Intelligent knowledge base system for hierarchical authority management

Abstract

The invention discloses an intelligent knowledge base system for hierarchical authority management, which relates to the technical field of hierarchical authority management and comprises a hierarchical authority data acquisition module, a hierarchical authority evaluation module, an authority feedback module, a hierarchical feedback module, a dynamic calibration module and a hierarchical authority management module. Collecting hierarchical authority data including user basic data, intelligent knowledge base source data, access data, user performance data and approval process data; the dynamic calibration module is used for dynamically adjusting the access level and the access permission of the intelligent knowledge block based on the output results of the access permission feedback model and the access level feedback model; the data entry technology, the hierarchical authority feedback technology, the hierarchical authority management technology and the model architecture technology in the system are closely combined with the modern information technology, and the intelligent degree in the process of hierarchical authority management of the intelligent knowledge base is remarkably enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of hierarchical permission management, and specifically 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 technology. 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; Although the existing technology has made great progress in the direction of managing intelligent knowledge bases, there are still some problems to be optimized. In the existing technology, when enterprises manage knowledge bases, it is difficult to combine multi-type 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 for the intelligent knowledge base is the problem to be solved by the present invention. Summary of the Invention

[0003] To achieve the above objectives, the present invention is realized through the following technical solutions: An intelligent knowledge base system for hierarchical permission management includes 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, wherein each module is communicatively connected; The hierarchical permission data acquisition module acquires 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 realization of subsequent module functions; The hierarchical permission evaluation module uses the preprocessed hierarchical permission data to allocate corresponding access levels and access permissions to intelligent knowledge blocks based on the actual work experience level and position of users, 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; The said access right feedback module calculates the access frequency of users to intelligent knowledge chunks through the preprocessed access data, and then constructs an access right feedback model, providing technical support for improving the accuracy of access rights to intelligent knowledge chunks; The said hierarchical feedback module obtains the satisfaction and complaint rates of customers through the preprocessed user performance data, and then constructs an access hierarchy feedback model, providing technical support for improving the accuracy of access hierarchies to intelligent knowledge chunks; The said dynamic calibration module dynamically adjusts the access hierarchy and access rights of intelligent knowledge chunks based on the output results of the access right feedback model and the access hierarchy feedback model; The said hierarchical access right management module combines the preprocessed approval process data to achieve hierarchical access right management of the intelligent knowledge base, solving the problem in the prior art that it is difficult to manage data by combining multiple types of hierarchical access rights, resulting in low utilization rate and low orderliness of the intelligent knowledge base.

[0004] A further improvement of the technical solution of the present invention lies in: the process of collecting hierarchical access right data by the said hierarchical access right data collection module includes: Dividing the intelligent knowledge base into several intelligent knowledge chunks, coding the divided intelligent knowledge chunks, and combining data entry technology and questionnaire survey technology to collect user basic data, intelligent knowledge base source data, access data, user performance data, and approval process data; Specifically, through data entry technology, selectively enter user basic data, intelligent knowledge base source data, access data, user KPIs, and approval process data from the enterprise intelligent knowledge base, and use questionnaire technology to collect the number of customer feedbacks, the number of customer satisfactions, and the number of customer complaints corresponding to users with different IDs; The said user basic data are the user ID, position, and working years; the intelligent knowledge base source data includes the sources of each intelligent knowledge chunk, where the sources of the intelligent knowledge chunks are composed of enterprise public resources, enterprise internal resources, and enterprise confidential resources; the access data includes the intelligent knowledge chunk numbers accessed by users, access times, 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 right, the applied access hierarchy, 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 access right data, integrate the preprocessed hierarchical access right data to generate a hierarchical access right data set, and divide the hierarchical access right data set into a training set and a test set, and the ratio of the training set to the test set is 8:2.

