Role-based knowledge base permission management and data access control method and system

Abstract

This invention provides a role-based knowledge base permission management and data access control method and system, relating to the field of knowledge base permission management technology. The method includes: performing dynamic permission allocation processing based on permission evaluation and adjustment parameters and historical adaptation accuracy parameters; dynamically verifying adaptation accuracy based on the dynamic processing results; if the dynamic verification passes, generating the dynamically processed role permissions; if the verification fails, dynamically adjusting the permission evaluation and adjustment parameter weights and re-executing the dynamic permission allocation process; dynamically evaluating the rationality of permissions based on the dynamically processed role permissions and the behavioral compliance parameters of the role users; dynamically optimizing permission allocation based on the dynamic evaluation results; and dynamically configuring caching strategies based on role data importance parameters during user access to role data.

Description

Technical Field

[0001] This invention relates to the field of knowledge base access control technology, and in particular to a role-based knowledge base access control and data access control method and system. Background Technology

[0002] Knowledge base access control and data access control together constitute a comprehensive enterprise data security system. The former focuses on authorization at the system function level, achieving separation of duties and content governance by defining user-executable operations; the latter delves into the data level, ensuring data privacy and trade secrets by restricting user access to specific data objects, achieving row-level security isolation. Role-based access control (RBAC) is the core mechanism integrating these two categories. It abstracts and aggregates dispersed functional permissions and data policies onto role entities. By assigning specific roles to users, the system can centrally and uniformly map functional authorization and data authorization, thereby systematically implementing the principle of least privilege and compliance requirements within complex organizational relationships, achieving closed-loop control from user identity to operational behavior to data entities.

[0003] Existing technologies primarily utilize role-based access control (RBAC) models to manage permissions and control data access in knowledge bases. Within this framework, the system first predefines roles and assigns corresponding functional operation permissions and data access policies to each role. In practice, access control lists are often used to finely control read and write permissions for specific documents or data records, and attribute-based access control enhances dynamic control capabilities, making real-time authorization decisions based on user attributes, resource attributes, and environment attributes. Finally, when a user accesses the system, the system matches their role with predefined policy rules and automatically grants them the corresponding function menus and data views.

[0004] For example, a role-access control system and method based on blockchain smart contracts, with announcement number CN110348202B, is logically divided into a data layer, an access control layer, and a presentation layer according to RBAC rules. The system is further divided into user, role, and permission modules based on functional modules. Before using the access control system, contract deployment, administrator initialization, and role and permission configuration must be completed. Only then can users register, and administrators review and assign roles. The process includes: Step 1: Deploying the smart contract for the role-access control system based on blockchain smart contracts in the blockchain system; Step 2: Configuring permissions and roles; Step 3: User account registration; Step 4: Reviewing user registration; Step 5: Administrator assigning roles to users; Step 6: Users logging in and accessing the system using their keys.

[0005] For example, a system permission management method based on a composite access control model, disclosed in CN119538234A, involves: acquiring user information and dynamically assigning roles and permissions to users; establishing associations between permissions and corresponding operations and objects; adding tasks and temporal constraints to the associations between operations and objects; controlling the length of the permission chain through a selection matrix based on system functions and user environment to associate system permissions; the permission chain includes one or more of users, roles, tasks, operations, and objects; establishing a data storage strategy matching the composite access control model; constructing a corresponding database based on the data storage strategy to store user, role, object, operation, and permission data; setting data sharing strategies; encrypting sensitive data in the database during data transmission; and setting access control strategies, auditing and monitoring strategies, and emergency response strategies.

[0006] However, this application has discovered that the above-mentioned technology has at least the following technical problems:

[0007] In existing technologies, when there is competition for permission resources, the rigid allocation of permissions and the lack of a continuous feedback mechanism lead to problems such as a disconnect between permission allocation and actual business needs, frequent permission redundancy and conflicts, and the inability to dynamically optimize and adjust based on role behavior and data characteristics. Summary of the Invention

[0008] In view of this, embodiments of the present invention provide a role-based knowledge base permission management and data access control method and system, which solves the problems in the prior art where, when permission resource competition occurs, the rigid permission allocation and lack of a continuous feedback mechanism lead to a disconnect between permission allocation and actual business needs, permission redundancy and frequent conflicts, and the inability to dynamically optimize and adjust according to role behavior and data characteristics. By introducing a closed-loop management system based on multi-dimensional parameter evaluation and dynamic verification, the static, one-time permission allocation is transformed into a dynamic process of continuous optimization and self-correction, thereby ensuring that in a resource competition environment, permissions can always be accurately, efficiently and securely allocated to the most suitable role.

[0009] This invention provides a role-based knowledge base permission management and data access control method, specifically including: performing dynamic permission allocation processing based on permission evaluation and adjustment parameters and historical adaptation accuracy parameters, and performing dynamic adaptation accuracy verification based on the dynamic permission allocation processing results. If the dynamic adaptation accuracy verification passes, the role permissions after dynamic permission allocation processing are generated; if the dynamic adaptation accuracy verification fails, the permission evaluation and adjustment parameter weights are dynamically adjusted, and the dynamic permission allocation processing is re-executed. Dynamic permission allocation processing means dynamically adjusting the role permission range based on permission priority weights to adapt to actual business needs and reduce permission redundancy and conflict risks; the role permissions after dynamic permission allocation processing are... The system dynamically assesses the rationality of permissions based on the compliance parameters of user behavior and role. Based on the results of this assessment, it dynamically optimizes permission allocation. The dynamic assessment of permission rationality means dynamically evaluating the rationality of role permissions after dynamic processing to determine whether they meet business needs, compliance standards, and usage effectiveness. The dynamic optimization of permission allocation means dynamically optimizing permission priority weights to precisely adjust role permission configurations and achieve continuous adaptation of permission allocation. During user access to role data, a caching strategy is dynamically configured based on the importance parameters of the role data. This dynamic configuration means dynamically adjusting cache granularity and cache lifetime to balance data access efficiency and data consistency.

[0010] This invention provides a role-based knowledge base permission management and data access control system, comprising: a permission allocation dynamic processing module, a permission allocation dynamic optimization module, and a caching strategy dynamic configuration module. The permission allocation dynamic processing module performs dynamic permission allocation processing based on permission evaluation and adjustment parameters and historical adaptation accuracy parameters, and performs dynamic verification of adaptation accuracy based on the dynamic processing results. If the dynamic verification of adaptation accuracy passes, the dynamic permission allocation for the role is generated; if the dynamic verification of adaptation accuracy fails, the weight of the permission evaluation and adjustment parameters is dynamically adjusted, and the dynamic permission allocation processing is re-executed. The permission allocation dynamic optimization module performs dynamic permission rationality evaluation based on the dynamic permission allocation for the role permissions and the behavioral compliance parameters of the role users, and performs dynamic permission allocation optimization based on the dynamic rationality evaluation results. The caching strategy dynamic configuration module performs dynamic configuration of the caching strategy based on the importance parameters of the role data during user access to role data.

[0011] The beneficial effects of the technical solutions provided by the embodiments of the present invention include at least the following:

[0012] 1. By dynamically processing permission allocation based on permission evaluation and adjustment parameters and historical adaptation accuracy parameters, and conducting dynamic verification of adaptation accuracy, and combining role-user behavior compliance parameters to dynamically evaluate the rationality of permissions and dynamically optimize permission allocation based on the results, while dynamically configuring caching strategies based on role data importance parameters during user data access, the system precisely adjusts the scope of role permissions to adapt to actual business needs, reduces permission redundancy and conflict risks, ensures that permission configuration meets compliance standards and usage effectiveness, and balances data access efficiency and data consistency. This achieves precise, compliant, and adaptive optimization of role-based knowledge base permission management and data access control, improving the overall system operating efficiency and user access experience while ensuring the secure, stable, and efficient operation of the knowledge base system.

