Centralized and distributed data management system and method

Abstract

The invention discloses a centralized + distributed data management system and method, and relates to the technical field of data management. Comprising a data strategy module, a management module, a standard module, a basic module, an execution module and an evaluation module. The data management system is constructed based on a centralized + distributed model, has the advantages of high management efficiency, short period, low cost and the like, and can be used as a large-scale organization for improving the data management efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of data management, and more particularly to a data management system and method based on centralized and distributed management. Background Art

[0002] A data management system is a systematic framework built by an organization to achieve full lifecycle management of data. It aims to ensure effective governance, secure application and maximum value of data by integrating strategies, processes, technologies and personnel.

[0003] The current data management system has a series of problems such as data silos and fragmentation, insufficient data quality and standardization, contradictions between data sharing and security, and shortages of technical and management talents, which affect the internal data management efficiency, data governance effectiveness and collaboration enthusiasm of the organization.

[0004] Most existing data management systems use centralized data management systems, where specialized data management teams are set up to carry out data management work. Business departments passively accept data management, which can lead to difficulties in coordination and implementation. Furthermore, the construction cycle is long, the adaptability is weak, and the initial investment required is high, while the management effect is difficult to guarantee. This invention studies and constructs a data management system based on centralized and distributed management. Business departments make data management decisions based on their own circumstances, and the data management department formulates a data management strategy that aligns management and business goals, forming a data management system with a short cycle, high efficiency, and low cost.

[0005] Therefore, it is an urgent problem for those skilled in the art to propose a data management system and method based on centralized + distributed to solve the difficulties existing in the prior art. Summary of the Invention

[0006] In view of this, the present invention provides a data management system and method based on centralization + distribution, aiming to construct a reasonable and feasible data management system that adapts to the development needs of organizations in the context of the big data era by investigating and sorting out mainstream data management systems at home and abroad.

[0007] In order to achieve the above object, the present invention provides the following technical solutions: A data management system based on centralized + distributed, including: data strategy module, management module, standard module, basic module, execution module and evaluation module; Data strategy module, used to clarify the vision and goals of data management; The management module is used to establish organizations or departments and define responsibilities, formulate systems, regulations, and processes, and manage and coordinate the data management system; The standard module is used to formulate unified specifications and solution standard SOPs based on various R&D standards, management standards, operation and maintenance standards, and security standards to guide and standardize daily data management work; Basic modules are used to provide technical support for metadata tools and system tools; The execution module is used to carry out data architecture, data application, data security, data quality control, and data governance throughout the data life cycle, and to achieve data management execution goals through pre-, mid-, and post-process management; The evaluation module is used to conduct a comprehensive evaluation of the feasibility of the data management system from three aspects: risk monitoring, indicator assessment and comprehensive evaluation.

[0008] Optionally, the data strategy module includes a data strategy planning unit, a data strategy implementation unit, and a data strategy evaluation unit connected in sequence; wherein, The data strategy planning unit is used by data stakeholders to clarify the direction of data management and application from both macro and micro levels; The data strategy implementation unit is used to decompose the task objectives of each stage in combination with the data management framework and formulate implementation plans; The data strategy assessment unit is used to track and evaluate the progress of each stage of strategy implementation.

[0009] Optionally, the management module includes a management organization and responsibility unit, a management system and a process unit connected in sequence; wherein, Management organization and responsibility unit, used to standardize the organization and responsibilities of data management; Management system and process unit, used to standardize the systems and processes of data management organizations or departments.

[0010] Optionally, the standard module includes a R&D standard unit, a management standard unit, an operation and maintenance standard unit, and a security standard unit connected in sequence; wherein, R&D Standards Unit, used to formulate standard specifications during system development; Management standards unit, used to formulate unified data management standards and specifications; Operation and maintenance standard unit, used to formulate platform operation and maintenance specifications for data management; The security standards unit is used to formulate standard specifications for data security in the data management process.