[0005] 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: 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. 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. Specifically, when the position level of the user is a low-level position, allocate the public level; when the position level of the user is a middle-level position, allocate the internal level; when the position level of the user is a high-level position, allocate the management level; Set the access permissions of each intelligent knowledge block. 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. Specifically, users with high work experience levels are users with KPI in the top 10% and working hours greater than 5 years, users with medium work experience levels are users with KPI between 10% and 50% and working hours between 3 and 5 years, and users with low work experience levels are users with KPI below 50% and working hours below three years; Allocate the browsing permission of some intelligent knowledge blocks to users with low work experience levels, and do not allocate the editing permission; allocate the browsing permission of all intelligent knowledge blocks to users with medium work experience levels, and allocate the editing permission of some intelligent knowledge blocks; allocate the browsing permission and editing permission of all intelligent knowledge blocks to users with high work experience levels.

[0006] A further improvement of the technical solution of the present invention lies in that: in the hierarchical permission evaluation module, the optimization process of the access level of each intelligent knowledge block includes: Correspond the 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 the current intelligent knowledge blocks, if the access level corresponding to the source of the current intelligent knowledge blocks is consistent with the allocated access level, do not optimize the allocated access level; if the access level corresponding to the source of the current intelligent knowledge blocks is inconsistent with the allocated access level, use the access level corresponding to the source of the current intelligent knowledge blocks to update the allocated access level.

[0007] A further improvement of the technical solution of the present invention lies in that: in the permission feedback module, the calculation process of the access frequency of users 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 by 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 access frequency of the user to the intelligent knowledge block, and deploy the access frequency of the user to the intelligent knowledge block to the hierarchical permission dataset. The calculation process includes:

[0008] Among them, is the access frequency of the user 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.

[0009] 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: Extract the access frequency of the user to the intelligent knowledge block in the hierarchical permission dataset, combine the training set data with the linear regression algorithm, use the access frequency of the user to the intelligent knowledge block as the input, and use the intelligent knowledge block access permission feedback index as the output to learn the linear relationship between the access frequency of the user to the intelligent knowledge block 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 the user to the intelligent knowledge block to output the corresponding intelligent knowledge block access permission feedback index; The expression of the access permission feedback model is as follows:

[0010] Among them, is the intelligent knowledge block access permission feedback index, is the regression coefficient of the access frequency of the user to the intelligent knowledge block, is the access frequency of the user to the intelligent knowledge block, and are the intercept term and error term of the access permission feedback model respectively.

[0011] A further improvement of the technical solution of the present invention lies in: for the hierarchical feedback module, the process of obtaining the satisfaction and complaint rate of the customer 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 rate and the complaint rate respectively, and integrate the obtained customer satisfaction rate and customer complaint rate into the hierarchical permission dataset.

[0012] A further improvement of the technical solution of the present invention lies in: for the hierarchical feedback module, the process of accessing the construction of the hierarchical feedback model includes: Extract the customer satisfaction rate and the customer complaint rate from the hierarchical permission dataset, use the training set data, and combine the multiple linear regression algorithm. Take the customer satisfaction rate and the customer complaint rate as inputs and the intelligent knowledge block hierarchical feedback index as the output, learn the linear relationship between the customer satisfaction rate, the customer complaint rate and the intelligent knowledge block access hierarchical feedback index, and train the access hierarchical feedback model; Input the test set data into the access hierarchical feedback model, adjust the intercept term and the regression coefficient of the access hierarchical feedback model, optimize the performance of the access hierarchical feedback model, obtain the final access hierarchical feedback model, and combine the current customer satisfaction rate and customer complaint rate to output the corresponding intelligent knowledge block access hierarchical feedback index; The expression of this access hierarchical feedback model is as follows:

[0013] Among them, is the access hierarchical feedback index of the intelligent knowledge block, and are the regression coefficients of the customer satisfaction rate and the customer complaint rate respectively, and are the customer satisfaction rate and the customer complaint rate respectively, and are the intercept term and the error term of the access hierarchical feedback model respectively.