[0013] 2. By integrating permission assessment and control parameters with historical adaptation accuracy parameters, and through basic priority weight matching, impact ratio quantification calculation, weight correction sorting, and role permission range adjustment, and then through historical adaptation accuracy index grading, impact ratio mapping matching, adaptation verification feedback, and dynamic correction of the impact ratio mapping table using the adaptation deviation index and correction coefficient, a closed-loop process for dynamic permission allocation is executed. This achieves accurate quantification and dynamic optimization of permission priority weights, avoids single-dimensional decision-making bias, effectively corrects historical adaptation deficiencies, reduces permission redundancy and conflict risks from the source, and ensures that permission allocation deeply aligns with historical adaptation experience and core business needs. This results in standardized, traceable, and adaptive iteration of dynamic permission allocation processing, significantly improving the accuracy, reliability, and scientific nature of permission allocation, and laying a solid foundation for core decision support in the role-based knowledge base permission management system.

[0014] 3. By integrating the dynamically processed role permissions and the behavioral compliance parameters of role users, a behavioral compliance index is obtained and graded. Then, differentiated actions are taken based on the evaluation results: First evaluation result: high-risk permissions are immediately stopped and an audit is triggered; second evaluation result: permission priority weights are optimized by matching the behavioral compliance deviation index with an adjustment value mapping table and then redistributed; third evaluation result: the current scheme is maintained. This accurately assesses the compliance level and business effectiveness of permission usage, specifically mitigating high compliance risks, optimizing local adaptation deficiencies, and retaining high-quality configurations. It avoids permission abuse, idleness, or non-compliance issues, thus achieving a closed-loop optimization and dynamic adaptation of permission configuration for compliance. This ensures the security and compliance of the knowledge base system's permissions while improving the efficiency of permission resource utilization, providing precise and efficient follow-up optimization support for role-based knowledge base permission management.

[0015] 4. By integrating three key data importance parameters—data change frequency, data access frequency, and data importance level—and through data basic priority weight matching, dual data priority weight adjustment value mapping, and comprehensive correction, differentiated caching granularities of coarse-grained, medium-grained, and fine-grained caching are defined based on the corrected data permission priority weights. Corresponding cache lifetimes are matched to dynamically configure caching strategies. This accurately adapts to the core value attributes, dynamic update status, and actual access needs of the data, achieving differentiated and efficient allocation of cache resources towards high-value data, avoiding excessive cache resource consumption by low-value data, and balancing data access response speed and data consistency. This results in adaptive optimization of knowledge base data access and improved system performance, reducing system IO pressure while ensuring a better user experience for different types of data. It provides efficient and stable data interaction support for a role-based knowledge base permission management system. Attached Figure Description

[0016] Figure 1 This is a flowchart of a role-based knowledge base permission management and data access control method provided in an embodiment of the present invention;

[0017] Figure 2 This is a flowchart illustrating the dynamic configuration of caching strategies for the role-based knowledge base permission management and data access control method provided in this embodiment of the invention.

[0018] Figure 3 This is a schematic diagram of the structure of the role-based knowledge base permission management and data access control system provided in the embodiments of the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limitations on the present invention. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] In the following description, references are made to "some embodiments," which describe a subset of all possible embodiments. However, it is understood that "some embodiments" can be the same subset or different subsets of all possible embodiments, and can be combined with each other without conflict. Figure 1The flowchart shown illustrates a role-based knowledge base permission management and data access control method. This method's processing flow can include the following steps: Dynamically processing permission allocation based on permission evaluation and adjustment parameters and historical adaptation accuracy parameters; dynamically verifying adaptation accuracy based on the dynamic processing results; if the dynamic accuracy verification passes, generating the dynamically processed role permissions; if the dynamic accuracy verification fails, dynamically correcting the permission evaluation and adjustment parameter weights, and re-executing the dynamic permission allocation. Dynamic permission allocation means dynamically adjusting the role permission range based on permission priority weights to adapt to actual business needs and reduce permission redundancy and conflict risks; based on the dynamic processing of permission allocation... After processing, the system dynamically evaluates the rationality of permissions based on the compliance parameters of role permissions and user behavior. Based on the results of this dynamic evaluation, it dynamically optimizes permission allocation. The dynamic evaluation of permission rationality means dynamically assessing the rationality of role permissions after dynamic processing to determine whether they meet business needs, compliance standards, and usage effectiveness. The dynamic optimization of permission allocation means dynamically optimizing permission priority weights to accurately adjust role permission configurations and achieve continuous adaptation of permission allocation. During user access to role data, the system dynamically configures caching strategies based on the importance parameters of role data. The dynamic configuration of caching strategies means dynamically adjusting cache granularity and cache lifetime to balance data access efficiency and data consistency.

[0021] In this embodiment, the preset mapping table, critical threshold, and various influence ratio parameters in this invention are determined comprehensively from multiple sources: their initial values ​​are derived from the analysis and summarization of industry security standards, internal compliance policies, and historical business operation data, and are calibrated through review by domain experts; after the system is put into operation, these preset values ​​will be dynamically optimized through continuous machine learning and feedback mechanisms. The system automatically collects actual effect data of permission allocation (such as adaptation accuracy index and behavior compliance index) and caching performance indicators. When these measured data deviate significantly from the preset values, the aforementioned dynamic correction process is triggered (for example, the influence ratio is corrected based on the adaptation accuracy deviation index), thereby enabling the entire permission management and caching strategy system to have self-evolution capabilities, gradually evolving from a static preset configuration to a dynamically optimized intelligent system. This invention dynamically processes permission allocation by integrating permission assessment and control parameters with historical adaptation accuracy parameters. Simultaneously, it dynamically verifies the adaptation accuracy based on the processing results. If the verification fails, the permission assessment and control parameter weights are dynamically adjusted, and the allocation process is repeated. This ensures that the scope of role permissions is precisely and dynamically adjusted based on permission priority weights. This deeply adapts to actual business scenario needs and significantly reduces the incidence of permission redundancy and conflicts from the source, preventing permission configuration from becoming disconnected from business requirements. Secondly, after permission allocation, a dynamic evaluation of permission rationality is conducted based on the behavioral compliance parameters of role users. This comprehensively determines whether the permission configuration meets core business needs, compliance standards, and actual effectiveness during use. Based on the evaluation results, the permission priority weights are dynamically optimized, enabling continuous adaptation and iterative upgrades of role permission configurations. This effectively avoids risks such as permission abuse, idleness, or non-compliant use, ensuring the security and efficiency of permission usage. Finally, throughout the user's access to role data, a caching strategy is dynamically configured based on the importance parameters of the role data. By flexibly adjusting the caching granularity and cache lifetime, efficient data access response is ensured while maintaining data consistency and real-time performance, avoiding access delays or data inaccuracies caused by rigid caching strategies. Through close collaboration and dynamic iteration across all stages, the overall solution achieves precise, compliant, and adaptive optimization of permission allocation, while also ensuring the efficiency and reliability of data access. This comprehensively enhances the scientific, secure, and practical nature of knowledge base permission management, providing users with different roles with customized and secure permission services. Simultaneously, it guarantees the stable and efficient operation of the knowledge base system's data access, realizing multi-dimensional and high-precision role-based knowledge base permission management and data access control.

