Cyber space data bank system and management method

Abstract

The application relates to the technical field of data management service, in particular to a network space data bank system and a management method, wherein the network space data bank system comprises an infrastructure layer, a data vault layer, a data bank layer, an operation management layer and an application business layer; the data vault layer comprises a data vault module and an analysis and mining processing module; the data bank layer provides data management service and access management service; the data vault layer is called to process target data assets through the data management service; the target data assets are formed into data futures contracts through the access management service; the operation management layer is used for sharing transaction operation of the data futures contracts; the application business layer is used for providing at least one data asset service, sending target data assets provided by a data service provider to the data bank layer in response to a data asset service selected by the data service provider, and realizing transaction between the data service provider and a data purchaser. Thus, the problems of poor data storage control reliability and safety in the related art are solved.

Description

Technical Field

[0001] This application relates to the field of data management service technology, and in particular to a cyberspace data bank system and management method. Background Technology

[0002] Cyberspace data banks are trusted intermediary platforms in data circulation and transactions, capable of providing financial services such as data ownership confirmation, secure storage, analysis and mining, hierarchical classification, value-added processing, trusted sharing, and transactions.

[0003] Among related technologies, traditional cloud storage, data warehouses, or data lakes are the core, which are essentially biased towards centralized data storage and basic management. They lack a complete financialization and trustworthy circulation system for data assets, have weak credit intermediation capabilities, high dispute rates, and are difficult to assume the role of a trustworthy third party. Summary of the Invention

[0004] This application provides a cyberspace data bank system and management method to solve problems such as poor reliability and security of data storage control in related technologies.

[0005] The first aspect of this application provides a cyberspace data bank system, comprising: an infrastructure layer, a data vault layer, a data bank layer, an operations management layer, and an application business layer. The data vault layer includes a data vault module and an analysis and mining processing module. The data vault module calls the equipment of the infrastructure layer to perform data storage, and the analysis and mining processing module calls the equipment of the infrastructure layer to perform data analysis and mining. The data bank layer provides data management services and access management services. The data management service calls the data vault layer to process target data assets, and the access management service forms data futures contracts from the target data assets. The operations management layer is used for shared trading operations of the data futures contracts. The application business layer provides at least one data asset service, responds to the data asset service selected by the data service provider, and sends the target data assets provided by the data service provider to the data bank layer, realizing transactions between the data service provider and the data purchaser.

[0006] Optionally, the infrastructure layer includes at least one computing device, a network device, and a storage device, wherein computing resources are provided through the computing device, network resources are provided through the network device, and storage resources are provided through the storage device.

[0007] Optionally, the data vault module includes at least one of a data lake, a data warehouse, a distributed database, and a blockchain-based evidence storage platform. The data lake receives and stores raw, semi-structured, and unstructured data, preserving the original format and metadata of the target data assets. The data warehouse is used for thematic aggregation, cleaning, transformation, indexing, and statistical analysis of the governed structured data. The distributed database is used for sharded storage, parallel querying, and copy backup of high-concurrency access data and large-scale target data assets. The blockchain-based evidence storage platform calls the infrastructure layer's devices to store at least one data operation: login, query, download, authorization, modification, and deletion.

[0008] Optionally, the analysis and mining processing module includes a data governance platform and a data processing and production platform. The data governance platform calls on the equipment of the infrastructure layer to perform at least one of structured data processing, semi-structured data processing, and unstructured data processing. The data processing and production platform is used to perform at least one of the following processes: data labeling, synthesis, matching, evaluation, and publication.

[0009] Optionally, the data vault layer also includes application production tools, which include analysis tools and data intelligence agents. The analysis tools are used to conduct multi-dimensional statistics, judgment and in-depth analysis on the processed data; the data intelligence agents are used to autonomously identify, intelligently adapt and automatically process the analysis results.

[0010] Optionally, the data bank layer includes a data bank center module and an access management node module. The data bank center module provides data management services, which include at least one of spatial account management, data catalog center, connector, data ownership center, and data audit center. The access management node module provides access management services, which include at least one of user management, data delivery, capability expansion, data management, policy control, data contracts, and connector security.

[0011] Optionally, the data bank layer also includes a trusted circulation platform, which provides at least one of the following: secure data exchange function, trusted execution environment, privacy computing function, and blockchain end-to-end notarization. The secure data exchange function establishes an encrypted transmission channel between the data service provider and the data purchaser, performs anonymization processing, format verification, transmission integrity verification, and recipient authentication on the exchanged data. The trusted execution environment decrypts the target data asset and performs computing tasks within the region. During the computing process, the original data is not visible to the operating system, database, and external applications. After the computing is completed, only at least one of the following is output: result or summary. The privacy computing function is used to complete joint statistical analysis, feature engineering, or model training, and outputs at least one of the following: computing result or encrypted gradient. The blockchain end-to-end notarization records the key operation records of the entire process of data asset management, from data governance, processing and production, contract generation, transaction matching, data delivery to audit compliance, and writes them into the blockchain notarization platform.

[0012] Optionally, the operations management layer includes a portal module, an asset level module, a rights management module, a compliance and supervision module, a demand center module, a data marketplace module, an application marketplace module, a metering and billing module, and a value-added service module. The operations management layer responds to user interactions and selects at least one module based on the interaction.

[0013] Optionally, the application business layer includes an application scenario module and an innovation scenario module. The application scenario module provides at least one of the following functions: emergency management, digital finance, smart city, and low-altitude economy. The innovation scenario module provides at least one of the following functions: model training, digital asset securitization, knowledge base construction, and data financial derivatives services.

[0014] A second aspect of this application provides a management method for a cyberspace data bank system. The method manages the aforementioned cyberspace data bank system, comprising: responding to a data asset service selected by a data service provider and obtaining a target data asset provided by the service provider; sending the target data asset provided by the data service provider to a data bank layer; processing the target data asset through the data bank layer, which provides data management services and access management services; calling a data vault layer to process the target data asset through the data management service; and forming a data futures contract from the target data asset through the access management service; and conducting shared trading operations of the data futures contract through an operations management layer to realize transactions between the data service provider and the data purchaser.

[0015] Therefore, this application has the following beneficial effects: This application establishes a multi-layered architecture comprising a data vault layer, a data bank layer, an operations management layer, and an application business layer. The data vault module relies on infrastructure to complete data storage; the analysis and mining module conducts data analysis and mining; the data bank layer, through data management services, calls upon the data vault layer to process target data assets and generates corresponding data futures contracts through access management services; the operations management layer is responsible for the sharing and trading of these data futures contracts; and the application business layer provides various data asset services, accepting target data assets submitted by data service providers and transmitting them to the data bank layer, thus completing the data asset transaction flow between supply and demand parties. This standardizes the management and flow of data assets, solves the problem of data ownership confirmation, ensures the orderly conduct of data asset transactions, achieves reliable registration and automated management of data ownership, improves data storage security, and promotes the compliant circulation and market application of data elements. Therefore, it solves the problems of poor reliability and security in data storage control in related technologies.

[0016] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0017] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a block diagram of a cyberspace data banking system according to an embodiment of this application; Figure 2 This is a block diagram of a data vault module according to an embodiment of this application; Figure 3 This is a block diagram of an analysis and mining processing module provided according to an embodiment of this application; Figure 4 This is a block diagram illustrating a data bank layer according to an embodiment of this application; Figure 5 This is a block diagram of an operation management layer according to an embodiment of this application; Figure 6 This is a block diagram illustrating an application service layer according to an embodiment of this application; Figure 7 This is a diagram illustrating the overall architecture of a cyberspace data banking system according to an embodiment of this application. Figure 8 This is a flowchart of a management method for a cyberspace data bank system provided according to an embodiment of this application. Detailed Implementation

[0018] The embodiments of this application are described in detail below. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0019] The following description, with reference to the accompanying drawings, illustrates a cyberspace data bank system and management method according to embodiments of this application. Addressing the issues of poor reliability and security in data storage control mentioned in the background section, this application provides a cyberspace data bank system. This system employs a multi-layered architecture comprising a data vault layer, a data bank layer, an operations management layer, and an application business layer. The data vault module relies on infrastructure to complete data storage; the analysis and mining module performs data analysis and mining; the data bank layer, through data management services, calls the data vault layer to process target data assets and generates corresponding data futures contracts through access management services; the operations management layer is responsible for the sharing and trading of these data futures contracts; and the application business layer provides various data asset services, accepting target data assets submitted by data service providers and transmitting them to the data bank layer, thus completing the data asset transaction flow between supply and demand parties. This system standardizes the management and flow of data assets, solves the problem of data ownership confirmation, ensures the orderly conduct of data asset transactions, achieves reliable registration and automated management of data ownership, improves data storage security, and promotes the compliant circulation and market application of data elements. Therefore, it solves the problems of poor reliability and security in data storage control in related technologies.

