Collaborative management authority rule calculation method and system based on community discovery

Abstract

A collaborative management authority rule optimization system based on community discovery comprises a feature extraction module, an organization role screening module and a data attribute screening and authority distribution control module. Generating a standardized user feature vector and a data vector through structured coding and semantic modeling; the organization role screening module constructs a user collaboration graph based on a community division algorithm, and reasones user optimal role mapping by using a community portrait model; the data attribute screening module is combined with a data classification tree and a structure dependency relationship extraction algorithm to recognize a semantic category and upstream and downstream dependency chains of target data; and the permission allocation control module performs reasoning in combination with a permission knowledge base according to roles, data classification and a dependency relationship, and finally generates a permission allocation table, so that dynamic permission configuration and strategy matching oriented to a collaborative scene are realized. Through automatic role mapping, data semantic association analysis and authority inheritance reasoning, accurate authority distribution and real-time strategy adjustment in a ship collaboration scene are realized, so that data security and collaboration efficiency are improved, and core support is provided for digital transformation of the ship industry.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of ship manufacturing, and particularly relates to a collaborative management permission rule calculation method and system based on community discovery. BACKGROUND

[0002] The static permission model is difficult to adapt to the multi-level collaborative scene of the ship industry, cannot dynamically perceive the evolution of the organizational structure and the data dependency relationship, causes the permission inheritance path to be broken, the cross-project and cross-organization sharing to be blocked, and the three-dimensional model review fine-grained access demand to be difficult to meet. SUMMARY

[0003] The application provides a collaborative management permission rule calculation method and system based on community discovery, which realizes accurate permission allocation and real-time strategy adjustment in the ship collaborative scene through automatic role mapping, data semantic association analysis and permission inheritance reasoning, so that the data security and collaboration efficiency are improved, and core support is provided for the digital transformation of the ship industry.

[0004] The application is realized through the following method:

[0005] The application relates to a collaborative management permission rule calculation system based on community discovery, which comprises a feature extraction module, an organizational role screening module, a data attribute screening and permission allocation control module, wherein: the feature extraction module generates standardized user feature vectors and data vectors through structured coding and semantic modeling according to user data, collaborative objects and data attribute information; the organizational role screening module constructs a user collaborative graph based on a community division algorithm, and infers the optimal role mapping of the user by using a community portrait model; the data attribute screening module identifies the semantic category and upstream and downstream dependency chain of target data by combining a data classification tree and a structure dependency relationship extraction algorithm; and the permission allocation control module generates a permission allocation table by reasoning according to the role, data classification and dependency relationship in combination with a permission knowledge base, so as to realize dynamic permission configuration and strategy matching for the collaborative scene.

[0006] The community portrait model adopts a hierarchical design of a community layer, an organization role layer, and a data layer. The community layer includes multiple users, and the users form a collaborative network through project cooperation and task coordination behavior. The organization role layer is based on a collaborative graph structure, and according to the role attributes and organization attributes of the users, adopts a dual division mechanism of a role community and an organization community, respectively identifies high-cohesion user groups from the functional attributes and the collaborative relationship, and generates a community portrait through feature aggregation, and dynamically maps the current user to an optimal collaborative role through similarity calculation and path behavior reasoning. The data layer is an accessible data resource, including a folder, a file, a BOM structure, and version information, and supports fine-grained operation permission control. The model integrates the division results of the role community and the organization community, realizes multi-dimensional accurate mapping of the user-role-data, and provides a dynamic permission allocation strategy for a complex collaborative scenario.

[0007] The feature extraction refers to extracting multi-dimensional features from the users, the collaborative scenarios, and the data objects, constructing high-discrimination features through structured coding, semantic modeling, and vectorization technology, and providing standardized input for subsequent permission calculation. The process adopts a method combining metadata analysis, graph embedding, and context coding.

[0008] The organization role screening refers to realizing dynamic role mapping based on the user feature vector, mining the user collaboration mode through community discovery technology, and deducing an optimal role allocation scheme in combination with multi-dimensional similarity calculation.

