Public data dominated urban data space system

Abstract

The invention provides a public data dominated urban data space system. The public data dominated urban data space system comprises a data aggregation and exchange facility and a data processing and opening facility, the data aggregation and exchange facility comprises longitudinal data application, transverse data application and government affair data application, and longitudinal, transverse and internal sharing exchange is realized through a data link center; the data processing and opening facility comprises a data resource pool, a data opening facility, a data resource registration facility, a data asset management facility and a data delivery and safety supervision facility; the data resource pool comprises a data governance and modeling tool oriented to converged longitudinal data and transverse data; the data opening facility is directly opened through a data set / API (Application Program Interface), and is used for realizing fusion, modeling and opening of government and enterprise data in a data sandbox opening mode; the data resource registration facility is used for supporting a data resource application permission issuing demand; the data asset management facility is used for solving the problem of identification and analysis of data assets and providing support of compliance analysis, cost collection and economic benefit analysis for data capitalization actions such as subsequent data asset transactions and data value-added services; and the data delivery and safety supervision facility is used for delivery application and supervision of data assets.

Description

Technical Field

[0001] The present invention relates to an urban data space, and in particular to an urban data space system dominated by public data. Background Art

[0002] As the relevant policies of "data space" are gradually implemented, the "city-level data space construction" has entered the exploratory stage. However, it is limited by unresolved problems such as "unclear data ownership", "original data interception and leakage", "uncontrollable use of data openness", and "difficult to trace the data use process". As a result, the construction of city-level integrated data space is still in a state of "dare not", "unwilling", and "unable". The main problems faced at this stage are as follows:

[0003] (1) It is impossible to clearly sort out the data rights and interests types, rights and interests attributes, and rights and interests scope of each participant in the urban data space, and the data property rights system has not been established in a standardized manner.

[0004] (2) It is impossible to strike a balance between data openness and privacy protection: When data enters the integration domain, data holders lose control over the data. It is difficult to ensure that the data is not copied during data governance, development, and operations. Therefore, it is necessary to minimize the allocation of permissions for data resource holding rights, data processing and use rights, and data product operating rights based on actual scenarios.

[0005] (3) Unable to guarantee the purpose and amount of data usage: There is a lack of effective review mechanism for the data demander's application and use of data, and identity verification, scenario review, data usage scope, and data usage cycle are unclear.

[0006] (4) The infrastructure for data storage, data processing, data connection, data sharing, and data openness has not been formed: the data storage and computing engines, data resource directories, data modeling platforms, data exchange platforms, and data security supervision tools of each participant are not unified, and there is a lack of standardization of functions and processes. In the process of data integration and cooperation, huge resources need to be invested in system transformation and docking, which makes data integration and cooperation difficult and the cooperation cycle long. Summary of the invention

[0007] The present invention provides a public data-dominated urban data space system, which solves the problem of data asset management and data assetization of data elements in urban data space. The technical solution is as follows:

[0008] An urban data space system dominated by public data, including data aggregation and exchange facilities, data processing and open facilities;

[0009] The data aggregation and exchange facilities include vertical data, horizontal data, and government data applications, and vertical, horizontal, and internal sharing and exchange are achieved through the data link hub;

[0010] The data processing and opening facilities include data resource pool, data opening facilities, data resource registration facilities, data asset management facilities, and data delivery and security supervision facilities; the data resource pool includes data governance and modeling tools for aggregated vertical data and horizontal data; the data opening facilities are used to realize the integration, modeling, and opening of government and enterprise data through direct opening of data sets / APIs and data sandbox opening modes; the data resource registration facilities are used to support the issuance of data resource application licenses; the data asset management facilities are used to solve the identification and analysis of data assets, and provide compliance analysis, cost collection, and economic benefit analysis support for subsequent data asset transactions, data value-added services, and other data assetization actions; the data delivery and security supervision facilities are used for the delivery, application, and supervision of data assets.

[0011] The vertical data include data from national ministries and commissions, provincial / district and county-level shared data, local public institution data, local enterprise data, and local personal data; the horizontal data include data from regional leading enterprises and urban agglomeration data; the government data applications include information systems that provide business services for emergency, transportation, medical, human resources and social security, market supervision, and fire protection industries or business fields, and also include data applications or data services for governments, enterprises, and individuals formed after data fusion.