[0022] Furthermore, the steps for dynamically processing permission allocation based on permission assessment and adjustment parameters and historical adaptation accuracy parameters include: permission assessment and adjustment parameters include permission allocation adaptation accuracy, role weight, and permission function level; matching the permission function level with a preset permission priority benchmark table to obtain the basic priority weight, where the permission priority benchmark table presets the basic priority weight corresponding to each permission function level; comprehensively evaluating the impact ratio of permission allocation adaptation accuracy based on historical adaptation accuracy parameters, and using the difference between the impact ratio of permission allocation adaptation accuracy and 1 as the impact ratio of role weight; obtaining the first priority weight adjustment value based on the product of the impact ratio of permission allocation adaptation accuracy and permission allocation adaptation accuracy; obtaining the second priority weight adjustment value based on the product of the impact ratio of role weight and role weight; obtaining the priority weight adjustment value based on the sum of the first and second priority weight adjustment values; adjusting the basic priority weight based on the priority weight adjustment value to obtain the adjusted permission priority weight; sorting the adjusted permission priority weights of each role in descending order, and dynamically adjusting the role permission range based on the sorting order, with roles ranked higher having higher permission priority. The system will prioritize assigning or expanding core and sensitive permissions to such roles; conversely, roles ranked lower will have lower permission priority, and the system will accordingly restrict or reduce their permissions, or revoke their non-core permissions. For example, knowledge base roles include students, instructors, government officials, and system administrators. Student permissions include student course Q&A scenarios, policy and legal system Q&A scenarios, Q&A feedback management, data evaluation management, and multi-mode settings. Instructor permissions include internal data management, data vectorization, student course Q&A scenarios, policy and legal system Q&A scenarios, teaching and research intelligent Q&A scenarios, and Q&A feedback. The system includes management, data evaluation management, and multi-mode settings. Corresponding permissions for government officials include internal data management, data vectorization, policy and regulatory Q&A scenarios, government management and office Q&A scenarios, Q&A feedback management, data evaluation management, and multi-mode settings. Corresponding permissions also include large language model training tasks, large language model services, external resource data management, internal data management, data vectorization, student course Q&A scenarios, policy and regulatory Q&A scenarios, teaching and research intelligent Q&A scenarios, government management and office Q&A scenarios, user management, role management, permission management, Q&A feedback management, data evaluation management, and multi-mode settings.

[0023] Based on the above role-permission correspondence, after calculating the corrected priority weights for each role, the system assigns the highest weight of 85 to instructors, followed by administrative staff at 80, system administrators at 78, and students the lowest at 65. This results in a descending order of priority for instructors, administrative staff, system administrators, and students, and dynamic adjustment of permission scopes is initiated: Instructors, with their highest weight, will receive the broadest range of teaching-related permissions. The system will automatically enhance their access level to the "Teaching and Research Intelligent Q&A Scenario" and temporarily authorize them to manage the "Policy, Regulations, and System Q&A Scenario," typically handled by administrative staff. Administrative staff's permissions are firmly confined to their core "Administrative Office Q&A Scenario." System administrators, with their weights lowered, will have their operational permissions monitored and restricted in real time; for example, high-risk operations such as "Large Language Model Training Tasks" will require additional approval. Students, with the lowest weight, may have their non-core functions, such as "Multi-Mode Settings," temporarily revoked by the system to ensure resources are allocated to higher-priority roles.

[0024] In this embodiment, the accuracy of permission allocation adaptation is the ratio of the number of permissions actually invoked after historical allocation to the total number of permissions allocated historically. The weight of a role is a quantitative coefficient set based on the importance of the role's responsibilities, and the permission function level is a level divided according to the coreness of the permission. This invention uses the permission function level as a basis, determines the basic priority weight by matching a preset permission priority benchmark table, ensuring the core logic of prioritizing the allocation of core permissions and meeting the differentiated needs of different levels of permissions in business operations. Then, it introduces historical adaptation accuracy parameters for comprehensive evaluation to obtain the influence ratio of permission allocation adaptation accuracy. Simultaneously, the difference between this influence ratio and 1 clarifies the influence ratio of the role's weight, achieving a dual consideration of historical adaptation experience and the importance of role responsibilities, avoiding permission allocation deviations caused by single-dimensional decision-making. Subsequently, by multiplying the permission allocation adaptation accuracy by the corresponding influence ratio and the role's weight by the corresponding influence ratio, two types of priority weight adjustment values ​​are obtained and summed to form a quantitative basis for priority weight adjustment, ensuring the basic... The priority weighting correction process is supported by clear data, significantly improving the accuracy of permission priority weighting calculation. Finally, by sorting the corrected permission priority weights of each role in descending order and dynamically adjusting the role permission range based on the sorting results, roles with higher weights receive more comprehensive and core permissions, while roles with lower weights only obtain the basic permissions necessary for business operations. This achieves differentiated and precise allocation of role permissions, effectively avoiding the abuse of core permissions and unnecessary permission redundancy, and reducing permission conflicts caused by improper permission allocation. It ensures that permission configuration is highly compatible with role responsibilities and business needs, significantly improving the scientific, targeted, and effective nature of knowledge base permission management, and laying a solid foundation for the compliance and efficiency of subsequent permission use.

[0025] Furthermore, the steps for comprehensively evaluating the impact ratio of permission allocation adaptation accuracy based on historical adaptation accuracy parameters include: historical adaptation accuracy parameters include permission matching degree, permission conflict rate, permission redundancy rate, and permission missing rate; obtaining preset adaptation accuracy critical parameters and adaptation accuracy impact ratio parameters, the adaptation accuracy critical parameters include permission matching degree threshold, permission conflict rate threshold, permission redundancy rate threshold, and permission missing rate threshold, and the adaptation accuracy impact ratio parameters include the impact ratio of permission matching degree, the impact ratio of permission conflict rate, the impact ratio of permission redundancy rate, and the impact ratio of permission missing rate; and respectively, the permission matching degree, permission conflict rate threshold, permission redundancy rate threshold, and permission missing rate... The ratios of the threshold to the permission matching degree threshold, permission conflict rate, permission redundancy rate, and permission missing rate are labeled as the permission matching degree influence parameter, permission conflict rate influence parameter, permission redundancy rate influence parameter, and permission missing rate influence parameter, respectively. The permission matching degree influence parameter, permission conflict rate influence parameter, permission redundancy rate influence parameter, and permission missing rate influence parameter are weighted using the adaptation accuracy influence ratio parameter, and then the weighting results are coupled to obtain the historical adaptation accuracy index. The historical adaptation accuracy index is a quantitative indicator reflecting the degree of adaptation and rationality of historical permission allocation with business needs. Based on the historical adaptation accuracy index, the permission allocation adaptation accuracy influence ratio is dynamically obtained.

[0026] The historical adaptation accuracy index is obtained as follows:

[0027] ;

[0028] In the formula, Indicates the historical adaptation accuracy index. , , and These represent the percentages affected by permission matching rate, permission conflict rate, permission redundancy rate, and permission missing rate, respectively. , , and These represent permission matching degree, permission conflict rate, permission redundancy rate, and permission missing rate, respectively. Permission matching degree is the ratio of the intersection of the actual assigned permissions and the permissions required by the business needs to the total number of permissions required by the business needs. Permission conflict rate is the ratio of the number of role combinations with mutually exclusive permissions to the total number of role combinations. Permission redundancy rate is the ratio of the number of permission items exceeding the business needs to the total number of permissions actually assigned. Permission missing rate is the ratio of the number of permission items not covering the business needs to the total number of permissions required by the business needs. , , and These represent the permission matching threshold, permission conflict rate threshold, permission redundancy rate threshold, and permission missing rate threshold, respectively.

