Data circulation system based on semantic interoperation model

Abstract

The invention discloses a data circulation system based on a semantic interoperation model. The data circulation system comprises a semantic interoperation model management layer, a technical support layer and a data source layer, the semantic interoperation model management layer comprises an information model management module, a namespace management module, a meta-model management module, a participant management module, a data resource management module and a data directory management module; the technical support layer comprises an OWL / RDF semantic reasoning module, a graph database module, a block chain evidence storage module and an encryption gateway module; the data source layer comprises a database module, an Internet of Things equipment module, an API service module, a file system module and a third-party platform module; the method has the advantages that interoperation between systems is achieved, data safety and compliance are guaranteed, and system performance and efficiency are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and particularly to a data circulation system based on a semantic interoperability model. Background Art

[0002] With the acceleration of digital transformation, the demand for cross-domain and cross-platform data sharing and system interoperability is increasing. However, the existing technologies still face two key challenges: insufficient semantic interoperability and imperfect data usage control, resulting in low data circulation efficiency and increased security risks.

[0003] The defects of the current technologies are as follows:

[0004] Semantic gap problem: The existing technologies cannot effectively solve cross-domain semantic differences. For example, the "blood pressure" data in the medical field and the "pressure" data in the industrial field may use the same measurement unit at the technical level, but their semantic meanings are completely different. Currently, there is a lack of a general semantic mapping mechanism, resulting in machines being unable to automatically understand the true meanings of cross-domain data;

[0005] Cross-trust domain problem: Data flows usually involve entities in different jurisdiction domains and trust domains. The existing technologies lack solutions that take into account both semantic interoperability and data security. How to achieve semantic understanding while ensuring data privacy is still an open question;

[0006] Domain knowledge dependence: Most of the existing semantic interoperability methods require profound domain knowledge to build ontologies, with a relatively high threshold. Small and medium-sized enterprises often lack relevant resources and find it difficult to participate in the data circulation ecosystem;

[0007] Insufficient two-way interoperability: The existing solutions mostly focus on semantic understanding at the data consumption end and ignore the semantic expression ability at the production end. Complete data circulation requires two-way semantic interoperability ability, but the current technologies have obvious shortcomings in this regard. Summary of the Invention

[0008] Aiming at the deficiencies of the above-mentioned existing technologies, the technical problem to be solved by this patent application is how to provide a data circulation system based on a semantic interoperability model that realizes interoperability between systems, ensures data security and compliance, and improves system performance and efficiency.

[0009] To solve the above technical problems, the present invention adopts the following technical solutions:

[0010] A data circulation system based on a semantic interoperability model, including a semantic interoperability model management layer, a technical support layer, and a data source layer; the semantic interoperability model management layer includes an information model management module, a namespace management module, a meta-model management module, a participant management module, a data resource management module, and a data directory management module; the technical support layer includes an OWL / RDF semantic reasoning module, a graph database module, a blockchain evidence storage module, and an encryption gateway module; the data source layer includes a database module, an Internet of Things device module, an API service module, a file system module, and a third-party platform module.

[0011] Preferably, the information model management module provides information model management functions to strictly define and maintain data elements and their relationships, ensuring that the system can accurately understand, interpret, and use data, thereby achieving effective data exchange and processing; the namespace management module provides namespace management functions to ensure the uniqueness and consistency of all data elements and concepts in the system; the meta-model management module provides meta-model management functions to define and maintain the most basic concepts and structures that make up the information model; the participant management module provides participant management functions to identify, assign roles, set permissions, and monitor the behaviors of all parties participating in data exchange and system interaction; the data resource management module provides data resource management functions for the comprehensive supervision of data assets, ensuring data quality and consistency, by implementing strict data governance policies, defining data life cycle management strategies, and taking appropriate security measures to protect data; the data directory management module provides data directory management functions for registering, indexing, and retrieving all available data resources, including classifying, annotating, and describing data resources.

[0012] Preferably, the OWL / RDF semantic reasoning module in the technical support layer constructs a formal knowledge representation and reasoning framework based on semantic web technology, supporting the automated parsing and logical reasoning of data semantics; where RDF, i.e., the Resource Description Framework, describes data relationships in the form of triples, i.e., subject-predicate-object, to achieve unified modeling of cross-domain data; OWL, i.e., the Web Ontology Language, defines domain ontologies, clarifies the logical constraints and semantic hierarchies of concepts, attributes, and relationships, and supports complex semantic reasoning.