[0012] The data link hub, including the vertical shared exchange library, the horizontal shared exchange library, the internal shared library and the shared data directory, is the data sharing channel of the urban data space. It introduces external data into the urban data resource pool and also provides localized data services to the outside world.

[0013] The data resource pool includes data governance and modeling tools, computing resource areas, storage resource areas, and security resource areas that support the operation of the tools, as well as data processing and governance results. The data processing and governance results include original libraries, standard libraries, basic libraries, theme libraries, and special libraries.

[0014] The computing resource area and storage resource area will centrally manage storage and computing resources to provide a foundation for data governance, application, sharing, and open services; the security resource area will centrally manage data security, network security software and hardware, and service capabilities to provide security resource guarantees.

[0015] For unconditionally open public data, the data opening facility directly provides government data services to the outside world through data sets or API interface services; for conditionally open public data, after desensitization operations, an independent training environment is allocated based on the data sandbox for model training, and then online data model services are deployed in the reasoning environment; the data opening facility is constructed with a data modeling and analysis module that provides two sets of environments: training and reasoning.

[0016] The data demander uses the data modeling and analysis module to perform model training and online services, including the following steps:

[0017] S1: The data demander applies for a task, including data, processor, memory, hard disk, image version, and uploads a self-sustaining training set;

[0018] S2: The data operator conducts review and samples and desensitizes the training data to the training sandbox;

[0019] S3: Data demanders perform model training in the sandbox;

[0020] S4: Data demanders apply for model release;

[0021] S5: Public data operators shall review the model security and availability;

[0022] S6: After review by the data operator, the model is deployed, the model is put online in the inference environment, and the data source is switched to the production database;

[0023] S7: The data demander calls the data product and provides a prediction data set;

[0024] S8: The data model provides online services and publishes prediction results.

[0025] The data resource registration facility completes the registration of data element data resource holding rights and data product data product operating rights through the first registration; then the data resource holder grants data processing and use rights and data product operating rights to others through license registration; and transfer registration realizes the transfer of data element resource holding rights and data product operating rights.

[0026] In the data asset management facilities, the leap from data resources to data assets requires data compliance analysis, data cost aggregation, and expected economic benefit analysis. The data compliance analysis is initiated from four dimensions, namely source compliance, content compliance, processing compliance, and operational compliance; the expected economic benefit analysis is initiated during internal empowerment and external monetization delivery. Data resources that meet the above conditions are identified as data assets.

[0027] The data delivery and security supervision infrastructure includes the following functions:

[0028] Data provision and data product delivery: When the government big data platform provides production data for model calculation, it will provide data access services through blockchain smart contracts; the model calculation results are uploaded to the blockchain and then provided to the data product delivery module through the blockchain; the data demander will obtain the results by calling the service interface encapsulated by the blockchain smart contract;

[0029] Data resource registration and certification: data ownership information and data feature information are stored through blockchain;

[0030] Core business anti-tampering: Build secondary indexes through analytical database plus on-chain data storage to achieve core data storage and regular comparison, and track and discover data tampering;

[0031] Key business process evidence storage: Provide evidence storage + query + verification smart contracts for task application, task review, task call, and data registration and confirmation;

[0032] Data lineage discovery: Explore the derived relationships of table-level data within a database, between databases, and between heterogeneous data sources, and form a data lineage map covering the three-layer system of data source, data warehouse, and data product;

[0033] User authorization notarization: For scenarios where data products and services involve outbound transfer of personal privacy data, authorization notarization is performed through blockchain.

[0034] The public data-led urban data space system is positioned to become a city-level data carrier that allows data to be "available, circulated, and used well", providing basic support for the reliable and efficient circulation of data between local and surrounding cities. It has the following advantages:

[0035] (1) The urban data space is based on local public data, supplemented by data from national ministries, provincial governments, city clusters, and regional leading enterprises. It builds a unified data resource directory through a data link hub, and realizes the entry registration, asset analysis, and data openness of data resources, thus opening up the entire chain of data aggregation, exchange, processing, and openness.

