An intelligent knowledge base system with hierarchical permission management

Abstract

The present invention discloses an intelligent knowledge base system with hierarchical permission management, which relates to the technical field of hierarchical permission management. It includes a hierarchical permission data collection module, a hierarchical permission evaluation module, a permission feedback module, a hierarchical feedback module, a dynamic calibration module, and a hierarchical permission management module. The hierarchical permission data collection module collects hierarchical permission data including user basic data, intelligent knowledge base source data, access data, user performance data, and approval process data. The dynamic calibration module dynamically adjusts the access level and access permission of intelligent knowledge blocks based on the output results of the access permission feedback model and the access level feedback model. The data entry technology, hierarchical permission feedback technology, hierarchical permission management technology, and model architecture technology in the system of the present invention are closely combined with modern information technology, significantly enhancing the degree of intelligence in the process of hierarchical permission management of the intelligent knowledge base.

Description

Technical Field

[0001] The present invention relates to the technical field of hierarchical permission management, and particularly relates to an intelligent knowledge base system for hierarchical permission management. Background Art

[0002] In the early knowledge bases, most of them only had simple permission management functions and were difficult to flexibly adapt to complex organizational structures and changing business scenarios. The intelligent knowledge base system for hierarchical permission management is an efficient information management system that emerged under the background of the rapid development of information technology. Its core background technologies include, but are not limited to, cloud computing, big data analysis, artificial intelligence, and network security technologies. First of all, cloud computing provides flexible and scalable storage and computing resources for the knowledge base, enabling enterprises to dynamically adjust resource allocation according to their own needs, not only reducing costs but also improving data processing efficiency. Secondly, big data analysis technology enables the system to extract valuable information from massive data. Finally, the application of artificial intelligence, especially machine learning algorithms, endows the system with the ability of self-learning and optimization, greatly improving the user experience. In summary, this intelligent knowledge base system that combines multiple cutting-edge technologies not only provides a safe and reliable knowledge sharing platform for enterprises, but also promotes the effective circulation of information and the continuous accumulation of knowledge, which is of great significance for promoting the innovative development of enterprises;

[0003] Although the prior art has made great progress in the direction of managing intelligent knowledge bases, there are still some problems to be optimized. In the prior art, when enterprises manage knowledge bases, it is difficult to combine multiple types of hierarchical permissions for data management, resulting in low utilization rate and orderliness of intelligent knowledge bases. Therefore, how to perform hierarchical permission management on the knowledge base to achieve dynamic adjustment of access permissions to the intelligent knowledge base is the problem to be solved by the present invention. Summary of the Invention

[0004] To achieve the above objectives, the present invention is realized through the following technical solutions: an intelligent knowledge base system for hierarchical permission management, including a hierarchical permission data collection module, a hierarchical permission evaluation module, a permission feedback module, a hierarchical feedback module, a dynamic calibration module, and a hierarchical permission management module, wherein each module is communicatively connected;

[0005] The hierarchical permission data collection module collects hierarchical permission data including user basic data, intelligent knowledge base source data, access data, user performance data, and approval process data, providing data support for the implementation of the functions of subsequent modules;

[0006] The hierarchical permission evaluation module uses the preprocessed hierarchical permission data to assign corresponding access levels and access permissions to intelligent knowledge blocks based on the user's actual work experience level and position, thereby optimizing the access levels of intelligent knowledge blocks and laying a foundation for improving the utilization rate and orderliness of the intelligent knowledge base;

[0007] The permission feedback module calculates the access frequency of users to intelligent knowledge blocks through the preprocessed access data, and then constructs an access permission feedback model, providing technical support for improving the accuracy of intelligent knowledge block access permissions;

[0008] The hierarchical feedback module obtains the customer satisfaction rate and complaint rate through the preprocessed user performance data, and then constructs an access level feedback model, providing technical support for improving the accuracy of intelligent knowledge block access levels;

[0009] The dynamic calibration module dynamically adjusts the access levels and access permissions of intelligent knowledge blocks based on the output results of the access permission feedback model and the access level feedback model;

[0010] The hierarchical permission management module combines the preprocessed approval process data to realize the hierarchical permission management of the intelligent knowledge base, solving the problem that it is difficult to combine multiple types of hierarchical permissions for data management in the prior art, resulting in low utilization rate and orderliness of the intelligent knowledge base.

