Dynamic knowledge graph driven cross-department government affair data collaboration method and system

Abstract

The embodiment of the invention provides a dynamic knowledge graph-driven cross-department government affair data collaboration method and a dynamic knowledge graph-driven cross-department government affair data collaboration system. The method is applied to the technical field of computer science and technology, and comprises the steps of obtaining cross-department government affair service data and corresponding policy rule data, constructing a data knowledge graph and a policy knowledge graph, performing incremental updating and consistency verification on the knowledge graphs based on preset event information, and extracting a newest service state and a newest policy rule. A minimum necessary data view and an authorization range are generated through joint reasoning, efficient and safe collaboration of cross-department government affair data is achieved on the basis, and accurate authorization and compliance management of data access are achieved. According to the scheme, only necessary data is provided and the access permission is strictly controlled in cross-department government affair data collaboration, so that the data sharing efficiency, security and compliance are remarkably improved, and meanwhile, the risks of unauthorized access and redundant data transmission are reduced.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of computer science and technology, in particular to a dynamic knowledge graph driven cross-department government data collaboration method and system. BACKGROUND

[0002] With the deepening of the digital reform of government affairs, cross-department business collaboration has become a key link to improve the efficiency of government governance. Government data is scattered in different department systems, and policy rules have dynamic adjustment characteristics. Traditional data sharing modes cannot meet the real-time, accuracy and compliance requirements. The existing technology lacks the ability to model the association between business data and policy rules, resulting in problems such as information lag, rule conflict, and ambiguous permissions in the cross-department data collaboration process, which restricts the level of government service integration and decision-making scientificity.

[0003] The existing cross-department government data collaboration technology mainly relies on traditional technical architecture: periodically extracting each department's business data to a central database for centralized storage through ETL tools, building a static data directory to describe data assets based on keyword matching technology, and manually sorting policy documents to form rule documents, and in the data sharing link, authorization management is performed through a pre-set permission template.

[0004] However, the data update mechanism of the existing technology relies on periodic batch processing, resulting in a lag in business status changes and policy rule adjustments; the permission management uses fixed templates and cannot dynamically adjust the authorization boundaries according to specific scenarios, posing a risk of data misuse; policy rule updates rely on offline manual processes, which are inefficient and prone to inconsistencies due to differences in human interpretation, and require a continuous investment of large amounts of human resources for maintenance, making it difficult to meet the core needs of "minimum necessary" government data sharing and precise authorization. SUMMARY

[0005] To solve the problems of the prior art, the present disclosure provides a dynamic knowledge graph driven cross-department government data collaboration method and system. The present disclosure solves the problems of the prior art that the data update mechanism relies on periodic batch processing, resulting in a lag in business status changes and policy rule adjustments; the permission management uses fixed templates and cannot dynamically adjust the authorization boundaries according to specific scenarios, posing a risk of data misuse; policy rule updates rely on offline manual processes, which are inefficient and prone to inconsistencies due to differences in human interpretation, and require a continuous investment of large amounts of human resources for maintenance, making it difficult to meet the core needs of "minimum necessary" government data sharing and precise authorization.

[0006] According to a first aspect of the present disclosure, a dynamic knowledge graph driven cross-department government data collaboration method is provided, comprising: acquiring cross-department government business data and corresponding policy rule data, constructing a data knowledge graph based on the cross-department government business data, and constructing a strategy knowledge graph based on the policy rule data; acquiring preset event information, performing incremental update and consistency check on the data knowledge graph and the strategy knowledge graph according to the preset event information, obtaining an updated data knowledge graph and an updated strategy knowledge graph; extracting the latest business status in the updated data knowledge graph and the latest policy rule in the updated strategy knowledge graph; performing joint reasoning based on the latest business status and the latest policy rule to obtain a minimum necessary data view and an authorized range, and performing cross-department government data collaboration based on the minimum necessary data view and the authorized range.

[0007] According to a second aspect of the present disclosure, a dynamic knowledge graph driven cross-department government data collaboration system is provided, configured to perform the method of the first aspect, comprising: a graph construction module configured to acquire cross-department government business data and corresponding policy rule data, construct a data knowledge graph based on the cross-department government business data, and construct a strategy knowledge graph based on the policy rule data; a dynamic update module configured to acquire preset event information, perform incremental update and consistency check on the data knowledge graph and the strategy knowledge graph according to the preset event information, and obtain an updated data knowledge graph and an updated strategy knowledge graph; an extraction module configured to extract the latest business status in the updated data knowledge graph and the latest policy rule in the updated strategy knowledge graph; a cross-department collaboration module configured to perform joint reasoning based on the latest business status and the latest policy rule to obtain a minimum necessary data view and an authorized range, and perform cross-department government data collaboration based on the minimum necessary data view and the authorized range.

[0008] According to a third aspect of the present disclosure, an electronic device is provided, comprising: a memory and a processor, wherein the memory stores a computer program, and the processor executes the program to implement the method as described above.

[0009] In the dynamic knowledge graph driven cross-department government data collaboration method and system as provided above, the embodiments of the present disclosure only provide necessary data and strictly control access rights in cross-department government data collaboration, significantly improving data sharing efficiency, security and compliance, while reducing the risk of unauthorized access and redundant data transmission. BRIEF DESCRIPTION OF DRAWINGS

[0010] In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are some embodiments of the present disclosure, and those skilled in the art can obtain other drawings according to these drawings without any creative effort.

[0011] Figure 1 A dynamic knowledge graph driven cross-department government data collaboration method flowchart is shown according to an embodiment of the present disclosure; Figure 2 A dynamic knowledge graph driven cross-department government data collaboration method flowchart is shown according to an embodiment of the present disclosure; Figure 3 A dynamic knowledge graph driven cross-department government data collaboration system schematic block diagram is shown according to an embodiment of the present disclosure; Figure 4 A structural schematic diagram of an electronic device according to an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0012] Various exemplary embodiments of the present disclosure will now be described in detail with reference to the accompanying drawings. Note that the relative arrangement, numerical expressions, and numerical values of the components and steps set forth in these embodiments do not limit the scope of the present disclosure unless otherwise specifically stated.

[0013] Those skilled in the art can understand that the terms "first", "second", and the like in the embodiments of the present disclosure are only used to distinguish different steps, devices, or modules, and do not represent any specific technical meaning, nor do they represent the inevitable logical sequence between them. It should also be understood that in the embodiments of the present disclosure, "multiple" can mean two or more, and "at least one" can mean one, two, or more. It should also be understood that for any component, data, or structure mentioned in the embodiments of the present disclosure, without explicit limitation or in the context of the preceding and following text giving the opposite indication, it can be understood as one or more in general. In addition, the term "and / or" in the present disclosure is only a description of the association between the associated objects, which means that there can be three relationships, for example, A and / or B can represent the existence of A alone, the existence of A and B together, and the existence of B alone. In addition, the character " / " in the present disclosure generally represents an "or" relationship between the front and rear associated objects. It should also be understood that the description of each embodiment of the present disclosure emphasizes the differences between each embodiment, and the same or similar parts can be referred to each other, and for the sake of brevity, they will not be repeated.

[0014] It should be understood that the dimensions of the various elements shown in the figures are not necessarily to scale and that the various elements can be larger or smaller than as shown in the figures. The following detailed description of at least one example embodiment is merely illustrative and is not limiting of the present disclosure and its applications or uses. Techniques, methods, and devices known to the relevant art can not be discussed in detail, but are contemplated to be part of the description of the present disclosure. It should be noted that like reference numerals and letters refer to like items in the various figures, and thus, once one item is defined in one figure, it need not be discussed further in subsequent figures.

[0015] In order to make the objects, technical solutions and advantages of the embodiments of the present disclosure clearer, the technical solutions in the embodiments of the present disclosure will be described clearly and completely below with reference to the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are only some of the embodiments of the present disclosure, rather than all the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present disclosure.

[0016] Figure 1 A dynamic knowledge graph driven cross-department government data collaboration method process schematic diagram is provided for the embodiments of the present disclosure. The method of the embodiments of the present disclosure aims to realize accurate detection of large and small goals for pictures.

[0017] In S101, cross-department government business data and corresponding policy rule data are acquired, a data knowledge graph is constructed based on the cross-department government business data, and a strategy knowledge graph is constructed based on the policy rule data.

[0018] The cross-department government business data can refer to business-related data generated, collected or stored by different government departments in the process of performing public management and social service functions, including population information, enterprise registration information, social security data, real estate registration data, financial tax data, medical insurance data, etc. Such data is usually stored in different department systems, and has the characteristics of inconsistent formats and standards.

[0019] The policy rule data can refer to laws, regulations, administrative rules, department rules, policy documents and business rules formulated and published by state organs or local governments for the purpose of regulating government business handling and service processes, including handling conditions, time limit requirements, material lists, applicable scope, approval constraints, etc., for guiding the collaborative processing of cross-department government business.