[0036] (2) The urban data space focuses on serving enterprise development and urban management. Enterprises are the livelihood of urban residents and are the top priority of development. The plan proposes a detailed plan for opening public data to enterprises, supplemented by data resource registration, data delivery and security supervision facilities, so that the public data opening to enterprises is compliant, safe and efficient.

[0037] (3) The transition from data resources to data assets is an inevitable trend. Building a data management model with data assets at its core is crucial for a city’s digital transformation. The data asset management infrastructure has achieved the transition from data resources to assets from the dimensions of compliance, cost, and economic benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 is a structural diagram of the data link hub;

[0039] Figure 2 is a schematic diagram of the structure of the data opening facility;

[0040] Figure 3 is a schematic diagram of the structure of the data resource registration facility;

[0041] Figure 4 is a schematic diagram of the structure of the data asset management facility;

[0042] Figure 5 It is a structural diagram of the data delivery and security supervision facilities. DETAILED DESCRIPTION

[0043] The public data-dominated urban data space system includes data aggregation and exchange facilities, and data processing and opening facilities.

[0044] The data aggregation and exchange facilities include vertical data, horizontal data, and government data applications. These three aspects of data are shared and exchanged vertically, horizontally, and internally through the data link hub, and data links are achieved without leaving the original domain through privacy computing methods.

[0045] The vertical data include national ministry data, provincial / district and county level shared data, local public institution data, local enterprise data, local personal data, etc.

[0046] The horizontal data include regional leading enterprise data and urban agglomeration data (Note: urban agglomeration data refers to data of surrounding cities, such as the northern Jiangsu urban agglomeration including Huai'an, Suqian and Lianyungang).

[0047] The government data applications mentioned above include information systems or other forms of intelligent applications that provide business services for industries or business fields such as emergency, transportation, medical care, human resources and social security, market supervision, and fire protection. They also include data applications or data services for governments, enterprises, and individuals such as one-network management, one-stop service, enterprise service platforms, and personal APPs formed after data integration.

[0048] like Figure 1 As shown, the data link hub includes a vertical sharing exchange library, a horizontal sharing exchange library, an internal sharing library, and a shared data directory. It is a data sharing channel for the urban data space, which introduces external data into the urban data resource pool and provides localized data services to the outside world. The vertical sharing exchange library is used to connect to the provincial sharing exchange library to form a vertical connection between the prefecture-level and provincial data; the horizontal sharing exchange library is used to complete the horizontal connection between regional leading enterprises and surrounding cities; the internal sharing exchange library is used to complete the connection between the commissions, offices, and bureaus within the prefecture-level cities.

[0049] The privacy computing is used to solve the data linking problem when the data cannot be left locally. For example, federated learning can solve the modeling needs of multiple parties’ data within the domain, and multi-party secure computing can solve the joint computing needs of multiple parties’ data within the domain.

[0050] The data processing and opening facilities include data resource pool, data opening facilities, data resource registration facilities, data asset management facilities, and data delivery and security supervision facilities.

[0051] The data resource pool includes data governance and modeling tools for aggregated vertical data, horizontal data, and government application data, computing resource areas, storage resource areas, and security resource areas that support the operation of the tools, as well as data processing and governance results, namely, original libraries, standard libraries, basic libraries, theme libraries, and special libraries.

[0052] The computing resource area and storage resource area will unify the management of storage and computing resources to provide a foundation for data governance, application, sharing, and open services; the security resource area will unify the management of data security, network security software and hardware, and service capabilities to provide security resource guarantees.

[0053] The data governance and modeling tools are used to solve the scenario-based needs of data governance and processing: for governance services for structured data, the service pipeline includes data exploration, data integration, data cleaning, and data processing; for governance services for unstructured video data, the service pipeline includes video access, data labeling, model training, and algorithm reasoning.

[0054] The data processing and governance results are the data resource areas formed after completing the data governance and processing. The formed data resource areas include original libraries, standard libraries, basic libraries, theme libraries, and special library.