[0011] A further improvement of the technical solution of the present invention lies in: the hierarchical permission data collection module, and the collection process of the hierarchical permission data includes:

[0012] The intelligent knowledge base is divided into several intelligent knowledge blocks, and the divided intelligent knowledge blocks are encoded. Combining data entry technology and questionnaire survey technology, user basic data, intelligent knowledge base source data, access data, user performance data, and approval process data are collected;

[0013] Specifically, through data entry technology, user basic data, intelligent knowledge base source data, access data, user KPIs, and approval process data are selectively entered from the enterprise intelligent knowledge base. Using questionnaire technology, the number of customer feedback times, customer satisfaction times, and customer complaint times corresponding to different ID users are collected;

[0014] The user basic data is the user's ID, position, and working years; the source data of the intelligent knowledge base includes the sources of each intelligent knowledge block, where the sources of the intelligent knowledge block are composed of enterprise public resources, enterprise internal resources, and enterprise confidential resources; the access data includes the intelligent knowledge block numbers accessed by the user, access time, and the number of accesses within 24 hours; the user performance data includes the KPIs of users with different IDs, the number of customer feedbacks, the number of customer satisfactions, and the number of customer complaints; the approval process data includes the approval application user ID, application access permission, application access level, approval time, and approval result, where the approval result is composed of approval passed and approval not passed;

[0015] Perform data cleaning and data standardization processing on the collected hierarchical permission data, integrate the preprocessed hierarchical permission data to generate a hierarchical permission data set, and divide the hierarchical permission data set into a training set and a test set, and the ratio of the training set to the test set is 8:2.

[0016] A further improvement of the technical solution of the present invention lies in that: in the hierarchical permission evaluation module, the process of allocating the access level and access permission of the intelligent knowledge block includes:

[0017] According to the enterprise management requirements, divide the user's position into high-level positions, middle-level positions, and low-level positions, and combine the current user basic data to obtain the user's position level;

[0018] Set the access levels of each intelligent knowledge block, where the access levels are composed of public level, internal level, and management level, and allocate the access levels of the intelligent knowledge block to the user according to the user's position level. Specifically, when the user's position level is a low-level position, allocate the public level; when the user's position level is a middle-level position, allocate the internal level; when the user's position level is a high-level position, allocate the management level;

[0019] Set the access permissions of each intelligent knowledge block, where the access permissions are composed of browsing permission and editing permission. Count the user's ID, working years, and KPI, draw a user work experience report, and screen out users with high work experience levels, medium work experience levels, and low work experience levels according to the user work experience report. Specifically, users with high work experience levels are users with KPIs in the top 10% and working hours greater than 5 years, medium work experience level users are users with KPIs between 10% and 50% and working hours between 3 and 5 years, and low work experience level users are users with KPIs below 50% and working hours less than three years;

[0020] Assign the browsing permission of some intelligent knowledge blocks to users with low work experience levels, and do not assign the editing permission; assign the browsing permission of all intelligent knowledge blocks to users with medium work experience levels, and assign the editing permission of some intelligent knowledge blocks; assign the browsing permission and editing permission of all intelligent knowledge blocks to users with high experience levels.

[0021] A further improvement of the technical solution of the present invention lies in: for the hierarchical permission evaluation module, the optimization process of the access level of each intelligent knowledge block includes:

[0022] Correspond enterprise public resources, enterprise internal resources and enterprise confidential resources to the public level, internal level and management level respectively;

[0023] Based on the source of each current intelligent knowledge block, if the access level corresponding to the source of each current intelligent knowledge block is consistent with the assigned access level, the assigned access level is not optimized; if the access level corresponding to the source of each current intelligent knowledge block is inconsistent with the assigned access level, the assigned access level is updated using the access level corresponding to the source of each current intelligent knowledge block.

[0024] A further improvement of the technical solution of the present invention lies in: for the permission feedback module, the calculation process of the user's access frequency to the intelligent knowledge block includes:

[0025] Extract the adjacent two access times of the user to the intelligent knowledge block, and obtain the access time interval of the intelligent knowledge block through the difference between the extracted adjacent two access times;

[0026] Weights are respectively assigned to the access time interval of the intelligent knowledge block and the number of accesses within 24 hours. Using the weighted summation method, calculate the user's access frequency to the intelligent knowledge block, and deploy the user's access frequency to the hierarchical permission dataset. The calculation process includes:

[0027]

[0028] Among them, is the user's access frequency to the intelligent knowledge block, and are respectively the weights of the access time interval of the intelligent knowledge block and the number of accesses within 24 hours, and are respectively the access time interval of the intelligent knowledge block and the number of accesses within 24 hours.