[0020] The data knowledge graph can refer to a graphed knowledge model constructed based on cross-department government business data, used to represent semantic relationships and business associations between different department data, including association relationships between objects such as personnel, enterprises, assets, and events, thereby realizing semantic fusion, unified modeling, and intelligent retrieval of cross-department data.

[0021] The policy knowledge graph can refer to a graphed knowledge model constructed based on policy rule data, used to represent association relationships between policy provisions, rule constraints, and conditional logic, including mapping relationships between policies and applicable objects, conditions, restrictions, and handling processes, thereby realizing structured expression, automatic reasoning, and compliance verification of policy rules.

[0022] First, cross-department government business data is obtained, which can be accessed through the establishment of a secure interface with a government data exchange platform, and data scattered in department databases such as population information libraries, business registration libraries, social security libraries, and real estate registration libraries is extracted. After data extraction, data cleaning and standardization processing techniques are used, including missing value filling, format unification, entity deduplication, and synonym merging, to ensure the structure and consistency of the data. After cleaning, entity recognition and relationship extraction are performed on the data, using natural language processing-based named entity recognition (NER) algorithms and dependency syntax analysis-based relationship extraction methods to identify entities such as personnel, enterprises, assets, and events and their semantic relationships. Subsequently, graph database modeling techniques (such as Neo4j) are used to model the extracted entities and relationships, resulting in a data knowledge graph that realizes semantic fusion and structured storage of cross-department data.

[0023] In the process of obtaining policy rule data, text data can be scraped or parsed from legal and regulatory libraries, government bulletins, and policy document management systems. First, optical character recognition (OCR) technology is used to recognize text from scanned documents, and then text segmentation and syntax analysis techniques are used to structure and analyze policy text. Based on rule extraction and machine learning methods, rule elements such as conditional constraints, applicable scope, and handling processes in policy provisions are extracted. For example, a method based on regular expressions and conditional logic modeling is used to analyze the constraint statement "the handling time limit shall not exceed 15 working days", which is converted into a formal conditional expression. Based on this, knowledge representation techniques (such as RDF / OWL ontology modeling) are used to construct multi-dimensional semantic relationships between policy provisions, handling conditions, objects, and rules, and stored and queried through a graph database, forming a policy knowledge graph.

[0024] S102, obtain preset event information, perform incremental update and consistency check on the data knowledge graph and the policy knowledge graph according to the preset event information, and obtain an updated data knowledge graph and an updated policy knowledge graph.

[0025] The preset event information can refer to a set of key events that can trigger knowledge graph update in the cross-department government business cooperation process, including but not limited to: data record events of newly added, modified or deleted data in the government data source; events of changes in department database structure or field rules; events of updates, abolitions or additions of related policies, regulations, department rules or work guidelines.

[0026] The updated data knowledge graph can refer to a knowledge graph instance formed after incremental update and consistency check on the original data knowledge graph based on newly added or modified cross-department government business data after detecting the preset event information.

[0027] The updated policy knowledge graph can refer to a knowledge graph instance formed after incremental update and consistency check on the original policy knowledge graph based on newly added or modified policy rule data after detecting the preset event information.

[0028] The preset event information can be captured by multi-source data change detection technology. For data update in the cross-department government business database, database triggers and change data capture mechanism can be used to record addition, modification and deletion operations; for changes in department database structure or field rules, metadata comparison algorithm can be used to periodically scan data table structure and compare differences with historical versions; for changes in policy rule data, natural language processing (NLP) technology can be used to automatically analyze newly published policy and regulation documents, extract rule clauses, applicable conditions and effective scope, and compare differences with existing rule sets. The above multi-type detection results are summarized by an event listening engine to form the preset event information.

[0029] Then, based on the preset event information, incremental update is performed on the data knowledge graph. Specifically, when detecting the addition or modification of cross-department government business data, entity recognition and relationship extraction algorithm (such as BERT-based named entity recognition and dependency syntax analysis-based relationship extraction) is used to perform semantic modeling on the changed data, generating new entity nodes and relationship edges; in the case of data deletion, the corresponding entity and its edge relationship are located and logical deletion is performed. The updated nodes and edges need to be verified by a consistency check mechanism, including structure constraint check based on graph database (such as mandatory primary key uniqueness, mandatory attribute integrity), and logical consistency check based on knowledge reasoning (such as OWL ontology rule reasoning, SPARQL query constraint detection), so as to ensure correct and non-conflicting data semantics.

[0030] Based on the preset event information, the policy knowledge graph is incrementally updated. When a change in policy rule data is detected, first, the rule extraction and logical modeling technology (such as regularized text parsing combined with logical conversion based on semantic role labeling) is used to convert the newly added clauses into formal rule nodes, and the association between the clauses and the applicable departments and business scenarios is established. For the policy clauses that are invalid or abolished, they are marked as “expired state” in the knowledge graph. After the update is completed, rule consistency checking is performed for detection, including logical reasoning based on ontology to determine whether there is a conflict between rules, and time sequence logic calculation to determine whether the application time period of the new and old clauses overlaps or contradicts. Finally, after the above incremental update and consistency checking processing, the updated data knowledge graph and the updated policy knowledge graph are obtained respectively.

[0031] On the basis of the above technical solutions, after the updated data knowledge graph and the updated policy knowledge graph are obtained, the method further includes: If the updated data knowledge graph and the updated policy knowledge graph do not meet the preset consistency rules, performing graph structure analysis on the updated data knowledge graph to obtain isolated node data and circular dependency relationship data; Performing logical constraint analysis on the updated policy knowledge graph to obtain cross-graph conflict relationship data; Performing node reconstruction processing on the isolated node data to obtain reconstructed node data; Performing edge redirection processing on the circular dependency relationship data to obtain redirected edge data; Performing redundant data elimination processing on the cross-graph conflict relationship data to obtain simplified data; Based on the reconstructed node data, the redirected edge data, and the simplified data, repairing the updated data knowledge graph and the policy knowledge graph to obtain the repaired updated data knowledge graph and the policy knowledge graph; Correspondingly, the latest business status is extracted from the updated data knowledge graph, and the latest policy rule is extracted from the updated policy knowledge graph, including: The latest business status is extracted from the repaired updated data knowledge graph, and the latest policy rule is extracted from the repaired updated policy knowledge graph.

[0032] In the scheme, the preset consistency rules can refer to a set of rules defined in advance to ensure that the data knowledge graph and the policy knowledge graph are coordinated in terms of semantics, logic and structure during the construction and updating process. The consistency rules include but are not limited to: node uniqueness rule (to prevent repeated definition of the same entity), edge relationship non-conflict rule (to avoid contradictory definition of the same relationship), semantic matching rule (to ensure semantic alignment of policy constraints and data entities), hierarchical integrity rule (to avoid missing upper or lower dependency relationships), and cross-graph logical consistency rule (to ensure that the mapping relationship between business data and policy constraints is conflict-free).

[0033] Isolated node data can refer to independent data entity nodes in the updated data knowledge graph that are not effectively connected to other nodes. Such nodes are often formed due to data missing, relationship breaking or rule conflict, and if not handled, will result in incomplete knowledge graph structure, thereby affecting the correctness of the reasoning result.

[0034] Circular dependency relationship data can refer to a ring-shaped dependency relationship that appears in the data knowledge graph, i.e., a data entity directly or indirectly depends on itself, resulting in a logical closed loop that cannot be resolved. For example, task A depends on task B, and task B depends on task A.

[0035] Cross-graph conflict relationship data can refer to mutually exclusive or contradictory relationships between the data knowledge graph and the policy knowledge graph. For example, the data knowledge graph shows that a certain data node can be shared, while the policy knowledge graph specifies that the data node is prohibited from external access.

[0036] Reconstruction node data can refer to a set of repaired nodes generated by introducing new semantic relationships, supplementing missing upstream and downstream associations, or merging redundant nodes based on similarity during the repair process of isolated nodes.

[0037] Redirected edge data can refer to a new set of edges generated by reallocating edge directions, adjusting dependency paths, or introducing virtual intermediate nodes during the repair of circular dependency relationships.

[0038] Simplified data can refer to a refined data set obtained by deleting redundant fields, removing contradictory edge relationships, and eliminating invalid policy constraints during the resolution of cross-graph conflicts.

[0039] The repaired updated data knowledge graph and the policy knowledge graph can refer to the knowledge graph version that meets the consistency rule requirements after node reconstruction, edge redirection and data simplification. This version of the graph maintains connectivity and order in structure, eliminates ambiguity and contradiction in semantics, and can be used as a reliable data support and policy constraint basis for subsequent business state extraction and policy rule extraction.

[0040] When the updated data knowledge graph and the updated policy knowledge graph do not meet the preset consistency rule, first, the updated data knowledge graph is subjected to graph structure analysis. In the specific implementation process, a combination of depth-first traversal and breadth-first traversal is adopted to detect nodes that do not form an effective edge relationship with other nodes, and extract isolated node data; at the same time, a strongly connected component decomposition algorithm is used to analyze the dependency relationship of each node in the graph, and if a self-loop or multi-node loop is found, the corresponding dependency set is marked as a circular dependency relationship data.