[0055] like Figure 2 As shown, the data openness facility is used to realize the integration, modeling and opening of government and enterprise data through direct opening of data sets / APIs and data sandbox opening modes. The data openness facility is positioned to integrate government and enterprise data, modeling and opening, and its main scenario application is to solve the opening of public data to enterprise needs.

[0056] For unconditionally open public data, government data services are provided directly through data sets or API interface services. For conditionally open public data, after desensitization, independent training environments are allocated based on data sandboxes for model training, and then online data model services are deployed in the inference environment. In order to ensure the security of public data and achieve data that is "available but not visible, available but not desirable, controllable and measurable", it is necessary to build a data modeling and analysis module that can provide two sets of environments for training and inference.

[0057] After obtaining the permission to use data from the public data operator, the public data management department and relevant commissions and bureaus, the data demander can obtain the integrated training sandbox environment of data, computing power and tools through task application, and put the model online through the process of model training, publishing, review and deployment, so as to provide computing support for data product services. In addition, this process involves the use of data to store evidence through blockchain.

[0058] The data demander uses the data modeling and analysis module for model training and online services. The process is as follows:

[0059] S1: The data demander applies for a task, including data, processor, memory, hard disk, image version, etc., and uploads a self-sustaining training set.

[0060] S2: The data operator conducts an audit and samples and desensitizes the training data to the training sandbox.

[0061] S3: Data demanders perform model training in the sandbox.

[0062] S4: Data demanders apply for model release.

[0063] S5: Public data operators shall review the security and availability of the model.

[0064] S6: After review by the data operator, the model is deployed, the model is put online in the inference environment, and the data source is switched to the production database.

[0065] S7: The data demander calls the data product and provides a prediction data set.

[0066] S8: The data model provides online services and publishes prediction results.

[0067] like Figure 3 As shown, the data resource registration facility is used to support the demand for issuing data resource "entry tickets", that is, the demand for issuing application licenses, and to provide compliance support for subsequent data assetization and capitalization.

[0068] With the gradual introduction of data assetization policies, data is gradually moving towards the evolution of "resourceization-assetization-capitalization". Data integration requires a credible entry ticket, namely the "data resource registration certificate". The issuance of this entry ticket requires the completion of two tasks. One is to examine data compliance from the combined perspective of "national laws + data element policies" and review the results of third-party data compliance analysis; the other is to analyze the value of data assets with the "data element + asset management" mindset and review the evaluation results of third-party asset appraisal agencies.

[0069] The data resource registration facilities and service system are based on the separation of the three rights proposed in the "Twenty Articles on Data": "the right to hold data resources, the right to process and use data, and the right to operate data products", to achieve the registration management of data resources and data products.

[0070] The first registration can complete the registration of data element data resource holding rights and data product data product operating rights; then, the data resource holder can grant data processing and use rights and data product operating rights to others through license registration; transfer registration can realize the transfer of data element resource holding rights and data product operating rights.

[0071] The registration results are issued in the form of electronic certificates, which clearly define the rights and interests subject, type of rights and interests, rights and interests attributes, and rights and interests scope, providing a credible entry ticket for "data integration". In the process of data resource registration, different subjects have successively claimed the right to hold data resources. The similarity of the two data should be obtained through data feature extraction tools under the premise of "not storing the original data and not comparing the original data", so as to fairly determine the data resource holder.

[0072] like Figure 4 As shown, the data asset management facility is used to solve the identification and analysis of data assets, and provide support in dimensions such as compliance analysis, cost collection, and economic benefit analysis for subsequent data asset transactions, data value-added services, and other data assetization actions.

[0073] The main function of data asset management facilities is to realize the identification and analysis of data assets, and to serve the actions of data asset entry, evaluation, credit financing, etc., aiming to realize external transactions and value-added services of data assets, as well as internal digital transformation support.

[0074] The leap from data resources to data assets requires actions such as data compliance analysis, data cost aggregation, and expected economic benefit analysis. The data compliance analysis is initiated from four dimensions, namely source compliance, content compliance, processing compliance, and operational compliance; the expected economic benefit analysis is mainly initiated from the perspective of internal empowerment and external monetization and delivery. Data resources that meet the above conditions can be identified as data assets.