[0020] Specifically, Figure 1 This is a block diagram of a cyberspace data banking system provided in an embodiment of this application.

[0021] like Figure 1 As shown, the cyberspace data bank system includes: infrastructure layer 101, data vault layer 102, data bank layer 103, operations management layer 104, and application business layer 105.

[0022] The data vault layer 102 includes a data vault module and an analysis and mining processing module. The data vault module calls the equipment of the infrastructure layer 101 to realize data storage, and the analysis and mining processing module calls the equipment of the infrastructure layer 101 to realize data analysis and mining. The data bank layer 103 provides data management services and access management services. Through the data management service, it calls the data vault layer 102 to process target data assets, and through the access management service, it forms data futures contracts from target data assets. The operation management layer 104 is used to conduct shared trading operations of data futures contracts. The application business layer 105 is used to provide at least one data asset service, respond to the data asset service selected by the data service provider, and send the target data assets provided by the data service provider to the data bank layer 103 to realize the transaction between the data service provider and the data purchaser.

[0023] This application embodiment constructs a multi-layered architecture consisting of a data vault layer 102, a data bank layer 103, an operations management layer 104, and an application business layer 105. The data vault module relies on infrastructure to complete data storage, while the analysis and mining module conducts data analysis and mining. The data bank layer 103 uses data management services to call the data vault layer 102 to process target data assets and generates corresponding data futures contracts through access management services. The operations management layer 104 is responsible for the sharing and trading of data futures contracts. The application business layer 105 provides various data asset services, accepts target data assets submitted by data service providers, and transmits them to the data bank layer 103, completing the data asset transaction flow between supply and demand parties. This can standardize the management and flow process of data assets, solve the problem of data ownership confirmation, ensure the orderly conduct of data asset transactions, realize the credible registration and automated management of data ownership, improve data storage security, and promote the compliant circulation and market application of data elements.

[0024] Furthermore, in embodiments of this application, the infrastructure layer 101 includes at least one computing device, a network device, and a storage device, wherein computing resources are provided through the computing device, network resources are provided through the network device, and storage resources are provided through the storage device.

[0025] It is understood that, by configuring at least one type of hardware facility among computing devices, network devices, and storage devices in the infrastructure layer 101, the present application provides basic computing, network, and storage resources respectively, thereby building a complete underlying hardware support system for business operation, stably supplying various basic operating resources, and ensuring the normal operation of upper-layer data processing, transmission, and storage-related work.

[0026] Specifically, such as Figure 2As shown, this embodiment of the application establishes an infrastructure layer 101 that includes at least one of computing devices, network devices, and storage devices. By relying on the division of labor among various hardware devices to supply underlying resources, this embodiment of the application can provide sufficient computing power support for the overall business operation of the system with the help of computing devices; build a stable and smooth network transmission link and provide dedicated network resources with the help of network devices; and use storage devices to realize the secure storage and retrieval of massive amounts of data, providing basic resource guarantees for all business modules such as upper-layer data processing, asset transfer, and transaction operation.

[0027] Meanwhile, the infrastructure layer 101 in this embodiment adopts a physically isolated storage unit deployment method, configuring core data storage resources with independent power supply systems, independent network links, and independent security domains to reduce the risk of external attacks and associated failures; it integrates to form the underlying resource capabilities of super network, super computing power, and super storage, solidifying the foundation for system operation from the hardware level. In addition, the system is configured with a multi-factor authentication mechanism, completing access control through multiple methods such as biometric recognition, digital certificate verification, and dynamic token verification, ensuring that only authorized entities can access core data. It is equipped with the highest level of physical protection and network security protection system to comprehensively resist security risks such as external network attacks, hardware damage, and data leakage. Through the dual support of hardware resource integration and security mechanisms, it stably outputs high-performance underlying resources.

[0028] Furthermore, in the embodiments of this application, the data vault module includes at least one of a data lake, a data warehouse, a distributed database, and a blockchain evidence storage platform. The data lake is used to receive and store unprocessed raw data, semi-structured data, and unstructured data, preserving the original format and metadata of the target data assets. The data warehouse is used to thematically collect, clean, transform, index, and statistically analyze the governed structured data. The distributed database is used to perform sharded storage, parallel querying, and copy backup of high-concurrency access data and large-scale target data assets. The blockchain evidence storage platform calls the devices of the infrastructure layer 101 to perform at least one data operation: login, query, download, authorization, modification, and deletion.

[0029] Among them, a data lake is an underlying data storage carrier that can store massive amounts of raw, multi-format, and unprocessed data, providing complete raw data support for subsequent data analysis and mining; a data warehouse is a data collection and processing platform that can integrate, clean, organize, and classify various types of business data, supporting efficient data querying and statistical analysis; a distributed database is a data storage and management database system used for distributed storage and parallel processing of massive amounts of data; and a blockchain evidence storage platform is a trusted evidence storage carrier that relies on the 101 storage resources of the infrastructure layer to solidify and store various operation data such as data login, query, download, authorization, modification, and deletion.

[0030] It is understood that, by configuring at least one of the functional carriers such as data lake, data warehouse, distributed database and blockchain evidence storage platform in the data vault module, this embodiment of the application can carry various data resources based on different data storage formats. At the same time, the blockchain evidence storage platform calls the infrastructure layer 101 device, which can achieve compatibility with different types and formats of data, meet the needs of massive data collection, organization, retrieval and management, effectively improve data storage capacity and read and write processing efficiency, and effectively maintain the standardization and security of data use and management.

[0031] Specifically, this application embodiment relies on the data vault layer 102 to operate. The data vault layer 102 is composed of a data vault module and an analysis and mining processing module. It utilizes at least one of the following carriers within the data vault module: data lake, data warehouse, distributed database, and blockchain evidence storage platform, to complete the collection and storage of various types of data. It establishes a data access audit log system to record the entire process of operations such as login, query, download, authorization, modification, and deletion, and ensures the authenticity of the logs through anti-tampering technology. In addition, the system is configured with data backup and disaster recovery mechanisms to support local high availability, off-site disaster recovery, and multi-site active-active deployment, so as to quickly restore business continuity in the event of equipment failure, network interruption, or disaster.

[0032] Furthermore, in the embodiments of this application, the analysis and mining processing module includes a data governance platform and a data processing and production platform. The data governance platform calls the equipment of the infrastructure layer 101 to perform at least one of structured data processing, semi-structured data processing, and unstructured data processing. The data processing and production platform is used to implement at least one of the following processes: data labeling, synthesis, matching, evaluation, and publication.

[0033] Structured data processing is a method of processing regular data with fixed formats, standardized fields, and standard structures; semi-structured data processing is a method of processing semi-regular data with basic data identifiers but no strict fixed format; and unstructured data processing is a method of processing fragmented raw data without fixed structure or unified fields.

[0034] It is understood that, in this embodiment of the application, a data governance platform and a data processing and production platform are set up in the analysis and mining processing module. The data governance platform calls the infrastructure layer 101 device to perform corresponding processing on structured, semi-structured, and unstructured data, and completes the regularization and sorting of various types of data. Then, the data processing and production platform is used to perform operations such as labeling, synthesis, matching, evaluation, and publishing of data, adapting to the processing needs of different types of data, effectively completing data standardization governance and in-depth processing, and improving data quality and utilization value.