[0041] Subsequently, the updated policy knowledge graph is subjected to logical constraint analysis. In the specific implementation, the predicate logic is used to analyze the policy constraint expression, and the SAT / SMT (satisfiability solving / theory solving) algorithm is used to determine whether there is a mutual exclusion or contradiction relationship between each policy rule. When it is detected that a certain business data node is marked as sharable in the data knowledge graph, but is marked as prohibited sharing in the policy knowledge graph, the contradictory constraint is extracted as cross-graph conflict relationship data.

[0042] For the isolated node data, node reconstruction processing is performed. Specifically, it includes: based on cosine similarity calculation and embedding representation learning (Graph Embedding, such as Node2Vec or TransE), the semantic similarity of isolated nodes and existing nodes is matched, and if the similarity exceeds a preset threshold, the isolated node is merged into the node group with the closest semantics; if there is no match, the missing relationship edge is inferred based on the knowledge completion algorithm (Knowledge Graph Completion, such as ComplEx or DistMult), and the upstream and downstream connections of the isolated node are supplemented to obtain reconstructed node data.

[0043] For the circular dependency relationship data, edge redirection processing is performed. In the specific implementation, a topological sorting algorithm is used to identify the key edges in the circular chain, and a dependency rewriting strategy (such as adjusting the execution order of tasks or introducing a virtual intermediate node to break the loop) is used to realize the unidirectionality of the dependency path, and finally the redirected edge data is obtained.

[0044] For the cross-graph conflict relationship data, redundant data elimination processing is performed. In the implementation, a priority constraint rule set is adopted, in which the security priority is higher than the sharing priority, and the compliance priority is higher than the business convenience priority. The conflicting edge relationship or attribute is determined according to the priority, and the redundant constraint or contradictory edge relationship with low priority will be eliminated to obtain the refined data.

[0045] Based on the reconstructed node data, the redirected edge data, and the simplified data, a repair operation is performed on the updated data knowledge graph and the policy knowledge graph. Specifically, the reconstructed nodes are reinserted into the graph main structure to replace the original isolated nodes; the redirected edge relationships are updated in the dependency chain to replace the circular dependency edges; and the simplified data after removing redundant constraints is written into the policy knowledge graph to form a repaired updated data knowledge graph and a policy knowledge graph that meet the consistency rules in structure, logic, and semantics.

[0046] Correspondingly, in the data extraction link, instead of directly extracting from the unrepaired knowledge graph, the latest business status is extracted based on the repaired updated data knowledge graph to ensure that the business entities and their relationships are complete in semantics and correct in logic; and the latest policy rules are extracted based on the repaired updated policy knowledge graph to ensure that the extracted policy constraints are conflict-free in logic and comply with compliance priorities, thereby providing a reliable foundation for subsequent joint reasoning and cross-departmental government data collaboration.

[0047] In this scheme, by analyzing the repaired graph defects, ensuring the consistency of data and policy collaboration, extracting the latest business status and policy rules, both data accuracy and compliance are guaranteed, and efficient task processing is supported, realizing precise linkage of data governance and business decision-making.

[0048] S103, extracting the latest business status in the updated data knowledge graph and the latest policy rules in the updated policy knowledge graph.

[0049] The latest business status can refer to the real-time and effective state of business data entities and their associated relationships extracted from the updated data knowledge graph after incremental updating and consistency checking of cross-departmental government business data. The latest business status includes but is not limited to: the latest numerical value of cross-departmental business data, structured field updates, business process execution progress, task node status, data association, and other information.

[0050] The latest policy rules can refer to the policy provisions and their logical constraints with current applicable effectiveness extracted from the updated policy knowledge graph after parsing, modeling, incremental updating, and consistency checking of policy rule data. The latest policy rules include but are not limited to: policy provisions, applicable object range, time limit conditions, execution constraint logic, priority order, and semantic association with related departments and business data.

[0051] For the updated data knowledge graph, a query statement is constructed based on the SPARQL, a graph database query language, with the timestamp, version number and data source identifier as the query conditions to search the business data entities in the graph. By comparing the timestamp fields of different version nodes, the data entities and their semantic relationships corresponding to the latest timestamp are selected, and the logical reasoning of the upstream and downstream dependencies involved is performed by combining the relationship reasoning technology, so as to extract the latest business status of each business process node, including the business execution progress, task completion, pending task node and its dependency constraints, etc.

[0052] Secondly, for the updated policy knowledge graph, a rule engine is used to analyze the policy clause entities in combination with the ontology reasoning technology. Specifically, the logical consistency of the policy clauses, condition restrictions and applicable objects in the policy knowledge graph is verified by using an OWL-based inference engine, and the latest effective date field, invalid date field and applicable scope label are used as retrieval conditions to filter out the invalid policy rules and extract the policy clauses that are still effective. By calculating the priority sorting field between the rules, the latest policy rules with actual binding force are obtained, including the applicable department, time effectiveness condition, execution order and associated business data constraint conditions.

[0053] S104, based on the latest business status and the latest policy rules, joint reasoning is performed to obtain the minimum necessary data view and the authorized range, and cross-departmental government data collaboration is performed based on the minimum necessary data view and the authorized range.

[0054] The minimum necessary data view can refer to the minimum data set necessary to meet the specific government business processing needs, which is obtained by joint reasoning of the latest business status and the latest policy rules when performing cross-departmental government business collaboration. The data view is presented in a structured or semi-structured manner, containing the key data fields and their logical relationships required for business processing, while excluding redundant data unrelated to the current business.

[0055] The authorized range can refer to the data access permission boundary determined according to the latest policy rules, which is used to restrict the access permissions of each participating department in cross-departmental government data collaboration. The authorized range usually includes the following dimensions: access subject (i.e. department or user identity), accessible data object, operable data type (query, write, update), time limit and data usage limit.

[0056] The semantic inference engine (such as an OWL inference machine or an RDF Schema-based inference tool) is used to semantically associate the latest business state with the latest policy rules, and to establish a logical constraint mapping relationship between the business entity nodes and the policy rule condition nodes. Through rule matching of the RDF triple by the inference engine, it can be identified which business entities belong to the necessary data supply range in the current scenario. Constraint solving methods are used to calculate the condition restrictions in the policy rules, such as legal subjects of data access, access frequency restrictions, data desensitization requirements, etc. Specifically, the policy rules are converted into computable constraint conditions using first-order logic expressions, and combined with the data attribute values in the business state, the Boolean logic solving is executed to obtain the candidate data set that satisfies all the constraint conditions.

[0057] A graph traversal and subgraph extraction algorithm is used to select a subgraph that meets the candidate conditions in the data knowledge graph, which forms the minimum necessary data view. Among them, a combination of breadth-first search (BFS) and depth-first search (DFS) is used to ensure that the extracted data set covers all necessary nodes while eliminating irrelevant nodes and redundant edges. Based on the access subject permission clauses in the latest policy rules, combined with the candidate data set, the role-based access control (RBAC) model and the attribute-enhanced access control (ABAC) mechanism are used to determine the data range and operation permissions that each access subject can access in the current business collaboration, thereby forming the authorized range. Among them, RBAC is used to determine the role permissions at the department level, and ABAC is used to impose attribute constraints on specific data objects at a fine-grained level, such as "only allow access to business flow data within the last three months".

[0058] According to the minimum necessary data view generated based on the joint inference result, the core data fields related to the current cross-department government collaboration matter are selected from the business databases of each department, such as the necessary fields of enterprise registration information, such as the name of the legal person and the unified social credit code, rather than the complete data table. In this way, redundant data is eliminated from the source, reducing unnecessary data exposure.

[0059] Secondly, the system strictly limits the data access permissions using the authorized range. The authorized range is obtained by analyzing the policy rules in the strategy knowledge graph, and specifically includes the access subject (department, role), the accessible data type, the access method (query, download, verification), and the access timeliness. The system uses attribute-based access control technology to combine the identity attributes of the access user, the task context, and the authorized range for multi-dimensional matching, ensuring that data access strictly complies with policy and business requirements.

[0060] Then, during the cross-department data transmission process, the system uses homomorphic encryption and secure multi-party computation technology to encrypt the transmitted data, enabling participants to complete joint computation without decryption, ensuring that data privacy is not disclosed. Meanwhile, combined with blockchain storage and hash verification technology, an immutable log record is generated for each cross-department access operation, ensuring traceability and auditability throughout the process.

[0061] Finally, in the collaborative execution phase, the system provides data interface calling services to the target department based on the aforementioned minimum necessary data view, and dynamically adjusts the access frequency and time limit of the interface according to the authorized scope. When the collaborative task is completed or the authorization time limit expires, the system automatically recovers access rights and cleans up temporary caches, ensuring a secure data loop.

[0062] The embodiments of the present application provide only necessary data and strictly control access rights in cross-department government data collaboration, significantly improving data sharing efficiency, security, and compliance, while reducing the risk of unauthorized access and redundant data transmission.