[0011] Optionally, the basic module includes a metadata tool unit and a system tool unit connected in sequence; wherein, Metadata tool unit, used to provide metadata storage and metadata services; System tool unit, used to manage governance tools and intelligent tools.

[0012] Optionally, the execution module includes an execution target unit, an execution content unit, and an execution process unit connected in sequence; wherein, Execution target unit, used to formulate data management goals; Execution content unit, which is used to establish data architecture, data application, data security, data quality control and data governance that meet strategic needs in line with the organization's data management strategic goals; The execution process unit is used for analysis and constraints before execution, monitoring and early warning during execution, and governance and evaluation after execution.

[0013] Optionally, the evaluation module includes a risk monitoring unit, an indicator evaluation unit and a comprehensive evaluation unit connected in sequence; wherein, The risk monitoring unit is used to monitor problems and risks that arise during the data management process; The indicator evaluation unit evaluates data quality and data value through a variety of standard indicators; The comprehensive evaluation unit is used to implement comprehensive evaluation of the data management system.

[0014] A centralized + distributed data management method, implementing any of the above-mentioned centralized + distributed data management systems, comprising the following steps: S1. Develop data strategy, which includes data strategy planning, data strategy implementation, and data strategy evaluation; S2. Standardize data management organizations and responsibilities, and develop data management systems and processes; S3. Carry out the formulation of standards for data management systems, focusing on management specifications, R&D specifications, operation and maintenance specifications, security specifications, and quality standards; S4. Conduct research and development of system basic modules; S5. Carry out the implementation of the data management system through the implementation of objectives, implementation content and implementation process; S6. Carry out evaluation of the data management system through risk monitoring, indicator assessment and comprehensive evaluation.

[0015] Optionally, the specific process of S4 is: S41. Build metadata tools through metadata warehousing and metadata services, build metadata warehousing by describing business metadata warehousing, technical metadata warehousing, and management metadata warehousing, and build metadata services from API interface services and message queue services; S42. Build system tools through the design of the basic platform architecture of the data management system. The basic platform architecture is designed from the aspects of data collection, data storage, infrastructure, data management, data application, and data services; The specific process of S5 is as follows: S51. Establish implementation objectives for the data management system; S52. Implement the data management system for data architecture, data application, data security, data quality control, and data governance; S53. On the basis of formulating execution objectives and implementing execution contents, the execution process of the data management system shall be carried out through pre-analysis and constraints, in-process monitoring and early warning, and post-event governance and evaluation.

[0016] Optionally, the specific process of S6 is as follows: S61. Conduct risk monitoring through problem monitoring and risk monitoring in the data management system; S62. Conduct indicator evaluation of data management systems; S63. Conduct a comprehensive evaluation of the data management system based on business needs, technical capabilities, and resource constraints.

[0017] It can be seen from the above technical solutions that, compared with the prior art, the present invention provides a data management system and method based on centralized + distributed management, which has the following beneficial effects: (1) Building a data management system based on a centralized + distributed model has the advantages of high management efficiency, short cycle, and low cost, and can be used by large organizations to improve data governance effectiveness; (2) It has the advantage of forming a data management model that aligns management and business goals, and can be promoted and applied. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0019] Figure 1 This is a structural diagram of a centralized + distributed data management system provided by the present invention; Figure 2 This is a diagram of the basic platform architecture of the data management system provided by the present invention; Figure 3 This is a diagram of the implementation process of the data management system provided by the present invention. DETAILED DESCRIPTION

[0020] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0021] See also Figure 1 As shown, the present invention discloses a data management system based on centralized + distributed, including: data strategy module, management module, standard module, basic module, execution module and evaluation module; wherein, Data strategy module, used to clarify the vision and goals of data management; The management module is used to establish organizations or departments and define responsibilities, formulate systems, regulations, and processes, and manage and coordinate the data management system; The standard module is used to formulate unified specifications and solution standard SOPs based on various R&D standards, management standards, operation and maintenance standards, and security standards to guide and standardize daily data management work; Basic modules are used to provide technical support for metadata tools and system tools; The execution module is used to carry out data architecture, data application, data security, data quality control, and data governance throughout the data life cycle, and to achieve data management execution goals through pre-, mid-, and post-process management; The evaluation module is used to conduct a comprehensive evaluation of the feasibility of the data management system from three aspects: risk monitoring, indicator assessment and comprehensive evaluation.