[0035] Specifically, such as Figure 3As shown, in this embodiment of the application, under the overall operating architecture of the data vault layer 102, after the basic data resources are collected and stored by the data vault module, the data governance and processing analysis work is carried out through the built-in analysis and mining processing module. Specifically, the data governance platform and the data processing production platform in the analysis and mining processing module work together to complete the data processing operation. The data governance platform calls the computing, storage, network and other hardware resources of the infrastructure layer 101. According to the actual business data type and processing requirements, it selectively classifies, identifies, organizes, cleans and corrects errors and standardizes the structured data, semi-structured data and unstructured data collected by the system, removes invalid, redundant and abnormal data, unifies the storage format and data standard of various heterogeneous data, generates standardized data after governance, and completes the standardized governance of massive and multi-type raw data.

[0036] After data governance is completed and data quality meets standards, the data processing and production platform connects to the data governance platform to receive standardized data that has undergone standardized governance. Using standardized data as the basic data source, subsequent processing and production work is carried out. Based on the actual usage needs of the data bank layer, the data processing and production platform selectively performs one or more of the following fine-grained processing operations on the standardized raw data: annotation, synthesis, matching, evaluation, and release. Through data annotation, data attribute information is improved; through data synthesis, multi-source data content is integrated; through data matching, data structure and proportion configuration are optimized; through data evaluation, data quality and usability are verified; and through data release, data compliance output and rights confirmation archiving are completed. This completes the in-depth processing and quality improvement of standardized data, and finally generates standardized target data assets that meet the format requirements and quality standards and can be directly accessed for storage, management, and reuse in the data bank layer.

[0037] Furthermore, in the embodiments of this application, the data vault layer 102 also includes application production tools, which include analysis tools and data intelligence agents. The analysis tools are used to perform multi-dimensional statistics, judgment and in-depth analysis on the processed data; the data intelligence agents are used to autonomously identify, intelligently adapt and automatically process the analysis results.

[0038] It is understood that, by adding application production tools containing analysis tools and data intelligence agents to the data vault layer 102, this embodiment of the application provides auxiliary capabilities for data processing, data mining and data analysis work based on the original data governance, processing and analysis functions, and realizes intelligent identification, autonomous processing and intelligent application adaptation of data by relying on data intelligence agents. This expands the intelligent operation capabilities of the data vault layer 102, enables automated assistance in completing data analysis and application production work, significantly improves the efficiency of data mining and analysis and the accuracy of data processing, reduces the cost of manual intervention and operational errors, and enriches the functional dimensions of the data vault layer 102.

[0039] Specifically, in this embodiment, after data governance and processing are completed in the data vault layer 102, the supporting application production tools are activated to carry out subsequent operations. The analysis tools are called to conduct multi-dimensional statistics, judgment and in-depth analysis on the processed data to mine the intrinsic value and related information of the data. Then, relying on the data intelligence agent, the analysis results are autonomously identified, intelligently adapted and automatically processed. According to business needs, the scheduling and optimization related to data application are completed. Relying on the collaboration of the two types of tools, the front-end data processing results are connected to complete the intelligent production of data application and improve the entire data processing chain.

[0040] Furthermore, in the embodiments of this application, the data bank layer 103 includes a data bank center module and an access management node module. The data bank center module provides data management services, which include at least one of spatial account management, data catalog center, connector, data ownership center, and data audit center. The access management node module provides access management services, which include at least one of user management, data delivery, capability expansion, data management, policy control, data contract, and connector security.

[0041] Specifically, after receiving a user access request, the access management node module first verifies the identity information, account status, role type, and authorization scope of the data service provider or data purchaser through the user management function, and generates a corresponding user identity identifier; then, it reads the metadata, data directory information, storage location, version information, and lifecycle status of the target data asset through the data management function, and matches the user identity identifier with the access permissions of the target data asset.

[0042] After the permission matching is successful, the policy control function intercepts, allows, or limits the user's data access requests based on the scope of use, access frequency, data download volume, usage period, interface call method, and operation type stipulated in the data contract. The data contract function parses the data category, quality level, delivery cycle, delivery method, pricing rules, and compliance requirements of the target data asset into executable control rules, and synchronizes the control rules to the policy control function and the data delivery function.

[0043] The data delivery function encapsulates the target data assets according to the control rules and completes the delivery in at least one of the following ways: application programming interface call, data file download, database read-only access, or encrypted transmission channel. During the delivery process, the connector security function performs two-way authentication, transmission encryption, message integrity verification, and operation log storage on the communication link between the data bank center module, the access management node module, and the external data service system.

[0044] The capability extension function is used to load plugins, register external service interfaces, add data format converters, extend security verification rules, or add delivery channels according to new business scenarios or external system access requirements. This enables the access management node module to adapt to different data source types, industry protocols, delivery methods, and compliance requirements. After the capability extension is completed, the new capabilities are registered in the data directory or service directory of the data bank center module and can be called by users in subsequent data access, data delivery, or data transaction processes.

[0045] The data bank center module is used to receive target data assets from the data vault layer, register the target data assets in the catalog, identify ownership, configure permissions and generate audit records, and send the target data asset information after registration and confirmation to the access management node module.

[0046] The access management node module is used to receive user access requests, perform access control, delivery encapsulation, transmission encryption and contractual processing on target data assets according to user identity, data permissions, contract rules and security policies, and feed the processing results back to the data bank center module for auditing and evidence storage.

[0047] Among them, the Data Ownership Confirmation Center is a management module that relies on the Data Bank Layer 103 Data Management Service to identify, register and divide the ownership, usage rights and rights boundaries of data assets, ensuring that the ownership of data assets is clear and the attribution is well-defined; the Data Audit Center is a regulatory module that relies on the Data Bank Layer 103 Data Management Service to record, trace and verify compliance of access, call, transaction, transfer and operation behaviors throughout the entire life cycle of data.

[0048] It is understood that this application embodiment constructs a data bank layer 103 comprising a data bank center module and an access management node module. The data bank center module provides multiple data management services, including space account management, data catalog center, connectors, data ownership confirmation center, and data audit center, to achieve ownership confirmation, compliance auditing, account control, catalog collection, and connection scheduling of data assets. Meanwhile, the access management node module provides access management services such as user management, data delivery, capability expansion, policy control, data contract management, and connector security protection, completing comprehensive control over user access, data delivery, contract control, and access security. This achieves an integrated control model for unified management, compliance confirmation, full-process auditing, secure access, and standardized delivery of data assets. It can clearly define data asset ownership, standardize data flow processes, strictly control data access and transaction risks, ensure full compliance and traceability of data asset flow, improve data connection security and transaction standardization, effectively reduce data asset circulation risks, and improve the management system for market-based trading of data assets.

[0049] Specifically, such as Figure 4As shown, the data bank layer includes a data bank center module and an access management node module. The data bank center module can provide at least one of the following services: space account management, data catalog center, connector, data ownership confirmation center, and data audit center. Space account management is specifically implemented through the account management system. The system supports the opening, information modification, permission adjustment, freezing, unfreezing, and cancellation management of data asset accounts, and can associate customer identity, asset list, and transaction records to achieve standardized management and control of the entire account lifecycle. The data catalog center, connector, data ownership confirmation center, and data audit center work together to complete the catalog collection, connection scheduling, ownership confirmation, and compliance audit of data assets.

[0050] The access management node module can provide at least one of the following services: user management, data delivery, capability expansion, data management, policy control, data contracts, and connector security. User management allows for the registration, authentication, permission allocation, and lifecycle management of data service providers and data purchasers, linking user identities with data operation records. Data delivery enables the encapsulation, encrypted transmission, breakpoint resumption, and delivery result confirmation of target data assets according to delivery methods stipulated in data futures contracts, such as APIs (Application Programming Interfaces), data file downloads, and read-only database accounts. Capability expansion allows the access management node module to dynamically add new data access protocols, data format converters, security verification rules, or delivery channels by loading plugins, registering external service interfaces, or integrating third-party analysis engines. This adapts to the expansion needs of different industries, data source types, and compliance requirements without modifying the core system code. Data management performs metadata extraction, version control, lifecycle status tracking, and storage location mapping for various accessed data assets, ensuring data security. Based on the asset's searchability, traceability, and recyclability; strategy control can be used to make real-time interception or approval judgments on data access frequency, data download volume, validity period, IP whitelist, and operation type (query / export / processing) according to the terms of data futures contracts and compliance regulatory requirements; data contracts can be used to parse the terms in data futures contracts (data category, quality level, delivery cycle, price, delivery method) into an executable rule set, and link them with user management, data delivery, strategy control and other modules to achieve automated performance; connector security can be used to perform two-way authentication, transmission encryption, message anti-tampering and operation log solidification and evidence storage on the communication link between the data bank center module and the access management node module.