[0013] Preferably, the graph database module stores and manages RDF triples or complex relationship networks in a graph structure, supporting efficient association query and path analysis; adopting a native graph database or an RDF graph database, supporting the SPARQL query language, realizing semantic data retrieval, a distributed architecture design, and key technologies for supporting the horizontal expansion of large-scale graph data; supporting the construction and query of cross-domain knowledge graphs and real-time path reasoning application scenarios.

[0014] Preferably, the blockchain evidence storage module: uses blockchain technology to achieve non-tamperable evidence storage in the data exchange process, ensuring the credibility and traceability of data circulation; adopts smart contract and cross-chain technology. The smart contract: automatically executes data exchange rules; the cross-chain technology: supports multi-chain interconnection.

[0015] Preferably, the encryption gateway module provides end-to-end data security transmission and access control, ensuring the confidentiality and integrity of cross-platform data circulation; adopts dynamic encryption, attribute-based encryption, and security protocol adaptation technology, supporting the desensitization of sensitive information during cross-domain data exchange and the application scenario of secure communication between Internet of Things devices and the cloud.

[0016] Preferably, the database module integrates heterogeneous data sources of relational databases and non-relational databases using a unified data connector, incremental data synchronization, and SQL conversion engine technology.

[0017] Preferably, the Internet of Things devices access the Internet of Things terminal data of sensors and industrial devices using edge computing, protocol adaptation, device identity authentication, and secure firmware upgrade technology, supporting real-time stream data processing.

[0018] Preferably, the API service module exposes data services through standardized APIs, enabling cross-system calls; adopts API gateway technology, OpenAPI specification technology, and response data format conversion technology.

[0019] Preferably, the file system module supports the unified management of structured and unstructured files; is implemented using a file parsing engine technology, distributed file storage technology, version control, and file fingerprint verification technology.

[0020] In summary, through the deep integration of the interoperability semantic model and usage control technology, this solution realizes the efficiency, security, and intelligence of cross-domain and cross-platform data circulation, with the following significant advantages:

[0021] 1. Semantic-driven refined access control

[0022] Through the semantic interoperability model, the meaning, context, and association relationships of data are standardized, enabling the usage control technology to implement dynamic and fine-grained access policies based on data semantics (rather than just format or tags);

[0023] Example: In the medical data sharing scenario, the system can automatically identify that the "gene test report" is more sensitive than regular physical examination data and dynamically adjust the access permission level

[0024] 2. Cross-domain semantic consistency guarantee

[0025] Provide a multi-domain ontology mapping framework to solve the problems of "homographs" (such as "blood pressure" in medicine and "pressure" in industry) and "synonyms with different forms" (such as "ID" and "identifier"), and reduce the cost of cross-domain data integration through semantic alignment algorithms. Actual measurements show that the data preprocessing time is reduced;

[0026] 3. Co-optimization of security and efficiency

[0027] Semantic compression technology reduces redundant data transmission. Hierarchical storage and routing optimization based on semantics enable high-value data to be processed preferentially. Security audit logs are automatically associated with semantic tags, improving the efficiency of compliance review;

[0028] This invention has made a breakthrough compared with the prior art: realizing the closed-loop linkage between semantic understanding and access control, overcoming the problem of maintaining semantic-policy consistency in a dynamic environment, achieving field-level data security control granularity through semantic enhancement, and significantly improving the cross-system interoperability efficiency while ensuring security. Brief Description of the Drawings

[0029] Figure 1 It is a system framework diagram of a data circulation system based on a semantic interoperability model according to the present invention. Detailed Description of the Invention

[0030] The present invention will be further described in detail below with reference to the accompanying drawings. In the description of the present invention, it should be understood that the orientation or positional relationship indicated by orientation words such as "upper, lower" and "top, bottom" is usually based on the orientation or positional relationship shown in the accompanying drawings, and is only for the convenience of describing the present invention and simplifying the description. Without contrary description, these orientation words do not indicate and imply that the device or element referred to must have a specific orientation or be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the protection scope of the present invention; the orientation words "inside, outside" refer to the inside and outside relative to the contour of each component itself.