[0022] Furthermore, the data strategy module includes a data strategy planning unit, a data strategy implementation unit, and a data strategy evaluation unit connected in sequence; wherein, The data strategy planning unit is used by data stakeholders to clarify the direction of data management and application from both macro and micro levels; Specifically, data strategic planning includes macro data strategic planning and micro data strategic planning; data stakeholders include data providers, users, and managers.

[0023] The data strategy implementation unit is used to decompose the task objectives of each stage in combination with the data management framework and formulate implementation plans; Specifically, data strategy implementation includes macro data strategy implementation and micro data strategy implementation.

[0024] The data strategy assessment unit is used to track and evaluate the progress of each stage of strategy implementation.

[0025] Specifically, data strategy assessment includes macro data strategy assessment and micro data strategy assessment.

[0026] Furthermore, the management module includes the management organization and responsibility unit, management system and process unit connected in sequence; wherein, Management organization and responsibility unit, used to standardize the organization and responsibilities of data management; Specifically, management organization and responsibilities include establishing a data management organization or department and defining the scope of responsibilities.

[0027] Management system and process unit, used to standardize the systems and processes of data management organizations or departments.

[0028] Specifically, management systems and processes include relevant systems and work processes of data management organizations or departments.

[0029] Furthermore, the standard module includes a R&D standard unit, a management standard unit, an operation and maintenance standard unit, and a safety standard unit connected in sequence; wherein, R&D Standards Unit, used to formulate standard specifications during system development; Specifically, the R&D standards include data access specifications and data classification specifications.

[0030] Management standards unit, used to formulate unified data management standards and specifications; Specifically, management standards include data management organization and responsibilities, data management system and data management process.

[0031] Operation and maintenance standard unit, used to formulate platform operation and maintenance specifications for data management; Specifically, operation and maintenance standards include platform operation and maintenance specifications.

[0032] The security standards unit is used to formulate standard specifications for data security in the data management process.

[0033] Specifically, security standards include data masking specifications and data security standards.

[0034] Furthermore, the basic module includes a metadata tool unit and a system tool unit connected in sequence; wherein, Metadata tool unit, used to provide metadata storage and metadata services; Specifically, metadata storage includes business metadata storage, technical metadata storage, and management metadata storage; metadata services include API interface services and message queue services.

[0035] System tool unit, used to manage governance tools and intelligent tools.

[0036] Specifically, governance tools include data panorama tools, data management workbench and data governance workbench; intelligent tools include model design, task development and task deployment.

[0037] Furthermore, the execution module includes an execution target unit, an execution content unit and an execution process unit connected in sequence; wherein, Execution target unit, used to formulate data management goals; Specifically, the execution target unit is used to formulate data management goals of ease of use, efficiency, quality assurance, and security. The execution targets include ease of use, efficiency, quality, and security.

[0038] Execution content unit, which is used to establish data architecture, data application, data security, data quality control and data governance that meet strategic needs in line with the organization's data management strategic goals; Specifically, data architecture includes data submission and sharing architecture, data model construction architecture and metadata management architecture; Data applications include data mining and analysis, data open sharing, and data services; Data security includes data security strategy, data security management and audit; Data quality control includes data quality requirements, data quality analysis and evaluation, and data quality optimization; Data governance includes organizational management, system construction, and communication and coordination.

[0039] The execution process unit is used for analysis and constraints before execution, monitoring and early warning during execution, and governance and evaluation after execution.

[0040] Specifically, the execution process includes before, during and after the event.