[0029] In this embodiment, the adaptation accuracy critical parameter is a benchmark threshold for judging whether the historical adaptation accuracy parameters meet the standards. It serves as a reference line for quantifying the rationality of adaptation. The adaptation accuracy influence ratio parameter assigns weight coefficients to each adaptation accuracy influence parameter, reflecting the degree of influence of different adaptation dimensions on the overall adaptation effect. This invention covers four core historical adaptation accuracy parameters: permission matching degree, permission conflict rate, permission redundancy rate, and permission missing rate. It comprehensively covers key pain points such as the matching degree between historical permission allocation and business needs, conflict risk, resource waste, and insufficient demand coverage, avoiding the one-sidedness caused by single-dimensional evaluation. Furthermore, by obtaining the preset adaptation accuracy critical parameter and adaptation accuracy influence ratio parameter, a standardized evaluation benchmark and dimension importance division rule are established to ensure that the evaluation process is systematic and avoids subjective arbitrariness. Subsequently, by quantitatively comparing each actual historical adaptation accuracy parameter with the corresponding threshold, a concrete influence parameter is generated, realizing the transformation of historical adaptation problems from qualitative description to quantitative analysis, allowing adaptation deviation to be accurately measured. Finally, the adaptation accuracy influence ratio parameter is used to assign weights to each influence parameter. This approach fully reflects the varying importance of different adaptation dimensions to the overall adaptation effect. Through coupling and integrating multi-dimensional quantitative results, a historical adaptation accuracy index is formed that comprehensively reflects the degree and rationality of historical permission allocation adaptation. This transforms complex historical adaptation situations into intuitive and comparable quantitative indicators. Finally, based on this index, the influence ratio of permission allocation adaptation accuracy is dynamically obtained, achieving a precise correlation between historical adaptation effects and current permission allocation adjustment criteria. This ensures that the influence ratio truly reflects historical lessons learned, avoiding repeated mistakes caused by ignoring historical data, and allowing current permission allocation to fully draw on high-quality historical adaptation experience. This provides solid data support for the scientific correction of subsequent permission priority weights, improving the adaptation accuracy of current permission allocation to business needs from the source and reducing problems such as permission conflicts, redundancy, and missing permissions.

[0030] Furthermore, the steps for dynamically obtaining the impact ratio of permission allocation adaptation accuracy based on the historical adaptation accuracy index include: obtaining a preset first threshold and a second threshold for historical adaptation accuracy; comparing the historical adaptation accuracy index with the first and second thresholds respectively; if the historical adaptation accuracy index is lower than the first threshold, the historical adaptation level is marked as the first adaptation level, indicating that there is serious redundancy, conflict, or missing permissions in the historical permission allocation; if the historical adaptation accuracy index is not lower than the first threshold but lower than the second threshold, the historical adaptation level is marked as the second adaptation level, indicating that the historical allocation basically meets the requirements but there is room for local optimization; if the historical adaptation accuracy index is not lower than the second threshold, the historical adaptation level is marked as the third adaptation level, indicating that the historical allocation is accurate. High accuracy and excellent adaptation effect; the historical adaptation levels are matched with the preset permission allocation adaptation accuracy impact ratio mapping table to obtain the corresponding permission allocation adaptation accuracy impact ratio. The permission allocation adaptation accuracy impact ratio mapping table is a relational data table that records the unique correspondence between each adaptation level and its corresponding permission allocation adaptation accuracy impact ratio; based on the permission allocation adaptation accuracy impact ratio, dynamic permission allocation processing is performed, and in the dynamic permission allocation processing, the adaptation accuracy index is recalculated based on the adaptation accuracy parameter. It is determined whether the recalculated adaptation accuracy index reaches the second threshold of historical adaptation accuracy. If so, subsequent permission allocation optimization is performed based on the permission allocation adaptation accuracy impact ratio; otherwise, the difference between the second threshold of historical adaptation accuracy and the adaptation accuracy index is marked as the adaptation accuracy deviation index, and the permission allocation adaptation accuracy impact ratio mapping table is dynamically corrected based on the adaptation accuracy deviation index.

[0031] In this embodiment, for the first adaptation level, the matching influence ratio reduces the reference weight of historical adaptation experience to avoid repeating past mistakes; for the third adaptation level, the influence ratio is increased to fully leverage high-quality adaptation experience; for the second adaptation level, an intermediate influence ratio is matched to balance experience reuse and local optimization. Subsequently, the influence ratio of permission allocation adaptation accuracy is obtained by matching the adaptation level with a preset mapping table, ensuring that the assignment of the influence ratio is standardized and traceable, avoiding subjective decision-making bias. More importantly, after performing dynamic permission allocation processing based on this influence ratio, the adaptation accuracy index is recalculated and compared with the second threshold of historical adaptation accuracy. If the verification passes, the influence ratio is confirmed to be valid, providing a reliable basis for subsequent optimization; if it fails, the degree of deviation is quantified by the adaptation accuracy deviation index, and the influence ratio mapping table is dynamically corrected, ensuring that the influence ratio corresponding to each adaptation level in the mapping table continuously aligns with the actual business scenario, gradually eliminating the deviation between parameters and actual needs. The overall process not only enables the scientific reuse of historical adaptation experience, but also continuously optimizes the impact ratio of the accuracy of permission allocation through dynamic verification and parameter correction mechanisms. This ensures that its adjustment effect on the current permission allocation is more precise and more in line with business needs, reducing the blindness of permission allocation from the source of decision-making, significantly improving the accuracy of permission allocation and business needs, and forming an adaptive capability of self-improvement of parameters, laying the core foundation for the continuous optimization of dynamic permission allocation processing.

[0032] Furthermore, the steps for dynamically correcting the permission allocation adaptation accuracy impact ratio mapping table based on the adaptation accuracy deviation index include: matching the adaptation accuracy deviation index with a preset impact ratio correction coefficient mapping table to obtain the corresponding impact ratio correction coefficient. The impact ratio correction coefficient mapping table is a relational data table that records the unique correspondence between each adaptation accuracy deviation index interval and its corresponding impact ratio correction coefficient; dynamically correcting the permission allocation adaptation accuracy impact ratio corresponding to the current historical adaptation level in the permission allocation adaptation accuracy impact ratio mapping table based on the impact ratio correction coefficient; multiplying the impact ratio correction coefficient with the permission allocation adaptation accuracy impact ratio to obtain the corrected permission allocation adaptation accuracy impact ratio; and performing dynamic permission allocation processing on the corrected permission allocation adaptation accuracy impact ratio and applying it to the permission allocation process. In the dynamic allocation process, the current adaptation accuracy index is calculated based on the adaptation accuracy parameter. It is then determined whether the current adaptation accuracy index has reached the second threshold of historical adaptation accuracy. If so, the corrected impact ratio of permission allocation adaptation accuracy is marked as an effective impact ratio, and updated to the permission allocation adaptation accuracy impact ratio mapping table corresponding to the historical adaptation level. If not, the difference between the second threshold of historical adaptation accuracy and the current adaptation accuracy index is remarked as the adaptation accuracy deviation index. Based on the adaptation accuracy deviation index, the permission allocation adaptation accuracy impact ratio mapping table is dynamically corrected again, and the cumulative repetition count is incremented by 1. If the cumulative repetition count reaches the preset repetition count threshold, but the current adaptation accuracy index still has not reached the second threshold of historical adaptation accuracy, a manual review process is triggered.

[0033] In this embodiment, the present invention matches the adaptation accuracy deviation index with a preset influence ratio correction coefficient mapping table. Through the unique correspondence between the preset deviation range and the correction coefficient, the standardization and data-driven assignment of the correction coefficient are achieved, avoiding subjective arbitrariness in the correction process and ensuring that each correction is supported by clear quantitative evidence. By multiplying the influence ratio correction coefficient with the influence ratio of the permission allocation adaptation accuracy corresponding to the current adaptation level, the corresponding influence ratio in the mapping table is precisely corrected, allowing the corrected influence ratio to accurately offset the adaptation deviation and effectively reduce the gap between the actual adaptation effect and the target threshold. The corrected influence ratio is then used to... The corrections are then reapplied to the dynamic processing of permission allocation, and the current accuracy index is calculated to ensure that the correction effect can be directly verified through actual allocation results, avoiding invalid corrections. For cases where the verification fails to meet the standard, the deviation index is re-marked and the correction process is repeated. The continuity of the correction action is ensured by accumulating the number of iterations, pushing the impact ratio to gradually approach the optimal value, so that the mapping table can continuously adapt to actual business changes. Finally, a threshold for the number of repetitions is set. When the iteration reaches the threshold and still fails to meet the standard, a manual review process is triggered. This not only avoids the waste of system resources caused by infinite iterations, but also provides a safety net to solve parameter optimization problems in complex scenarios through manual intervention, preventing parameter solidification or deviation expansion. The overall process automates and quantifies the correction of the mapping table for the impact ratio of permission allocation accuracy, allowing the parameters in the mapping table to dynamically adapt to changes in business needs and feedback on historical adaptation effects, continuously improving the accuracy of the impact ratio in guiding permission allocation. Furthermore, through closed-loop verification and manual oversight, the effectiveness of the correction process and system stability are ensured. Ultimately, the impact ratio of permission allocation accuracy maintains a consistently high level of accuracy, providing a more reliable basis for subsequent dynamic permission allocation processing, further improving the overall quality of permission allocation and its adaptation to business needs, and reducing the recurrence of issues such as permission redundancy, conflicts, and missing permissions.