[0009] The community portrait model jointly models the collaboration behavior consistency, the role semantic consistency, and the organization semantic consistency, and each community node simultaneously carries three types of homogeneous information: role-role similarity, organization-organization similarity, and the corresponding collaboration mode, thereby expanding the traditional single collaboration graph into a role-organization dual-channel heterogeneous graph. The following methods are used to obtain the same:

[0010] a. Graph structure initialization: constructing a heterogeneous graph , taking a user as a node , the node attributes including a role vector , an organization vector , and a project list; constructing three types of edges: a role similarity edge , a weight =cos( , ), an organization similarity edge , a weight =cos( , ), and a collaboration edge , a weight , wherein: 、 、 The number of common projects, the frequency of communication, and the recent collaboration time are weighted factors, respectively.

[0011] b. Community division: In two Louvain hierarchical detection are performed in parallel: the first one gets the role community set with as the weight , and the second one gets the organization community set with as the weight

[0012] . c. Community portrait generation: for any role community and the corresponding organization community , the role vector and organization vector of the members are aggregated to get the role center and the organization center , and the high-frequency role label , organization label and typical collaboration path Paths(g) are extracted; finally, a dual portrait is formed, which is used for subsequent dual similarity calculation and permission condition merging.

[0013] The community similarity is calculated in the role-organization dual-channel heterogeneous graph framework, and is independently obtained for the role community and the organization community . Specifically, it is obtained in the following way:

[0014] a. Vector space similarity: calculate the cosine similarity between the user feature vector and the community center vector : , which reflects the matching degree of the user in the role, organization, and project static attributes.

[0015] b. Behavior path distance: based on the user operation sequence and the typical path of the community , the dynamic time warping (DTW) is used to calculate the behavior pattern difference: , the smaller the distance, the higher the similarity, which is used to capture dynamic collaboration features.

[0016] c. Collaboration frequency overlap rate: statistics of the actual collaboration record overlap between the user and the community members: , where: is the similarity score of the user and the community in the collaboration history, the larger the value, the closer the social relationship; : is the historical collaboration user set of the user , that is, all users who have collaborated with the user a set of other users who have co-participated in projects or tasks with the user; : a set of users who have historically collaborated with all members of the community .

[0017] d. Comprehensive similarity calculation: By weighted fusion of multi-dimensional similarity indicators, define the comprehensive similarity of user to community : , where: is the comprehensive similarity score of user to community . Finally, according to the comprehensive similarity score, output a list of the top K communities most similar to the user : , where: is the th community in the candidate community set C, is the normalized comprehensive similarity score of the th community. This comprehensive scoring mechanism realizes the unified expression of multi-dimensional similarity, enabling individual users to quickly locate the community group with the closest behavior pattern, structural attribute and collaboration tendency in the complex organizational collaboration network.

[0018] The data attribute filtering refers to automatically parsing and aggregating the attributes, categories and structural relationships of the accessed data objects, and assisting in generating data context labels that can be used for permission reasoning through multi-level semantic classification, dependency extraction and dynamic constraint verification.

[0019] The dependency extraction includes:

[0020] a. Structure graph construction. According to the BOM structure, hierarchical directory, construct a directed graph , where: the node set V is all data entities with permission management significance, and the edge set E is the structural dependency, reference relationship or hierarchical subordination relationship between data objects. The directed graph G serves as the basic framework for dependency extraction.

[0021] b. Target node identification. Receive the data object identifier involved in the user's current operation request , locate its corresponding node in the graph structure G. This node serves as the starting point for dependency path traversal and is the key entry for subsequent extraction of its upstream and downstream associated data. This step can support precise positioning of data objects through unique identifiers such as file ID, document number, object path.

[0022] c. Upstream and downstream path traversal. Using the depth-first search (DFS) algorithm, starting from the target node , recursively traverse all downstream nodes and upstream nodes, and record all reachable paths: .

[0023] The dynamic constraint check mentioned above refers to: based on the dependency legality condition: ,in: , For dependency paths The declared start and end objects, The first one in the user operation log sequence The object and conditions affected by this actual operation This ensures that the order of operations aligns with the direction of dependencies. After the above checks, the set of valid context dependencies is filtered out: .