[0063] Based on the above technical solutions, the latest business status and the latest policy rules are used to perform joint reasoning to obtain the minimum necessary data view and the authorized scope, including: A target task is obtained, and a task subgraph related to the target task is generated in the updated data knowledge graph, and a compliance constraint subgraph related to the target task is generated in the updated policy knowledge graph; The intersection of the task subgraph and the compliance constraint subgraph is clipped to obtain a candidate data subgraph; The candidate data subgraph is optimized based on a preset constraint priority rule to generate the minimum necessary data view; A department role mapping relationship is obtained, the field access rights are determined based on the data sensitivity level in the minimum necessary data view, and the authorized scope is determined based on the field access rights and the department role mapping relationship.

[0064] In this scheme, the target task can refer to a specific government collaboration matter triggered by a business process or driven by a user request in a cross-department government business scenario, such as enterprise cross-regional establishment approval, social security information comparison, or cross-department joint law enforcement, etc. The target task usually includes a specific task type, a processing flow, and data requirements involved.

[0065] The task subgraph can refer to a local graph structure extracted from the updated data knowledge graph according to the data requirements of the target task, which includes entity nodes, attribute nodes, and relationship edges directly related to the target task. For example, when the target task is enterprise approval, the task subgraph can include enterprise nodes, legal person nodes, and their association with department data such as industry and commerce, taxation, etc.

[0066] The compliance constraint subgraph can refer to a constraint knowledge subgraph related to the target task extracted from the updated policy knowledge graph according to the policy and regulation, privacy protection requirements and inter-department cooperation rules involved in the target task. The subgraph includes data use constraints, access permission restrictions, time effectiveness rules and compliance verification logic, etc.

[0067] The candidate data subgraph can refer to a graph structure obtained by performing an intersection clipping operation on the task subgraph and the compliance constraint subgraph. The candidate data subgraph only retains entities and relationships that meet both the data requirements of the target task and the compliance constraint conditions, and is a preliminary candidate result for the generation of the minimum necessary data view.

[0068] The preset constraint priority rule can refer to a preset constraint execution priority order when there are multiple policy rule conflicts or different constraint conditions in parallel. The rule usually includes priority based on the level of laws and regulations (such as national laws taking precedence over departmental regulations), priority based on the level of data sensitivity (such as privacy protection rules taking precedence over general business rules), etc., for ensuring that the result is compliant and reasonable when optimizing the candidate data subgraph.

[0069] The department role mapping relationship can refer to the corresponding relationship between the government department and its internal roles or positions, which is used to define the permission range of different roles in the cross-department collaboration process. For example, an ordinary clerk of a department can only query part of the basic information, while an auditor can view higher sensitivity data fields.

[0070] The data sensitivity level can refer to a classification system for hierarchical management of data fields according to their importance, privacy and potential risks. Common sensitivity levels include public data, internal data, sensitive data and highly sensitive data, etc., for determining the strictness of field access permissions.

[0071] The field access permission can refer to the access operation permission given to a specific data field based on the data sensitivity level and the department role mapping relationship, including query, modification, download or prohibition of access, etc., to ensure that the data use complies with the minimum necessary principle and compliance requirements.

[0072] The target task can be defined by a government approval event triggered by a business process engine, a cross-department comparison request or a user-submitted business application. The system uses natural language processing (NLP) technology to perform intent recognition on the task description, and based on task semantic analysis and entity extraction methods, maps the task content to specific entities and relationships in the updated data knowledge graph, thereby identifying the data range involved in the task. Subsequently, in the updated data knowledge graph, a task subgraph related to the target task is generated using subgraph matching and graph traversal algorithms. The task subgraph includes the core business entities corresponding to the target task and their upstream and downstream relationship chains.

[0073] At the same time, in the updated policy knowledge graph, the policy constraint nodes and edges related to the target task are extracted using rule matching, logical reasoning and OWL-based semantic constraint reasoning technology, and the corresponding compliance constraint subgraph is generated, which includes access restriction conditions, sensitive field marking, timeliness rules and cross-department collaboration requirements.

[0074] Subsequently, the task subgraph and the compliance constraint subgraph are intersected and cropped, and the specific method is as follows: through graph isomorphism detection and constraint consistency comparison algorithm, only the entity nodes and relationship edges that appear in both task requirements and compliance constraints are retained, so as to obtain a data range that meets business requirements and compliance, and form a candidate data subgraph.

[0075] The system predefines a set of constraint priority rules, which are sorted in multiple dimensions according to the hierarchy of laws and regulations, the level of privacy, the urgency of business and the security of data sharing, for example: the priority of legal and regulatory constraints is the highest, better than departmental rules; the priority of privacy protection and sensitive field constraints is higher than that of general business convenience constraints; the priority of cross-departmental urgent business task constraints is higher than that of daily non-urgent tasks; the priority of security risk control rules is higher than that of access efficiency rules.

[0076] In the optimization process, the system first adds constraint labels to each node and edge in the candidate data subgraph, including constraint source (law, policy, departmental rule), constraint strength (mandatory, recommended, optional) and sensitivity level. Subsequently, the system uses a multi-level priority sorting algorithm to sort the constraint labels globally, for example, using a weight-based analytic hierarchy process (AHP) or a weighted directed graph topology sorting algorithm, to obtain the priority order of each constraint.

[0077] In the candidate data subgraph, when there is a constraint conflict, for example, a task requirement requires access to field A, while the compliance constraint requires field A to be prohibited from sharing, the system uses the following optimization strategy: if the conflicting constraints belong to different levels, the constraint with higher priority is retained, and the constraint node or edge with lower priority is deleted or shielded; if the conflicting constraints are at the same level but have different priority weights, the optimal solution is retained according to the weight value; if the conflicting constraints are completely opposite and have the same priority, an artificial review or intelligent arbitration mechanism is triggered, and an artificial approval or intelligent agent recommendation is used to replace the solution (such as desensitization, partial field sharing).

[0078] After optimization, the system performs pruning and reconstruction operations on the candidate data subgraph: delete nodes and edges that do not meet the constraints, retain legal data paths, and generate a structured, minimal-scale but still supporting the target task data subgraph through graph rewriting techniques. The result of the pruning and reconstruction is the minimal necessary data view, which is characterized by containing only the minimum data set required for target task execution, and all data access meets the requirements of the constraint priority rules.

[0079] After obtaining the minimal necessary data view, the system obtains the department role mapping relationship, which can be constructed through the organizational structure directory and role permission system. Then, for each data field in the minimal necessary data view, the data sensitivity level is determined, and the sensitivity classification algorithm (such as a hybrid method based on rule labeling and risk model) is used to divide the field into four levels: public, internal, sensitive, and highly sensitive. According to the sensitivity level, the system uses access control policy generation technology (RBAC / ABAC model combination) to automatically determine the field access permission of each field, for example, only authorized specific roles with legal approval access to highly sensitive data, while ordinary roles can only access low sensitivity or desensitized data. Finally, combined with the field access permission and the department role mapping relationship, the corresponding authorized range is generated to ensure that the cross-department collaborative data sharing process strictly follows the principle of minimal necessity and security compliance requirements.

[0080] In this scheme, by constructing the association graph of tasks and compliance constraints and performing intelligent pruning optimization, the minimal necessary data range can be accurately identified to avoid data redundancy; combined with sensitivity classification and role permission mapping, dynamic permission control is realized, which not only guarantees data compliance but also improves access efficiency, and finally forms a safe and controllable, efficient and adaptive data authorization system to business needs.

[0081] On the basis of the above technical solutions, optionally, after cross-departmental government data collaboration based on the minimal necessary data view and authorized range, the method further comprises: Obtain cross-department data access records, and determine access time, access user, and access department information according to the cross-department data access records; Based on the minimal necessary data view, the access time, access user, and access department information are encrypted and processed with tamper-proof log records to obtain traceable access credentials.

[0082] In this solution, the cross-department data access record can refer to the access behavior log generated by the system during the cross-department government data interaction process, specifically including the interface call request, data reading request, or full-process record of data transmission operation. This record reflects the data interaction between different departments, covering access initiator, access target, access duration, request content summary, and processing result information. As the basis for generating access credentials, the cross-department data access record ensures the completeness and accuracy of subsequent traceability.

[0083] Access time can refer to the specific time point or time interval when the cross-department data access behavior occurs, including the timestamp of request initiation, response return time, and processing completion time.

[0084] Access user can refer to the specific user entity that initiates the data request during the cross-department data access process, which can be a natural person, legal representative, or authorized system account.

[0085] Access department information can refer to the identification of the administrative agency or business department to which the access user belongs, representing the organizational relationship of cross-department data interaction.

[0086] Traceable access credentials can refer to access credentials generated based on cross-department data access records, combined with access time, access user, and access department information, and processed by encryption and tamper-proof logs. This credential has uniqueness, integrity, and non-repudiation, enabling verification and traceability of access behavior after the fact, ensuring compliance and security of cross-department data access.