[0034] Furthermore, the steps for dynamically evaluating the rationality of permissions based on the behavioral compliance parameters of role permissions and role users after dynamic processing of permission allocation include: behavioral compliance parameters include permission usage frequency, effective call rate, and compliant operation ratio; obtaining preset behavioral compliance first threshold, behavioral compliance second threshold, behavioral compliance critical parameter, and behavioral compliance influence ratio parameter, wherein the behavioral compliance critical parameter includes permission usage frequency threshold, effective call rate threshold, and compliant operation ratio threshold, and the behavioral compliance influence ratio parameter includes the influence ratio of permission usage frequency, the influence ratio of effective call rate, and the influence ratio of compliant operation ratio; and marking the ratio of each behavioral compliance parameter to its corresponding behavioral compliance critical parameter as a behavioral compliance influence parameter, wherein the behavioral compliance influence parameters include the influence parameter of permission usage frequency, the influence parameter of effective call rate, and the influence parameter of compliant operation ratio. The operation proportion affects the parameter; the parameter affecting the behavior compliance is weighted using the behavior compliance impact ratio parameter, and then the weighting results are coupled to obtain the behavior compliance index. The behavior compliance index is a quantitative indicator reflecting the compliance degree of role user permission usage and business effectiveness. The behavior compliance index is compared with the preset first behavior compliance threshold and second behavior compliance threshold respectively; if the behavior compliance index is lower than the first behavior compliance threshold, the dynamic evaluation result of permission rationality is marked as the first evaluation result; if the behavior compliance index is not lower than the first behavior compliance threshold but lower than the second behavior compliance threshold, the dynamic evaluation result of permission rationality is marked as the second evaluation result; if the behavior compliance index is not lower than the second behavior compliance threshold, the dynamic evaluation result of permission rationality is marked as the third evaluation result.

[0035] The behavioral compliance index is obtained in the following ways:

[0036] ;

[0037] In the formula, Indicator of behavioral compliance index , and These represent the percentages affected by the frequency of permission usage, the percentages affected by the effective call rate, and the percentages affected by compliant operations, respectively. , and These represent the frequency of permission usage, the effective call rate, and the proportion of compliant operations, respectively. Permission usage frequency is the ratio of the total number of times a user invoked the target permission within the historical evaluation period to the product of the preset evaluation period duration and the number of users. The historical evaluation period is dynamically set based on permission type: 7 days for core permissions, 15 days for general permissions, and 30 days for peripheral permissions. The effective call rate is the ratio of the number of times a user effectively achieved the business objective after invoking the permission within the historical evaluation period to the total number of permission calls. The criterion for an effective call is that the call result matches the preset business scenario output requirements. The proportion of compliant operations is the ratio of the number of operations performed by a user that comply with the permission usage rules within the historical evaluation period to the total number of permission operations. Permission usage rules include compliance with operation time periods, compliance with data access scope, and compliance with operation procedures. , and These represent the threshold for permission usage frequency, the threshold for effective call rate, and the threshold for the proportion of compliant operations, respectively.

[0038] In this embodiment, the behavioral compliance threshold parameter serves as the benchmark threshold for judging whether a user's permission usage is compliant and effective, acting as a reference line for quantifying the rationality of usage. The behavioral compliance influence ratio parameter assigns weight coefficients to each behavioral compliance influence parameter, reflecting the degree of influence of different usage behavior dimensions on the rationality of permissions. This invention selects three core behavioral compliance parameters: permission usage frequency, effective call rate, and compliant operation ratio, comprehensively covering the frequency rationality of permission usage, business effectiveness, and operational compliance, avoiding judgment bias caused by single-dimensional evaluation and ensuring the completeness of the evaluation perspective. Each actual behavioral compliance parameter is quantitatively compared with its corresponding threshold parameter to generate a concrete behavioral compliance influence parameter, realizing the transformation of permission usage behavior from qualitative description to quantitative analysis, allowing for precise measurement of issues such as compliance deviation and insufficient effectiveness. Through weighting processing, the weight value of core dimensions is fully highlighted, and then through coupling processing, multi-dimensional quantitative results are integrated to form a behavioral compliance index that comprehensively reflects the compliance degree of user's permission usage and business effectiveness, making complex rights... The restricted usage status is transformed into an intuitive and comparable quantitative indicator, avoiding the confusion caused by the dispersion of multi-dimensional data. Finally, by comparing the behavior compliance index with two preset thresholds, three evaluation levels are clearly defined. This not only accurately defines the rationality level of the current permission configuration, but also provides a clear decision-making basis for subsequent targeted dynamic optimization of permission allocation. It avoids ineffective optimization or excessive intervention caused by ambiguous evaluation, and can accurately identify compliance risks, idle permissions, insufficient business adaptation, and other issues. This lays a solid foundation for the dynamic adjustment of permission priority weights, effectively reduces the risks of permission abuse and violations, and improves the utilization efficiency of permission resources, ensuring that permission configurations continuously meet the actual usage needs and compliance requirements of role users.

[0039] Furthermore, the steps for dynamically optimizing permission allocation based on the dynamic evaluation results of permission rationality include: if the dynamic evaluation result of permission rationality is the first evaluation result, then the high-risk permissions of the corresponding role (such as data modification, batch export permissions, etc.) are immediately stopped, and the permission audit process is triggered, specifically including: tracing the permission operation logs of the past 30 days to clarify the type of violation and the responsible user; after the audit is completed, basic permissions are reassigned based on the audit results (only the query permissions necessary for business are retained), and a 30-day observation period is set. During the observation period, dynamic evaluation of permission rationality is performed daily. If the dynamic evaluation of permission rationality at the end of the observation period is not lower than the first threshold for compliance of the behavior, some permissions are restored; otherwise, the observation period is extended or the case is transferred to manual processing; if the dynamic evaluation result of permission rationality is the second evaluation result, then... The difference between the second threshold for behavioral compliance and the behavioral compliance index is marked as the behavioral compliance deviation index. The compliance deviation index is matched with a preset permission priority weight adjustment value mapping table, which is a relational data table that records the unique correspondence between each compliance deviation index interval and its corresponding permission priority weight adjustment value. Based on the matched permission priority weight adjustment value, the permission priority weight of the role is adjusted, and the dynamic permission allocation process is re-executed based on the adjusted permission priority weight. If the dynamic evaluation result of permission rationality is the third evaluation result, the current permission allocation scheme is maintained, and the permission configuration of this role is used as a reference template for similar roles. The reference weight of the corresponding level in the permission allocation adaptation accuracy impact ratio mapping table is updated synchronously.