[0024] The aforementioned permission allocation and control refers to: based on the user's optimal role mapping result, the classification tags of the target data and their dependencies, and combined with a preset permission rule knowledge base, comprehensively inferring the user's operable permissions on a specific data object.

[0025] The classification labels and their dependencies of the target data are obtained in the following way:

[0026] a. Rule matching and filtering: Based on the user's optimal role mapping results Data classification tags and the set of legal dependency paths In the permission knowledge base Perform multi-dimensional condition filtering to select all permission rule items that meet the conditions. Among them: requirements rules Character Collection Optimal set of user roles A subset of, and rules Data Classification Category labels of target data objects Totally consistent.

[0027] b. Generate a permission set: For rule items that match successfully... Extract the permission set field from each rule item. Perform a set union operation on the permission set of all matching items to form the initial inherited permission set, specifically: This set represents the operational permissions that a user can directly infer based on roles and data tags in the current context, and it forms the basis for subsequent dependency extension logic.

[0028] c. Dependency chain permission expansion: In the initial permission set Based on this, a depth-first search (DFS) traversal is used. Each dependency path in , recursively check the permission inheritance requirement of each node on the path : if the permission rule corresponding to the node is marked as transitive, the relevant permissions on its parent node are transmitted to the current node through the inheritance mechanism, and the transmission path and the source of the inheritance are recorded; if a certain edge in the dependency path is defined as a strong constraint, the system will simultaneously verify whether the permission set of the predecessor node on the path contains the prerequisite permission set required by the edge.

[0029] The standard permission assignment entry includes user ID, role name, data object ID, data category, granted permission type (such as read, write, copy, delete, and approval), permission effective range, and permission source.

[0030] Technical effects

[0031] The present application introduces a social discovery algorithm into permission management, which changes the originally static role configuration into a dynamic mapping that evolves with the organization structure and collaborative behavior in real time, and continuously maintains the accurate matching of roles and permissions in a shipbuilding environment with multiple projects running in parallel without manual maintenance by administrators; meanwhile, the graph-driven data semantic analysis uniformly encodes files, BOM nodes, and their upstream and downstream dependencies into context tags, so that the permission rules can be inferred based on fine-grained data structures and semantic relationships, thereby avoiding redundant authorization and permission omission; further, the incremental graph update mechanism ensures that when personnel, projects, or data relationships change, only partial recalculation is required to complete permission adjustment, significantly improving the expansion capability of the collaborative management permission rule calculation method; and the finally output standardized permission assignment table can be seamlessly integrated into existing systems to realize fine-grained security collaboration across organizations and break through the application limitations of traditional RBAC models in dynamic collaboration scenarios. BRIEF DESCRIPTION OF DRAWINGS

[0032] Figure 1 is a structural schematic diagram of the present application;

[0033] Figure 2 is a community portrait model schematic diagram;

[0034] Figure 3 is an embodiment schematic diagram. DETAILED DESCRIPTION

[0035] As shown in Figure 1 , the present embodiment relates to a collaborative management permission rule calculation system based on community discovery, which includes a feature extraction module, an organization role screening module, a data attribute screening and permission assignment control module.

[0036] The feature extraction module includes: a user data parsing unit, a collaborative scenario modeling unit, a data attribute parsing unit, and a feature standardization processing unit. Specifically: the user data parsing unit collects user information, including role descriptions, organization identifiers, and project tags, combining these multi-dimensional fields to generate user vectors; the collaborative scenario modeling unit extracts operation type, collaborative object type, and access frequency temporal features based on user operation logs. Context modeling is performed using a lightweight Transformer encoder to generate dynamic collaborative semantic vectors; the data attribute parsing unit analyzes the type identifier, version number, and organization attribute in the metadata of the accessed data object. A hierarchical embedding strategy is used: structured attributes are encoded using regularization, while unstructured attributes are extracted using a BERT fine-tuning model to extract semantic features. The final generated multi-dimensional vector of the data object is: The feature standardization processing unit processes user vectors. and data vector Normalization was performed separately to eliminate dimensional differences. After mapping the eigenvalues ​​of each dimension to the [0,1] interval using Min-Max Scaling, principal component analysis (PCA) was used to compress the high-dimensional vector to a uniform dimension. To reduce computational complexity, one-hot encoding is used to enhance the separability of discrete features, and the final output is a standardized feature vector: as well as This ensures that the similarity calculation and cluster analysis of subsequent modules have numerical stability.