[0031] As Figure 1 shown, a data circulation system based on a semantic interoperability model includes a semantic interoperability model management layer, a technical support layer, and a data source layer; the semantic interoperability model management layer includes an information model management module, a namespace management module, a meta-model management module, a participant management module, a data resource management module, and a data directory management module; the technical support layer includes an OWL / RDF semantic reasoning module, a graph database module, a blockchain evidence storage module, and an encryption gateway module; the data source layer includes a database module, an Internet of Things device module, an API service module, a file system module, and a third-party platform module.

[0032] In this embodiment, the information model management module provides information model management functions, strictly defines and maintains data elements and their mutual relationships, ensures that the system can accurately understand, interpret, and use data, so as to achieve effective data exchange and processing. The naming space management module provides naming space management functions to ensure the uniqueness and consistency of all data elements and concepts in the system. The meta-model management module provides meta-model management functions to define and maintain the most basic concepts and structures that make up the information model. The participant management module provides participant management functions to identify, assign roles, set permissions, and monitor the behaviors of all parties participating in data exchange and system interaction. The data resource management module provides data resource management functions for the comprehensive supervision of data assets, ensuring data quality and consistency. By implementing strict data governance policies, defining data life cycle management strategies, and taking appropriate security measures to protect data. The data catalog management module provides data catalog management functions for registering, indexing, and retrieving all available data resources, including classifying, labeling, and describing data resources.

[0033] Specifically, the key activities of the information model management module include designing a consistent data model structure, establishing standardized rules for data naming and definition, implementing metadata management to describe the characteristics and semantics of data, and maintaining version control and evolution history of the data model; it also involves controlling the permissions for data access and operations to ensure data security and compliance.

[0034] Specifically, the naming space management module avoids naming conflicts by assigning a unique identifier to each data item, allowing data from different sources to be correctly distinguished and associated. It also involves defining and maintaining a set of rules and conventions to guide how to create, assign, and manage naming spaces within the organization and across organizational boundaries, which helps to simplify the data integration process and improve data discoverability and interoperability.

[0035] Specifically, in the meta-model management module, the meta-model is a set of rules that describe how the data model is composed, including data types, attributes, relationships, and how they interact with each other. Through meta-model management, an organization can ensure the consistency, accuracy, and scalability of the data model. Meta-model management includes controlling model changes, version management, and ensuring backward compatibility of the model.

[0036] Specifically, the participant management module includes defining the responsibilities, permissions, and requirements of each participant, establishing trust relationships, and ensuring that they comply with established data usage protocols and standards. Through participant management, cross-organizational and cross-platform cooperation can be promoted, the efficiency and security of data sharing can be improved, and potential compatibility and coordination issues can be reduced, thus achieving smoother and more reliable semantic interoperability.

[0037] Specifically, the data resource management module is also involved in data classification, indexing, and retrieval optimization, as well as providing accurate and rich metadata descriptions for data, thereby enhancing data discoverability and accessibility.

[0038] Specifically, the data catalog management module also includes monitoring data usage, updating data metadata, and managing data access permissions, thereby ensuring data security and compliance.

[0039] In this embodiment, the OWL / RDF semantic reasoning module in the technical support layer constructs a formal knowledge representation and reasoning framework based on semantic web technology, supporting automated parsing and logical reasoning of data semantics; among them, RDF is the Resource Description Framework: describing data relationships in the form of triples, i.e., subject-predicate-object, to achieve unified modeling of cross-domain data; OWL is the Web Ontology Language: defining domain ontologies, clarifying the logical constraints and semantic hierarchies of concepts, attributes, and relationships, and supporting complex semantic reasoning.

[0040] Specifically, a semantic reasoning engine is adopted, including but not limited to Apache Jena and Pellet, to achieve rule-driven automated reasoning, support ontology alignment technology, which is the key technology to solve semantic conflicts between different domain models, and support application scenarios for automatically identifying implicit associations between heterogeneous data (such as the equivalence relationship between "customer ID" and "user number") and verifying the consistency of data models (such as checking for missing required fields or logical contradictions).