[0040] In this embodiment, the present invention addresses the first assessment result of the dynamic evaluation of permission rationality by immediately suspending high-risk permissions, tracing the operation logs of the past 30 days to clarify the responsibility for violations, and reallocating basic permissions containing only business-essential query permissions. A 30-day daily evaluation observation period is also set. This achieves rapid risk mitigation, preventing greater losses from violations or invalid permissions. Furthermore, the design of minimum permission configuration and continuous monitoring ensures the effectiveness of rectification. If the observation period meets the standards, some permissions are restored; if not, the observation period is extended or manual intervention is required, balancing business continuity and risk control. For the second assessment result, a quantitative behavioral compliance deviation index is used, matched with a preset permission priority weight adjustment value mapping table, to prioritize role permissions. The precise adjustment and re-execution of hierarchical weights, followed by dynamic re-allocation of permissions based on these adjusted weights, avoids a one-size-fits-all approach to optimization. This allows permission adjustments to precisely offset compliance deviations, addressing specific issues of insufficient adaptation or lack of compliance, and ensuring a scientific and traceable optimization process. For the third assessment results, the current permission allocation scheme is maintained and used as a reference template for similar roles. Simultaneously, the reference weights for the corresponding levels in the permission allocation accuracy impact ratio mapping table are updated. This enables the rapid reuse of high-quality permission configuration experience, reducing repetitive workload and trial-and-error costs for similar roles. Furthermore, updating the reference weights allows subsequent permission allocations to continuously absorb high-quality experience, driving the iterative upgrade of the overall permission management system. Through differentiated and precise processing of different assessment results, issues such as permission abuse, compliance risks, and insufficient adaptation are effectively addressed. Dynamic optimization and experience accumulation in permission configuration are achieved, ensuring that role permissions always align with business needs, compliance standards, and practical effectiveness. This significantly improves the efficiency and scientific nature of permission management, reduces manual intervention costs, and provides strong support for the long-term stable operation of the knowledge base permission system.

[0041] like Figure 2The diagram shows a dynamic configuration flowchart of the caching strategy for the role-based knowledge base permission management and data access control method provided in this embodiment of the invention. The weight and threshold comparison in the diagram indicates that the corrected data permission priority weight is compared with the first threshold of permission priority weight and the second threshold of data permission priority weight. The first threshold is the first threshold of permission priority weight, and the second threshold is the second threshold of data permission priority weight. The steps for dynamically configuring a caching strategy based on role data importance parameters include: Role data importance parameters include data change frequency, data access frequency, and data importance level; matching the data importance level with a preset data permission priority benchmark table to obtain the basic data priority weight, where the data permission priority benchmark table presets the basic data priority weight corresponding to each data importance level; matching the data change frequency with a preset first data priority weight adjustment value mapping table to obtain the corresponding first data priority weight adjustment value, where the first data priority weight adjustment value mapping table is an association data table recording the unique correspondence between each data change frequency interval and its corresponding first data priority weight adjustment value; matching the data access frequency with a preset second data priority weight adjustment value mapping table to obtain the corresponding second data priority weight adjustment value, where the second data priority weight adjustment value mapping table is an association data table recording the unique correspondence between each data access frequency interval and its corresponding second data priority weight adjustment value; obtaining a data priority weight adjustment value based on the sum of the first and second data priority weight adjustment values; adjusting the basic data priority weight based on the data priority weight adjustment value to obtain the adjusted data permission priority weight; and dynamically configuring the caching strategy based on the adjusted data permission priority weight.

[0042] In this embodiment, the present invention selects three core data importance parameters—data change frequency, data access frequency, and data importance level—to comprehensively cover the core value attributes of data, avoiding the rigidity of caching strategies caused by single-dimensional parameters and ensuring that the strategy adaptation can take into account both the core value of the data and its real-time usage status. Next, the data importance level is matched with a preset benchmark table to obtain the basic data priority weight, clarifying the caching priority of core data and aligning with the knowledge base's core requirements for efficient access and secure storage of core data. Simultaneously, by matching the data change frequency with a first adjustment value mapping table and the data access frequency with a second adjustment value mapping table, a two-way dynamic adjustment of the basic weight is achieved. Adjustments with high data change frequency weaken the cache priority, preventing inconsistencies between the cache and source data due to frequent data updates; adjustments with high data access frequency strengthen the cache priority, ensuring that frequently accessed data can respond quickly. This allows the weight adjustment to both adapt to dynamic data changes and maintain a standard. The system establishes a quantitative and traceable basis for data priority. Subsequently, by summing the data priority weights, the system adjusts the base weights to form a revised data permission priority weight that accurately reflects the overall importance and usage status of the data. This provides clear quantitative decision support for caching strategy configuration, avoiding subjective arbitrariness. Finally, based on this revised weight, the system dynamically configures the caching strategy, enabling high-weight data to receive finer caching granularity and a reasonable cache lifetime, while low-weight data adopts a simplified caching strategy (reducing system resource consumption). This achieves differentiated and efficient allocation of cache resources, avoiding waste or insufficient caching of core data. Furthermore, by dynamically adapting to changes in data attributes, it ensures efficient data access response while maintaining data consistency, significantly improving user experience and the overall performance of the knowledge base system. This provides stable and efficient technical support for data access within the permission management system, achieving dynamic matching between caching strategies and role data attributes.

[0043] Furthermore, the steps for dynamically configuring the caching strategy based on the corrected data permission priority weights include: obtaining a preset first threshold and a second threshold for data permission priority weights; comparing the corrected data permission priority weights with the first and second thresholds respectively; if the corrected data permission priority weights are lower than the first threshold, the corresponding data caching granularity is marked as coarse-grained (data table level); if the corrected data permission priority weights are not lower than the first threshold but lower than the second threshold, the corresponding data caching granularity is marked as medium-grained (data subset level); if the corrected data permission priority weights are not lower than the second threshold, the corresponding data caching granularity is marked as fine-grained (single data item level); matching the corrected data permission priority weights with a preset cache lifetime mapping table to obtain the corresponding cache data lifetime, wherein the cache lifetime mapping table is an association data table that records the unique correspondence between each corrected data permission priority weight interval and its corresponding cache data lifetime.

[0044] In this embodiment, coarse-grained caching directly caches the entire data table. This caching logic is simple and fast, but resource consumption is relatively high. It is suitable for scenarios where data changes very infrequently and overall queries are frequently required, such as a company employee basic information table (containing fields such as name, department, and start date, rarely modified, and frequently requiring overall export or query by department). Medium-grained caching only caches a subset of data in the data table that meets specific conditions, balancing access efficiency and resource consumption. It is suitable for scenarios with fixed query dimensions and no need for the entire table, such as "online sales data of a certain brand in the third quarter of 2024" on an e-commerce platform (a subset filtered by brand and time dimensions, frequently queried and analyzed by operations personnel, without needing to cache the full sales data). Fine-grained caching precisely caches individual data records or specific fields, with minimal resource consumption and the fastest query response speed. It is suitable for scenarios with high-frequency access to single key data, such as user account balances, real-time order completion counts, and key configuration parameters of core business systems (only a single data item needs to be queried each time, and extremely high response speed is required). Low-value data with a weight below the first threshold uses coarse-grained caching, which significantly reduces the consumption of cache resources by low-importance, low-access-frequency data, avoiding resource waste. Medium-value data with a weight between the two thresholds uses medium-grained caching, which meets the efficiency requirements of regular access without excessively consuming cache resources, achieving a balance between efficiency and resource consumption. High-value data with a weight not lower than the second threshold uses fine-grained caching, which can accurately locate and cache key data items, ensuring fast response and accurate querying of core data, and improving the user experience. At the same time, by matching the modified data permission priority weight with a preset cache lifecycle mapping table, the cache lifecycle is precisely linked to the overall importance, change frequency, and access frequency of the data. High-weight data corresponds to a reasonably long lifecycle, reducing the overhead of repeated cache building; low-weight data corresponds to a short lifecycle, avoiding consistency problems caused by long-term unupdated cache data, and ensuring that cached data is synchronized with source data. The overall process achieves differentiated and efficient allocation of cache resources through dynamic adaptation of both cache granularity and lifetime, allowing limited cache resources to be tilted towards high-value data, while also taking into account data access efficiency and data consistency. This effectively reduces system IO pressure, improves the overall response speed and stability of knowledge base data access, and provides solid technical support for efficient data interaction under the permission management system. It also achieves a high degree of fit between caching strategies and the comprehensive value of data and usage scenarios.