[0037] The aforementioned combination of multidimensional fields generates user vectors by uniformly encoding role descriptions, organization identifiers, and project tags to construct a standard input vector. An entity recognition method based on rule templates or keyword dictionaries is then used to identify role and organization entities, forming triples. A multi-level mapping mechanism is used to establish the association between roles and permission models. Finally, multi-dimensional fields are combined to generate user vectors. This is used to support subsequent permission matching and modeling.

[0038] The organizational role selection module includes: a community profile model construction unit, a community similarity calculation unit, a role-organization mapping reasoning unit, and an optimal role selection unit. Specifically: the community profile model construction unit constructs a community profile model G based on historical collaborative behavior data of the organization; the community similarity calculation unit calculates the similarity between the current user's feature vector and the community nodes in the community profile model G, using vector space similarity, behavioral path distance, and collaborative frequency overlap rate as metrics to calculate community similarity. The role-organization mapping inference unit extracts role tags from Top-K communities and generates a candidate role probability distribution by weighting the distribution based on community similarity. The output probability-ordered candidate role set The optimal role selection unit outputs all roles that meet the conditions from the candidate set R in combination with the organizational constraints and project requirements. The multi-role mapping relationship is completed to achieve dynamic role allocation.

[0039] The data attribute filtering module includes a data classification clustering unit, a classification tree matching unit, a data dependency relationship extraction unit, and a dynamic dependency verification unit. The data classification clustering unit extracts a key attribute set from the data attribute vector input by the feature extraction module , generates an attribute vector set by a semantic embedding method. Calculate the semantic similarity between any attribute pairs, and use the DBSCAN clustering algorithm to divide the attributes into semantically consistent categories to obtain a preliminary classified node set ; the classification tree matching unit calculates the cosine similarity between the candidate class node vector and each node vector in the classification tree according to the system's built-in data classification tree containing multiple nodes with pre-trained semantic vectors . Select the class node with the highest similarity as the standard classification label of the data object, which is used as the basis for subsequent permission matching and rule association. This process ensures that unstructured or heterogeneous data can be normalized in permission management; the data dependency relationship extraction unit constructs a directed graph for the dependency relationship between data objects, and traverses the graph by using the depth-first search (DFS) algorithm to identify the upstream and downstream dependency paths of the target data object; the dynamic dependency verification unit verifies whether the dependency path is legal in the actual operation context in combination with the user operation sequence and the dependency path .

[0040] The permission allocation and control module includes a permission knowledge base construction unit, a dependent permission inheritance unit, an explicit authorization override unit, and a permission allocation table construction unit. The permission knowledge base construction unit extracts structured permission rule items from historical authorization logs and system policy templates: , organizes the rules into a permission knowledge base for subsequent permission matching and reasoning; the dependent permission inheritance unit performs rule matching and filtering according to the user optimal role mapping result , data classification label , and legal dependency path set and generates a permission set, and extends the dependent chain permission on this basis; the explicit authorization override unit queries the explicit authorization record table G, and if there is a manual authorization record of the user to the target object , then the explicit authorization permission Cover inheritance result, get the most right permission set ; The permission assignment table construction unit according to the optimal role of the user , its associated project and organization information, the unique identifier of the target data object and its classification result , all authorized items are output as standard permission assignment items, forming the final permission assignment table result of the system.