[0051] Furthermore, in the embodiments of this application, the data bank layer 103 also includes a trusted circulation platform, wherein the trusted circulation platform is used to provide at least one of the following: secure data exchange function, trusted execution environment, privacy computing function, and blockchain end-to-end notarization. The secure data exchange function establishes an encrypted transmission channel between the data service provider and the data purchaser, and performs desensitization processing, format verification, transmission integrity verification, and recipient authentication on the exchanged data. The trusted execution environment decrypts the target data asset and performs computing tasks within the area. During the computing process, the original data is not visible to the operating system, database, and external applications. After the computing is completed, only at least one of the results or summaries is output. The privacy computing function is used to complete joint statistical analysis, feature engineering, or model training, and outputs at least one of the computing results or encrypted gradients. The blockchain end-to-end notarization records the key operation records of the entire process of data asset from data governance, processing and production, contract generation, transaction matching, data delivery to audit compliance into the blockchain notarization platform.

[0052] Among them, the secure data exchange function is the ability to achieve compliant interoperability of multi-party data and isolate the transmission control of original plaintext; the trusted execution environment is a secure operating carrier built on hardware isolation to ensure that the computing process is not stolen or tampered with; the privacy computing function is a computing technology that completes joint modeling and data value mining without disclosing the original data; and the blockchain full-link evidence storage is a means of evidence storage guarantee that solidifies the information of the entire data flow process on the chain and realizes tamper-proof traceability.

[0053] It is understood that the embodiments of this application integrate multiple capabilities such as secure data exchange, trusted execution environment, privacy computing, and blockchain end-to-end notarization through a trusted circulation platform. This enables the standardization of the entire process of cross-entity data flow under the data bank architecture. It strengthens the data transmission boundary by relying on secure data exchange to prevent plaintext leakage, constrains the data computing environment by relying on the trusted execution environment to prevent data theft and tampering during operation, enables collaborative data computing and value mining under the condition that the original data does not leave the domain, and retains the circulation vouchers at each step through blockchain end-to-end notarization. This comprehensively reduces the security risks and compliance hazards in the data circulation process and effectively improves the security, credibility, and practicality of data asset circulation in the data bank.

[0054] Specifically, this application embodiment establishes a trusted circulation platform through the data bank layer 103. The platform's secure data exchange, trusted execution environment, privacy computing, and blockchain-based end-to-end data circulation security capabilities serve as fundamental support. A multi-module system is then integrated to achieve comprehensive security management and governance. This application embodiment uses a security situation awareness platform to collect and integrate various log data of network, host, and user access behavior, as well as external threat intelligence resources in real time. It conducts all-weather dynamic monitoring and timely early warning of various security risks and hidden dangers such as abnormal access, network attacks, system vulnerabilities, and data leakage that occur during the operation of the data bank, and captures various security threats.

[0055] This application embodiment utilizes the standardized automated handling process built into the security incident response system to automatically complete risk control operations such as host isolation, network connection blocking, freezing of unauthorized accounts, and system configuration rollback for various security threats identified by the situational awareness platform, and simultaneously pushes information to inform management personnel.

[0056] Meanwhile, the compliance audit system strictly adheres to data security, personal information protection, cybersecurity, and relevant industry regulatory standards. It conducts routine and continuous compliance audits of platform permission allocation and management, data flow throughout the entire process, log information retention, and various business operation processes, and automatically generates compliance reports to ensure the platform operates legally and compliantly.

[0057] Furthermore, the trusted circulation platform proposed in this application is equipped with a security training and exercise platform. Through routine personnel security training, practical attack and defense drills, emergency plan simulations, and comprehensive security capability assessments, the platform continuously improves the security operation and maintenance capabilities of operation and maintenance personnel and the overall security level of the platform, and comprehensively builds a data bank security governance system that integrates monitoring, handling, auditing, and protection.

[0058] Furthermore, in the embodiments of this application, the operation management layer 104 includes an operation portal module, an asset level module, a rights confirmation management module, a compliance supervision module, a demand center module, a data market module, an application market module, a metering and billing module, and a value-added service module. The operation management layer 104 responds to user interaction actions and selects at least one module of the operation management layer 104 according to the interaction actions.

[0059] The system comprises the following modules: Operation Portal Module, a comprehensive user interface that integrates various operational management functions; Asset Classification Module, a management module that classifies and categorizes data assets, defining their security levels and control standards; Rights Management Module, a rights management module that provides ownership verification, registration, and traceability for data assets; Compliance and Supervision Module, a supervision module that coordinates compliance verification, risk control, and standardized supervision throughout the entire data operation process; Demand Center Module, a demand management module that collects, organizes, and connects user data with business needs; Data Market Module, a data trading platform that provides services for listing, displaying, trading, and circulating data assets; Application Market Module, a service module that aggregates various data applications and supports application access, selection, and deployment; Metering and Billing Module, a quantitative billing module that statistically analyzes data and service usage and calculates fees according to rules; and Value-Added Services Module, a service expansion module that leverages data resources to develop derivative services and enhance the platform's operational value.

[0060] It is understood that this application embodiment, by configuring various functional modules such as the operation portal, asset level, rights management, compliance supervision, demand center, data market, application market, metering and billing, and value-added services in the operation management layer 104, and relying on the operation management layer 104 to respond to user interaction actions in real time and call the corresponding functional modules on demand to carry out business processing, can simplify the user's usage steps. Through asset level and rights management, it can realize the hierarchical control and standardized filing of data assets and ownership, control operational compliance risks, collect and integrate multiple business needs through the demand center to realize supply and demand matching, and implement the listing, circulation and trading of data assets and data applications in the data market and application market respectively. Relying on the metering and billing module, it can accurately calculate service fees according to actual usage, and further utilize the value-added service module to explore the added value of data and expand the platform's revenue channels. Relying on the on-demand module calling operation mode, it can avoid redundant functions occupying resources, comprehensively improve the full-chain operation and control capabilities of the data bank, realize the closed-loop management of data assets from level and rights confirmation, compliance control, supply and demand matching, transaction billing to value-added operation, and effectively improve the platform's operational standardization and overall operating efficiency.

[0061] Specifically, such as Figure 5As shown in the embodiment of this application, the operation management layer 104 is equipped with multiple business modules, including operation portal, asset level, rights management, compliance supervision, demand center, data market, application market, metering and billing, and value-added services. The overall architecture flexibly calls the corresponding modules to complete various business processes based on the user's interactive instructions. After the user initiates an operation request through the unified operation portal, the platform automatically matches the relevant modules such as asset level, ownership registration, compliance review, demand collection, asset and application listing and trading, usage billing, or value-added services according to the business scenario to carry out corresponding processing, and complete the entire process of data asset operation. At the same time, the operation analysis dashboard continuously summarizes the operation data of the entire platform, automatically captures core operation indicators such as the number of customers, the scale of existing data assets, the total market transaction amount, the actual utilization rate of software and hardware resources, the composition of various platform revenues, and the health status of system operation, and presents them in real time for visualization. Relying on the collaboration of multiple modules to implement daily operation business, and combined with the quantitative data of the dashboard, it intuitively reflects the actual operation of the platform, which makes it easier for managers to make judgments and decisions based on detailed and dynamic operation data.