[0045] like Figure 3As shown in the embodiment of this application, a role-based knowledge base permission management and data access control system includes a permission allocation dynamic processing module, a permission allocation dynamic optimization module, and a caching strategy dynamic configuration module. The permission allocation dynamic processing module performs dynamic permission allocation processing based on permission evaluation and control parameters and historical adaptation accuracy parameters, and performs dynamic verification of adaptation accuracy based on the dynamic processing results. If the dynamic verification of adaptation accuracy passes, the dynamic permission allocation for the role is generated; if the dynamic verification of adaptation accuracy fails, the weight of the permission evaluation and control parameters is dynamically adjusted, and the dynamic permission allocation processing is re-executed. The permission allocation dynamic optimization module performs dynamic permission rationality evaluation based on the dynamic permission allocation for the role permissions and the behavioral compliance parameters of the role users, and performs dynamic permission allocation optimization based on the dynamic rationality evaluation results. The caching strategy dynamic configuration module performs dynamic caching strategy configuration based on the importance parameters of the role data during user access to role data.

[0046] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0047] It should be understood that determining B based on A does not mean determining B solely based on A; it also means determining B based on A and other information.

[0048] Those skilled in the art will understand that all or part of the steps of the above embodiments can be implemented by hardware or by a program instructing related hardware. The program can be stored in a computer-readable storage medium, such as a read-only memory, a disk, or an optical disk.

[0049] The above description is only an optional embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A role-based knowledge base permission management and data access control method, characterized in that, The method includes: Dynamic permission allocation is performed based on permission evaluation and adjustment parameters and historical adaptation accuracy parameters. The permission evaluation and adjustment parameters include permission allocation adaptation accuracy, role weight, and permission function level. The historical adaptation accuracy parameters include permission matching degree, permission conflict rate, permission redundancy rate, and permission missing rate. Dynamic verification of adaptation accuracy is performed based on the dynamic permission allocation results. If the dynamic verification of adaptation accuracy passes, the role permissions after dynamic permission allocation are generated. If the dynamic verification of adaptation accuracy fails, the permission evaluation and adjustment parameter weights are dynamically adjusted, and dynamic permission allocation is re-executed. The dynamic permission allocation means dynamically adjusting the role permission range based on permission priority weights to adapt to actual business needs and reduce permission redundancy and conflict risks. The rationality of permissions is dynamically evaluated based on the compliance parameters of role permissions and role user behavior after dynamic processing of permission allocation. The compliance parameters include permission usage frequency, effective call rate, and proportion of compliant operations. Based on the dynamic evaluation results of permission rationality, dynamic optimization of permission allocation is performed. The dynamic evaluation of permission rationality means to dynamically evaluate the rationality of role permissions after dynamic processing of permission allocation to determine whether they meet business needs, compliance standards, and usage effectiveness. The dynamic optimization of permission allocation means to dynamically optimize permission priority weights to accurately adjust role permission configuration and achieve continuous adaptation of permission allocation. During the user's access to role data, a caching strategy is dynamically configured based on the role data importance parameters. These parameters include data change frequency, data access frequency, and data importance level. The dynamic configuration of the caching strategy means dynamically adjusting the cache granularity and cache lifetime to balance data access efficiency and data consistency.

2. The role-based knowledge base permission management and data access control method as described in claim 1, characterized in that, The steps for dynamically processing permission allocation based on permission evaluation and control parameters and historical adaptation accuracy parameters include: The permission function level is matched with the preset permission priority benchmark table to obtain the basic priority weight. The permission priority benchmark table presets the basic priority weight corresponding to each permission function level. The impact ratio of permission allocation adaptation accuracy is obtained by comprehensively evaluating the historical adaptation accuracy parameters. The difference between the impact ratio of permission allocation adaptation accuracy and 1 is used as the weight impact ratio of the role. The first priority weight adjustment value is obtained by multiplying the impact ratio of permission allocation adaptation accuracy with the permission allocation adaptation accuracy. The second priority weight adjustment value is obtained by multiplying the influence ratio of the role's weight by the role's weight. The priority weight adjustment value is obtained by summing the first priority weight adjustment value and the second priority weight adjustment value. The basic priority weight is then adjusted based on the priority weight adjustment value to obtain the adjusted permission priority weight. The revised permission priority weights of each role are sorted in descending order, and the permission range of the role is dynamically adjusted based on the sorting order.

3. The role-based knowledge base permission management and data access control method as described in claim 2, characterized in that, The step of obtaining the impact ratio of permission allocation adaptation accuracy based on a comprehensive evaluation of historical adaptation accuracy parameters includes: Obtain preset critical parameters for adaptation accuracy and parameters for the proportion of adaptation accuracy impact. The critical parameters for adaptation accuracy include a permission matching degree threshold, a permission conflict rate threshold, a permission redundancy rate threshold, and a permission missing rate threshold. The parameters for the proportion of adaptation accuracy impact include the proportion of the proportion of the permission matching degree impact, the proportion of ... The ratios of permission matching degree, permission conflict rate threshold, permission redundancy rate threshold, and permission missing rate threshold to permission matching degree threshold, permission conflict rate, permission redundancy rate, and permission missing rate are respectively labeled as permission matching degree influence parameter, permission conflict rate influence parameter, permission redundancy rate influence parameter, and permission missing rate influence parameter. The parameters affecting permission matching degree, permission conflict rate, permission redundancy rate, and permission missing rate are weighted using the adaptation accuracy impact ratio parameter, respectively. The weighting results are then coupled to obtain the historical adaptation accuracy index, which is a quantitative indicator reflecting the degree of adaptation and rationality of historical permission allocation with business needs. The proportion of the impact of historical adaptation accuracy on permission allocation is dynamically obtained based on the historical adaptation accuracy index.

4. The role-based knowledge base permission management and data access control method as described in claim 3, characterized in that, The step of dynamically obtaining the impact ratio of permission allocation adaptation accuracy based on historical adaptation accuracy index includes: Obtain the preset first threshold and second threshold for historical adaptation accuracy; If the historical adaptation accuracy index is lower than the first threshold of historical adaptation accuracy, then the historical adaptation level will be marked as the first adaptation level. If the historical adaptation accuracy index is not lower than the first threshold of historical adaptation accuracy, but lower than the second threshold of historical adaptation accuracy, then the historical adaptation level will be marked as the second adaptation level. If the historical adaptation accuracy index is not lower than the second threshold of historical adaptation accuracy, then the historical adaptation level will be marked as the third adaptation level. The historical adaptation levels are matched with the preset permission allocation adaptation accuracy impact ratio mapping table to obtain the corresponding permission allocation adaptation accuracy impact ratio. The permission allocation adaptation accuracy impact ratio mapping table is an association data table that records the unique correspondence between each adaptation level and its corresponding permission allocation adaptation accuracy impact ratio. Based on the impact ratio of the accuracy of permission allocation, dynamic processing of permission allocation is performed, and the accuracy index is recalculated based on the accuracy parameter to determine whether the recalculated accuracy index reaches the second threshold of historical accuracy. If so, then subsequent permission allocation optimizations will be performed based on the impact ratio of the accuracy of the permission allocation adaptation. Otherwise, the difference between the second threshold of historical adaptation accuracy and the adaptation accuracy index is marked as the adaptation accuracy deviation index, and the mapping table of the proportion of the impact of the adaptation accuracy on the allocation of permissions is dynamically corrected based on the adaptation accuracy deviation index.