[0041] Furthermore, the evaluation module includes a risk monitoring unit, an indicator evaluation unit and a comprehensive evaluation unit connected in sequence; wherein, The risk monitoring unit is used to monitor problems and risks that arise during the data management process; Specifically, it includes problem monitoring and risk monitoring.

[0042] The indicator evaluation unit evaluates data quality and data value through a variety of standard indicators; Specifically, it includes data quality assessment and data value assessment.

[0043] The comprehensive evaluation unit is used to implement comprehensive evaluation of the data management system.

[0044] Specifically, the comprehensive evaluation includes a comprehensive evaluation of the necessity, feasibility, sustainability, etc. of the data management system.

[0045] More specifically, the output of the data strategy planning unit is connected to the input of the data strategy implementation unit to generate an overall data strategy plan; The output end of the data strategy implementation unit is connected to the input end of the data strategy evaluation unit to generate a data strategy implementation plan; The output of the data strategy assessment unit is connected to the input of the management organization and responsibility unit to generate the data strategy assessment results; The output of the management organization and responsibility unit is connected to the input of the management system and process unit to generate data management related management organizations and their responsibility specifications; The output of the management system and process unit is connected to the input of the R&D standard unit to generate management systems and processes related to data management; The output of the R&D standard unit is connected to the input of the management specification unit to generate relevant standard specifications in the system development process; The output of the management specification unit is connected to the input of the operation and maintenance specification unit to generate relevant standard specifications in the system management process; The output of the operation and maintenance specification unit is connected to the input of the safety standard unit to generate relevant standard specifications in the system operation and maintenance process; The output of the security standard unit is connected to the input of the metadata tool unit to generate data security related standard specifications; The output end of the metadata tool unit is connected to the input end of the system tool unit to generate metadata storage and metadata services; The output of the system tool unit is connected to the input of the execution target unit to generate governance tools and intelligent tools; The output end of the execution target unit is connected to the input end of the execution content unit to generate a data management execution target that is easy to use, efficient, and guarantees quality and security; The output of the execution content unit is connected to the input of the execution process unit to generate data architecture, data application, data security, data quality control and data governance that meet the needs of data strategy; The output of the execution process unit is connected to the input of the risk monitoring unit to generate pre-event constraints and analysis, in-event monitoring and early warning, and post-event governance and evaluation; The output of the risk monitoring unit is connected to the input of the indicator evaluation unit to generate data management process problem monitoring and risk monitoring; The output of the indicator evaluation unit is connected to the input of the comprehensive evaluation unit to generate a metric evaluation of data quality and data value.

[0046] The output end of the comprehensive evaluation unit outputs comprehensive evaluation results on the necessity, feasibility, sustainability and other aspects of the data management system.

[0047] and Figure 1 Corresponding to the system, the present invention also provides a data management method based on centralized + distributed Figure 1 The specific implementation of the system includes the following steps: S1. Develop data strategy, which includes data strategy planning, data strategy implementation, and data strategy evaluation; S2. Standardize data management organizations and responsibilities, and develop data management systems and processes; S3. Carry out the formulation of standards for data management systems, focusing on management specifications, R&D specifications, operation and maintenance specifications, security specifications, and quality standards; S4. Conduct research and development of system basic modules; S5. Carry out the implementation of the data management system through the implementation of objectives, implementation content and implementation process; S6. Carry out evaluation of the data management system through risk monitoring, indicator assessment and comprehensive evaluation.

[0048] Furthermore, the specific process of S4 is as follows: S41. Build metadata tools through metadata warehousing and metadata services, build metadata warehousing by describing business metadata warehousing, technical metadata warehousing, and management metadata warehousing, and build metadata services from API interface services and message queue services; S42. Build system tools through the design of the basic platform architecture of the data management system. The basic platform architecture is designed from the aspects of data collection, data storage, infrastructure, data management, data application, and data services; The specific process of S5 is as follows: S51. Establish implementation objectives for the data management system; S52. Implement the data management system for data architecture, data application, data security, data quality control, and data governance; S53. On the basis of formulating execution objectives and implementing execution contents, the execution process of the data management system shall be carried out through pre-analysis and constraints, in-process monitoring and early warning, and post-event governance and evaluation.