[0075] Urban data space is not only the infrastructure for the aggregation, processing, exchange and opening of urban data resources, but also has the ability to enable data resources to leap into data assets, promoting the gradual upgrade of data towards "resourceization-assetization-capitalization".

[0076] like Figure 5 As shown, the data delivery and security supervision facilities include data product delivery, data resource registration and evidence storage, core business tamper-proofing, key business process evidence storage, data lineage discovery, user authorization evidence security assurance, supervision and inspection, traceability and other tool service capabilities.

[0077] The data delivery and security supervision infrastructure includes the following functional supports:

[0078] Data provision and data product delivery: When the government big data platform provides production data for model calculation, it will provide data access services through blockchain smart contracts; the model calculation results are uploaded to the blockchain, and then provided to the data product delivery module through the blockchain; the data demander will obtain the results by calling the service interface encapsulated by the blockchain smart contract.

[0079] Data resources are registered and notarized, and data ownership information and data feature information are stored through blockchain, building a "data feature chain and data property rights chain" to ensure credibility.

[0080] To prevent core business from being tampered with, a secondary index is built through "analytical database + on-chain data storage" to achieve core data storage and regular comparison, and track and discover data tampering.

[0081] The key business process evidence storage provides "evidence storage + query + verification" smart contracts for task application, task review, task call, data registration and confirmation, and builds an "on-chain twin" of historical business flows.

[0082] Data lineage discovery explores the derived relationships of table-level data within a database, between databases, and between heterogeneous data sources, forming a data lineage map covering the three-layer system of "data source, data warehouse, and data product."

[0083] User authorization notarization: For scenarios where data product services involve outbound transfer of personal privacy data, authorization notarization is performed through blockchain.

[0084] The present invention has the following characteristics:

[0085] (1) It proposes an urban data space dominated by public data, which divides the urban data space into two main parts: "data aggregation and exchange facilities" and "data processing and open facilities". It also innovatively proposes a data link hub consisting of "vertical sharing library, horizontal sharing library, internal sharing library and privacy computing tools" to solve the problem of data interconnection and interoperability between the national, provincial, prefectural, municipal, surrounding cities, regional leading enterprises, and local commissions and bureaus.

[0086] (2) It innovatively proposes specific implementation paths for data resource registration and data openness, solves compliance issues for data resource entry through data resource registration infrastructure, and solves data fusion, modeling and openness requirements for government and enterprise data through the training and reasoning environment of the data sandbox.

[0087] (3) Innovatively proposed data asset management facilities and implementation paths for data resources to be transformed into data assets, including sub-paths such as compliance analysis, cost aggregation, and expected economic benefit analysis. Urban data space is not only a facility for the aggregation, exchange, processing, and opening of data resources, but also a facility for the transformation of data resources into data assets.

[0088] (4) In response to the demand for data security supervision during the data processing and opening process of urban data space, the main needs and solutions for data delivery and security supervision facilities are proposed, including core functions such as data product delivery, data resource registration and notarization, core business tamper-proofing, key business process notarization, data lineage discovery, and user authorization notarization.

Claims

1. A public data-led urban data space system, characterized by: Including data aggregation and exchange facilities, data processing and opening facilities; The data aggregation and exchange facilities include vertical data, horizontal data, and government data applications, and vertical, horizontal, and internal sharing and exchange are achieved through the data link hub; The data processing and opening facilities include data resource pool, data opening facilities, data resource registration facilities, data asset management facilities, data delivery and security supervision facilities; the data resource pool includes data governance and modeling tools for aggregated vertical and horizontal data; the data opening facilities are directly opened through data sets / APIs and data sandbox opening modes, which are used to realize the integration, modeling and opening of government and enterprise data; The data resource registration facility is used to support the demand for issuing data resource application licenses; the data asset management facility is used to solve the identification and analysis of data assets, and provide support for compliance analysis, cost collection, and economic benefit analysis for subsequent data asset transactions, data value-added services, and other data assetization actions; the data delivery and security supervision facility is used for the delivery, application, and supervision of data assets.