[0014] A further improvement of the technical solution of the present invention lies in: for the dynamic calibration module, the process of dynamically adjusting the access level and access permission of the intelligent knowledge block includes: Analyze the access permission feedback index of the intelligent knowledge block, divide the access permission feedback range, and perform corresponding dynamic adjustments on the access permissions of the intelligent knowledge blocks in different access permission feedback ranges; According to the intelligent knowledge block access hierarchical feedback index, divide the access hierarchical feedback range, and perform corresponding dynamic adjustments on the access levels of the intelligent knowledge blocks in different access hierarchical feedback ranges; 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 for allocating access permissions to users with low work experience levels; 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 for allocating access permissions to users with medium work experience levels; 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 for allocating access permissions to users with high work experience levels. 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; 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.

[0015] A further improvement of the technical solution of the present invention lies in: for the hierarchical permission management module, the hierarchical permission management process of the intelligent knowledge base includes: According to the approval process data, when there is an application for access permission by an approval user, correct the access permission of the intelligent knowledge block according to the approval result. If the approval result is approval, replace the access permission of the intelligent knowledge block after dynamic adjustment with the applied 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 approval user, correct the access level of the intelligent knowledge block according to the approval result. If the approval result is approval, replace the access level of the intelligent knowledge block after dynamic adjustment with the applied 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.

[0016] 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. By constructing 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 with 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 enterprises manage knowledge bases, it is difficult to manage data in combination with multiple types of hierarchical permissions, resulting in low utilization rate and orderliness of intelligent knowledge bases. 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 degree of intelligence in the process of hierarchical permission management of intelligent knowledge bases. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] 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 for use in the embodiments. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings.

[0018] 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

[0019] 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. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0020] 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; 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; The hierarchical permission evaluation module uses the preprocessed hierarchical permission data to allocate 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; 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 for intelligent knowledge blocks; 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 for intelligent knowledge blocks; 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; The hierarchical permission management module combines the preprocessed approval process data to implement hierarchical permission management of the intelligent knowledge base, solving the problem in the prior art that it is difficult to manage data by combining multiple types of hierarchical permissions, resulting in low utilization rate and orderliness of the intelligent knowledge base.

[0021] The process of collecting hierarchical permission data by the hierarchical permission data acquisition module includes: Dividing the intelligent knowledge base into several intelligent knowledge blocks, coding 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; Specifically, through data entry technology, selectively enter user basic data, intelligent knowledge base source data, access data, user KPIs, and approval process data from the enterprise intelligent knowledge base, and use questionnaire technology to collect the number of customer feedback times, customer satisfaction times, and customer complaint times corresponding to different ID users; 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. Among them, 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, the 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 user applying for approval, the applied access permission, the applied access level, the approval time, and the approval result. Among them, the approval result consists of approved and not approved. 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. The ratio of the training set to the test set is 8:2.

[0022] Hierarchical permission evaluation module. The process of allocating the access level and access permission of intelligent knowledge blocks includes: 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; Set the access levels of each intelligent knowledge block. The access levels consist of public level, internal level, and management level, and allocate the access levels of 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; Set the access permissions of each intelligent knowledge block. 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 those with KPIs in the top 10% and working hours greater than 5 years, users with medium work experience levels are those with KPIs between 10% and 50% and working hours between 3 and 5 years, and users with low work experience levels are those with KPIs below 50% and working hours less than three years; Allocate the browsing permission of some intelligent knowledge blocks to users with low work experience levels and do not allocate the editing permission; allocate the browsing permission of all intelligent knowledge blocks to users with medium work experience levels and allocate the editing permission of some intelligent knowledge blocks; allocate the browsing permission and editing permission of all intelligent knowledge blocks to users with high experience levels.

[0023] 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, if the access levels corresponding to the sources of current intelligent knowledge blocks are the same as the assigned access levels, the assigned access levels are not optimized; if the access levels corresponding to the sources of current intelligent knowledge blocks are different from the assigned access levels, the assigned access levels are updated using the access levels corresponding to the sources of current intelligent knowledge blocks.

[0024] 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. 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:

[0025] 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.

