Lever information multi-protection right management and control system for mobile office

Abstract

The invention discloses a cadre information multi-protection authority management and control system for mobile office, relates to the technical field, and is used for solving the problems that the safety and the use efficiency are reduced, the authority setting basis is single, and dynamic adjustment is difficult. Basic data of all cadre members in an office space are collected through a data collection module and transmitted to an information classification module, cadres are divided into different categories according to a preset rule, a high-level cadre list is returned to trigger behavior data collection, and meanwhile all cadre category information is transmitted to a permission setting module; the permission setting module sets a permission level according to the category and the behavior data, sets permission feedback time, continuously collects permission use and adaptation data in a permission feedback period and transmits the data to the management and control adjustment module, and the management and control adjustment module models and evaluates cadre permission dominance features and determines whether to adjust the permission level; accurate distribution and dynamic control of the authority are realized, and mobile office authority management efficiency and response capability are improved.

Description

Technical Field

[0001] This invention relates to the field of multi-dimensional access control technology, and more specifically, to a multi-dimensional access control system for cadre information for mobile office use. Background Technology

[0002] With the development of e-government and mobile office scenarios, officials' work activities are gradually shifting from fixed terminals to multiple devices and scenarios, especially with a continuous increase in the proportion of office activities conducted on mobile terminals such as smartphones and tablets. In order to ensure the information security of officials and the standardization of access control, the e-government system needs to implement access control over dimensions such as officials' identity information, job responsibilities, and historical behavior.

[0003] The existing technology has the following shortcomings:

[0004] Currently, most government office systems only provide initial authorization functions and lack feedback collection and dynamic adjustment functions based on the effectiveness and adaptability of permission usage. This makes it difficult to cope with permission optimization issues in scenarios such as changes in cadre positions and responsibilities, resulting in decreased system security and efficiency. Permission settings are based on a single basis and are difficult to adjust dynamically. Therefore, a multi-dimensional permission control system for cadre information for mobile office is proposed.

[0005] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0006] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide a multi-dimensional access control system for cadre information for mobile office use, which solves the problems mentioned in the background art by employing an access control strategy that integrates behavioral data-driven and category-dynamic mapping.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a multi-dimensional access control system for cadre information for mobile office use, comprising a data acquisition module, an information classification module, an access control module, and a control adjustment module, the functions of which are as follows:

[0008] The data acquisition module is used to collect basic data of all cadres and members in the office and transmit it to the information classification module. After receiving the list of cadres and members transmitted by the information classification module, it collects the behavioral data of each cadre and member in the list and transmits it to the permission setting module.

[0009] The information classification module is used to receive basic data and classify cadres and members, send the list of cadres and members belonging to the category of senior cadres back to the data collection module, and pass the classification of each cadre and member to the permission setting module.

[0010] The permission setting module is used to set different permission levels based on the classification of cadres and members and their behavioral data, set permission feedback time, collect permission usage data and permission adaptation data of each cadre and member within the permission feedback time, and transmit them to the control and adjustment module.

[0011] The control and adjustment module is used to receive permission usage data and permission adaptation data to assess the permission control characteristics of the corresponding cadres and members, and to determine whether to adjust or change their permission level based on the permission control characteristics of each cadre and member.

[0012] In a preferred embodiment, the data acquisition module presets a collection time window, acquires all meeting records held within the collection time window, and compiles a list of participating cadres for each meeting.

[0013] All meetings and their corresponding lists of participating cadres are merged into a set of collaborative relationships.

[0014] The workload of cadres and members within the collection time window is used as the workload coefficient, which is the ratio of the workload of cadres and members to the preset standard workload of their positions.

[0015] The set of collaborative relationships and the workload coefficients of cadres and members are used as the basic data of cadres and members, and then passed into the information classification module.

[0016] In a preferred embodiment, after receiving the list of cadres and members from the information classification module, the data acquisition module collects the behavioral data of each cadre and member in the list, including the project responsibility ratio and the approved amount of resource application.

[0017] The project responsibility ratio is obtained by comparing the total number of projects managed by cadres within the data collection time window with the total number of projects managed by all cadres.

[0018] The total budget approved by cadres and members within the data collection window is used as the approved resource application quota.

[0019] Pass the project responsibility ratio and resource application approval amount to the permission settings module.