[0041] As Figure 3 shown, it is a collaborative management permission rule management device based on the above system, including: a Web application layer, a collaborative management permission rule calculation system based on social discovery as a business processing layer and a data layer, wherein: the Web application layer realizes the user interface and system interaction function, supports the configuration of permission rules, the monitoring of operation logs and the display and export of permission results; the collaborative management permission rule calculation system based on social discovery integrates feature extraction, organization role screening, data attribute screening and permission assignment control multiple function modules, completes the whole process calculation from user data input, feature modeling, permission reasoning to permission assignment table output; the data layer carries all the multi-modal data related to permissions, including structured user information, unstructured metadata, graph structure collaboration network and dependency path, supports unified management and efficient retrieval of different types of data. The whole system runs through three layers of cooperation, realizes the automation, dynamic and refinement of permission assignment in collaborative scenarios, and adapts to complex and variable project organization relationships and task structures.

[0042] The Web application layer is realized based on the Vue.js framework, and the ElementPlus component library is used to complete the permission rule configuration, permission state display and interactive form design. The system supports users to upload JSON files containing user information, collaborative scenario information and data object information through the front-end interface, and the front-end parses and initiates a permission calculation request to the back-end FastAPI service through Axios. In the request process, RESTful API parameter transmission and structure verification, WebSocket is used to listen to the calculation state and result return. After the permission calculation is completed, the result is returned to the front-end in a standardized JSON format, and the user can choose to export it as a CSV file for subsequent analysis. The front-end of the system also integrates permission rule template management function, supports to display the mapping relationship between users, data objects and permissions in a visual way, and allows users to add, delete and modify rules.

[0043] The business processing layer is the core functional module of the system, which completes all the calculation logic from the original input to the final permission output. The system first parses the user information and data object information, calls the feature extraction module to generate user feature vectors and data feature vectors. Then the collaboration graph structure is constructed, combined with the collaboration frequency and communication behavior in the historical operation log, the heterogeneous graph structure is generated in Neo4j, and the Louvain algorithm is used to divide the community in the role dimension and the organization dimension respectively, forming the role community and the organization community portrait. The system infers the optimal role set of the current user according to the vector similarity and path coincidence degree index. In the data processing stage, the system matches the classification tree nodes according to the data vector, extracts the upstream and downstream paths of the data object in the dependency graph, and judges whether the dependency path is legal according to the actual operation sequence of the user. The permission control module matches the corresponding rules in the permission knowledge base according to the optimal role, data classification label and legal dependency path, generates the permission set and outputs the structured result.

[0044] The data layer adopts a collaborative working mode of three types of databases composed of PostgreSQL, MongoDB and Neo4j, which are used to manage structured information, document type permission data and graph structure dependency respectively. PostgreSQL is used to store user information, role attributes, organization relationship and data object metadata, support transaction query and conditional filtering operation. MongoDB saves permission rule items, user authorization records and intermediate results in document format, which is suitable for various field structures and supports conditional retrieval by item or user dimension. Neo4j graph database is used to record user collaboration graph and data dependency graph, the system calls built-in DFS and Louvain algorithm through graph query language to complete dependency path extraction and community discovery operation. The system data layer structure supports incremental update and concurrent access, each permission request of the front end will trigger the backend to query the three data sources, complete the whole process of data support from feature extraction to rule matching.

[0045] Table 1 Comparison of technical characteristics

[0046] The application is superior to the existing permission management method in integrity, accuracy and flexibility through the synergistic integration of multiple core algorithms and key technical characteristics. In terms of integrity, the user dynamic behavior characteristics are completely captured by the Transformer time series modeling in feature extraction, the organizational role screening comprehensively covers the organizational collaboration relationship through the Louvain community division, and the data attribute screening constructs the data semantic association through the DBSCAN clustering and Neo4j graph analysis, forming a trinity of permission decision basis. In terms of accuracy, the organizational role screening calculates the threefold weighted calculation of feature similarity, behavior path matching degree and collaboration frequency overlap rate, combines the dependent path verification and explicit authorization coverage mechanism of permission allocation, and ensures that the accurate user permission set can still be output in the multiple role scene; in terms of flexibility, the organizational role screening realizes local graph reconstruction through the incremental calculation function of the Louvain algorithm, and when the user organizational relationship changes are detected, the algorithm automatically triggers the local update of community division instead of full recalculation; the data attribute screening dynamically adjusts the data dependency relationship network through the real-time indexing mechanism of the Neo4j graph database, so that the collaborative management permission rule calculation method can automatically adapt to the typical multi-role change scene of the ship industry, and solves the failure problem of the traditional manual maintenance method in the large-scale complex organization. The application not only effectively solves the fine-grained permission control problem in the cross-organizational collaboration scene, but also provides an innovative solution for designing a safe, reliable and scalable collaborative management permission rule optimization system, and has wide application value and significant technical advantages.

