement learning algorithm, and the assessment score of the changed employee is regenerated, and this iteration is repeated to make the assessment score of the changed employee reasonable.

8. The method according to claim 1, characterized in that After constructing the metadata version tree of the structured change log through a directed acyclic graph, the method further includes: Verify the integrity of the metadata version tree using a Merkle tree; When the metadata version tree is complete, a timestamp is added to the metadata version tree so as to trace back the organizational structure snapshot at any historical time point through the timestamp.

9. A performance appraisal device based on RBAC, characterized in that: include: at least one processor; as well as, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the RBAC-based performance appraisal method described in any one of claims 1 to 8.

10. A performance appraisal storage medium based on RBAC, storing computer-executable instructions, characterized in that: The computer-executable instructions can execute the RBAC-based performance appraisal method described in any one of claims 1 to 8.

Images