[0020] In a preferred embodiment, after receiving the basic data of the cadres and members, the information classification module classifies the cadres and members by constructing a collaborative relationship network centrality method. The specific steps are as follows:

[0021] The number of times any two cadres jointly participate in a meeting is counted by the set of collaborative relationships. Whether cadres jointly participate in a meeting is used as the basis for the edge, and the cadres are treated as cadres nodes.

[0022] If any two cadres participate in at least one meeting together, then there is an edge between the corresponding cadres, and the value of the edge is set to 1; otherwise, the value of the edge is set to 0.

[0023] Establish the connection relationship between cadre nodes and edges based on the set of collaborative relationships, and construct a cadre collaborative relationship network.

[0024] In a preferred embodiment, the summation of the edge values ​​between the cadre node and the remaining cadre nodes is used as the degree centrality value of the cadre member.

[0025] The average degree centrality of all cadres is taken as the average degree centrality.

[0026] In a preferred embodiment, if the degree centrality value of a number of members is greater than the average degree centrality value, and the workload coefficient of the cadres is greater than a preset workload coefficient threshold, then the cadres are classified as senior cadres.

[0027] Conversely, if the criteria are not met, the cadre member is classified as an ordinary cadre.

[0028] In a preferred embodiment, the permission setting module receives the classification of each cadre member and the corresponding behavioral data of the cadre member classified as a senior cadre.

[0029] The behavioral data was processed using the Z-score normalization method to obtain standardized behavioral data.

[0030] For cadres classified as senior cadres, the authority assessment score of cadres is calculated using a nonlinear hyperbolic tangent function based on standardized behavioral data.

[0031] Based on the authority assessment score and preset threshold, senior cadres are divided into different authority levels, which are defined in ascending order as low authority, medium authority and high authority.

[0032] For cadres classified as ordinary cadres, assign them low-level permissions.

[0033] In a preferred embodiment, a permission feedback time is set for all cadre members;

[0034] Collect the frequency of access to authorized permissions by cadres and members within the permission feedback period, and use it as permission usage data;

[0035] The ratio of the number of tasks completed by cadres and members within the permission feedback time to the total number of times permissions were invoked is collected as permission adaptation data.

[0036] In a preferred embodiment, the permission usage data and permission adaptation data are standardized to obtain standardized permission usage data and permission adaptation data, and an input feature vector is constructed.

[0037] The input feature vector is fed into the logistic regression model trained based on historical permission behavior samples, and the permission dominance feature is obtained through the decision function.

[0038] When the authority control characteristic is greater than or equal to the preset control threshold, the authority level of the cadre member will be adjusted and changed.

[0039] Conversely, no changes will be made to the authority levels of cadres and members.

[0040] The technical effects and advantages of this invention are as follows:

[0041] This invention collects basic data of all staff members in the office space through a data acquisition module and transmits this data to an information classification module. The information classification module performs structured analysis on the received basic data, classifies staff members into different categories according to preset classification rules, and returns the list of senior staff members to the data acquisition module to trigger further collection of behavioral data. Simultaneously, the category information of all staff members is transmitted to a permission setting module. The permission setting module sets corresponding permission levels based on staff member categories and behavioral data, and sets permission feedback time parameters. During the permission feedback time period, the permission setting module continuously collects permission usage data and permission adaptation data of staff members and transmits the above data to a control and adjustment module. The control and adjustment module performs comprehensive modeling of the permission usage data and adaptation data, evaluates the permission control characteristics of staff members, and determines whether to adjust permission levels based on these characteristics. This achieves precise allocation and dynamic control of permission resources, thereby improving the efficiency of staff permission management and system responsiveness in mobile office scenarios, and meeting the needs of multi-dimensional refined permission management in mobile office scenarios. Attached Figure Description

[0042] Figure 1 This is a flowchart illustrating the implementation of a multi-dimensional access control system for cadre information in mobile office environments, as described in this invention.

[0043] Figure 2 This is a schematic diagram illustrating the steps of a multi-dimensional access control system for cadre information for mobile office work according to the present invention. Detailed Implementation

[0044] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0045] This invention collects basic data of all staff members in the office space through a data acquisition module and transmits this data to an information classification module. The information classification module performs structured analysis on the received basic data, classifies staff members into different categories according to preset classification rules, and returns the list of senior staff members to the data acquisition module to trigger further collection of behavioral data. Simultaneously, the category information of all staff members is transmitted to the permission setting module. The permission setting module sets corresponding permission levels based on staff member categories and behavioral data, and sets permission feedback time parameters. During the permission feedback time period, the permission setting module continuously collects permission usage data and permission adaptation data of staff members and transmits the above data to the management and adjustment module. The management and adjustment module performs comprehensive modeling on the permission usage data and adaptation data, evaluates the permission control characteristics of staff members, and determines whether to adjust the permission level based on these characteristics. This achieves precise allocation and dynamic control of permission resources, thereby improving the efficiency of staff permission management and system responsiveness in mobile office scenarios.