[0047] The above specific embodiments can be adjusted in different ways by those skilled in the art without departing from the principles and purposes of the application, the protection scope of the application is subject to the claims and is not limited by the above specific embodiments, and each implementation scheme within the scope is subject to the constraints of the application.

Claims

1. A collaborative management permission rule calculation system based on community discovery, characterized in that, include: The system comprises a feature extraction module, an organizational role selection module, a data attribute selection module, and a permission allocation and control module. Specifically: the feature extraction module generates standardized user feature vectors and data vectors based on user data, collaborating objects, and data attribute information through structured encoding and semantic modeling; the organizational role selection module constructs a user collaboration graph based on a community segmentation algorithm and uses a community profile model to infer the optimal user role mapping; the data attribute selection module combines data classification trees and structural dependency extraction algorithms to identify the semantic categories and upstream / downstream dependency chains of target data; and the permission allocation and control module, based on roles, data classifications, and dependencies, and combined with a permission knowledge base, performs reasoning to ultimately generate a permission allocation table, enabling dynamic permission configuration and strategy matching for collaborative scenarios. The community profiling model adopts a layered design of community, organizational role, and data layers. The community layer contains multiple system users who form a collaborative network through project collaboration and task coordination. The organizational role layer is based on a collaboration graph structure and uses a dual segmentation mechanism of role communities and organizational communities according to user role attributes and organizational attributes. It identifies highly cohesive user groups from the perspectives of functional attributes and collaborative relationships, and generates community profiles through feature aggregation. Combining similarity calculation and path behavior reasoning, it dynamically maps the current user to the optimal collaborative role. The data layer consists of accessible data resources in the system, including folders, files, BOM structure, and version information, supporting fine-grained operation permission control. By integrating the segmentation results of role communities and organizational communities, this model achieves multi-dimensional and accurate mapping of users, roles, and data, providing dynamic permission allocation strategies for complex collaborative scenarios.

2. The collaborative management permission rule calculation system based on community discovery according to claim 1, characterized in that, The feature extraction module includes: a user data parsing unit, a collaborative scenario modeling unit, a data attribute parsing unit, and a feature standardization processing unit. Specifically: the user data parsing unit collects user information from role descriptions, organization identifiers, and project tags, combining these multi-dimensional fields to generate user vectors; the collaborative scenario modeling unit extracts operation type, collaborative object type, and access frequency temporal features from user operation logs, and performs contextual modeling using a lightweight Transformer encoder to generate dynamic collaborative semantic vectors; the data attribute parsing unit analyzes the type identifier, version number, and organization attribute in the metadata of the accessed data object, and uses a hierarchical embedding strategy: structured attributes are encoded using regularization, and unstructured attributes are extracted using a BERT fine-tuning model to extract semantic features, ultimately generating a multi-dimensional vector of the data object. The feature standardization processing unit processes user vectors. and data vector Normalization was performed separately to eliminate dimensional differences. After mapping the eigenvalues ​​of each dimension to the [0,1] interval using Min-Max Scaling, principal component analysis (PCA) was used to compress the high-dimensional vector to a uniform dimension. To reduce computational complexity, one-hot encoding is used to enhance the separability of discrete features, and the final output is a standardized feature vector: as well as This ensures that the similarity calculation and cluster analysis of subsequent modules have numerical stability.

3. The collaborative management permission rule calculation system based on community discovery according to claim 2, characterized in that, The aforementioned combination of multidimensional fields generates user vectors by uniformly encoding role descriptions, organization identifiers, and project tags to construct a standard input vector. Then, an entity recognition method based on rule templates or keyword dictionaries is used to identify role and organization entities, forming triples. ; A multi-level mapping mechanism is used to establish the association between roles and permission models, and finally, multi-dimensional fields are combined to generate user vectors. This is used to support subsequent permission matching and modeling.