[0041] In this embodiment, the graph database module stores and manages RDF triples or complex relationship networks in a graph structure, supporting efficient association queries and path analysis; adopting a native graph database or an RDF graph database, supporting the SPARQL query language, realizing semantic data retrieval, and a distributed architecture design, which is the key technology to support horizontal expansion of large-scale graph data; supporting the construction and query of cross-domain knowledge graphs and real-time path reasoning application scenarios.

[0042] Specifically, the native graph databases include but are not limited to Neo4j and Amazon Neptune; the RDF graph databases include but are not limited to Stardog and Virtuoso.

[0043] In this embodiment, the blockchain evidence storage module: uses blockchain technology to achieve tamper-proof evidence storage in the data exchange process, ensuring the credibility and traceability of data circulation; adopts smart contracts and cross-chain technology, where the smart contract: automatically executes data exchange rules; the cross-chain technology: supports multi-chain interconnection.

[0044] Specifically, the automated execution data exchange rules include, but are not limited to, permission verification and compliance checking; the cross-chain technology solves key technologies such as interoperability issues between heterogeneous blockchain platforms, privacy protection mechanisms such as zero-knowledge proofs and homomorphic encryption, and balances data transparency and privacy.

[0045] Specifically, the blockchain evidence storage module supports key data operations, such as the full-process evidence storage of contract signing and ownership change, and the application scenarios of audit tracking for cross-organization data sharing.

[0046] In this embodiment, the encryption gateway module provides end-to-end data security transmission and access control, ensuring the confidentiality and integrity of cross-platform data circulation; it adopts dynamic encryption, attribute-based encryption, and security protocol adaptation technologies, and supports the desensitization of sensitive information during cross-domain data exchange and the secure communication application scenarios between IoT devices and the cloud.

[0047] Specifically, the dynamic encryption technology: dynamically selects encryption algorithms according to the data sensitivity level, including but not limited to AES-256 and the national cipher SM4; the attribute-based encryption technology, i.e., ABE: dynamically authorizes decryption permissions based on data attributes and user roles; the security protocol adaptation includes but is not limited to MQTT over TLS and HTTPS two-way authentication.

[0048] Specifically, the desensitization of sensitive information during cross-domain data exchange includes but is not limited to the masking of ID numbers in financial data; the secure communication between IoT devices and the cloud includes but is not limited to the encrypted upload of industrial sensor data.

[0049] In this embodiment, the database module integrates heterogeneous data sources of relational databases and non-relational databases by using unified data connectors, incremental data synchronization, and SQL conversion engine technologies.

[0050] Specifically, the relational databases include but are not limited to MySQL and Oracle, and the non-relational databases include but are not limited to MongoDB and Redis.

[0051] Specifically, the unified data connector technology: adapts to multiple types of databases through JDBC / ODBC or custom drivers; the incremental data synchronization technology: realizes low-latency data synchronization based on CDC (Change Data Capture) technology; the SQL conversion engine technology: automatically converts cross-database query statements into the dialects of target databases.

[0052] In this embodiment, the IoT devices access the IoT terminal data of sensors and industrial devices by using edge computing, protocol adaptation, device identity authentication, and secure firmware upgrade technologies, and support real-time stream data processing.

[0053] Specifically, the edge computing technology performs data preprocessing on the device side, such as filtering and downsampling; the protocol adaptation technology is compatible with Internet of Things communication protocols such as MQTT, CoAP, and Modbus; the device identity authentication includes but is not limited to X.509 certificates.

[0054] In this embodiment, the API service module exposes data services through standardized APIs to enable cross-system calls, and adopts API gateway technology, OpenAPI specification technology, and response data format conversion technology.

[0055] Specifically, the standardized APIs include but are not limited to RESTful and GraphQL; the API gateway technology uniformly manages the API lifecycle, such as registration, authentication, rate limiting, and monitoring; the OpenAPI specification technology automatically generates standardized interface documents, such as Swagger / YAML; the response data format conversion technology, such as XML to JSON.

[0056] In this embodiment, the file system module supports the unified management of structured and unstructured files, and is implemented by using file parsing engine technology, distributed file storage technology, and version control and file fingerprint verification technology.

[0057] Specifically, the structured files include but are not limited to CSV and Excel; the unstructured files include but are not limited to PDF and images; the file parsing engine technology extracts file content and converts it into structured data, such as OCR for identifying invoice information; the distributed file storage technology is based on HDFS or object storage, such as Amazon S3, to achieve massive file storage; the version control and file fingerprint verification technology includes but is not limited to SHA-256.