[0026] Permission feedback module. The construction process of the access permission feedback model includes: Extract the access frequency of the user to the intelligent knowledge block in the hierarchical permission dataset. Combine the training set data with the linear regression algorithm. Use the access frequency of the user to the intelligent knowledge block 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 the user to the intelligent knowledge block 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 the user to the intelligent knowledge block to output the corresponding intelligent knowledge block access permission feedback index. The expression of this access permission feedback model is as follows:

[0027] Among them, is the feedback index of the access permission for the intelligent knowledge block, is the regression coefficient of the access frequency of the user to the intelligent knowledge block, is the access frequency of the user to the intelligent knowledge block, and are respectively the intercept term and the error term of the access permission feedback model.

[0028] Hierarchical 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.

[0029] Hierarchical feedback module. The process of constructing the access hierarchical feedback model includes: Extract the customer satisfaction and customer complaint rate from the hierarchical permission dataset. Using the training set data and combining with the multiple linear regression algorithm, take the customer satisfaction and customer complaint rate as inputs and the access hierarchical feedback index of the intelligent knowledge block as the output, learn the linear relationship between the customer satisfaction, customer complaint rate and the access hierarchical feedback index of the intelligent knowledge block, and train the access hierarchical feedback model; Input the test set data into the access hierarchical feedback model, adjust the intercept term and regression coefficient of the access hierarchical feedback model, optimize the performance of the access hierarchical feedback model, obtain the final access hierarchical feedback model, and combine the current customer satisfaction and customer complaint rate to output the corresponding access hierarchical feedback index of the intelligent knowledge block; The expression of this access hierarchical feedback model is as follows:

[0030] Among them, is the access hierarchical feedback index of the intelligent knowledge block, and are respectively the regression coefficients of the customer satisfaction and customer complaint rate, and are respectively the customer satisfaction and customer complaint rate, and are respectively the intercept term and the error term of the access hierarchical feedback model.

[0031] Dynamic calibration module. The dynamic adjustment process of the access hierarchy and access permission of the intelligent knowledge block includes: Analyze the access permission feedback index of intelligent knowledge chunks, divide the access permission feedback range, and make corresponding dynamic adjustments to the access permissions of intelligent knowledge chunks in different access permission feedback ranges; Based on the access level feedback index of intelligent knowledge chunks, divide the access level feedback range, and make corresponding dynamic adjustments to the access levels of intelligent knowledge chunks in different access level feedback ranges; When the access permission feedback index of an intelligent knowledge chunk is lower than 0.4, adjust the access permission of the intelligent knowledge chunk according to the standard for allocating access permissions to users with low work experience levels; when the access permission feedback index of the intelligent knowledge chunk is between 0.4 and 0.6, adjust the access permission of the intelligent knowledge chunk according to the standard for allocating access permissions to users with medium work experience levels; when the access permission feedback index of the intelligent knowledge chunk is higher than 0.6, adjust the access permission of the intelligent knowledge chunk according to the standard for allocating access permissions to users with high work experience levels. The above adjustments change with the change of the access permission feedback index of the intelligent knowledge chunk, realizing the dynamic adjustment of the access permission of the intelligent knowledge chunk; When the access level feedback index of an intelligent knowledge chunk is lower than 0.3, adjust the access level of the intelligent knowledge chunk to the public level; when the access level feedback index of the intelligent knowledge chunk is between 0.3 and 0.6, adjust the access level of the intelligent knowledge chunk to the internal level; when the access level feedback index of the intelligent knowledge chunk is higher than 0.6, adjust the access level of the intelligent knowledge chunk to the management level. The above adjustments change with the change of the access level feedback index of the intelligent knowledge chunk, realizing the dynamic adjustment of the access level of the intelligent knowledge chunk.

[0032] 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 from an approval user, according to the approval result, correct the access permission of the intelligent knowledge chunk. If the approval result is approval passed, replace the access permission of the intelligent knowledge chunk after dynamic adjustment with the applied access permission of the corresponding user; if the approval result is approval not passed, do not change the access permission of the intelligent knowledge chunk after dynamic adjustment; When there is an application for access level from an approval user, according to the approval result, correct the access level of the intelligent knowledge chunk. If the approval result is approval passed, replace the access level of the intelligent knowledge chunk after dynamic adjustment with the applied access level of the corresponding user; if the approval result is approval not passed, do not change the access level of the intelligent knowledge chunk after dynamic adjustment; Through the combination of each intelligent knowledge chunk, the hierarchical permission management of the intelligent knowledge base is realized.