[0062] In this embodiment, the operation portal module receives user interaction commands, routes them to other corresponding modules based on command type, collects the processing results from each module and presents them uniformly to the user, and summarizes the platform's overall operational data, including customer numbers, data asset scale, total transaction amount, resource utilization rate, system health, etc., generating a visual operation dashboard; the asset grading module classifies data assets according to sensitivity, data volume, update frequency, industry attributes, and compliance requirements, automatically labels asset grading, and sets corresponding access control policies, storage isolation policies, and audit frequencies based on the grading; the rights management module receives data source certificates, authorization chains, and ownership statements submitted by data service providers, verifies, registers, and files the ownership of data assets in conjunction with blockchain evidence records, generates a unique asset ownership certificate, and supports ownership changes, transfers, and traceability queries; the compliance supervision module conducts real-time compliance reviews of the entire process of data assets from access, governance, transaction to delivery, including automatic comparison and risk warning of data source legality, personal information authorization scope, cross-border transmission restrictions, and industry regulatory requirements, and regularly generates compliance audit reports; the demand center module is used for The system collects, aggregates, and categorizes data demand information released by data purchasers. This information includes data categories, quality requirements, delivery cycles, and budget ranges. It automatically matches and recommends data to listed assets in the data marketplace module. The data marketplace module provides listing, display, retrieval, order placement, and transaction matching functions for data assets. It manages the listing of data futures contracts, records transaction intentions, bidding processes, and transaction results, and creates a transaction ledger. The application marketplace module aggregates data application products developed based on data assets, such as analytical models, visualization reports, and API services. The product supports application release, version management, online trial, authorized deployment, and usage statistics; the metering and billing module is used to perform real-time metering based on dimensions such as data storage capacity, computing resource consumption, network traffic, API call count, subscription period, and service level, and automatically generate bills according to pricing rules, supporting multi-period settlement, revenue sharing, and overdue payment alerts; the value-added services module is used to provide derivative services such as data consulting, data compliance assessment, data value assessment, data talent training, and data finance support services around data assets, recording service needs, service processes, and service results, and incorporating them into the metering and billing system.

[0063] The portal module receives user interaction commands, routes them to other corresponding modules based on command type, collects the processing results from each module and presents them to the user in a unified manner, and summarizes the platform's operational data (number of customers, data asset scale, total transaction amount, resource utilization rate, system health, etc.) to generate a visual operational dashboard. Asset Classification Module: Classifies data assets according to sensitivity, data volume, update frequency, industry attributes and compliance requirements, automatically labels asset classes, and sets corresponding access control policies, storage isolation policies and audit frequencies based on the class. The rights management module receives data source certificates, authorization chains, and ownership declarations submitted by data service providers. It verifies, registers, and files the ownership of data assets by combining blockchain evidence records, generates unique asset ownership certificates, and supports ownership changes, transfers, and traceability inquiries. Compliance and supervision module: Conducts real-time compliance review of data assets from access, governance, transaction to delivery, including automatic comparison and risk warning of data source legality, scope of personal information authorization, cross-border transmission restrictions, and industry regulatory requirements, and generates compliance audit reports regularly; The Demand Center module collects, aggregates, and categorizes data demand information (including data categories, quality requirements, delivery cycles, budget ranges, etc.) published by data purchasers, and automatically matches and recommends demands with the data assets already listed in the Data Marketplace module. Data Market Module: Provides functions for listing, displaying, searching, placing orders and matching transactions for data assets, manages the listing of data futures contracts, records transaction intentions, bidding processes and transaction results, and forms a transaction ledger; Application Marketplace Module: This module aggregates data application products (such as analytical models, visualization reports, API services, etc.) developed based on data assets, and supports application release, version management, online trial, authorized deployment, and usage statistics. Metering and billing module: Real-time metering is performed based on dimensions such as data storage capacity, computing resource consumption, network traffic, API call count, subscription period and service level. Bills are automatically generated according to pricing rules, and multi-period settlement, revenue sharing and overdue payment alerts are supported. Value-added services module: Provide derivative services such as data consulting, data compliance assessment, data value assessment, data talent training and data finance support services around data assets, record service needs, service process and service results, and incorporate them into the metering and billing system.

[0064] Furthermore, in the embodiments of this application, the application service layer 105 includes an application scenario module and an innovation scenario module. The application scenario module provides at least one of the following functions: emergency management, digital finance, smart city, and low-altitude economy. The innovation scenario module provides at least one of the following functions: model training, digital asset securitization, knowledge base construction, and data financial derivatives services.

[0065] Among them, the emergency management function is an application function that relies on data resources to support the monitoring, handling, and overall dispatch of emergencies; the digital finance function is a function that relies on compliant data to empower credit risk control and investment and financing business; the smart city function is a function that integrates multi-source data to realize the intelligent construction of urban governance and public services; the low-altitude economy function is an application function that relies on data to coordinate and manage airspace operations, aircraft maintenance, and industrial landing; the model training function is an innovative R&D function that relies on platform-collected data to complete algorithm iteration and optimization; the digital asset securitization function is an innovative service function that revolves around compliant data assets to complete asset confirmation, splitting, and securitization operations; the knowledge base construction function is an innovative construction function that accumulates industry data and business experience to form standardized knowledge resources; and the data financial derivatives service function is an innovative business function that designs derivative financial products based on compliant data assets.

[0066] It is understood that this application embodiment divides the application business layer 105 into two types of business carriers: application scenario modules and innovation scenario modules. The application scenario modules implement mature business functions such as emergency management, digital finance, smart cities, and low-altitude economy. Leveraging the data resources accumulated on the platform, it empowers various traditional industries to achieve digital transformation and upgrading, meeting the routine data implementation needs of various real-world industries. Simultaneously, the innovation scenario modules carry innovative functions related to model training, digital asset securitization, knowledge base construction, and data financial derivatives services, supporting cutting-edge technology research and development and the incubation of new data business models. This forms a two-tiered business layout: mature businesses stabilize the platform's basic revenue, while innovative businesses explore the incremental value of data. This approach allows for the rapid revitalization of existing data assets and the broadening of data implementation paths through mature application scenarios, effectively leveraging the practical value of data in empowering the real economy. Furthermore, it enables the continuous exploration of deeper data value and the discovery of diversified new business models through innovative scenarios, promoting the extension of data assets from basic use to capitalization and productization. This enriches the business coverage and profit model of the application business layer 105, enhancing the scenario adaptability and long-term market competitiveness of the entire data banking system.

[0067] Specifically, such as Figure 6 As shown, this embodiment of the application sets up an application business layer 105 in the overall architecture and refines it into two major functional modules: application scenario module and innovation scenario module, so as to implement data business applications in a layered manner. In actual operation, relying on the emergency management, digital finance, smart city, and low-altitude economy related functions carried by the application scenario module, it makes full use of the compliant data resources gathered by the platform to provide data empowerment services for real-world fields such as urban governance, financial services, low-altitude industry, and emergency prevention and control.

[0068] The emergency management function of the application scenario module is used to retrieve disaster monitoring data, emergency resource distribution data, population heat map and real-time traffic data from the data bank layer, combine them with the preset emergency model to predict the impact range of the emergency, plan the resource dispatch path and determine the emergency response level, output the emergency command plan, and feed the handling results back to the operation management level for effect evaluation and resource verification.

[0069] The digital finance function of the application scenario module is used to obtain compliant and anonymized enterprise operating data, personal credit authorization data, and upstream and downstream transaction data of the industrial chain from the data bank layer. It uses risk identification models to conduct credit scoring, anti-fraud analysis, and post-loan risk warning, outputs credit suggestions or bad behavior reports, and sends the calculation results back to the data bank layer for data asset value assessment.

[0070] The smart city function of the application scenario module is used to call urban IoT data, government data, traffic flow data and environmental monitoring data from the data bank layer, use urban digital twin models to predict traffic congestion, diagnose public facility failures and trace environmental quality, output urban governance decision-making suggestions, and store the analysis results in the data bank layer for other applications to call.

[0071] The low-altitude economic function of the application scenario module is used to obtain low-altitude aircraft operation data from the data bank layer. The low-altitude aircraft operation data includes flight trajectory, altitude, speed, airspace meteorological data, and no-fly zone data. The flight plan is pre-approved, real-time conflict alarms are issued, and route optimization is performed through airspace conflict detection algorithms. Flight permits or avoidance suggestions are output, and flight logs are stored on the blockchain.