[0058] Specifically, the third-party platform module interfaces with external SaaS services and government open data platforms, and adopts data sandbox, protocol conversion middleware, and data compliance inspection technology; the external SaaS services include but are not limited to WeChat and Alipay; the data sandbox technology isolates third-party data to avoid polluting the internal environment; the protocol conversion middleware technology solves the interface protocol differences between different platforms, such as SOAP to REST; the data compliance inspection technology includes but is not limited to GDPR and the Personal Information Protection Law.

[0059] The interoperability semantic model and usage control technology are interrelated and complementary, and jointly serve the efficient operation of the system and the secure sharing of data, which is specifically reflected in the following aspects:

[0060] Achieving Interoperability between Systems: The interoperability semantic model is mainly used to solve the problems of data semantic consistency and understanding between different systems or applications, ensuring that data can be accurately interpreted and processed during the process of exchange and sharing. The usage control technology manages and controls the use of system resources, including operations such as data access, processing, and transmission. The usage control technology relies on the interoperability semantic model to accurately understand the meaning and operation requirements of data, so as to implement fine-grained access control policies. For example, in the Internet of Things system, the semantic interoperability model provides a unified semantic standard for data exchange between different devices and applications, and the usage control technology determines which devices or applications can access specific data and what operations to perform based on this semantic information;

[0061] Ensuring Data Security and Compliance: By clarifying the semantics and context of data, the interoperability semantic model helps the usage control technology to more accurately identify the sensitivity and importance of data, thus formulating more reasonable security policies. The usage control technology uses the information provided by the interoperability semantic model to monitor and audit the use of data, ensuring that the use of data complies with security regulations and laws and regulations. For example, in the medical field, when sharing patient data between different medical institutions, the interoperability semantic model enables all parties to accurately understand the meaning of the data, and the usage control technology controls the access and use of data based on semantic information according to the patient privacy policy and relevant regulations, preventing data leakage and abuse;

[0062] Improving System Performance and Efficiency: The interoperability semantic model optimizes the representation and exchange methods of data, reducing errors and redundancies in data transmission and processing, making the usage control technology more efficient in performing access control and operation management. The usage control technology ensures the safe and stable transmission of data during the interoperability process by reasonably allocating system resources, improving the overall performance and efficiency of the system. For example, in the integration of enterprise information systems, the interoperability semantic model unifies the data semantics of different business systems, and the usage control technology allocates resources according to business rules and data importance, giving priority to ensuring the interoperability process of key data and improving enterprise operation efficiency.

[0063] This solution realizes the high efficiency, security, and intelligence of cross-domain and cross-platform data circulation by deeply integrating the interoperability semantic model and the usage control technology, and has the following remarkable advantages:

[0064] 1. Semantic-driven Fine-grained Access Control

[0065] Standardize the description of data meaning, context, and correlation relationship through the semantic interoperability model, enabling the usage control technology to implement dynamic and fine-grained access policies based on data semantics (rather than just format or tags);

[0066] Example: In the scenario of medical data sharing, the system can automatically identify that the sensitivity of a "gene test report" is higher than that of regular physical examination data and dynamically adjust the access privilege level.

[0067] 2. Cross - domain semantic consistency guarantee

[0068] Provide a multi - domain ontology mapping framework to solve problems of "same form but different meanings" (such as "blood pressure" in medicine and "pressure" in industry) and "same meaning but different forms" (such as "ID" and "identifier"), reduce the cost of cross - domain data integration through semantic alignment algorithms, and actual measurements show that the data pre - processing time is reduced.

[0069] 3. Co - optimization of security and efficiency

[0070] Semantic compression technology reduces redundant data transmission. Hierarchical storage and routing optimization based on semantics enable high - value data to be processed preferentially. Security audit logs are automatically associated with semantic tags, improving the efficiency of compliance review.

[0071] Compared with the prior art, the present invention has a breakthrough progress: realizing the closed - loop linkage of semantic understanding and access control, overcoming the problem of maintaining semantic - policy consistency in a dynamic environment, achieving a field - level data security control granularity through semantic enhancement, and significantly improving the cross - system interoperability efficiency on the premise of ensuring security.

[0072] Finally, it should be noted that those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these changes and modifications.