[0087] First, the cross-department data access record is obtained, which is composed of network request logs and application call logs generated during the cross-department data interaction process. It is usually collected using a unified log collection mechanism, such as real-time capture of interface call data based on a distributed log collection protocol (such as Fluentd or Apache Kafka log collection framework) and storage in a distributed log database. This cross-department data access record contains original access request information, including caller identification, target data identification, request initiation time, and response status.

[0088] Subsequently, the system parses the cross-department data access record to extract access time, access user, and access department information. Among them, access time can be obtained by reading the timestamp field in the log and converting it to a standard time format (such as ISO8601 time format); access user information can be obtained by parsing the user token (Token) or digital certificate identification generated by the identity authentication system; access department information is matched by the system's pre-set user and department correspondence table, thereby realizing the binding of access subject and department affiliation.

[0089] After the above information extraction is completed, the system securely processes the access time, access user and access department information based on the minimum necessary data view. Specifically, first, symmetric encryption algorithm (such as AES-256) is used to encrypt the above information to ensure confidentiality during transmission and storage; at the same time, in order to ensure subsequent non-repudiation, the system further generates a digital signature for the encryption result using asymmetric encryption technology (such as RSA or national standard SM2), thereby preventing the access subject from denying its access behavior.

[0090] At the log level, in order to achieve tamper-proof log processing, the system writes the encrypted and signed access data into a blockchain-like log storage structure, such as a chain-like hash storage method based on MerkleTree. Specifically, a unique hash value is generated for each record, and is linked with the hash value of the previous record, thereby forming a chain structure; if any record is tampered with, it will cause all subsequent hash value checks to fail, achieving immediate detection of tampering behavior. In addition, the system can also use a WORM (Write Once Read Many) storage medium to ensure that the log is not modifiable at the physical level.

[0091] Finally, the system generates traceable access credentials based on the above encryption and tamper-proof log processing results. The credentials consist of encrypted access information, digital signatures and blockchain-like log indexes, which can be used to confirm and trace access behavior after the fact through decryption and signature verification operations, thereby achieving secure audit and responsibility tracing of the entire cross-department data access process.

[0092] In this scheme, the confidentiality and authenticity of the access information are ensured through encryption and digital signature, preventing unauthorized access and data leakage; through tamper-proof log recording and blockchain-like storage, complete tracking and auditing of access behavior are achieved, thereby providing high reliability, security and compliance assurance for cross-department government data collaboration.

[0093] Figure 2 A flowchart of a dynamic knowledge graph driven cross-department government data collaboration method provided by the embodiments of the present disclosure is provided. The method can include the following steps: S201, cross-department government business data and corresponding policy rule data are obtained, a data knowledge graph is constructed based on the cross-department government business data, and a strategy knowledge graph is constructed based on the policy rule data.

[0094] S202, preset event information is obtained, and incremental updating and consistency checking of the data knowledge graph and the strategy knowledge graph are performed according to the preset event information, to obtain an updated data knowledge graph and an updated strategy knowledge graph.

[0095] S203, extract the latest business status in the updated data knowledge graph and the latest policy rules in the updated policy knowledge graph.

[0096] S204, perform joint reasoning based on the latest business status and the latest policy rules to obtain a minimum necessary data view and an authorized range, and perform cross-departmental government data collaboration based on the minimum necessary data view and the authorized range.

[0097] S205, obtain interface state information of each interface, determine interface response time and availability data of each interface according to the interface state information, perform average response time calculation and fault frequency analysis on the interface response time and availability data, and obtain an interface delay weight table.

[0098] An interface can refer to a unified access portal for data exchange and call between different business systems, application services or databases in a cross-departmental government data collaboration system, usually including request parameter definition, call protocol (such as HTTP, gRPC, RESTful API) and return data format, etc., used to realize data interaction and business linkage between systems.

[0099] Interface state information can refer to the running status information collected in real time or periodically during the running process of the interface, including the number of interface calls, the number of successful returns, the number of failed returns, the number of timeouts, the distribution of abnormal codes, the current load level and the timestamp, etc., used to reflect the running performance of the interface in a certain time period.

[0100] Interface response time can refer to the time interval from sending a request to the interface from the client to returning the result of the interface, usually in milliseconds (ms), used to measure the efficiency of the interface in processing requests; Availability data can refer to the proportion of time that the interface remains normal and returns business results correctly within the monitoring period, usually expressed in percentage form, which is a key indicator for measuring the reliability and stability of the interface.

[0101] Average response time calculation can refer to the weighted or arithmetic average of response time data based on multiple interface calls within a monitoring time window to obtain the overall response time level of the interface; Fault frequency analysis can refer to the number of times the interface appears to be timed out, returned with an abnormal code or unreachable within the same monitoring window, used to characterize the performance of the interface in terms of stability.

[0102] Interface delay weight table can refer to the delay weight of each interface in the data access scheduling process, which is calculated comprehensively according to the average response time calculation result and the fault frequency analysis result of each interface, combined with the set delay evaluation index system, and finally forms a weight mapping table.

[0103] First, the interface state information of each interface is acquired, and the interface state information includes timestamp of interface call, return state code, request response time, error type, and request failure number, etc. Based on the interface state information, the system determines the interface response time and availability data of each interface, wherein the interface response time refers to the average time consumed by the interface to complete the request response in a unit time window, and the availability data refers to the success rate or failure rate of the interface in normally responding to the request in the time window. Subsequently, the average response time calculation is performed on the interface response time, that is, the arithmetic average of the response time of the interface in multiple time windows is taken to obtain the interface overall response performance index. At the same time, the fault number analysis is performed on the availability data, that is, the request failure number, abnormal error code frequency, and interface unavailable duration of the interface in a preset time period are counted. The system will comprehensively evaluate the average response time and fault number calculated in the foregoing and the availability data of the interface. Specifically, the system first normalizes the average response time of each interface, so that its value is mapped to a unified performance quantization scale, and normalizes the fault number to eliminate the influence of different interface call frequency differences on the evaluation result. Subsequently, the normalized average response time, the standardized fault number, and the interface availability data are combined by a preset weight coefficient, wherein the weight coefficient of the availability data is usually higher than that of the fault number and the response time, in order to reflect the interface stability in priority. The system divides each interface into different delay weight grades according to the size of the comprehensive score, for example, the interface can be divided into five grades of high, higher, medium, lower, and low, and the boundary value of the grade division is determined by historical performance statistical data and business response demand. For the interface with high comprehensive score, short response time, and high availability, the system assigns a lower delay weight grade, so as to preferentially select the interface for data access; for the interface with low comprehensive score, slow response, or frequent faults, the system assigns a higher delay weight grade, so as to reduce the access priority in subsequent cross-department data calling. Finally, the system records each interface and its corresponding delay weight grade in the complete interface delay weight table, and the interface delay weight table associates each interface with its corresponding delay weight grade one by one.

[0104] In S206, the access task priority and historical access frequency data of each department are acquired, and the access task priority and historical access frequency data are combined and sorted by weight to obtain a preliminary task access list.

[0105] The access task priority can refer to the task urgency or importance degree assigned by each department in the cross-department government data calling process, and is used to reflect the priority order of different tasks in scheduling.

[0106] The historical access frequency data can refer to the statistics of the number of accesses of each department to the cross-departmental interface or data resource within a certain time window in the past, which is used to measure the activity and dependence of the department on the specific interface or data call.

[0107] The preliminary task access list can refer to the sorted set of data access tasks of each department generated after the weighted combination and sorting of the access task priority and the historical access frequency data. The list is used to reflect the preliminary execution order of the tasks of each department in the cross-departmental data collaboration process, providing a basic reference for subsequent optimization sorting and access scheduling.

[0108] The system first obtains the access task priority and the historical access frequency data of each department. The access task priority includes the importance level and urgency information of the tasks of each department, which is used to reflect the processing priority of the tasks in the cross-departmental data access; the historical access frequency data records the number of accesses and access patterns of each task within a past time period, which is used to reflect the actual access demand of the task.

[0109] After obtaining the above data, the system performs standardization processing on the access task priority, unifying the priority indicators of different departments and different tasks to the same evaluation scale to eliminate the differences in dimensions and data ranges; at the same time, the historical access frequency data is normalized to ensure that the tasks with more access times will not produce unreasonable sorting deviation due to quantity differences.

[0110] After completing the standardization and normalization processing, the system performs comprehensive evaluation on the tasks of each department to determine their sorting priority in the cross-departmental data access. The specific operation is as follows: The system first assigns two independent evaluation indicators to each task: one based on the standardized task priority, reflecting the importance and urgency of the task; the other based on the normalized historical access frequency data, reflecting the activity of the task in the past access. Then, the system comprehensively considers the two indicators according to the preset optimization strategy: tasks belonging to key business or high importance level are given higher weight to ensure that core tasks can have priority access to data access resources; tasks with high historical access frequency are given additional weight to ensure that frequently called tasks will not be delayed due to low priority; by adjusting the proportion of core task weight and access frequency weight, the balance between importance and access demand is achieved, so that the tasks that are both important and active can get the highest comprehensive score; during the evaluation process, the comprehensive score of each task can be adjusted appropriately according to the department resource capacity, network load and current task accumulation, to ensure that the system can still reasonably schedule under high load. After completing the comprehensive evaluation, the system sorts the tasks according to the comprehensive score from high to low, generating the preliminary task access list.