[0072] The innovative scenario module provides at least one of the following functions: model training, digital asset securitization, knowledge base construction, and data financial derivatives services. Under a trusted execution environment or privacy computing framework, the model training function is used to apply for training datasets from the data bank layer, call the computing resources of the data vault layer to perform iterative training of the model, generate model parameters or algorithm services, store the model file as a new data asset back to the data vault layer, and notify the operations management to register the asset.

[0073] The digital asset securitization function of the innovative scenario module is used to obtain data assets with confirmed ownership from the data bank layer, such as data futures contracts and standardized data packages. Combined with the value assessment report provided by the operation management, asset pool construction, cash flow calculation and risk rating are carried out, securitization product design scheme is output, and product information is published to the data market module for share listing. The knowledge base construction function of the innovative scenario module is used to extract multi-source data from the data bank layer, such as industry reports, business rules, historical cases, and labeled data. Through knowledge extraction and knowledge fusion technologies, it constructs an entity relationship network, forms a standardized knowledge graph or question-and-answer knowledge base, and stores the constructed knowledge base as a data asset in the data vault layer to support upper-layer applications in querying.

[0074] The data financial derivatives service function of the innovative scenario module is used to obtain data on the open positions, price fluctuations and market supply and demand indicators of data futures contracts from the data bank layer, calculate the theoretical prices of derivatives such as data options and data swaps through risk pricing models, output derivative contract design schemes, and complete the listing, trading and clearing through the data market module of the operations management team.

[0075] The application business layer of the application scenario module calls the data assets and computing resources of the data vault layer through the data management services and access management services of the data bank layer. The processing results or newly generated data assets are then put on the shelf, traded and settled through the asset registration, metering and billing and market modules of the operation management layer, realizing a closed-loop business flow between data service providers and data purchasers.

[0076] Emergency management function: retrieve disaster monitoring data, emergency resource distribution data, population heat map and real-time traffic data from the data bank layer, combine them with the preset emergency model to predict the impact range of the emergency, plan the resource dispatch route and determine the emergency response level, output the emergency command plan, and feed the handling results back to the operation management level for effect evaluation and resource write-off. Digital financial functions: Obtain compliant and anonymized enterprise operating data, personal credit authorization data, and upstream and downstream transaction data of the industrial chain from the data bank layer; conduct credit scoring, anti-fraud analysis, and post-loan risk warning through risk identification models; output credit granting suggestions or bad behavior reports; and send the calculation results back to the data bank layer for data asset value assessment. Smart city functions: It calls urban IoT data, government data, traffic flow data and environmental monitoring data from the data bank layer, uses the city digital twin model to predict traffic congestion, diagnose public facility failures and trace environmental quality, outputs urban governance decision-making suggestions, and stores the analysis results in the data bank layer for other applications to call; Low-altitude economic function: Obtain low-altitude aircraft operation data (flight trajectory, altitude, speed), airspace meteorological data and no-fly zone data from the data bank layer, conduct flight plan pre-review, real-time conflict alarm and route optimization through airspace conflict detection algorithm, output flight permission or avoidance suggestions, and store flight logs on the blockchain for evidence. The innovative scenario module provides at least one of the following functions: model training, digital asset securitization, knowledge base construction, and data financial derivatives services. Model training function: Request training datasets from the data bank layer (in a trusted execution environment or privacy computing framework), call the computing resources of the data vault layer to perform iterative training of the model, generate model parameters or algorithm services, store the model files as new data assets back to the data vault layer, and notify the operations management to register the assets. Digital asset securitization function: Obtain confirmed data assets (such as data futures contracts and standardized data packages) from the data bank layer, combine them with the value assessment report provided by the operation management to construct asset pools, calculate cash flow and risk rating, output securitization product design schemes, and publish product information to the data market module for share listing; Knowledge base construction function: Extract multi-source data (industry reports, business rules, historical cases, labeled data) from the data bank layer, construct entity relationship network through knowledge extraction and knowledge fusion technology, form a standardized knowledge graph or question-and-answer knowledge base, and store the constructed knowledge base as a data asset in the data vault layer to support upper-layer application queries; Data financial derivatives service functions: Obtain data on open positions, price fluctuations and market supply and demand indicators of data futures contracts from the data bank layer, calculate the theoretical prices of derivatives such as data options and data swaps through risk pricing models, output derivative contract design schemes, and complete listing, trading and clearing through the data market module of the operations management team; The application business layer calls the data assets and computing resources of the data vault layer through the data management services and access management services of the data bank layer, and completes the asset listing, trading and settlement of the processing results or newly generated data assets through the asset registration, metering and billing and market modules of the operation management layer, so as to realize the closed-loop business flow between data service providers and data purchasers.

[0077] Meanwhile, this application embodiment relies on the model training, digital asset securitization, knowledge base construction, and data financial derivatives service functions configured in the innovative scenario module. Based on the platform's high-quality data resources, it carries out intelligent technology iteration and new data business model innovation. Through model training, it continuously optimizes intelligent algorithm capabilities; through the construction of a standardized knowledge base, it accumulates industry experience and data results; and through digital asset securitization and data financial derivatives services, it explores the capitalization and financialization value of data assets, breaking through the traditional single-application model of data. This architecture distinguishes between mature application scenarios and innovative R&D scenarios, enabling the parallel advancement of basic business implementation and cutting-edge business expansion. It standardizes the application implementation path of data business, which can not only stably output implementable industry data services and broaden the coverage of data applications, but also continuously incubate innovative data products and new business models, fully releasing the deep value of data assets, improving the 105-dimensional service capabilities of the application business layer, and enhancing the overall platform's data application diversity, innovation, and market adaptability.

[0078] To better understand the solution of this application, the cyberspace data bank system of this application is described below through a specific embodiment, as follows: The cyberspace data bank system provided in this application adopts a layered architecture design, which includes an infrastructure layer, a data vault layer, a data bank layer, and an application business layer from bottom to top.

[0079] (i) The infrastructure layer is used to provide underlying resource support, including super networks, supercomputing power, super storage, and the highest level of physical and network security.

[0080] (ii) The data vault layer includes a data vault module and an analysis and mining processing module, which are used to realize data governance, processing and analysis.

[0081] (iii) The data bank layer includes a data ownership management module, a value-added processing module, and a trusted sharing transaction module, which are used to complete data asset ownership confirmation, secure custody, value mining, asset processing, and cross-domain trusted circulation.

[0082] (iv) The operations management layer includes an operations portal module, which is mainly used for data sharing and transaction operations.

[0083] (v) The application business layer mainly realizes innovative services such as data asset securitization, data futures, data options, and data pledge financing.

[0084] The overall technology of the cyberspace data bank can support the realization of related system prototypes, including a data vault system, a data bank operation and maintenance system, a data bank security system, and a data trading platform.

[0085] (i) The data vault system is used to meet the security storage needs of high-value data assets. It adopts a physically isolated storage unit deployment method, and configures core data storage resources with independent power supply systems, independent network links and independent security domains to reduce the risk of external attacks and associated failures. The system is equipped with a multi-factor authentication mechanism, which completes access control through multiple methods such as biometric recognition, digital certificate verification and dynamic token verification to ensure that only authorized subjects can access core data. At the same time, a data access audit log system is established to record the entire process of operations such as login, query, download, authorization, modification and deletion, and ensures the authenticity of logs through anti-tampering technology. In addition, the system is equipped with a data backup and disaster recovery mechanism to support local high availability, off-site disaster recovery and multi-site active deployment, so as to quickly restore business continuity in the event of equipment failure, network interruption or disaster.

[0086] (ii) The data bank operation and management system is used to ensure the daily operation and business management of the platform. The system is configured with a unified resource management platform to centrally monitor network, computing power, storage, security equipment and application services, and collect real-time operating status, performance indicators and capacity usage to realize resource scheduling, fault early warning and elastic expansion. The account management system supports the opening, information change, permission adjustment, freezing, unfreezing and cancellation management of data asset accounts, and can be associated with customer identity, asset list and transaction records. The billing and settlement system automatically bills according to storage capacity, computing resource consumption, network traffic, number of calls and service level, and supports multi-period settlement, electronic billing and revenue sharing functions. The operation analysis dashboard displays key indicators such as number of customers, asset size, transaction amount, resource utilization rate, revenue structure and system health in real time to support operation decision-making.