[0049] Furthermore, the specific process of S6 is as follows: S61. Conduct risk monitoring through problem monitoring and risk monitoring in the data management system; S62. Conduct indicator evaluation of data management systems; S63. Conduct a comprehensive evaluation of the data management system based on business needs, technical capabilities, and resource constraints.

[0050] In a specific embodiment: The working method of this system includes the following steps: Step 1: Data strategy formulation, which includes data strategy planning, data strategy implementation, and data strategy evaluation. Specifically, the data generated by multiple departments within a tertiary hospital, including outpatient and emergency treatment data, inpatient data, imaging data, tissue sample data, scientific research data, personnel data, and education and teaching data over the past five years, are converted into system input data resource A. A data strategy is formulated for data resource A from the aspects of data strategy planning, data strategy implementation, and data strategy evaluation.

[0051] Step 2: Based on the data strategy formed in step 1, standardize the hospital's internal data management organization and its responsibilities, and develop relevant data management systems and processes; Step 3: Based on the standardized management organization and its responsibilities, relevant systems and processes in Step 2, develop standards for the data management system. Standard development should focus on management standards, R&D standards, operation and maintenance standards, security standards, and quality standards. Step 4: Based on the data management system standards obtained in step 3, develop the basic modules of the data management system, which include metadata tools and system tools. Specifically, the method includes the following steps: Step 41: Combine the multi-source and multi-departmental data resources within the hospital and build metadata tools through metadata warehousing and metadata services. Build metadata warehousing from aspects such as business metadata warehousing, technical metadata warehousing, and management metadata warehousing. Build metadata services from aspects such as API interface services and message queue services. Step 42: Build system tools through the design of the basic platform architecture of the data management system. The basic platform architecture is designed from the aspects of multi-source data collection, data storage, infrastructure, data management, data application, and data services within the hospital. Step 5: Based on the basic system modules obtained in step 4, carry out the implementation of the data management system from the aspects of implementation objectives, implementation content, implementation process, etc. Specifically, the method includes the following steps: Step 51: Integrate the entire data lifecycle and develop implementation goals for the data management system from the perspectives of usability, efficiency, quality, and security, focusing on managers, team management, daily management, demand management, and fault management. Step 52: Implement the data management system in terms of data architecture, data application, data security, data quality control, and data governance; Step 53: Based on the establishment of implementation goals and implementation content, the implementation process of the data management system is carried out through pre-analysis and constraints, in-process monitoring and early warning, and post-event governance and evaluation; Step 6: Based on the completion of the data management system implementation in step 5, conduct an evaluation of the data management system from the aspects of risk monitoring, indicator assessment, and comprehensive evaluation.

[0052] Specifically, the method includes the following steps: Step 61: Conduct risk monitoring of the data management system through problem monitoring and risk monitoring; Step 62: Based on the relevant indicators of the data management system standard obtained in step 3, carry out indicator evaluation of the data management system; Step 63: Considering business needs, technical capabilities, and resource constraints, conduct a comprehensive evaluation of the data management system from the perspectives of necessity, technical feasibility, and sustainability.

[0053] The details are as follows: In combination with the characteristics of the hospital's internal multi-source data resources, the data management system standards are formulated from the aspects of management standards, R&D specifications, operation and maintenance specifications, safety specifications, and quality standards, as shown in Table 1.

[0054] Table 1 Contents covered by the development of data management system standards

[0055] The basic platform architecture of the data management system is designed from the aspects of data collection, data storage, infrastructure, data management, data application, data service, etc. Figure 2 By building infrastructure such as the MongoDB big data architecture, Oracle database architecture, and application service architecture, we will carry out a series of tasks such as data collection, data storage, and data management. Furthermore, we will carry out data application construction such as data query, browsing, visualization, and data product design, as well as research data services for key scenarios such as image duplication detection, embedded tools, and journal evaluation.