Claims

1. A data circulation system based on a semantic interoperability model, characterized in that, It includes a semantic interoperability model management layer, a technology support layer, and a data source layer; the semantic interoperability model management layer includes an information model management module, a namespace management module, a meta-model management module, a participant management module, a data resource management module, and a data catalog management module; the technology support layer includes an OWL / RDF semantic reasoning module, a graph database module, a blockchain evidence storage module, and an encryption gateway module; the data source layer includes a database module, an Internet of Things device module, an API service module, a file system module, and a third-party platform module.

2. The data circulation system based on the semantic interoperability model according to claim 1, characterized in that The information model management module provides information model management functions, strictly defines and maintains data elements and their mutual relationships, ensures that the system can accurately understand, interpret, and use data, so as to achieve effective data exchange and processing. The namespace management module provides namespace management functions to ensure the uniqueness and consistency of all data elements and concepts in the system. The meta-model management module provides meta-model management functions to define and maintain the most basic concepts and structures that make up the information model. The participant management module provides participant management functions to identify, assign roles, set permissions, and monitor the behaviors of all parties participating in data exchange and system interaction. The data resource management module provides data resource management functions for the comprehensive supervision of data assets, ensures the quality and consistency of data, defines data life cycle management strategies by implementing strict data governance policies, and takes appropriate security measures to protect data. The data catalog management module provides data catalog management functions for registering, indexing, and retrieving all available data resources, including classifying, labeling, and describing data resources.

3. A data circulation system based on a semantic interoperability model according to claim 2, characterized in that, The OWL / RDF semantic reasoning module in the technology support layer constructs a formal knowledge representation and reasoning framework based on semantic web technology, supporting the automatic parsing of data semantics and logical reasoning; among them, RDF, that is, the Resource Description Framework, describes data relationships in the form of triples, that is, subject-predicate-object, to achieve unified modeling of cross-domain data; OWL, that is, the Web Ontology Language, defines domain ontologies, clarifies the logical constraints and semantic hierarchies of concepts, attributes, and relationships, and supports complex semantic reasoning.

4. A data circulation system based on a semantic interoperability model according to claim 3, characterized in that The graph database module stores and manages RDF triples or complex relationship networks in a graph structure, supports efficient association queries and path analysis; adopts a native graph database or an RDF graph database, supports the SPARQL query language, realizes semantic data retrieval, and is a key technology for distributed architecture design to support the horizontal expansion of large-scale graph data; supports the construction and query of cross-domain knowledge graphs and real-time path reasoning application scenarios.

5. A data circulation system based on a semantic interoperability model according to claim 4, characterized in that, The blockchain evidence storage module: uses blockchain technology to achieve tamper-proof evidence storage in the data exchange process, ensuring the credibility and traceability of data circulation; adopts smart contracts and cross-chain technology, the smart contract: automatically executes data exchange rules; cross-chain technology: supports multi-chain interconnection.

6. The data circulation system based on a semantic interoperability model according to claim 5, characterized in that, The encryption gateway module provides end-to-end data security transmission and access control to ensure the confidentiality and integrity of cross-platform data circulation; it adopts dynamic encryption, attribute-based encryption, and security protocol adaptation technologies, and supports the desensitization of sensitive information during cross-domain data exchange and the application scenario of secure communication between IoT devices and the cloud.

7. A data circulation system based on a semantic interoperability model according to claim 6, characterized in that The database module integrates heterogeneous data sources of relational databases and non-relational databases by using unified data connectors, incremental data synchronization, and SQL conversion engine technologies.

8. A data circulation system based on a semantic interoperability model according to claim 7, characterized in that, The IoT devices access the IoT terminal data of sensors and industrial devices by using edge computing, protocol adaptation, device identity authentication, and secure firmware upgrade technologies, and support real-time stream data processing.

9. A data circulation system based on a semantic interoperability model according to claim 8, characterized in that, The API service module exposes data services through standardized APIs to enable cross-system calls; it adopts API gateway technology, OpenAPI specification technology, and response data format conversion technology.

10. A data circulation system based on a semantic interoperability model according to claim 9, characterized in that, The file system module supports the unified management of structured and unstructured files; it is implemented by using file parsing engine technology, distributed file storage technology, version control, and file fingerprint verification technology.

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