[0087] (iii) The data bank security system is used to achieve comprehensive security governance. The system is equipped with a security situation awareness platform, which continuously collects network logs, host logs, access behavior logs and threat intelligence information, and conducts real-time monitoring and early warning of abnormal access, attack behavior, vulnerability risks and signs of data leakage. The security incident response system has built-in automated handling processes, which can automatically perform response actions such as isolating hosts, blocking connections, freezing accounts, rolling back configurations and notifying administrators when threats are discovered, thereby improving the efficiency of security handling. The compliance audit system continuously audits access management, data flow, log retention and business processes in accordance with data security, personal information protection, network security and industry regulatory requirements and outputs compliance reports. At the same time, a security training and exercise platform is configured to support personnel training, attack and defense exercises, emergency plan simulation and security capability assessment.

[0088] (iv) The data trading platform supports the market-based circulation of data assets. The platform includes a data asset listing system, which can publish, display, classify and search data products, data services, data interfaces and derivative data assets and disclose information; an intelligent matching engine that automatically matches and recommends transactions based on factors such as supply and demand, industry preferences, price range, transaction history and credit rating, to improve transaction efficiency; a transaction clearing and settlement system that supports order processing, fund clearing, profit distribution and delivery management for spot trading, subscription trading, authorized trading and data futures trading, and can realize real-time settlement or periodic settlement; at the same time, the platform has a data futures trading mechanism, which supports the listing, matching, margin management, risk control, maturity delivery and hedging of data asset delivery contracts, and promotes the financial operation of data assets.

[0089] In summary, the cyberspace data bank system proposed in this application adopts a multi-layered architecture consisting of a data vault layer, a data bank layer, an operations management layer, and an application business layer. The data vault module relies on infrastructure to complete data storage, while the analysis and mining module conducts data analysis and mining. The data bank layer uses data management services to call the data vault layer to process target data assets and generates corresponding data futures contracts through access management services. The operations management layer is responsible for the sharing and trading of data futures contracts. The application business layer provides various data asset services, accepts target data assets submitted by data service providers, and transmits them to the data bank layer, completing the data asset transaction flow between supply and demand parties. This system can standardize the management and flow process of data assets, solve the problem of data ownership confirmation, ensure the orderly conduct of data asset transactions, realize the credible registration and automated management of data ownership, improve data storage security, and promote the compliant circulation and market application of data elements.

[0090] Next, referring to the accompanying drawings, a management method for a cyberspace data banking system according to an embodiment of this application is described.

[0091] Figure 8 This is a flowchart of a management method for a cyberspace data bank system according to an embodiment of this application.

[0092] The method is applied to the management of the aforementioned cyberspace data banking system, such as Figure 8 As shown, the method includes the following steps: In step S801, responding to the data asset service selected by the data service provider, the target data asset provided by the service provider is obtained.

[0093] Among them, data service providers are professional suppliers that provide various data resources and services in compliance with regulations; data asset services are data supply services that data service providers provide to external parties that can be called and used; target data assets are compliant data resources that users can obtain for business applications by selecting data asset services.

[0094] It is understood that, by responding to the user's selection of a data service provider and matching the corresponding data asset service, the target data assets provided by the data service provider can be obtained. This enables targeted acquisition of data resources and allows for flexible selection of suitable service providers and data service types based on actual business needs. This effectively improves the accuracy and efficiency of data resource integration. At the same time, by relying on standardized data asset services provided by compliant service providers, the legality of the source of the acquired target data assets is guaranteed, effectively improving the standardization and practicality of data resource acquisition on the overall platform.

[0095] Specifically, this application provides a management method for a cyberspace data bank system for government agencies, corporate clients, and other data rights holders. It provides data asset services according to dimensions such as storage capacity, storage duration, access frequency, security level, and backup level. By responding to the data service provider selected by the user and the corresponding data asset services, it connects with compliant data supply entities and then obtains the target data assets output by the service provider.

[0096] In step S802, the target data assets provided by the data service provider are sent to the data bank layer, which processes the target data assets. The data bank layer provides data management services and access management services. The data management service calls the data vault layer to process the target data assets, and the access management service forms data futures contracts from the target data assets.

[0097] It is understood that the embodiments of this application By pushing target data assets provided by data service providers to the data bank layer for centralized and standardized processing, and by using data management services to schedule and call the data vault layer to complete the processing and standardization of target data assets, standardized governance and long-term storage management of data assets are achieved. By using access management services to encapsulate the processed target data assets into contracts, physical data assets are transformed into standardized data futures contracts. This achieves two-way collaboration between the governance of original data assets and the financialization of data. By leveraging the professional processing capabilities of the data vault layer, the quality of data assets is strengthened, and systematic management of data assets is achieved, thus improving the data bank's processing system from data access and standardized governance to financial asset transformation.

[0098] Specifically, the data bank layer operates based on the supercomputing resources and analysis engines provided by the data bank. Billing is based on indicators such as computing resource consumption, task execution time, model complexity, data scale, and number of calls. It can provide customers with pre-built standard analysis models, such as user profile analysis, market trend prediction, risk identification analysis, and business indicator analysis, as well as provide industry-specific and scenario-specific customized analysis services according to customer needs.

[0099] The data management service provided by the data bank layer proactively calls the data vault layer to process the acquired target data assets. Relying on the data governance and storage capabilities of the data vault layer, the target data assets are organized, quality verified, compliance screened, cleaned, optimized and structured. The qualifications of the transaction entities, the legality of the data source, the scope of authorization, the purpose of use, cross-border circulation restrictions and industry regulatory requirements are automatically or manually reviewed to ensure that the transaction process is legal and compliant.

[0100] The access management service provided by the data bank layer takes the target data assets after standardized processing as the underlying asset. It sorts out the contract elements by combining key information such as data category, quality level, delivery cycle, and pricing rules. It completes the drafting and standardized packaging of contract terms in accordance with the platform's transaction specifications, transforms the physical data assets into data futures contracts with compliant trading qualifications, clarifies the delivery method, performance details and risk control-related agreements, completes the contract filing and registration, and generates contract content such as the data category, delivery cycle, update frequency, quality level, delivery method and price terms for listing and circulation.

[0101] In step S803, the data futures contract is shared and traded by the operations management team to realize transactions between data service providers and data purchasers.

[0102] Among them, shared transaction operation is a coordinated operation model that connects supply and demand sides to carry out contract transfer transactions.

[0103] It is understood that the embodiments of this application coordinate the shared trading operations of data futures contracts through the operation management layer. Relying on the coordinated linkage of various functional modules of the operation layer, a standardized connection channel is built for data service providers and data purchasers. The entire process of data futures contract listing, supply and demand matching, and transaction management is carried out in an orderly manner, which smoothly facilitates the transaction of contracts between supply and demand parties, improves the circulation efficiency of data futures contracts, and effectively restrains transaction chaos by relying on the operation management layer to standardize the supervision of the entire transaction process.

[0104] Specifically, the shared transaction operation relies on the data bank transaction platform to realize the circulation of data assets and adopts a transaction commission model to obtain revenue, that is, to charge a certain percentage or fixed amount of service fees based on the transaction amount, transaction scale, matching service level or transaction frequency; the platform supports one-time transaction mode and subscription transaction mode, where one-time transactions are suitable for single data delivery, special data procurement or targeted analysis services, and subscription transactions are suitable for continuously updated data interfaces, periodic industry data packages and long-term data services.

[0105] To better understand the solution of this application, the management method or execution flow of the cyberspace data bank system of this application is described below through a specific embodiment, as follows: The prototype data bank system can realize multiple service management models, including data hosting services, data analysis services, data trading services, and data futures services.

[0106] (i) Data hosting services provide data asset custody capabilities to government agencies, enterprise clients and other data rights holders. Billing is based on dimensions such as storage capacity, storage duration, access frequency, security level and backup level. It supports two service models: standardized hosting solutions and customized hosting solutions. Standardized solutions are suitable for general data storage needs, while customized solutions can provide dedicated deployment, dedicated security policies and dedicated disaster recovery architecture according to the client's industry characteristics, compliance requirements and business continuity needs. At the same time, the system availability, response latency, recovery time, data reliability and operation and maintenance support capabilities are guaranteed through SLA service level agreements.