[0111] S207, obtain department priorities of each department, and generate an optimized task access list according to the department priorities and the preliminary task access list.

[0112] The department priority can refer to the access priority level of each department in cross-department government data collaboration, and is used to reflect the importance, urgency or resource allocation priority of the department in the overall business process.

[0113] The optimized task access list can refer to a task scheduling order list obtained by re-adjusting the task order based on the preliminary task access list and the department priorities of each department. The optimized task access list comprehensively considers the importance of the task, the historical access frequency and the priority of the department to which the task belongs, and aims to realize the efficiency, rationality and fairness of cross-department data access, and to provide basic data for the determination of subsequent interface access frequency and batch transmission strategy.

[0114] Obtaining the department priorities of each department first involves quantitatively evaluating the access importance and resource allocation demand of each department in cross-department government data collaboration. The system determines the department priorities in the form of numerical values or levels by using the analytic hierarchy process (AHP), weighted scoring or multi-dimensional sorting technology according to the business functions, historical access records, task urgency and policy regulations of each department, and generates a department priority data set. In this process, for each department, the system will count the completion efficiency, request frequency, proportion of key tasks and other information in historical task access, and standardize each index to eliminate the influence of dimension differences on comprehensive evaluation.

[0115] Subsequently, the system performs an optimization sorting operation based on the department priorities and the preliminary task access list. Specifically, the system associates the department priority data of the department to which each task entry in the preliminary task access list belongs, and then uses a sorting and weighted merging strategy to place the task entries with higher department priorities in a more forward position, while taking into account the original order of the tasks in the preliminary list and the historical access frequency to ensure that both the department weight and the task execution urgency and frequency regularity are considered. The optimization sorting process can be implemented using a priority queue, a tree structure or a graph sorting algorithm to ensure that the task order can be dynamically adjusted and the weight evaluation can be updated in real time during the list generation process. The final optimized task access list ensures the efficiency and fairness of cross-department access while providing data basis for subsequent interface access frequency determination and batch transmission strategy.

[0116] S208, obtain current network load information, determine the data access frequency and batch transmission strategy of each interface based on the current network load information, the interface delay weight table and the optimized task access list, and control the data access order and access frequency of each interface based on the data access frequency and batch transmission strategy of each interface.

[0117] The current network load information can refer to the real-time state information of the network environment in which each interface is located in the cross-department government data collaboration process, including network bandwidth occupancy, data packet queue length, delay fluctuation, and throughput, etc. parameters, used to reflect the tension and available capacity of network resources.

[0118] The data access frequency can refer to the number of interface requests or access periods set by the system per unit time under specific network load conditions and department task priorities for each interface.

[0119] The batch transmission strategy can refer to the data block size and transmission method set for each interface for one-time processing, including data packaging method, sending interval and transmission order.

[0120] The data access order can refer to the order in which each interface performs access operations according to the optimized task access list and access strategy in the cross-department data calling process.

[0121] The access frequency can refer to the number of accesses per unit time in the actual execution process of the interface, reflecting the calling rhythm and access intensity of the interface.

[0122] The system groups all interfaces according to response speed and stability based on the delay level defined in the interface delay weight table. For interfaces with lower delay weight (i.e. fast response speed and high stability), the system reserves higher allocable access frequency space for them; for interfaces with higher delay weight, the system reduces the upper limit of their access frequency to avoid dragging the overall task execution efficiency.

[0123] Secondly, the system combines the task ordering in the optimized task access list to improve the access frequency of the interfaces called by high-priority tasks in their own delay group. For example, if a task is in the front position in the optimized task access list and its associated interface belongs to the medium delay group, the access frequency of the interface will be preferentially improved to a level close to that of the high-level interface within the group. The system realizes this process through a priority queue scheduling algorithm, dynamically calculates the comprehensive value of task priority weight and interface delay weight during scheduling, and thus obtains the target access frequency interval of each interface.

[0124] Then, the system dynamically corrects the above target access frequency in combination with the current network load information. Specifically, when it is detected that the network bandwidth utilization is low and the link is in a smooth state, the system allows the interface access frequency to be adjusted upward within the target interval; on the contrary, when the network load tends to be saturated, the system automatically reduces the access frequency of the interface to prevent link congestion. This dynamic correction process is realized through sliding time window statistics and exponential weighting adjustment technology, so as to ensure that the frequency allocation matches the real-time network state. Thus, the data access frequency of each interface is finally determined.

[0125] For the determination of the batch transmission strategy, the system first refers to the delay grouping information in the interface delay weight table, assigns larger single transmission data blocks to low-delay interfaces to reduce the additional overhead caused by frequent requests, and assigns smaller data blocks to high-delay interfaces to reduce the loss caused by transmission failure or retransmission. Subsequently, the system combines the bandwidth margin in the current network load information to adaptively adjust the data block size: when the bandwidth is idle, the single transmission block is increased to improve the throughput efficiency; when the congestion risk increases, the system automatically reduces the block size and increases the batch number to relieve the network pressure.

[0126] Finally, the system combines the execution order and priority of the tasks in the optimization task access list to customize the selection of the batch transmission method. For the interfaces corresponding to high-priority tasks, the system preferentially adopts the TCP parameter optimization strategy with higher reliability (such as window expansion and rate adaptive control) to ensure stable transmission of critical data; for low-priority but large-volume task interfaces, the UDP batch transmission strategy with high throughput is preferred to reduce the overall delay. Thus, based on the current network load information, the interface delay weight table, and the optimization task access list, the system finally determines the data access frequency and batch transmission strategy for each interface.

[0127] The system uses the dynamic rate limiting mechanism to control the interface access by taking the data access frequency as the rate constraint condition. The scheduler monitors the actual call number of the interface in real time during execution and compares it with the target frequency. When the access number of an interface approaches or exceeds the target value, the system automatically postpones the subsequent call of the interface, thereby avoiding network congestion or interface blocking caused by excessive access.

[0128] Secondly, the system takes the batch transmission strategy as the scheduling basis for the transmission scale. For interfaces allocated larger transmission blocks, the scheduler reserves longer transmission time windows for them in the access order to ensure that the single batch data can be transmitted completely; for interfaces using small block transmission strategy, the scheduler allows them to be frequently interleaved in multiple transmission periods, thereby improving the flexibility of transmission and the overall link utilization.

[0129] The data access order is determined by the frequency and batch demand of the interface. The system determines the call interval of execution according to the target access frequency of the interface during scheduling, and adjusts the order of calls in combination with the batch transmission strategy. For example, interfaces with higher frequency but smaller batch size are evenly distributed in multiple time slots; interfaces with lower frequency but larger batch size are arranged to be executed in periods with lower network load.

[0130] In this embodiment, by integrating interface performance, department task priority and network load dynamic data, the delay weight table and the optimized access list are intelligently generated, the resource allocation and network load are accurately balanced, the high-priority task access is preferentially guaranteed, the dynamic adaptation of data access frequency and transmission strategy is realized, and finally the system response efficiency and stability are improved.

[0131] On the basis of the above technical solutions, based on the current network load information, the interface delay weight table and the optimized task access list, the data access frequency and the batch transmission strategy of each interface are determined, comprising: The bandwidth occupancy rate and the queue length data in the current network load information are obtained, the bandwidth occupancy rate, the queue length data, the interface response time and the availability data are normalized, and the load adaptability score of each interface is calculated according to the normalized bandwidth occupancy rate, the queue length data, the interface response time, the availability data and the preset load adaptability score calculation formula; According to the load adaptability score of each interface, each interface is divided into a corresponding access frequency interval to obtain the access frequency level of each interface; Based on the access frequency level and the task priority in the optimized task access list, the task access order of each interface is weighted and adjusted to obtain the target task access order; According to the bandwidth occupancy rate, the queue length data and the delay weight value in the interface delay weight table, the batch transmission parameters of each interface are determined; Based on the access frequency level and the batch transmission parameters, the data access frequency and the batch transmission strategy of each interface are determined.

[0132] In this scheme, the bandwidth occupancy rate can refer to the ratio between the actual usage of the current network bandwidth and the total available bandwidth, which is used to reflect the real-time occupancy degree of network resources. The higher the bandwidth occupancy rate, the less bandwidth available for new task transmission, and the system needs to take flow limiting or batch transmission measures when scheduling.

[0133] The queue length data can refer to the number of data packets waiting for transmission or processing in network devices such as switches, routers or interface caches.

[0134] The load adaptability score can refer to the interface performance adaptability index generated by the system according to the preset score calculation formula after considering factors such as bandwidth occupancy rate, queue length data, interface response time and availability data. The score reflects the stability and schedulability of the interface under the current network load condition, and provides a basis for subsequent access frequency division and transmission strategy generation.