[0029] A further improvement of the technical solution of the present invention lies in: for the permission feedback module, the construction process of the access permission feedback model includes:

[0030] Extract the access frequency of users to intelligent knowledge blocks in the hierarchical permission dataset. Combine the training set data with the linear regression algorithm. Use the access frequency of users to intelligent knowledge blocks as the input and the intelligent knowledge block access permission feedback index as the output to learn the linear relationship between the access frequency of users to intelligent knowledge blocks and the intelligent knowledge block access permission feedback index, and train the access permission feedback model;

[0031] Input the test set data into the access permission feedback model, adjust the intercept term and regression coefficient of the access permission feedback model, optimize the performance of the access permission feedback model, obtain the final access permission feedback model, and combine the current access frequency of users to intelligent knowledge blocks to output the corresponding intelligent knowledge block access permission feedback index;

[0032] The expression of the access permission feedback model is as follows:

[0033]

[0034] Wherein, is the intelligent knowledge block access permission feedback index, is the regression coefficient of the access frequency of users to intelligent knowledge blocks, is the access frequency of users to intelligent knowledge blocks, and are the intercept term and error term of the access permission feedback model respectively.

[0035] A further improvement of the technical solution of the present invention lies in: for the hierarchical feedback module, the process of obtaining the customer satisfaction and complaint rate includes:

[0036] Extract the number of customer feedbacks, the number of customer satisfactions, and the number of customer complaints from the preprocessed user performance data;

[0037] By calculating the proportion of the number of customer satisfactions in the number of customer feedbacks and the proportion of the number of customer complaints in the number of customer feedbacks, obtain the customer satisfaction and complaint rate respectively, and integrate the obtained customer satisfaction and customer complaint rate into the hierarchical permission dataset.

[0038] A further improvement of the technical solution of the present invention lies in: for the hierarchical feedback module, the construction process of the access hierarchical feedback model includes:

[0039] Extract the customer satisfaction and customer complaint rate in the hierarchical permission dataset, use the training set data, and combine the multiple linear regression algorithm. Use the customer satisfaction and customer complaint rate as the input and the intelligent knowledge block hierarchical feedback index as the output to learn the linear relationship between the customer satisfaction, customer complaint rate and the intelligent knowledge block access hierarchical feedback index, and train the access hierarchical feedback model;

[0040] Input the test set data into the access level feedback model, adjust the intercept term and regression coefficients of the access level feedback model, optimize the performance of the access level feedback model, obtain the final access level feedback model, and combine the current customer satisfaction and customer complaint rate to output the corresponding intelligent knowledge block access level feedback index;

[0041] The expression of the access level feedback model is as follows:

[0042]

[0043] Wherein, is the access level feedback index of the intelligent knowledge block, and are the regression coefficients of customer satisfaction and customer complaint rate respectively, and are customer satisfaction and customer complaint rate respectively, and are the intercept term and error term of the access level feedback model respectively.

[0044] A further improvement of the technical solution of the present invention is that: in the dynamic calibration module, the dynamic adjustment process of the access level and access permission of the intelligent knowledge block includes:

[0045] Analyze the access permission feedback index of the intelligent knowledge block, divide the access permission feedback range, and perform corresponding dynamic adjustment on the access permission of the intelligent knowledge block in different access permission feedback ranges;

[0046] According to the access level feedback index of the intelligent knowledge block, divide the access level feedback range, and perform corresponding dynamic adjustment on the access level of the intelligent knowledge block in different access level feedback ranges;

[0047] When the access permission feedback index of the intelligent knowledge block is lower than 0.4, adjust the access permission of the intelligent knowledge block according to the standard of allocating access permission to users with low work experience level; when the access permission feedback index of the intelligent knowledge block is between 0.4 and 0.6, adjust the access permission of the intelligent knowledge block according to the standard of allocating access permission to users with medium work experience level; when the access permission feedback index of the intelligent knowledge block is higher than 0.6, adjust the access permission of the intelligent knowledge block according to the standard of allocating access permission to users with high work experience level. The above adjustments change with the change of the access permission feedback index of the intelligent knowledge block, realizing the dynamic adjustment of the access permission of the intelligent knowledge block;

[0048] When the access level feedback index of the intelligent knowledge block is lower than 0.3, adjust the access level of the intelligent knowledge block to the public level; when the access level feedback index of the intelligent knowledge block is between 0.3 and 0.6, adjust the access level of the intelligent knowledge block to the internal level; when the access level feedback index of the intelligent knowledge block is higher than 0.6, adjust the access level of the intelligent knowledge block to the management level. The above adjustments change with the change of the access level feedback index of the intelligent knowledge block, realizing the dynamic adjustment of the access level of the intelligent knowledge block.