2. The public data-driven urban data space system according to claim 1 is characterized by: The vertical data include data from national ministries and commissions, provincial / district and county-level shared data, local public institution data, local enterprise data, and local personal data; the horizontal data include data from regional leading enterprises and urban agglomeration data; the government data applications include information systems that provide business services for emergency, transportation, medical, human resources and social security, market supervision, and fire protection industries or business fields, and also include data applications or data services for governments, enterprises, and individuals formed after data fusion.

3. The public data-driven urban data space system according to claim 1 is characterized by: The data link hub, including the vertical shared exchange library, the horizontal shared exchange library, the internal shared library and the shared data directory, is the data sharing channel of the urban data space. It introduces external data into the urban data resource pool and also provides localized data services to the outside world.

4. The public data-driven urban data space system according to claim 1 is characterized by: The data resource pool includes data governance and modeling tools, computing resource areas, storage resource areas, and security resource areas that support the operation of the tools, as well as data processing and governance results. The data processing and governance results include original libraries, standard libraries, basic libraries, theme libraries, and special libraries.

5. The public data-driven urban data space system according to claim 4 is characterized by: The computing resource area and storage resource area will centrally manage storage and computing resources to provide a foundation for data governance, application, sharing, and open services; the security resource area will centrally manage data security, network security software and hardware, and service capabilities to provide security resource guarantees.

6. The public data-driven urban data space system according to claim 1 is characterized by: For unconditionally open public data, the data opening facility directly provides government data services to the outside world through data sets or API interface services; for conditionally open public data, after desensitization operations, an independent training environment is allocated based on the data sandbox for model training, and then online data model services are deployed in the reasoning environment; the data opening facility is constructed with a data modeling and analysis module that provides two sets of environments: training and reasoning.

7. The public data-driven urban data space system according to claim 6 is characterized by: The data demander uses the data modeling and analysis module to perform model training and online services, including the following steps: S1: The data demander applies for a task, including data, processor, memory, hard disk, image version, and uploads a self-sustaining training set; S2: The data operator conducts review and samples and desensitizes the training data to the training sandbox; S3: Data demanders perform model training in the sandbox; S4: Data demanders apply for model release; S5: Public data operators shall review the model security and availability; S6: After review by the data operator, the model is deployed, the model is put online in the inference environment, and the data source is switched to the production database; S7: The data demander calls the data product and provides a prediction data set; S8: The data model provides online services and publishes prediction results.

8. The public data-driven urban data space system according to claim 1 is characterized by: The data resource registration facility mentioned above completes the registration of data element data resource holding rights and data product data product operation rights for the first time; The data resource holder then grants the right to process and use data and the right to operate data products to others through license registration; transfer registration realizes the transfer of data element resource holding rights and data product operating rights.

9. The public data-driven urban data space system according to claim 1 is characterized by: In the data asset management facilities, the leap from data resources to data assets requires data compliance analysis, data cost aggregation, and expected economic benefit analysis. The data compliance analysis is initiated from four dimensions, namely source compliance, content compliance, processing compliance, and operational compliance; the expected economic benefit analysis is initiated during internal empowerment and external monetization delivery. Data resources that meet the above conditions are identified as data assets.

10. The public data-driven urban data space system according to claim 1 is characterized by: The data delivery and security supervision infrastructure includes the following functions: Data provision and data product delivery: When the government big data platform provides production data for model calculation, it will provide data access services through blockchain smart contracts; the model calculation results are uploaded to the blockchain and then provided to the data product delivery module through the blockchain; the data demander will obtain the results by calling the service interface encapsulated by the blockchain smart contract; Data resource registration and certification: data ownership information and data feature information are stored through blockchain; Core business anti-tampering: Build secondary indexes through analytical database plus on-chain data storage to achieve core data storage and regular comparison, and track and discover data tampering; Key business process evidence storage: Provide evidence storage + query + verification smart contracts for task application, task review, task call, and data registration and confirmation; Data lineage discovery: Explore the derived relationships of table-level data within a database, between databases, and between heterogeneous data sources, and form a data lineage map covering the three-layer system of data source, data warehouse, and data product; User authorization notarization: For scenarios where data products and services involve outbound transfer of personal privacy data, authorization notarization is performed through blockchain.

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