[0046] Example 1: A multi-dimensional access control system for cadre information for mobile office use, such as... Figures 1 to 2 As shown, it includes a data acquisition module, an information classification module, a permission setting module, and a control and adjustment module, with each module connected by electrical signals;

[0047] The functions of each module are as follows:

[0048] The data acquisition module collects basic data of all cadres and members in the office and transmits it to the information classification module. After receiving the list of cadres and members from the information classification module, it collects the behavioral data of each cadre and member in the list and transmits it to the permission setting module.

[0049] The information classification module is used to receive basic data and classify cadres and members, send the list of cadres and members belonging to the category of senior cadres back to the data collection module, and pass the classification of each cadre and member to the permission setting module.

[0050] The permission setting module is used to select and set different permission levels based on the classification of cadres and members and their behavioral data, set the permission feedback time, and collect the permission usage data and permission adaptation data of each cadre and member within the permission feedback time and transmit them to the control and adjustment module.

[0051] The control and adjustment module receives permission usage data and permission adaptation data to assess the permission control characteristics of the corresponding cadres and members, and determines whether to adjust or change their permission levels based on the permission control characteristics of each cadre and member.

[0052] The specific implementation is as follows:

[0053] The data acquisition module has a preset acquisition time window. It uses online collaboration tools to obtain all meeting records held within the acquisition time window, and counts the corresponding participating cadres of each meeting. All meetings and their corresponding participating cadres are then merged into a set of collaboration relationships.

[0054] The task management system is used to obtain the workload of cadres and members within the collection time window, and the ratio of the workload of cadres and members to the preset standard workload of their positions is used as the workload coefficient.

[0055] The set of collaborative relationships and the workload coefficients of cadres and members are used as the basic data of cadres and members, and then passed into the information classification module.

[0056] After receiving the list of cadres and members from the information classification module, collect the behavioral data of each cadre and member in the list, including the project responsibility ratio and the approved amount of resource application.

[0057] The project responsibility ratio is the percentage of projects for which cadres are project leaders. It is obtained by using the project management system to obtain the total number of projects for cadres and the total number of projects for all cadres within the data collection time window. The ratio of the total number of projects for cadres to the total number of projects for all cadres is used as the project responsibility ratio.

[0058] The total budget approved by cadres and members within the collection time window is used as the approved amount for resource applications through the budget management system.

[0059] Pass the project responsibility ratio and resource application approval amount to the permission settings module.

[0060] By acquiring basic and behavioral data of cadres and members within a preset collection time window, the authenticity, completeness, and traceability of data sources are ensured, which is beneficial to the accuracy and dynamism of subsequent information classification and access control.

[0061] It should be noted that online collaboration tools refer to software systems used by cadres and employees for information exchange and meeting communication in the office environment, used to record the meeting participation, collaborative behavior and communication records of cadres and members within the collection time window; project management systems refer to management platforms used to record and manage information on various work tasks, responsible persons and participants within the organization; preset job standard task quantities refer to standardized task workload thresholds pre-set for different job responsibilities, which can be job standard task quantities formulated by professionals or obtained through historical average task quantities; budget management systems refer to electronic management platforms used to record, approve and archive various budget applications.

[0062] After receiving the basic data of the cadres and members, the information classification module categorizes the cadres and members by constructing a collaborative relationship network centrality method. The specific steps are as follows:

[0063] The number of times any two cadres participate in a meeting together is counted by the set of collaborative relationships. Whether cadres participate in a meeting together is used as the basis for the edge. Cadre members are cadres nodes. If any two cadres participate in a meeting together at least once, then there is an edge between the corresponding cadres and the edge value is set to 1. Otherwise, the edge value is set to 0.