[0135] The access frequency interval can refer to a plurality of access frequency ranges divided by the system according to the load adaptability score, each interval corresponding to a different data access rate. For example, the high frequency interval corresponds to frequent calls, the medium frequency interval corresponds to moderate calls, and the low frequency interval corresponds to sparse calls.

[0136] The access frequency level can refer to the specific access interval level into which the interface is divided according to its load adaptability score, which is usually divided into high, medium and low frequency levels, representing the access frequency of the interface per unit time.

[0137] The task priority can refer to the importance level of the task in the overall data collaboration determined by the optimization task access list. The task priority is set according to factors such as department demand urgency, business sensitivity and historical access frequency, and is used to guide the task scheduling order.

[0138] The task access order can refer to the preliminary execution order generated by the system in the process of cross-department data calling according to the task priority and related scheduling strategies, reflecting the calling sequence of different tasks in the time dimension.

[0139] The target task access order can refer to the final task access execution order generated after combining the access frequency level and the task priority weight adjustment.

[0140] The delay weight value can refer to the interface performance indicator recorded in the interface delay weight table, reflecting the comprehensive performance of the interface in response time and stability.

[0141] The batch transmission parameter can refer to the batch data transmission related parameter set by the system for the interface, including the size of the single transmission block, the number of batches and the transmission method.

[0142] First, the current network load information is obtained, including bandwidth occupancy and queue length data. The bandwidth occupancy can be obtained based on real-time traffic collection technology at the network link layer, such as extracting the current bandwidth usage from routers and switches using SNMP protocol or sFlow traffic sampling technology, and then calculating the ratio based on the maximum available bandwidth of the link; the queue length data can be obtained in real time through queue monitoring instructions or device port buffer statistics information, reflecting the number of data packets waiting for transmission. Subsequently, the system normalizes the bandwidth occupancy, queue length data, interface response time and availability data obtained, which can use the minimum-maximum scaling or Z-score standardization method to convert data of different dimensions into a unified numerical interval for subsequent comprehensive calculation.

[0143] After normalization, the system evaluates each interface based on a preset load adaptability score calculation formula. Specifically, the score calculation combines the normalized bandwidth occupancy, queue length data, interface response time, and availability data to calculate a comprehensive score according to predefined weight proportions, obtaining the load adaptability score of each interface, which reflects the interface's adaptation ability and scheduling priority under the current network state. According to the load adaptability score, the system divides each interface into different access frequency intervals and generates the corresponding access frequency level for each interface, such as dividing high-score interfaces into high-frequency levels and low-score interfaces into low-frequency levels, thereby determining the call rate range of each interface within a unit time.

[0144] Next, the system adjusts the task access order of each interface by combining the above access frequency level and the task priority in the optimization task access list. Specifically, the system uses a priority queue scheduling algorithm to assign higher access weights to interfaces corresponding to high-priority tasks during scheduling, while referring to the access frequency level to avoid over-calling low-adaptability interfaces under high-load conditions, thereby generating a comprehensive and balanced target task access order.

[0145] After determining the access order, the system further determines the batch transmission parameters of each interface based on the bandwidth occupancy, queue length data, and delay weight values in the interface delay weight table. The setting process of batch transmission parameters uses an adaptive block transmission method, which increases the data block size of single transmission appropriately when the bandwidth occupancy is low and the queue length is short to improve overall throughput efficiency; while in the case of increasing network congestion risk and long queue length, the system automatically reduces the data block size of single transmission and increases the transmission batch number to reduce the risk of congestion. The delay weight value serves as an adjustment factor to ensure that high-delay interfaces use a more conservative block strategy, while low-delay interfaces can carry larger data block transmission.

[0146] The system determines the call number range of each interface within a unit time based on the access frequency level. For example, for high-level interfaces, the system assigns a higher call number range to ensure that critical tasks can be completed within a shorter time; for low-level interfaces, it assigns a lower call number range to reduce resource occupation. This allocation process can use a dynamic rate limiting mechanism, which monitors the cumulative access number of the interface when it is called, and when the threshold set by the level is reached, the system delays or queues subsequent requests, thereby achieving frequency control.

[0147] Subsequently, the system determines the data transmission mode in a single access in combination with the batch transmission parameters corresponding to each interface. Specifically, it dynamically sets the data block size based on the bandwidth occupancy rate and queue length data, and further corrects it according to the delay weight value of the interface. For example, when the interface is in a high access frequency level but the delay weight value is large, the system will reduce the transmission size of a single data block and increase the transmission batch number to balance the delay and throughput; when the interface is in a high access frequency level and the delay weight value is small, the network state is good, the system will increase the data block transmission size, thereby improving the overall bandwidth utilization.

[0148] Finally, the system combines the call rate determined by the access frequency level with the data block size and transmission batch number defined by the batch transmission parameters to obtain the data access frequency and batch transmission strategy of each interface. The strategy not only specifies the call rhythm of the interface in a unit of time, but also specifies the transmission mode of data in a single call, ensuring that the interface scheduling can be dynamically optimized according to the current network state and interface characteristics, thereby achieving the purpose of reducing delay and improving the overall throughput efficiency of the system.

[0149] In this scheme, by integrating network load (bandwidth occupancy rate, queue length) and interface performance (response time, availability) data, dynamically calculating interface load adaptability and dividing access frequency levels, adjusting access order in combination with task priority, and determining batch transmission parameters, precise dynamic adaptation of data access strategy is realized, critical task execution is prioritized, resource allocation is optimized, and system efficiency and stability are improved.

[0150] On the basis of the above technical scheme, optionally, the preset load adaptability score calculation formula is:

[0151] Among them, is the load adaptability score; is the normalized bandwidth occupancy rate; is the normalized queue length data; is the normalized interface response time; is the normalized availability data; is the preset bandwidth factor weight; is the preset queue factor weight; is the preset response time factor weight; is the preset availability factor weight.

[0152] In this scheme, The weight coefficient pre-set for different indicators (bandwidth occupancy, queue length, interface response time, availability data) in the process of calculating the load adaptability score. These coefficients are determined through performance testing and simulation evaluation before system deployment, and can be set by expert experience or data-driven methods. For example, when the system goal is to reduce latency, the interface response time weight corresponding to γ can be increased; when the goal is to improve stability, the availability weight corresponding to δ can be increased. This pre-setting method is usually calibrated based on multi-objective optimization experiments (such as weighted scoring method, AHP analysis hierarchy method) to ensure that the final score objectively reflects the actual adaptability of the interface under complex load.

[0153] Indicates the availability of the remaining bandwidth resources. When close to 0 (low occupancy), close to 1, indicating that the interface has good availability; when close to 1 (high occupancy), close to 0, indicating that the interface has almost no available bandwidth.

[0154] The correction for the queue length term, when smaller, tends to 1, indicating that the interface processing pressure is low; when larger, tends to 0, indicating that the interface is severely queued and the adaptability is reduced.

[0155] The correction for the response time term, using an exponential decay form, indicating that the longer the response time, the faster the adaptability score decays. When close to 0 (fast response), close to 1; when increases (slow response), e tends to 0. This design can highlight the advantages of "fast interfaces" and suppress the weight of "slow interfaces".

[0156] Normalization refers to mapping the original indicator values such as bandwidth occupancy, queue length, interface response time, and availability data to a unified interval (such as [0, 1]). Its functions include: eliminating dimensional differences: the dimensions of bandwidth (Mbps), queue length (number), response time (milliseconds) are different, and direct comparison will cause bias. Ensure fairness: after normalization, each indicator is combined and calculated in the same order of magnitude, avoiding the dominance of a certain indicator due to the large range of values. Improve sensitivity: small changes after normalization can be reflected in the score, allowing the system to respond to load fluctuations in a timely manner.

[0157] Figure 3A dynamic knowledge graph driven cross-department government data collaboration system schematic block diagram is provided for an embodiment of the present disclosure. The system comprises: A graph construction module 301 is configured to acquire cross-department government business data and corresponding policy rule data, construct a data knowledge graph based on the cross-department government business data, and construct a strategy knowledge graph based on the policy rule data. A dynamic updating module 302 is configured to acquire preset event information, perform incremental updating and consistency checking on the data knowledge graph and the strategy knowledge graph according to the preset event information, and obtain an updated data knowledge graph and an updated strategy knowledge graph. An extraction module 303 is configured to extract a latest business state from the updated data knowledge graph and extract a latest policy rule from the updated strategy knowledge graph. A cross-department collaboration module 304 is configured to perform joint reasoning based on the latest business state and the latest policy rule, obtain a minimum necessary data view and an authorized range, and perform cross-department government data collaboration based on the minimum necessary data view and the authorized range.

[0158] Figure 4 An electronic device 400 provided by an embodiment of the present application is shown, which comprises a processor 401, a memory 402, and a program or instructions stored in the memory 402 and executable on the processor 401. The program or instructions are executed by the processor 401 to implement various processes of the above-mentioned dynamic knowledge graph driven cross-department government data collaboration method embodiment, and achieve the same technical effects. To avoid repetition, details are not described here.