[0049] A further improvement of the technical solution of the present invention lies in that: for the hierarchical permission management module, the hierarchical permission management process of the intelligent knowledge base includes:

[0050] According to the approval process data, when there is an application for access permission by the approval user, the access permission of the intelligent knowledge block is corrected according to the approval result. If the approval result is approval, the application access permission of the corresponding user is used to replace the access permission of the intelligent knowledge block after dynamic adjustment; if the approval result is disapproval, the access permission of the intelligent knowledge block after dynamic adjustment remains unchanged;

[0051] When there is an application for access level by the approval user, the access level of the intelligent knowledge block is corrected according to the approval result. If the approval result is approval, the application access level of the corresponding user is used to replace the access level of the intelligent knowledge block after dynamic adjustment; if the approval result is disapproval, the access level of the intelligent knowledge block after dynamic adjustment remains unchanged;

[0052] Through the combination of each intelligent knowledge block, the hierarchical permission management of the intelligent knowledge base is realized.

[0053] The beneficial effects of the present invention are as follows: In the intelligent knowledge base system with hierarchical permission management of the present invention, compared with the traditional intelligent knowledge base system with hierarchical permission management, the data entry technology, hierarchical permission feedback technology, hierarchical permission management technology, and model architecture technology in the system of the present invention are closely combined with modern information technology to accurately capture user basic data, intelligent knowledge base source data, access data, user performance data, and approval process data, and then obtain the access level and access permission of intelligent knowledge blocks, the access frequency of users to intelligent knowledge blocks, as well as the customer satisfaction rate and complaint rate. Through the construction of the access permission feedback model and the access level feedback model, the access permission feedback index of intelligent knowledge blocks and the access level feedback index of intelligent knowledge blocks are obtained, achieving dynamic adjustment of the access level and access permission of intelligent knowledge blocks. Combining the actual approval process data, the access level and access permission of intelligent knowledge blocks are corrected, improving the reliability of hierarchical permission management, and solving the problem in the prior art that when an enterprise conducts knowledge base management, it is difficult to manage data in combination with multiple types of hierarchical permissions, resulting in low utilization rate and orderliness of the intelligent knowledge base. It ensures that the method in the present invention can refine the dynamic monitoring standard for an intelligent knowledge base system with hierarchical permission management within a more accurate range, making the monitored data more accurate indicators under the same conditions. The research and application of this method significantly enhance the intelligence level in the process of hierarchical permission management of the intelligent knowledge base. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required in the embodiments. Obviously, the drawings described below are only some embodiments recorded in the present invention, and those of ordinary skill in the art can also obtain other drawings based on these drawings.

[0055] Figure 1 It is a block diagram of an intelligent knowledge base system with hierarchical permission management of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0056] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0057] Such as Figure 1As shown in the figure, the present invention provides an intelligent knowledge base system with hierarchical permission management, including a hierarchical permission data acquisition module, a hierarchical permission evaluation module, a permission feedback module, a hierarchical feedback module, a dynamic calibration module, and a hierarchical permission management module. Among them, each module is communicatively connected;

[0058] The hierarchical permission data acquisition module collects hierarchical permission data including user basic data, intelligent knowledge base source data, access data, user performance data, and approval process data, providing data support for the implementation of subsequent module functions;

[0059] The hierarchical permission evaluation module uses the preprocessed hierarchical permission data to assign corresponding access levels and access permissions to intelligent knowledge blocks based on the user's actual work experience level and position, and then optimizes the access levels of intelligent knowledge blocks, laying a foundation for improving the utilization rate and orderliness of the intelligent knowledge base;

[0060] The permission feedback module calculates the access frequency of users to intelligent knowledge blocks through the preprocessed access data, and then constructs an access permission feedback model, providing technical support for improving the accuracy of access permissions of intelligent knowledge blocks;

[0061] The hierarchical feedback module obtains the customer satisfaction rate and complaint rate through the preprocessed user performance data, and then constructs an access level feedback model, providing technical support for improving the accuracy of access levels of intelligent knowledge blocks;

[0062] The dynamic calibration module dynamically adjusts the access levels and access permissions of intelligent knowledge blocks based on the output results of the access permission feedback model and the access level feedback model;

[0063] The hierarchical permission management module combines the preprocessed approval process data to implement hierarchical permission management of the intelligent knowledge base, solving the problem that it is difficult to combine multiple types of hierarchical permissions for data management in the prior art, resulting in low utilization rate and orderliness of the intelligent knowledge base.

[0064] The hierarchical permission data acquisition module, the acquisition process of hierarchical permission data includes:

[0065] The intelligent knowledge base is divided into several intelligent knowledge blocks, and the divided intelligent knowledge blocks are encoded. Combining data entry technology and questionnaire survey technology, user basic data, intelligent knowledge base source data, access data, user performance data, and approval process data are collected;

[0066] Specifically, through data entry technology, user basic data, intelligent knowledge base source data, access data, user KPIs, and approval process data are selectively entered from the enterprise intelligent knowledge base. Using questionnaire technology, the number of customer feedback times, customer satisfaction times, and customer complaint times corresponding to different ID users are collected;

[0067] The user basic data includes the user's ID, position, and working years; the source data of the intelligent knowledge base includes the sources of each intelligent knowledge block, where the sources of the intelligent knowledge blocks consist of enterprise public resources, enterprise internal resources, and enterprise confidential resources; the access data includes the intelligent knowledge block numbers accessed by the user, access time, and the number of accesses within 24 hours; the user performance data includes the KPIs of users with different IDs, the number of customer feedbacks, the number of customer satisfactions, and the number of customer complaints; the approval process data includes the ID of the approval application user, the applied access permission, the applied access level, the approval time, and the approval result, where the approval result consists of approval passed and approval not passed.