[0056] In combination with the entire life cycle of data, the implementation of the data management system is carried out from the aspects of managers, demand management, team management, fault management, etc. Figure 3 As shown in the figure, the system aims to quickly understand data, improve management efficiency, identify problems promptly, and formulate implementation goals for corresponding countermeasures. Users are categorized into several categories, including hospital team managers, general management personnel, and professional and technical personnel. Team management, labor efficiency analysis management, daily operations analysis management, and fault analysis management are carried out. This forms a centralized and distributed data management model with comprehensive indicator system monitoring, focus on key scenarios, and standardized daily operations.

[0057] Based on the development of a standard indicator system, the data management system's indicator completion status is evaluated from the perspectives of data quality, security, usability, cost, and value, as shown in Table 2. The data quality indicator completion evaluation covers basic core indicators, timeliness and availability, logic and rule indicators, authenticity, and integrity. Data security is assessed from the perspectives of basic attributes, technical protection, management compliance, and risk assessment. Usability includes aspects such as data accessibility, data structure and format, metadata and document integrity, data processing efficiency, and data understandability.

[0058] Table 2 Comprehensive evaluation indicators of data management system

[0059] From the three aspects of necessity, technical feasibility and sustainability, combined with business needs, technical capabilities and resource constraints, a feasibility analysis and comprehensive evaluation of the data management system are carried out.

[0060] (1) Necessity analysis The justification analysis for a data management system involves factors such as demand alignment and risk-value balancing. Regarding demand alignment, a systematic analysis is conducted to determine whether the data management system meets core requirements for data strategy planning, management decision-making, and process optimization. Regarding risk-value balancing, the assessment considers the potential for decision-making bias and compliance risks associated with data management, as well as the potential for resource optimization after the data management system is established.

[0061] (2) Technical feasibility Technical feasibility is considered from the perspectives of technical maturity and data quality baselines. Technical maturity requires verifying the compatibility and implementation difficulty of data management tools (such as metadata management and quality oversight assessment) with the data management platform. Regarding data quality baselines, quantifiable data quality objectives are defined based on dimensions such as accuracy, completeness, and consistency.

[0062] (3) Sustainability analysis Demonstrate the sustainability of the data management system from the perspectives of resource assurance, risk prediction, and response. Regarding resource assurance, clarify the division of responsibilities among different roles within the data management team (e.g., data provider, user, and manager), study the adaptability of the organizational structure, and budget the input-output ratio and development cycle for data management tool development, personnel training, and process optimization. Regarding risk prediction and response, promptly identify potential implementation risks, such as potential obstacles to technology implementation (e.g., data silos) and resistance to collaboration between business departments. Develop a data quality anomaly warning mechanism and a contingency plan for responding to issues.

[0063] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Reference can be made to the common and similar parts between the various embodiments. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method description.

[0064] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A data management system based on centralized + distributed, characterized in that, include: Data strategy module, management module, standard module, basic module, execution module and evaluation module; among them, Data strategy module, used to clarify the vision and goals of data management; The management module is used to establish organizations or departments and define responsibilities, formulate systems, regulations, and processes, and manage and coordinate the data management system; The standard module is used to formulate unified specifications and solution standard SOPs based on various R&D standards, management standards, operation and maintenance standards, and security standards to guide and standardize daily data management work; Basic modules are used to provide technical support for metadata tools and system tools; The execution module is used to carry out data architecture, data application, data security, data quality control, and data governance throughout the data life cycle, and to achieve data management execution goals through pre-, mid-, and post-process management; The evaluation module is used to conduct a comprehensive evaluation of the feasibility of the data management system from three aspects: risk monitoring, indicator assessment and comprehensive evaluation.

2. A centralized + distributed data management system according to claim 1, characterized in that: The data strategy module includes a data strategy planning unit, a data strategy implementation unit, and a data strategy evaluation unit connected in sequence; among them, The data strategy planning unit is used by data stakeholders to clarify the direction of data management and application from both macro and micro levels; The data strategy implementation unit is used to decompose the task objectives of each stage in combination with the data management framework and formulate implementation plans; The data strategy assessment unit is used to track and evaluate the progress of each stage of strategy implementation.