[0033] First, combine data entry technology and questionnaire survey technology to collect user basic data, intelligent knowledge base source data, access data, user performance data, and approval process data. Secondly, use the preprocessed user basic data and the user's KPIs 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 implement hierarchical permission management of the intelligent knowledge base.

[0034] 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 should be subject to the protection scope of the claims.

Claims

1. An intelligent knowledge base system for hierarchical authority management, comprising a hierarchical authority data acquisition module, a hierarchical authority evaluation module, an authority feedback module, a hierarchical feedback module, a dynamic calibration module and a hierarchical authority management module, wherein: Each module is connected in communication, characterized by: The hierarchical authority data collection module collects hierarchical authority data including user basic data, intelligent knowledge base source data, access data, user performance data and approval process data; The hierarchical authority evaluation module uses the pre-processed hierarchical authority data to assign corresponding access levels and access rights to the intelligent knowledge blocks, thereby optimizing the access levels of the intelligent knowledge blocks; The permission feedback module calculates the user's access frequency to the intelligent knowledge block through the preprocessed access data, and then constructs an access permission feedback model; The hierarchical feedback module obtains customer satisfaction and complaint rate through pre-processed user performance data, and then constructs an access hierarchical feedback model; The dynamic calibration module dynamically adjusts the access level and access rights of the intelligent knowledge block based on the output results of the access right feedback model and the access level feedback model; The hierarchical authority management module realizes hierarchical authority management of the intelligent knowledge base in combination with the pre-processed approval process data.

2. The intelligent knowledge base system for hierarchical rights management according to claim 1, characterized in that: The hierarchical authority data collection module includes: Divide the intelligent knowledge base into several intelligent knowledge blocks, encode the divided intelligent knowledge blocks, and combine data entry technology and questionnaire survey technology to collect user basic data, intelligent knowledge base source data, access data, user performance data and approval process data; The user basic data includes the user's ID, position and years of work experience; the intelligent knowledge base source data includes the source of each intelligent knowledge block, wherein the source of the intelligent knowledge block consists of enterprise public resources, enterprise internal resources and enterprise confidential resources; the access data includes the number of the intelligent knowledge block accessed by the user, the access time and the number of visits within 24 hours; the user performance data includes the KPI 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 application access rights, the application access level, the approval time and the approval result, wherein the approval result consists of approval passed and approval failed; The collected hierarchical permission data is cleaned and standardized, the pre-processed hierarchical permission data is integrated to generate a hierarchical permission data set, and the hierarchical permission data set is divided into a training set and a test set.

3. The intelligent knowledge base system for hierarchical authority management according to claim 2 is characterized by: The hierarchical authority evaluation module, the process of allocating the access level and access authority of the intelligent knowledge block includes: According to the enterprise management requirements, the user's position is divided into high-level positions, middle-level positions and low-level positions, and the user's position level is obtained by combining the current user basic data; Setting the access level of each smart knowledge block, which consists of a public level, an internal level and a management level, and assigning the access level of the smart knowledge block to the user according to the user's position level; Set access rights for each intelligent knowledge block, which consists of browsing rights and editing rights, count the user's ID, years of work experience and KPI, draw a user work experience report, and screen out users with high work experience levels, users with medium work experience levels and users with low work experience levels based on the user work experience report; Assign browsing rights to some smart knowledge blocks but not editing rights to users with low working experience levels; assign browsing rights to all smart knowledge blocks and editing rights to some smart knowledge blocks to users with medium working experience levels; assign browsing rights and editing rights to all smart knowledge blocks to users with high working experience levels.

4. The intelligent knowledge base system for hierarchical authority management according to claim 3 is characterized by: In the hierarchical authority evaluation module, the optimization process of the access level of each intelligent knowledge block includes: The enterprise's public resources, internal resources and confidential resources are respectively matched to the public level, internal level and management level; Based on the current sources of each intelligent knowledge block, if the access level corresponding to the current sources of each intelligent knowledge block is consistent with the allocated access level, the allocated access level is not optimized; if the access level corresponding to the current sources of each intelligent knowledge block is inconsistent with the allocated access level, the allocated access level is updated using the access level corresponding to the current sources of each intelligent knowledge block.