[0068] Perform data cleaning and data standardization processing on the collected hierarchical permission data, integrate the preprocessed hierarchical permission data to generate a hierarchical permission data set, and divide the hierarchical permission data set into a training set and a test set, and the ratio of the training set to the test set is 8:2.

[0069] Hierarchical permission evaluation module, the process of allocating the access level of the intelligent knowledge block and the access permission includes:

[0070] According to the enterprise management requirements, divide the user's position into high-level positions, middle-level positions, and low-level positions, and combine the current user basic data to obtain the user's position level;

[0071] Set the access levels of each intelligent knowledge block, where the access levels consist of public level, internal level, and management level, and allocate the access levels of the intelligent knowledge blocks to users according to the user's position level. Specifically, when the user's position level is a low-level position, allocate the public level; when the user's position level is a middle-level position, allocate the internal level; when the user's position level is a high-level position, allocate the management level;

[0072] Set the access permissions of each intelligent knowledge block, where the access permissions consist of browsing permission and editing permission, count the user's ID, working years, and KPI, draw a user work experience report, and screen out users with high work experience levels, medium work experience levels, and low work experience levels according to the user work experience report. Specifically, users with high work experience levels are users with KPIs in the top 10% and working hours greater than 5 years, medium work experience level users are users with KPIs between 10% and 50% and working hours between 3 and 5 years, and low work experience level users are users with KPIs below 50% and working hours below three years;

[0073] Assign the browsing permission of some intelligent knowledge blocks to users with low work experience levels, and do not assign the editing permission; assign the browsing permission of all intelligent knowledge blocks to users with medium work experience levels, and assign the editing permission of some intelligent knowledge blocks; assign the browsing and editing permissions of all intelligent knowledge blocks to users with high experience levels.

[0074] Hierarchical permission evaluation module. The optimization process of the access level of each intelligent knowledge block includes:

[0075] Correspond the enterprise's public resources, internal resources, and confidential resources to the public level, internal level, and management level respectively;

[0076] Based on the source of each current intelligent knowledge block, if the access level corresponding to the source of each current intelligent knowledge block is consistent with the assigned access level, do not optimize the assigned access level; if the access level corresponding to the source of each current intelligent knowledge block is inconsistent with the assigned access level, use the access level corresponding to the source of each current intelligent knowledge block to update the assigned access level.

[0077] Permission feedback module. The calculation process of the user's access frequency to the intelligent knowledge block includes:

[0078] Extract the adjacent two access times of the user to the intelligent knowledge block, and obtain the access time interval of the intelligent knowledge block through the difference between the extracted adjacent two access times;

[0079] Assign weights to the access time interval of the intelligent knowledge block and the number of accesses within 24 hours respectively, and use the weighted summation method to calculate the user's access frequency to the intelligent knowledge block, and deploy the user's access frequency to the hierarchical permission dataset. The calculation process includes:

[0080]

[0081] Among them, is the user's access frequency to the intelligent knowledge block, and are the weights of the access time interval of the intelligent knowledge block and the number of accesses within 24 hours respectively, and are the access time interval of the intelligent knowledge block and the number of accesses within 24 hours respectively.

[0082] Permission feedback module. The construction process of the access permission feedback model includes:

[0083] Extract the access frequency of users to intelligent knowledge blocks in the hierarchical permission dataset. Combine the training set data with the linear regression algorithm. Use the access frequency of users to intelligent knowledge blocks as the input and the intelligent knowledge block access permission feedback index as the output to learn the linear relationship between the access frequency of users to intelligent knowledge blocks and the intelligent knowledge block access permission feedback index, and train the access permission feedback model;

[0084] Input the test set data into the access permission feedback model, adjust the intercept term and regression coefficient of the access permission feedback model, optimize the performance of the access permission feedback model, obtain the final access permission feedback model, and combine the current access frequency of the user to the intelligent knowledge block to output the corresponding intelligent knowledge block access permission feedback index;

[0085] The expression of the access permission feedback model is as follows:

[0086]

[0087] Among them, is the intelligent knowledge block access permission feedback index, is the regression coefficient of the access frequency of users to intelligent knowledge blocks, is the access frequency of users to intelligent knowledge blocks, and are the intercept term and error term of the access permission feedback model respectively.