[0064] The generated cadre collaboration network is as follows: , , ,in, For the i-th cadre member, The total number of cadres and members, Gathering of cadres and members, Let be the set of edges between any two cadre members. Let i be the edge between the i-th cadre member and the j-th cadre member;

[0065] The degree centrality of a cadre member is measured by calculating the centrality value of the cadre node. The number of collaborative connections between cadres within the data collection time window is measured by calculating the centrality value of the edges between the cadre node and the remaining cadre nodes.

[0066] The average degree centrality value of each cadre member is used as the average degree centrality value, and a workload coefficient threshold is preset.

[0067] If the degree centrality value of a cadre member is greater than the average degree centrality value, and the workload coefficient of a cadre member is greater than the workload coefficient threshold, then the cadre member is classified as a senior cadre.

[0068] Conversely, if the criteria are not met, the cadre member is classified as an ordinary cadre.

[0069] The list of cadres categorized as senior cadres is sent back to the data collection module, and the classification of each cadre member is passed to the permission setting module.

[0070] Based on the construction of a collaborative relationship network of cadres and the analysis of centrality indicators, and combined with the workload coefficient, cadres are scientifically classified to improve the pertinence and adaptability of permission allocation and meet the multi-dimensional management and control needs under mobile office conditions.

[0071] It should be noted that the preset workload coefficient threshold is used to measure whether the task load level of cadres and members within the data collection time window meets the job standard requirements, and is specifically set by professionals; the collaborative relationship network centrality method refers to establishing the connection relationship between nodes and edges among cadres and members based on their joint participation in meetings, constructing a cadre collaborative relationship network, and calculating the hub degree of cadres and members in organizational communication and collaboration based on the degree of connection of each node.

[0072] In the permission setting module, the classification of each cadre member is received. The classification is a classification label calculated by the information classification module based on the basic data of the cadre members, including senior cadres and ordinary cadres. At the same time, the behavioral data corresponding to the cadre members classified as senior cadres is received, including the project responsibility ratio and the approved amount of resource application.

[0073] To eliminate the influence of dimensions and improve the compatibility of parameter comparisons, the Z-score standardization method is used to process the behavioral data, as shown in the following formula:

[0074] ;

[0075] in, and These are the standardized project responsibility ratio and the approved amount of resource requests. ,and These are the average of the project responsibility ratio and the approved amount of resource applications, respectively. and These are the standard deviations of the project responsibility ratio and the approved amount of resource applications, respectively.

[0076] For cadres classified as senior cadres, their authority assessment score is calculated using a nonlinear hyperbolic tangent function based on the standardized project responsibility ratio and approved resource application quota. The specific calculation formula is as follows:

[0077] ;

[0078] Where S is the permission assessment score. This represents the hyperbolic tangent function, with an output range of... ; These are the adjustment parameters for the project responsibility ratio and the approved amount of resource applications, respectively. A nonlinear amplification effect is introduced to distinguish high-frequency scheduling behavior; Amplitude convergence control is introduced to suppress the perturbation of the model by extreme values.

[0079] Based on the permission assessment score S corresponding to a preset threshold range, different permission levels are defined. The permission levels are defined in ascending order as low-level permission, medium-level permission, and high-level permission. The permission level division rules are as follows:

[0080]

[0081] in, and For the preset threshold, satisfy This information was obtained by professionals through historical data, and will not be elaborated upon here.

[0082] For cadres classified as ordinary cadres, behavioral data processing and scoring will no longer be performed. The permission setting module will directly assign low-level permissions to these cadres to ensure that system access is restricted and to maintain resource scheduling efficiency.

[0083] The permission settings module further sets permission feedback time for all cadres and members, and continuously collects permission usage data and permission adaptation data for all cadres and members during the permission feedback time.

[0084] The permission setting module collects the frequency of calls made by cadres and members to authorized permissions within the permission feedback time, i.e., the ratio of the total number of times the cadre or member calls the permission to the permission feedback time, forming permission usage data. At the same time, it collects the ratio of the number of tasks completed by cadres and members within the permission feedback time to the total number of times the permission is called, i.e., permission usage efficiency, as permission adaptation data. The permission usage data and permission adaptation data are then transmitted to the management and adjustment module.

[0085] Based on the classification of cadres and members and standardized behavioral data, the system calculates permission evaluation scores using nonlinear functions, scientifically classifies permission levels, and dynamically collects permission usage data and adaptation data based on feedback on permission usage and task completion. This enables more refined, dynamic, and adaptive allocation and control of cadre permissions, enhancing the system's ability to manage the matching degree of cadre permissions.