5. The intelligent knowledge base system for hierarchical authority management according to claim 4 is characterized in that: The calculation process of the permission feedback module for the user's access frequency to the intelligent knowledge block includes: Extracting two consecutive access times of the user to the intelligent knowledge block, and obtaining the access time interval of the intelligent knowledge block by the difference between the two consecutive access times; Weights are assigned to the access time interval and the number of accesses within 24 hours of the smart knowledge block respectively. The weight summation method is used to calculate the user's access frequency to the smart knowledge block, and the user's access frequency to the smart knowledge block is deployed in the hierarchical permission dataset.

6. The intelligent knowledge base system for hierarchical authority management according to claim 5, characterized in that: The permission feedback module and the construction process of the access permission feedback model include: The access frequency of users to smart knowledge blocks in the hierarchical permission data set is extracted, and the training set data is combined with the linear regression algorithm. The user's access frequency to the smart knowledge block is used as input, and the smart knowledge block access permission feedback index is used as output. The linear relationship between the user's access frequency to the smart knowledge block and the smart knowledge block access permission feedback index is learned, and the access permission feedback model is trained. 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 output the corresponding smart knowledge block access permission feedback index based on the current user's access frequency to the smart knowledge block.

7. The intelligent knowledge base system for hierarchical authority management according to claim 6, characterized in that: In the hierarchical feedback module, the process of obtaining customer satisfaction and complaint rate includes: Extract the number of customer feedbacks, customer satisfaction and customer complaints from the pre-processed user performance data; By calculating the proportion of customer satisfaction times in the number of customer feedback times and the proportion of customer complaints in the number of customer feedback times, we can obtain customer satisfaction and complaint rates respectively, and integrate the obtained customer satisfaction and customer complaint rates into the hierarchical permission data set.

8. The intelligent knowledge base system for hierarchical authority management according to claim 7, characterized in that: The hierarchical feedback module accesses the hierarchical feedback model construction process including: The customer satisfaction and customer complaint rate in the hierarchical permission data set are extracted. The training set data is used in combination with the multivariate linear regression algorithm. The customer satisfaction and customer complaint rate are used as inputs, and the intelligent knowledge block hierarchical feedback index is used as output. The linear relationship between customer satisfaction, customer complaint rate and the intelligent knowledge block access hierarchical feedback index is learned to train the access hierarchical 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.

9. The intelligent knowledge base system for hierarchical authority management according to claim 8, characterized in that: The dynamic calibration module and the dynamic adjustment process of the access level and access rights of the intelligent knowledge block include: Analyze the access permission feedback index of the smart knowledge block, divide the access permission feedback range, and dynamically adjust the access permission of the smart knowledge block in different access permission feedback ranges accordingly; According to the access level feedback index of the intelligent knowledge block, the access level feedback range is divided, and the access level of the intelligent knowledge block in different access level feedback ranges is dynamically adjusted accordingly.

10. The intelligent knowledge base system for hierarchical authority management according to claim 9, characterized in that: The hierarchical authority management module and the hierarchical authority management process of the intelligent knowledge base include: According to the approval process data, when the approving user has applied for access rights, the access rights of the intelligent knowledge block are modified according to the approval result. If the approval result is approved, the access rights of the dynamically adjusted intelligent knowledge block are replaced with the applied access rights of the corresponding user; if the approval result is not approved, the access rights of the dynamically adjusted intelligent knowledge block are not changed; When there is a user application access level in the approval, the access level of the intelligent knowledge block is modified according to the approval result. If the approval result is approved, the access level of the dynamically adjusted intelligent knowledge block is replaced with the application access level of the corresponding user; if the approval result is not approved, the access level of the dynamically adjusted intelligent knowledge block is not changed; Through the combination of various intelligent knowledge blocks, hierarchical permission management of the intelligent knowledge base can be achieved.

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