[0107] (ii) Data analysis services are operated based on the supercomputing resources and analysis engines provided by the data bank. Billing is based on indicators such as computing resource consumption, task execution time, model complexity, data scale and number of calls. It can provide customers with pre-built standard analysis models, such as user profile analysis, market trend forecasting, risk identification analysis, and business indicator analysis, as well as industry-specific and scenario-specific customized analysis services according to customer needs. The system supports outputting analysis results in the form of visual dashboards, dynamic charts, intelligent reports, early warning notifications and API interfaces to help customers quickly form a basis for business decisions.

[0108] (iii) Data trading services rely on the data bank trading platform to realize the circulation of data assets and adopt a transaction commission model to obtain revenue, that is, a certain percentage or fixed amount of service fees are charged according to the transaction amount, transaction scale, matching service level or transaction frequency; the platform supports one-time transaction mode and subscription transaction mode, of which one-time transactions are suitable for single data delivery, special data procurement or targeted analysis services, and subscription transactions are suitable for continuously updated data interfaces, periodic industry data packages and long-term data services; at the same time, the system is configured with a transaction compliance review mechanism to automatically or manually review the qualifications of the transaction entities, the legality of the data source, the scope of authorization, the purpose of use, cross-border circulation restrictions and industry regulatory requirements to ensure that the transaction process is legal and compliant.

[0109] (iv) Data futures services provide standardized financial services to meet future data resource allocation needs. By establishing forward data delivery contracts, data resources that can be generated within a certain future time period are pre-sold and listed for trading. The contract content includes data categories, delivery cycles, update frequencies, quality levels, delivery methods, and price terms. During the trading process, a margin system and risk control mechanism are adopted, collecting performance margin from both buyers and sellers, and reducing default risk by combining price fluctuation monitoring, position limits, risk warnings, and forced liquidation mechanisms. After the contract expires, the system can execute data delivery according to the choice of the trading parties, that is, deliver the corresponding data access permissions, data interfaces, or data packages. It can also support hedging and closing positions through reverse transactions, thereby improving the efficiency of data resource allocation and market liquidity.

[0110] In summary, the management method of the cyberspace data bank system proposed in this application involves connecting with data service providers to select corresponding data asset services to obtain the required target data assets, then pushing the obtained target data assets to the data bank layer. The data management service configured at the data bank layer, in conjunction with the data vault layer, completes the processing of the target data assets. The access management service generates data futures contracts based on the processed target data assets. Finally, the operation management layer implements the shared trading operation of the data futures contracts, completing the data asset transaction between data service providers and data purchasers. This method leverages dedicated services at each level to transform data assets from raw resources into standardized tradable assets. The data futures contracts standardize the rules for data asset trading, avoiding various risks arising from fragmented data transactions without standardized constraints. This improves the market-based trading system for data assets and promotes the market-based allocation and value release of data elements according to standardized models.

[0111] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0112] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0113] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0114] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any of the following techniques known in the art, or a combination thereof: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (FPGAs), field-programmable gate arrays (FPGAs), etc.

[0115] Those skilled in the art will understand that all or part of the steps of the methods implementing the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0116] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A cyberspace data banking system, characterized in that, include: The infrastructure layer, data vault layer, data bank layer, operations management layer, and application business layer are as follows: The data vault layer includes a data vault module and an analysis and mining processing module. The data vault module calls the devices of the infrastructure layer to realize data storage, and the analysis and mining processing module calls the devices of the infrastructure layer to realize data analysis and mining. The data bank layer provides data management services and access management services. The data management service calls the data vault layer to process the target data assets, and the access management service forms data futures contracts from the target data assets. The operations management layer is used to conduct shared trading operations for the data futures contracts. The application service layer is used to provide at least one data asset service, respond to the data asset service selected by the data service provider, and send the target data asset provided by the data service provider to the data bank layer to realize the transaction between the data service provider and the data purchaser.

2. The cyberspace data banking system according to claim 1, characterized in that, The infrastructure layer includes at least one computing device, a network device, and a storage device, wherein computing resources are provided through the computing device, network resources are provided through the network device, and storage resources are provided through the storage device.

3. The cyberspace data banking system according to claim 1, characterized in that, The data vault module includes at least one of a data lake, a data warehouse, a distributed database, and a blockchain-based evidence storage platform. The data lake receives and stores raw, semi-structured, and unstructured data, preserving the original format and metadata of the target data assets. The data warehouse is used for thematic aggregation, cleaning, transformation, indexing, and statistical analysis of the managed structured data. The distributed database is used for sharded storage, parallel querying, and copy backup of high-concurrency access data and large-scale target data assets. The blockchain-based evidence storage platform invokes devices in the infrastructure layer to store at least one data operation: login, query, download, authorization, modification, and deletion.

4. The cyberspace data banking system according to claim 1, characterized in that, The analysis and mining processing module includes a data governance platform and a data processing and production platform. The data governance platform calls the equipment of the infrastructure layer to perform at least one of structured data processing, semi-structured data processing, and unstructured data processing. The data processing and production platform is used to perform at least one of the following processes: data labeling, synthesis, matching, evaluation, and publication.

5. The cyberspace data banking system according to claim 1, characterized in that, The data vault layer also includes application production tools, which include analysis tools and data intelligence agents. The analysis tools are used to perform multi-dimensional statistics, judgment, and in-depth analysis on the processed data; the data intelligence agents are used to autonomously identify, intelligently adapt, and automatically process the analysis results.

6. The cyberspace data banking system according to claim 1, characterized in that, The data bank layer includes a data bank center module and an access management node module, wherein, The data bank center module provides data management services, which include at least one of the following: spatial account management, data catalog center, connector, data ownership confirmation center, and data audit center. The access management node module provides access management services, which include at least one of user management, data delivery, capability expansion, data management, policy control, data contracts, and connector security.

7. The cyberspace data banking system according to claim 6, characterized in that, The data bank layer also includes a trusted circulation platform, which provides at least one of the following: secure data exchange, trusted execution environment, privacy computing, and end-to-end blockchain notarization. Specifically, the secure data exchange function establishes an encrypted transmission channel between the data service provider and the data purchaser, performing data anonymization, format verification, transmission integrity verification, and recipient authentication on the exchanged data. The trusted execution environment decrypts the target data asset and executes computational tasks within a hardware-isolated or logically isolated secure execution area. During computation, the original data is invisible to the operating system, database, and external applications; upon completion, only at least one result and summary are output. The privacy computing function performs joint statistical analysis, feature engineering, or model training, outputting at least one computational result and encrypted gradient. The end-to-end blockchain notarization records key operations throughout the entire data asset process, from data governance, processing and production, contract generation, transaction matching, data delivery to audit compliance, on the blockchain notarization platform.

8. The cyberspace data banking system according to claim 1, characterized in that, The operation management layer includes an operation portal module, an asset level module, a rights confirmation management module, a compliance supervision module, a demand center module, a data market module, an application market module, a metering and billing module, and a value-added service module. The operation management layer responds to user interaction actions and selects at least one module of the operation management layer based on the interaction actions.

9. The cyberspace data banking system according to claim 1, characterized in that, The application business layer includes an application scenario module and an innovation scenario module. The application scenario module provides at least one of the following functions: emergency management, digital finance, smart city, and low-altitude economy. The innovation scenario module provides at least one of the following functions: model training, digital asset securitization, knowledge base construction, and data financial derivatives services.

10. A management method for a cyberspace data bank system, characterized in that, The method is used to manage the cyberspace data banking system according to any one of claims 1-9, wherein the method includes the following steps: Respond to the data asset services selected by the data service provider and obtain the target data assets provided by the service provider; The target data assets provided by the data service provider are sent to the data bank layer, which processes the target data assets. The data bank layer provides data management services and access management services. The data management service calls the data vault layer to process the target data assets, and the access management service forms data futures contracts from the target data assets. The data service provider and the data purchaser can realize transactions by the operation management team through shared trading operations of the data futures contracts.

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