[0086] It should be noted that Z-score standardization is a method for uniformly scaling raw data with different dimensions or distributions. The hyperbolic tangent function is a continuously differentiable function commonly used in nonlinear mappings, and it has mathematical properties such as interval compression, gradient continuity, and parity.

[0087] The control and adjustment module receives permission usage data and permission adaptation data from the permission setting module, and performs standardization processing using the Z-Score standardization method to obtain standardized permission usage data and permission adaptation data, and constructs an input feature vector.

[0088] The input feature vector is fed into a logistic regression model trained based on historical permission behavior samples, and its decision function is:

[0089] ;

[0090] in, The probability of the output classification result being y=1 is used as the authority dominance feature. For the input feature vector, For standardized access control data, To adapt the standardized permission data, It is the sigmoid activation function. and b is the weighting coefficient, and b is the bias term;

[0091] The classification result is y=1. When the classification result is y=1, it means that the frequency of permission calls and the efficiency of permission use by cadres and members in the feedback period both exceed the preset upper limit threshold, which indicates that the dominant ownership of permission resources is significantly high, and permission level needs to be revoked or reconfigured. When the classification result is y=0, it means that the frequency of permission calls and the efficiency of permission use by cadres and members in the feedback period do not reach the minimum dominance limit of the system, which indicates that the control rate of permission resources is insufficient and does not meet the behavioral performance standards required for the current permission level.

[0092] When the authority control characteristic is greater than or equal to the control threshold, the authority level of the cadre member will be adjusted and changed; when the authority control characteristic is less than the control threshold, the authority level of the cadre member will not be adjusted and changed.

[0093] The dominance threshold was obtained by professionals through cross-validation and will not be elaborated here.

[0094] By standardizing permission usage data and permission adaptation data, and combining the logistic regression model trained based on historical permission behavior samples to calculate the permission control characteristics of cadres, quantitative analysis and dynamic judgment of cadres' permission usage behavior can be achieved. Based on the comparison results of permission control characteristics and preset thresholds, permission levels can be automatically adjusted or maintained, thereby improving the adaptability and flexibility of cadre permission configuration and preventing permission abuse or configuration imbalance.

[0095] It should be noted that the logistic regression model is a generalized linear model used for binary classification. Its basic idea is to use a logistic function to map the linear combination result into a probability value, and then complete the binary classification task accordingly.

[0096] This invention introduces a cadre classification mechanism that combines collaborative relationship network centrality analysis with workload coefficients. It uses standardized and nonlinear function models to accurately evaluate cadre behavior data, dynamically sets permission levels, and constructs a logistic regression model based on permission usage frequency and adaptation efficiency. This enables quantitative judgment and automatic adjustment of cadre permission control, effectively improving the scientific, dynamic, and adaptable nature of permission configuration. It also enhances the system's ability to prevent and control permission abuse, resource waste, and permission mismatch, meeting the needs of multi-dimensional and refined permission management in mobile office scenarios.

[0097] Finally, it should be noted that in this paper, relational terms such as first and second are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations.

[0098] Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "including a…" does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0099] In this document, the singular forms “a,” “an,” and “the” may also include the plural forms unless the context clearly indicates otherwise. It should also be understood that the terms “comprising / including” or “having” specify the presence of the stated features, integrals, steps, operations, components, parts, or combinations thereof, but do not preclude the possibility of the presence or addition of one or more other features, integrals, steps, operations, components, parts, or combinations thereof. Meanwhile, the term “and / or” as used in this specification includes any and all combinations of the associated listed items.

[0100] The various embodiments in this specification are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. The various embodiments can be combined as needed, and the same or similar parts can be referred to each other.

[0101] The above description of the disclosed embodiments will enable those skilled in the art to make or use various modifications to these embodiments. It will be readily apparent to those skilled in the art that the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A multi-dimensional access control system for cadre information for mobile office use, characterized in that: It includes a data acquisition module, an information classification module, a permission setting module, and a control adjustment module. The functions of each module are as follows: The data acquisition module is used to collect basic data of all cadres and members in the office and transmit it to the information classification module. After receiving the list of cadres and members transmitted by the information classification module, it collects the behavioral data of each cadre and member in the list and transmits it to the permission setting module. The information classification module is used to receive basic data and classify cadres and members, send the list of cadres and members belonging to the category of senior cadres back to the data collection module, and pass the classification of each cadre and member to the permission setting module. The permission setting module is used to set different permission levels based on the classification of cadres and members and their behavioral data, set permission feedback time, and collect permission usage data and permission adaptation data of each cadre and member within the permission feedback time and transmit them to the control and adjustment module. The control and adjustment module is used to receive permission usage data and permission adaptation data, assess the permission control characteristics of the corresponding cadres and members, and determine whether to adjust or change their permission levels based on the permission control characteristics of each cadre and member.