3. A centralized + distributed data management system according to claim 1, characterized in that: The management module includes the management organization and responsibility unit, management system and process unit connected in sequence; among them, Management organization and responsibility unit, used to standardize the organization and responsibilities of data management; Management system and process unit, used to standardize the systems and processes of data management organizations or departments.

4. A centralized + distributed data management system according to claim 1, characterized in that: The standard module includes the R&D standard unit, management standard unit, operation and maintenance standard unit and security standard unit connected in sequence; among them, R&D Standards Unit, used to formulate standard specifications during system development; Management standards unit, used to formulate unified data management standards and specifications; Operation and maintenance standard unit, used to formulate platform operation and maintenance specifications for data management; The security standards unit is used to formulate standard specifications for data security in the data management process.

5. The centralized + distributed data management system according to claim 1, characterized in that: The basic module includes a metadata tool unit and a system tool unit connected in sequence; wherein, Metadata tool unit, used to provide metadata storage and metadata services; System tool unit, used to manage governance tools and intelligent tools.

6. The centralized + distributed data management system according to claim 1, characterized in that: The execution module includes an execution target unit, an execution content unit and an execution process unit connected in sequence; wherein, Execution target unit, used to formulate data management goals; Execution content unit, which is used to establish data architecture, data application, data security, data quality control and data governance that meet strategic needs in line with the organization's data management strategic goals; The execution process unit is used for analysis and constraints before execution, monitoring and early warning during execution, and governance and evaluation after execution.

7. The centralized + distributed data management system according to claim 1, characterized in that: The evaluation module includes a risk monitoring unit, an indicator evaluation unit and a comprehensive evaluation unit connected in sequence; among them, The risk monitoring unit is used to monitor problems and risks that arise during the data management process; The indicator evaluation unit evaluates data quality and data value through a variety of standard indicators; The comprehensive evaluation unit is used to implement comprehensive evaluation of the data management system.

8. A data management method based on centralized + distributed, characterized in that Executing a centralized + distributed data management system according to any one of claims 1 to 7 comprises the following steps: S1. Develop data strategy, which includes data strategy planning, data strategy implementation, and data strategy evaluation; S2. Standardize data management organizations and responsibilities, and develop data management systems and processes; S3. Carry out the formulation of standards for data management systems, focusing on management specifications, R&D specifications, operation and maintenance specifications, security specifications, and quality standards; S4. Conduct research and development of system basic modules; S5. Carry out the implementation of the data management system through the implementation of objectives, implementation content and implementation process; S6. Carry out evaluation of the data management system through risk monitoring, indicator assessment and comprehensive evaluation.

9. The centralized + distributed data management method according to claim 8, characterized in that: The specific process of S4 is: S41. Build metadata tools through metadata warehousing and metadata services, build metadata warehousing by describing business metadata warehousing, technical metadata warehousing, and management metadata warehousing, and build metadata services from API interface services and message queue services; S42. Build system tools through the design of the basic platform architecture of the data management system. The basic platform architecture is designed from the aspects of data collection, data storage, infrastructure, data management, data application, and data services; The specific process of S5 is as follows: S51. Establish implementation objectives for the data management system; S52. Implement the data management system for data architecture, data application, data security, data quality control, and data governance; S53. On the basis of formulating execution objectives and implementing execution contents, the execution process of the data management system shall be carried out through pre-analysis and constraints, in-process monitoring and early warning, and post-event governance and evaluation.

10. The centralized + distributed data management method according to claim 8, characterized in that: The specific process of S6 is as follows: S61. Conduct risk monitoring through problem monitoring and risk monitoring in the data management system; S62. Conduct indicator evaluation of data management systems; S63. Conduct a comprehensive evaluation of the data management system based on business needs, technical capabilities, and resource constraints.

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