[0088] Hierarchical feedback module. The process of obtaining the customer satisfaction and complaint rate includes:

[0089] Extract the number of customer feedbacks, the number of customer satisfactions, and the number of customer complaints from the preprocessed user performance data;

[0090] By calculating the proportion of the number of customer satisfactions in the number of customer feedbacks and the proportion of the number of customer complaints in the number of customer feedbacks, obtain the customer satisfaction and complaint rate respectively, and integrate the obtained customer satisfaction and customer complaint rate into the hierarchical permission dataset.

[0091] Hierarchical feedback module. The construction process of the access hierarchical feedback model includes:

[0092] Extract the customer satisfaction and customer complaint rate from the hierarchical permission dataset, use the training set data, combine with the multiple linear regression algorithm, use the customer satisfaction and customer complaint rate as the input, and the intelligent knowledge block hierarchical feedback index as the output to learn the linear relationship between the customer satisfaction, customer complaint rate and the intelligent knowledge block access hierarchical feedback index, and train the access hierarchical feedback model;

[0093] Input the test set data into the access level feedback model, adjust the intercept term and regression coefficients of the access level feedback model, optimize the performance of the access level feedback model, obtain the final access level feedback model, and combine the current customer satisfaction and customer complaint rate to output the corresponding intelligent knowledge block access level feedback index;

[0094] The expression of the access level feedback model is as follows:

[0095]

[0096] Where, is the access level feedback index of the intelligent knowledge block, and are the regression coefficients of customer satisfaction and customer complaint rate respectively, and are customer satisfaction and customer complaint rate respectively, and are the intercept term and error term of the access level feedback model respectively.

[0097] Dynamic calibration module, the dynamic adjustment process of the access level and access permission of the intelligent knowledge block includes:

[0098] Analyze the access permission feedback index of the intelligent knowledge block, divide the access permission feedback range, and make corresponding dynamic adjustments to the access permissions of the intelligent knowledge blocks in different access permission feedback ranges;

[0099] According to the access level feedback index of the intelligent knowledge block, divide the access level feedback range, and make corresponding dynamic adjustments to the access levels of the intelligent knowledge blocks in different access level feedback ranges;

[0100] When the access permission feedback index of the intelligent knowledge block is lower than 0.4, adjust the access permission of the intelligent knowledge block according to the standard of allocating access permission to users with low work experience level; when the access permission feedback index of the intelligent knowledge block is between 0.4 and 0.6, adjust the access permission of the intelligent knowledge block according to the standard of allocating access permission to users with medium work experience level; when the access permission feedback index of the intelligent knowledge block is higher than 0.6, adjust the access permission of the intelligent knowledge block according to the standard of allocating access permission to users with high work experience level. The above adjustments change with the change of the access permission feedback index of the intelligent knowledge block, realizing the dynamic adjustment of the access permission of the intelligent knowledge block;

[0101] When the intelligent knowledge block access level feedback index is lower than 0.3, adjust the access level of the intelligent knowledge block to the public level; when the intelligent knowledge block access level feedback index is between 0.3 and 0.6, adjust the access level of the intelligent knowledge block to the internal level; when the intelligent knowledge block access level feedback index is higher than 0.6, adjust the access level of the intelligent knowledge block to the management level. The above adjustments change with the change of the intelligent knowledge block access level feedback index, realizing the dynamic adjustment of the intelligent knowledge block access level.

[0102] Hierarchical permission management module. The hierarchical permission management process of the intelligent knowledge base includes:

[0103] According to the approval process data, when there is an application for access permission by the approval user, correct the access permission of the intelligent knowledge block according to the approval result. If the approval result is approval passed, replace the access permission of the intelligent knowledge block after dynamic adjustment with the application access permission of the corresponding user; if the approval result is approval not passed, do not change the access permission of the intelligent knowledge block after dynamic adjustment;

[0104] When there is an application for access level by the approval user, correct the access level of the intelligent knowledge block according to the approval result. If the approval result is approval passed, replace the access level of the intelligent knowledge block after dynamic adjustment with the application access level of the corresponding user; if the approval result is approval not passed, do not change the access level of the intelligent knowledge block after dynamic adjustment;

[0105] Through the combination of each intelligent knowledge block, the hierarchical permission management of the intelligent knowledge base is realized.