2. The multi-dimensional access control system for cadre information for mobile office as described in claim 1, characterized in that: The data acquisition module has a preset acquisition time window, which acquires all meeting records held within the acquisition time window and compiles a list of participating cadres for each meeting. All meetings and their corresponding lists of participating cadres are merged into a set of collaborative relationships. The workload of cadres and members within the collection time window is used as the workload coefficient, which is the ratio of the workload of cadres and members to the preset standard workload of their positions. The set of collaborative relationships and the workload coefficients of cadres and members are used as the basic data of cadres and members, and then passed into the information classification module.

3. The multi-dimensional access control system for cadre information for mobile office as described in claim 1, characterized in that: After receiving the list of cadres and members from the information classification module, the data acquisition module collects the behavioral data of each cadre and member in the list, including the project responsibility ratio and the approved amount of resource applications. The project responsibility ratio is obtained by comparing the total number of projects managed by cadres within the data collection time window with the total number of projects managed by all cadres. The total budget approved by cadres and members within the data collection window is used as the approved resource application quota. Pass the project responsibility ratio and resource application approval amount to the permission settings module.

4. A multi-dimensional access control system for cadre information for mobile office as described in claim 2, characterized in that: After receiving the basic data of the cadres and members, the information classification module categorizes the cadres and members by constructing a collaborative relationship network centrality method. The specific steps are as follows: The number of times any two cadres participate in a meeting is counted by the set of collaborative relationships. Whether cadres participate in a meeting together is used as the basis for the edge, and the cadres are treated as cadres nodes. If any two cadres participate in at least one meeting together, then there is an edge between the corresponding cadres, and the value of the edge is set to 1; otherwise, the value of the edge is set to 0. Establish the connection relationship between cadre nodes and edges based on the set of collaborative relationships, and construct a cadre collaborative relationship network.

5. A multi-dimensional access control system for cadre information for mobile office as described in claim 4, characterized in that: The sum of the edge values ​​between the cadre node and the remaining cadre nodes is used as the degree centrality value of the cadre member; The average degree centrality of all cadres is taken as the average degree centrality.

6. A multi-dimensional access control system for cadre information for mobile office as described in claim 5, characterized in that: If the degree centrality value of a cadre member is greater than the average degree centrality value, and the workload coefficient of a cadre member is greater than the preset workload coefficient threshold, then the cadre member is classified as a senior cadre. Conversely, if the criteria are not met, the cadre member is classified as an ordinary cadre.

7. A multi-dimensional access control system for cadre information for mobile office as described in claim 6, characterized in that: In the permission settings module, the classification of each cadre member is received, along with the behavioral data corresponding to the cadre members classified as senior cadres. The behavioral data was processed using the Z-score normalization method to obtain standardized behavioral data. For cadres classified as senior cadres, the authority assessment score of cadres is calculated using a nonlinear hyperbolic tangent function based on standardized behavioral data. Based on the authority assessment score and preset threshold, senior cadres are divided into different authority levels, which are defined in ascending order as low authority, medium authority and high authority. For cadres classified as ordinary cadres, assign them low-level permissions.

8. A multi-dimensional access control system for cadre information for mobile office as described in claim 1, characterized in that: Set feedback time limits for all cadres and members; Collect the frequency of access to authorized permissions by cadres and members within the permission feedback period, and use it as permission usage data; The ratio of the number of tasks completed by cadres and members within the permission feedback time to the total number of times permissions were invoked is collected as permission adaptation data.

9. A multi-dimensional access control system for cadre information for mobile office as described in claim 8, characterized in that: The permission usage data and permission adaptation data are standardized to obtain standardized permission usage data and permission adaptation data, and an input feature vector is constructed. The input feature vector is fed into the logistic regression model trained based on historical permission behavior samples, and the permission dominance feature is obtained through the decision function. When the authority control characteristic is greater than or equal to the preset control threshold, the authority level of the cadre member will be adjusted and changed. Conversely, no changes will be made to the authority levels of cadres and members.

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