[0159] It should be noted that the electronic device in the embodiment of the present application includes the mobile electronic device and the non-mobile electronic device described above.

[0160] A readable storage medium provided by an embodiment of the present application is provided, and the readable storage medium stores a program or instructions. The program or instructions are executed by a processor to implement various processes of the above-mentioned dynamic knowledge graph driven cross-department government data collaboration system embodiment, and achieve the same technical effects. To avoid repetition, details are not described here.

[0161] The processor is the processor in the electronic device described in the above embodiments. The readable storage medium includes a computer readable storage medium, such as a computer readable memory (Read-Only Memory, ROM), a random access memory (Random Access Memory, RAM), a magnetic disk or an optical disk, etc.

[0162] It should be noted that, in the present document, the terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus. An element proceeded by "comprises a", "comprising", or "includes a", does not, without more constraints, preclude the existence of additional identical elements in the process, method, article, or apparatus that comprises the element. Additionally, it should be noted that the methods and systems described in the present embodiments are not limited to the order of execution as described or suggested in the present document, and that any order of execution is possible depending on the functional requirements of the particular application. Furthermore, the features described in relation to certain examples can be combined in other examples.

[0163] From the above description of the embodiments, it is clear that the above-described method of the embodiments can be realized by means of software and the necessary universal hardware platform, of course, it can also be realized by hardware, but in many cases, the former is a better embodiment. Based on such understanding, the technical solutions of the present application can be embodied in the form of a computer software product in essence or in the form of a part of the prior art that makes a contribution. The computer software product is stored in a storage medium (such as ROM / RAM, magnetic disc, optical disc), and includes a plurality of instructions for causing a terminal (which can be a mobile phone, computer, server, or network device, etc.) to execute the method described in each embodiment of the present application.

[0164] The embodiments of the present application are described above in combination with the drawings, but the present application is not limited to the above-described specific embodiments, and the above-described specific embodiments are only illustrative, not restrictive. Those skilled in the art can make many forms under the inspiration of the present application without departing from the scope of the present application and the protection scope of the claims.

[0165] The above is only the preferred embodiment of the present application and the technical principle used. The present application is not limited to the specific embodiments described herein. Various obvious changes, re-adjustments, and replacements made by those skilled in the art do not deviate from the protection scope of the present application. Therefore, although the present application is described in more detail through the above embodiments, the present application is not limited to the above embodiments, and can include more other equivalent embodiments without deviating from the concept of the present application. The scope of the present application is determined by the scope of the claims.

Claims

1. A dynamic knowledge graph driven cross-department government data collaboration method, characterized in that, The method comprises: acquiring cross-departmental government service data and corresponding policy rule data, constructing a data knowledge graph based on the cross-departmental government service data, and constructing a strategy knowledge graph based on the policy rule data; acquiring preset event information, performing incremental updating and consistency checking on the data knowledge graph and the strategy knowledge graph according to the preset event information, to obtain an updated data knowledge graph and an updated strategy knowledge graph; extracting the latest business status in the updated data knowledge graph and the latest policy rules in the updated strategy knowledge graph; performing joint reasoning based on the latest business status and the latest policy rules to obtain a minimum necessary data view and an authorized range, and performing cross-departmental government data collaboration based on the minimum necessary data view and the authorized range.

2. The method of claim 1, wherein, Wherein, performing joint reasoning based on the latest business status and the latest policy rules to obtain a minimum necessary data view and an authorized range comprises: acquiring a target task, generating a task subgraph related to the target task in the updated data knowledge graph, and generating a compliance constraint subgraph related to the target task in the updated strategy knowledge graph; performing intersection clipping on the task subgraph and the compliance constraint subgraph to obtain a candidate data subgraph; optimizing the candidate data subgraph based on a preset constraint priority rule to generate the minimum necessary data view; acquiring a department role mapping relationship, determining field access permissions based on the data sensitivity level in the minimum necessary data view, and determining the authorized range based on the field access permissions and the department role mapping relationship.

3. The method of claim 1, wherein, Wherein, after obtaining the updated data knowledge graph and the updated strategy knowledge graph, the method further comprises: if the updated data knowledge graph and the updated strategy knowledge graph do not meet a preset consistency rule, performing graph structure analysis on the updated data knowledge graph to obtain isolated node data and circular dependency relationship data; performing logical constraint analysis on the updated strategy knowledge graph to obtain cross-graph conflict relationship data; performing node reconstruction processing on the isolated node data to obtain reconstructed node data; performing edge redirection processing on the circular dependency relationship data to obtain redirected edge data; performing redundant data elimination processing on the cross-graph conflict relationship data to obtain simplified data; repairing the updated data knowledge graph and the strategy knowledge graph based on the reconstructed node data, the redirected edge data, and the simplified data to obtain repaired updated data knowledge graph and strategy knowledge graph; Correspondingly, extracting the latest business status in the updated data knowledge graph and the latest policy rules in the updated strategy knowledge graph comprises: extracting the latest business status in the repaired updated data knowledge graph and the latest policy rules in the repaired updated strategy knowledge graph.

4. The method of claim 1, wherein, Wherein, after performing cross-departmental government data collaboration based on the minimum necessary data view and the authorized range, the method further comprises: Obtaining interface state information of each interface, determining interface response time and availability data of each interface according to the interface state information, performing average response time calculation and fault frequency analysis on the interface response time and the availability data, and obtaining an interface delay weight table; Obtaining access task priorities and historical access frequency data of each department, performing weighted combination sorting on the access task priorities and the historical access frequency data, and obtaining a preliminary task access list; Obtaining department priorities of each department, and generating an optimized task access list according to the department priorities and the preliminary task access list; Obtaining current network load information, determining data access frequency and batch transmission strategy of each interface based on the current network load information, the interface delay weight table and the optimized task access list, and controlling data access order and access frequency of each interface based on the data access frequency and the batch transmission strategy of each interface.

5. The method of claim 4, wherein, Wherein, Determining data access frequency and batch transmission strategy of each interface based on the current network load information, the interface delay weight table and the optimized task access list comprises: Obtaining bandwidth occupancy rate and queue length data in the current network load information, performing normalization processing on the bandwidth occupancy rate, the queue length data, the interface response time and the availability data, calculating load adaptability scores of each interface according to the normalized bandwidth occupancy rate, the queue length data, the interface response time, the availability data and a preset load adaptability score calculation formula; According to the load adaptability scores of each interface, each interface is divided into a corresponding access frequency interval to obtain an access frequency level of each interface; Based on the access frequency level and the task priority in the optimized task access list, the task access order of each interface is weighted and adjusted to obtain a target task access order; According to the bandwidth occupancy rate, the queue length data and the delay weight value in the interface delay weight table, the batch transmission parameters of each interface are determined; Based on the access frequency level and the batch transmission parameters, the data access frequency and the batch transmission strategy of each interface are determined.

6. The method of claim 5, wherein, Wherein, The preset load adaptability score calculation formula is: wherein, is a load adaptivity score; is a normalized bandwidth occupancy; is a normalized queue length data; is a normalized interface response time; is a normalized availability data; is a preset bandwidth factor weight; is a preset queue factor weight; is a preset response time factor weight; is a preset availability factor weight.

7. The method of claim 1, wherein, Wherein, After cross-departmental government data collaboration based on the minimum necessary data view and the authorized range, the method further comprises: Obtaining cross-departmental data access records, and determining access time, access user and access department information according to the cross-departmental data access records; Based on the minimum necessary data view, the access time, the access user and the access department information are encrypted and processed by an unforgeable log record to obtain traceable access credentials.

8. A dynamic knowledge graph driven cross-department government affair data collaboration system, characterized in that, The system comprises: A graph construction module is configured to obtain cross-departmental government business data and corresponding policy rule data, construct a data knowledge graph based on the cross-departmental government business data, and construct a strategy knowledge graph based on the policy rule data; A dynamic update module is configured to obtain preset event information, perform incremental update and consistency verification on the data knowledge graph and the strategy knowledge graph according to the preset event information, and obtain an updated data knowledge graph and an updated strategy knowledge graph; extracting the latest business status in the updated data knowledge graph and the latest policy rule in the updated policy knowledge graph; a cross-department collaboration module configured to perform joint reasoning based on the latest business status and the latest policy rule to obtain a minimum necessary data view and an authorized range, and perform cross-department government data collaboration based on the minimum necessary data view and the authorized range.

9. An electronic device, comprising: The computer device comprises a processor, a memory, and a program or instruction stored in the memory and executable on the processor, and the program or instruction, when executed by the processor, implements the steps of the dynamic knowledge graph driven cross-department government data collaboration method according to any one of claims 1-7.

10. A readable storage medium, characterized by, The readable storage medium stores a program or instruction, and the program or instruction, when executed by the processor, implements the steps of the dynamic knowledge graph driven cross-department government data collaboration method according to any one of claims 1-7.

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