4. The collaborative management permission rule calculation system based on community discovery according to claim 1, characterized in that, The organizational role selection module includes: a community profile model construction unit, a community similarity calculation unit, a role-organization mapping reasoning unit, and an optimal role selection unit. Specifically: the community profile model construction unit constructs a community profile model G based on historical collaborative behavior data of the organization; the community similarity calculation unit calculates the similarity between the current user's feature vector and the community nodes in the community profile model G, using vector space similarity, behavioral path distance, and collaborative frequency overlap rate as metrics to calculate community similarity. The role-organization mapping inference unit extracts role tags from Top-K communities and generates a candidate role probability distribution by weighting the distribution based on community similarity. Output the candidate character set sorted by probability. The optimal role selection unit, combining organizational constraints and project requirements, outputs all roles that meet the conditions from the candidate set R, and finally outputs... Multiple role mapping relationships enable dynamic role allocation.

5. The collaborative management permission rule calculation system based on community discovery according to claim 1, characterized in that, The data attribute filtering module includes: a data classification and clustering unit, a classification tree matching unit, a data dependency extraction unit, and a dynamic dependency verification unit, wherein: the data classification and clustering unit receives the data attribute vector input from the feature extraction module. Extract key attribute set Generate attribute vector sets through semantic embedding methods The semantic similarity between any two attribute pairs is calculated, and the DBSCAN clustering algorithm is used to classify the attributes into semantically consistent categories, resulting in a preliminary set of classification nodes. The classification tree matching unit uses a built-in system of nodes containing multiple pre-trained semantic vectors. Data classification tree Calculate the candidate class node vector The class node with the highest cosine similarity to the vector of each node in the classification tree is selected as the standard classification label for the data object, which is used as the basis for subsequent permission matching and rule association. This process ensures that unstructured or heterogeneous data can be normalized in permission management. The data dependency extraction unit constructs a directed graph to represent the dependencies between data objects and traverses the graph using a depth-first search (DFS) algorithm to identify the upstream and downstream dependency paths of the target data object. The dynamic dependency verification unit combines user operation sequences... With dependency path Verify whether the dependency path is valid in the actual operation context.

6. The collaborative management permission rule calculation system based on community discovery according to claim 1, characterized in that, The permission allocation and control module includes: a permission knowledge base construction unit, a dependent permission inheritance unit, an explicit authorization overriding unit, and a permission allocation table construction unit, wherein: the permission knowledge base construction unit extracts structured permission rule items from historical authorization logs and system policy templates. The rules are summarized and organized into a permission knowledge base. This is used for subsequent permission matching and inference; it depends on the permission inheritance unit based on the user's optimal role mapping result. Data classification tags and the set of legal dependency paths Perform rule matching and filtering to generate a permission set, and then extend the dependency chain permissions based on this set; the explicit authorization overriding unit queries the explicit authorization record table G, and if there is a user's manual authorization record for the target object, it checks if such a record exists. Then grant explicit permissions. Override the inheritance result to obtain the final permission set. The permission allocation table construction unit is based on the user's optimal role. The associated project and organizational information, the unique identifier of the target data object, and its classification results. Each authorization item is output as a standard permission allocation entry, forming the final permission allocation table of the system.

7. A collaborative management permission rule management device for a system according to any one of claims 1-6, characterized in that, include: The system consists of a web application layer, a community-based collaborative management permission rule calculation system (serving as the business processing layer), and a data layer. The web application layer is used for interaction between users and the system. The business processing layer, implemented through the collaborative management permission rule optimization system, performs dynamic role mapping, permission boundary deduction, and optimized output of the permission allocation table by comprehensively processing user characteristics, collaborative relationships, and data dependencies. This generates data calculation and dynamic permission rules for the entire process from user input to permission output. The data layer optimizes storage and enables efficient access by classifying multimodal data characteristics for permission management scenarios.

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