[0106] First, combine the data entry technology and the questionnaire survey technology to collect user basic data, intelligent knowledge base source data, access data, user performance data and approval process data; second, use the preprocessed user basic data and the user's KPI to assign corresponding access levels and access permissions to the intelligent knowledge blocks, and then optimize the access levels of the intelligent knowledge blocks; immediately afterwards, construct an access permission feedback model and an access level feedback model respectively through the preprocessed access data and user performance data; then, based on the output results of the access permission feedback model and the access level feedback model, dynamically adjust the access levels and access permissions of the intelligent knowledge blocks; finally, combine the preprocessed approval process data to realize the hierarchical permission management of the intelligent knowledge base.

[0107] The above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should all be covered within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. An intelligent knowledge base system with hierarchical permission management, including a hierarchical permission data collection module, a hierarchical permission evaluation module, a permission feedback module, a hierarchical feedback module, a dynamic calibration module, and a hierarchical permission management module, where, Each module is communicatively connected, characterized in that: The hierarchical permission data collection module collects hierarchical permission data including user basic data, intelligent knowledge base source data, access data, user performance data, and approval process data; The hierarchical permission evaluation module uses the preprocessed hierarchical permission data to assign corresponding access levels and access permissions to intelligent knowledge blocks, thereby optimizing the access levels of intelligent knowledge blocks; The permission feedback module calculates the access frequency of users to intelligent knowledge blocks through the preprocessed access data, and then constructs an access permission feedback model; The permission feedback module, the construction process of the access permission feedback model includes: Extract the access frequency of users to intelligent knowledge blocks in the hierarchical permission dataset, combine the training set data with the linear regression algorithm, use the access frequency of users to intelligent knowledge blocks as the input, and the intelligent knowledge block access permission feedback index as the output, learn the linear relationship between the access frequency of users to intelligent knowledge blocks and the intelligent knowledge block access permission feedback index, and train the access permission feedback model; Input the test set data into the access permission feedback model, adjust the intercept term and regression coefficient of the access permission feedback model, optimize the performance of the access permission feedback model, obtain the final access permission feedback model, and combine the current access frequency of users to intelligent knowledge blocks to output the corresponding intelligent knowledge block access permission feedback index; The expression of this access permission feedback model is as follows: R = α0 + α1f + ∈ Where, R is the intelligent knowledge block access permission feedback index, α1 is the regression coefficient of the access frequency of users to intelligent knowledge blocks, f is the access frequency of users to intelligent knowledge blocks, and α0 and ∈ are the intercept term and error term of the access permission feedback model respectively; The hierarchical feedback module obtains the customer satisfaction and complaint rate through the preprocessed user performance data, and then constructs an access level feedback model; The hierarchical feedback module, the construction process of the access level feedback model includes: Extract the customer satisfaction and customer complaint rate in the hierarchical permission dataset, use the training set data, combine the multiple linear regression algorithm, use the customer satisfaction and customer complaint rate as the input, and the intelligent knowledge block hierarchical feedback index as the output, learn the linear relationship between the customer satisfaction, customer complaint rate and the intelligent knowledge block access level feedback index, and train the access level feedback model; Input the test set data into the access level feedback model, adjust the intercept term and regression coefficient of the access level feedback model, optimize the performance of the access level feedback model, obtain the final access level feedback model, and combine the current customer satisfaction and customer complaint rate to output the corresponding intelligent knowledge block access level feedback index; The expression of this access level feedback model is as follows: Y = β0 + β1y1 + β2y2 + δ Where, Y is the intelligent knowledge block access level feedback index, β1 and β2 are the regression coefficients of customer satisfaction and customer complaint rate respectively, y1 and y2 are customer satisfaction and customer complaint rate respectively, and β0 and δ are the intercept term and error term of the access level feedback model respectively; The dynamic calibration module dynamically adjusts the access level and access permissions of intelligent knowledge blocks based on the output results of the access permission feedback model and the access level feedback model; The hierarchical permission management module realizes the hierarchical permission management of the intelligent knowledge base in combination with the preprocessed approval process data.

2. The intelligent knowledge base system with hierarchical permission management according to claim 1, characterized in that: The hierarchical permission data collection module, the process of collecting hierarchical permission data includes: Dividing the intelligent knowledge base into several intelligent knowledge blocks, encoding the divided intelligent knowledge blocks, and collecting user basic data, intelligent knowledge base source data, access data, user performance data, and approval process data by combining data entry technology and questionnaire technology; The user basic data is the user's ID, position, and working years; the intelligent knowledge base source data includes the sources of each intelligent knowledge block, where the sources of the intelligent knowledge blocks are composed of enterprise public resources, enterprise internal resources, and enterprise confidential resources; the access data includes the intelligent knowledge block numbers accessed by the user, access time, and the number of accesses within 24 hours; the user performance data includes the KPIs of users with different IDs, the number of customer feedbacks, the number of customer satisfactions, and the number of customer complaints; the approval process data includes the approval application user ID, the applied access permission, the applied access level, the approval time, and the approval result, where the approval result is composed of approval passed and approval not passed; Perform data cleaning and data standardization processing on the collected hierarchical permission data, integrate the preprocessed hierarchical permission data, generate a hierarchical permission data set, and divide the hierarchical permission data set into a training set and a test set.