5. The role-based knowledge base permission management and data access control method as described in claim 4, characterized in that, The steps of dynamically correcting the permission allocation adaptation accuracy impact ratio mapping table based on the adaptation accuracy deviation index include: The adaptation accuracy deviation index is matched with the preset influence ratio correction coefficient mapping table to obtain the corresponding influence ratio correction coefficient. The influence ratio correction coefficient mapping table is an association data table that records the unique correspondence between each adaptation accuracy deviation index interval and its corresponding influence ratio correction coefficient. The impact ratio of permission allocation adaptation accuracy is dynamically adjusted based on the impact ratio correction coefficient in the current historical adaptation level corresponding to the impact ratio of permission allocation adaptation accuracy in the impact ratio mapping table, so as to obtain the corrected impact ratio of permission allocation adaptation accuracy. The impact ratio of the corrected permission allocation adaptation accuracy will be dynamically processed by permission allocation, and the current adaptation accuracy index will be calculated based on the adaptation accuracy parameter during the dynamic processing of permission allocation. Determine whether the current adaptation accuracy index has reached the second threshold of historical adaptation accuracy. If so, update the corrected permission allocation adaptation accuracy impact ratio to the permission allocation adaptation accuracy impact ratio value corresponding to the historical adaptation level in the permission allocation adaptation accuracy impact ratio mapping table. If not, the difference between the second threshold of historical adaptation accuracy and the current adaptation accuracy index will be re-marked as the adaptation accuracy deviation index. The permission allocation adaptation accuracy impact ratio mapping table will be dynamically corrected again based on the adaptation accuracy deviation index, and the cumulative number of repetitions will be incremented by 1. If the cumulative number of repetitions reaches the preset repetition threshold, but the current adaptation accuracy index has not yet reached the second threshold of historical adaptation accuracy, then a manual review process will be triggered.

6. The role-based knowledge base permission management and data access control method as described in claim 1, characterized in that, The steps for dynamically evaluating the rationality of permissions based on the compliance parameters of role permissions and role user behavior after dynamic processing of permission allocation include: Obtain preset first threshold for behavior compliance, second threshold for behavior compliance, critical parameters for behavior compliance, and proportional parameters for behavior compliance impact. The critical parameters for behavior compliance include a threshold for permission usage frequency, a threshold for effective call rate, and a threshold for the proportion of compliant operations. The proportional parameters for behavior compliance impact include the proportional impact of permission usage frequency, the proportional impact of effective call rate, and the proportional impact of compliant operations. The ratios of the behavioral compliance parameters to their corresponding behavioral compliance threshold parameters are labeled as behavioral compliance impact parameters. The behavioral compliance impact parameter is weighted using the behavioral compliance impact ratio parameter, and the weighting results are then coupled to obtain the behavioral compliance index, which is a quantitative indicator reflecting the compliance degree of role user permission usage and business effectiveness. If the behavior compliance index is lower than the first threshold for behavior compliance, the dynamic assessment result of the authority rationality will be marked as the first assessment result. If the behavior compliance index is not lower than the first threshold of behavior compliance, but lower than the second threshold of behavior compliance, then the dynamic assessment result of the authority rationality will be marked as the second assessment result. If the behavioral compliance index is not lower than the second threshold for behavioral compliance, the dynamic assessment result of the authority rationality will be marked as the third assessment result.

7. The role-based knowledge base permission management and data access control method as described in claim 6, characterized in that, The steps for dynamically optimizing permission allocation based on the dynamic evaluation results of permission rationality include: If the dynamic assessment result of the reasonableness of the permissions is the first assessment result, the high-risk permissions of the corresponding role will be immediately stopped and the permission audit process will be triggered. If the dynamic evaluation result of the permission rationality is the second evaluation result, then the difference between the second threshold of behavior compliance and the behavior compliance index is marked as the behavior compliance deviation index. The compliance deviation index is matched with the preset permission priority weight adjustment value mapping table. The permission priority weight adjustment value mapping table is an association data table that records the unique correspondence between each compliance deviation index interval and its corresponding permission priority weight adjustment value. The permission priority weight of the role is adjusted based on the permission priority weight adjustment value obtained by matching, and the permission allocation dynamic processing is re-executed based on the adjusted permission priority weight. If the dynamic evaluation result of the rationality of permissions is the third evaluation result, then the current permission allocation scheme shall be maintained.

8. The role-based knowledge base permission management and data access control method as described in claim 1, characterized in that, The steps for dynamically configuring the caching strategy based on the importance parameter of role data include: The role data importance parameters include data change frequency, data access frequency, and data importance level; The data importance level is matched with a preset data permission priority benchmark table to obtain the basic data priority weight. The data permission priority benchmark table presets the basic data priority weight corresponding to each data importance level. The data change frequency is matched with the preset first data priority weight adjustment value mapping table to obtain the corresponding first data priority weight adjustment value. The first data priority weight adjustment value mapping table is an associated data table that records the unique correspondence between each data change frequency interval and its corresponding first data priority weight adjustment value. The data access frequency is matched with the preset second data priority weight adjustment value mapping table to obtain the corresponding second data priority weight adjustment value. The second data priority weight adjustment value mapping table is an associated data table that records the unique correspondence between each data access frequency interval and its corresponding second data priority weight adjustment value. The data priority weight adjustment value is obtained based on the first data priority weight adjustment value and the second data priority weight adjustment value. The basic data priority weight is then corrected based on the data priority weight adjustment value to obtain the corrected data permission priority weight. The caching strategy is dynamically configured based on the revised data permission priority weights.

9. The role-based knowledge base permission management and data access control method as described in claim 8, characterized in that, The steps for dynamically configuring the caching strategy based on the corrected data permission priority weights include: Obtain the preset data permission priority weight first threshold and data permission priority weight second threshold; The corrected data permission priority weight is compared with the first threshold of permission priority weight and the second threshold of data permission priority weight. If the corrected data permission priority weight is lower than the first threshold of permission priority weight, the corresponding data cache granularity is marked as coarse granularity. If the corrected data permission priority weight is not lower than the first threshold of permission priority weight, but lower than the second threshold of data permission priority weight, then the corresponding data cache granularity will be marked as medium granularity. If the corrected data permission priority weight is not lower than the second threshold of the data permission priority weight, then the corresponding data cache granularity will be marked as fine-grained. The corrected data permission priority weights are matched with a preset cache lifetime mapping table to obtain the corresponding cache data lifetime. The cache lifetime mapping table is an association data table that records the unique correspondence between each corrected data permission priority weight range and its corresponding cache data lifetime.

10. A role-based knowledge base access control and data access management system, applied in the role-based knowledge base access control and data access control method as described in any one of claims 1 to 9, characterized in that, This includes a dynamic permission allocation processing module, a dynamic permission allocation optimization module, and a dynamic caching strategy configuration module. The permission allocation dynamic processing module is used to perform dynamic permission allocation processing based on permission evaluation and control parameters and historical adaptation accuracy parameters. The permission evaluation and control parameters include permission allocation adaptation accuracy, role weight, and permission function level. The historical adaptation accuracy parameters include permission matching degree, permission conflict rate, permission redundancy rate, and permission missing rate. The module performs dynamic verification of adaptation accuracy based on the dynamic permission allocation processing results. If the dynamic verification of adaptation accuracy passes, the role permissions after dynamic permission allocation processing are generated. If the dynamic verification of adaptation accuracy fails, the permission evaluation and control parameter weights are dynamically corrected, and the dynamic permission allocation processing is re-executed. The permission allocation dynamic optimization module is used to dynamically evaluate the rationality of permissions based on the compliance parameters of role permissions and role users' behavior after dynamic processing of permission allocation. The compliance parameters include permission usage frequency, effective call rate and compliant operation ratio. Based on the dynamic evaluation results of permission rationality, dynamic optimization of permission allocation is performed. The dynamic configuration module for caching strategies is used to dynamically configure caching strategies based on role data importance parameters during user access to role data. These role data importance parameters include data change frequency, data access frequency, and data importance level.

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