3. An intelligent knowledge base system with hierarchical permission management according to claim 2, characterized in that: The hierarchical permission evaluation module, the process of allocating the access level and access permissions of intelligent knowledge blocks includes: According to the enterprise management requirements, divide the positions of users into high-level positions, middle-level positions, and low-level positions, and combine the current user basic data to obtain the position levels of users; Set the access levels of each intelligent knowledge block, where the access levels are composed of public level, internal level, and management level, and allocate the access levels of intelligent knowledge blocks to users according to the position levels of users; Set the access permissions of each intelligent knowledge block, where the access permissions are composed of browsing permission and editing permission, count the user ID, working years, and KPI of users, draw a user work experience report, and screen out users with high work experience levels, medium work experience levels, and low work experience levels according to the user work experience report; Allocate the browsing permission of some intelligent knowledge blocks to low work experience level users without allocating the editing permission; allocate the browsing permission of all intelligent knowledge blocks to medium work experience level users and allocate the editing permission of some intelligent knowledge blocks; allocate the browsing permission and editing permission of all intelligent knowledge blocks to high work experience level users.

4. An intelligent knowledge base system with hierarchical permission management according to claim 3, characterized in that: The hierarchical permission evaluation module, the optimization process of the access level of each intelligent knowledge block includes: Correspond enterprise public resources, enterprise internal resources, and enterprise confidential resources to the public level, internal level, and management level respectively; Based on the sources of current intelligent knowledge blocks, when the access levels corresponding to the sources of current intelligent knowledge blocks are consistent with the assigned access levels, the assigned access levels are not optimized; when the access levels corresponding to the sources of current intelligent knowledge blocks are inconsistent with the assigned access levels, the assigned access levels are updated using the access levels corresponding to the sources of current intelligent knowledge blocks.

5. The intelligent knowledge base system with hierarchical permission management according to claim 4, characterized in that: For the permission feedback module, the calculation process of the user's access frequency to intelligent knowledge blocks includes: Extract the adjacent two access times of the user to the intelligent knowledge block, and obtain the access time interval of the intelligent knowledge block through the difference between the extracted adjacent two access times; Assign weights to the access time interval of the intelligent knowledge block and the number of accesses within 24 hours respectively, and use the weighted summation method to calculate the user's access frequency to the intelligent knowledge block, and deploy the user's access frequency to the intelligent knowledge block to the hierarchical permission dataset.

6. An intelligent knowledge base system with hierarchical permission management according to claim 5, characterized in that: For the level feedback module, the process of obtaining the customer satisfaction and complaint rate includes: Extract the number of customer feedbacks, the number of customer satisfactions, and the number of customer complaints from the preprocessed user performance data; By calculating the proportion of the number of customer satisfactions in the number of customer feedbacks and the proportion of the number of customer complaints in the number of customer feedbacks, obtain the customer satisfaction and complaint rate respectively, and integrate the obtained customer satisfaction and customer complaint rate into the hierarchical permission dataset.

7. An intelligent knowledge base system with hierarchical permission management according to claim 6, characterized in that: For the dynamic calibration module, the dynamic adjustment process of the access level and access permission of intelligent knowledge blocks includes: Analyze the access permission feedback index of intelligent knowledge blocks, divide the access permission feedback range, and make corresponding dynamic adjustments to the access permissions of intelligent knowledge blocks in different access permission feedback ranges; According to the access level feedback index of intelligent knowledge blocks, divide the access level feedback range, and make corresponding dynamic adjustments to the access levels of intelligent knowledge blocks in different access level feedback ranges.

8. An intelligent knowledge base system with hierarchical permission management according to claim 7, characterized in that: For the hierarchical permission management module, the hierarchical permission management process of the intelligent knowledge base includes: Based on the approval process data, when there is an application for access permission by an approved user, according to the approval result, correct the access permission of the intelligent knowledge block. If the approval result is approval, replace the access permission of the intelligent knowledge block after dynamic adjustment with the application access permission of the corresponding user; if the approval result is disapproval, do not change the access permission of the intelligent knowledge block after dynamic adjustment; When there is an application for access level by an approved user, according to the approval result, correct the access level of the intelligent knowledge block. If the approval result is approval, replace the access level of the intelligent knowledge block after dynamic adjustment with the application access level of the corresponding user; if the approval result is disapproval, do not change the access level of the intelligent knowledge block after dynamic adjustment; Through the combination of each intelligent knowledge block, the hierarchical permission management of the intelligent knowledge base is realized.

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