Industrial data governance and development synchronization method and system oriented to data-in-data platform

Abstract

The invention provides an industrial data management and development synchronization method and system for a data-in-data station, and the method comprises the steps: S1, constructing a data architecture of the data-in-data station according to different demands of an enterprise; s2, based on the data architecture, establishing a data management and development collaboration mechanism of the data-in-data platform; s3, based on the data architecture and the collaboration mechanism, making a construction method for data management and development of the data-in-the-data platform; and S4, based on the data architecture, the collaboration mechanism and the construction method, formulating a work specification of data management and development of the data-in-the-data platform. The method comprises a collaboration mechanism, a construction method, a work specification and the like of data architecture design, treatment and development of the data in the data platform, and different requirements of enterprises on data unified management, public data asset construction, personalized data application construction and the like can be met by designing a three-layer logic data architecture of the data in the data platform.

Description

Technical Field

[0001] The present invention relates to the field of data management technology, and in particular to an industrial data governance and development synchronization method and system for a data middle platform. Background Art

[0002] In today's era of vigorous development of data elements, industrial data has become a vital asset and core key element for industrial manufacturing companies. With the continuous advancement of the digital transformation of enterprises, the use of data middle platforms to carefully build the data capability base of enterprises, and then continuously gather and precipitate high-quality data assets to provide solid support for the high-quality development of enterprises, has become the mainstream trend for many industrial manufacturing companies to seek development.

[0003] However, judging from the actual situation of data construction in the data middle platform of enterprises, there are many problems that need to be solved urgently. On the one hand, although enterprises are aware of the importance of the data middle platform and actively invest in its construction, the lack of a comprehensive and systematic method as a guide throughout the process often makes the two key tasks of data governance and data development appear disconnected in the actual implementation process. Due to the lack of effective coordination mechanism and systematic method guidance, the two cannot work closely together. This makes it difficult to truly implement data governance effectively, and it is impossible to effectively ensure that data can meet high-quality standard requirements in all links, and thus it is impossible to effectively support enterprises to form truly high-quality data assets, and it is difficult to give full play to the role of data as an important asset in promoting enterprise development.

[0004] Through searching patent documents, it is found that the invention patent with publication number CN116522095A discloses a master data governance method based on data middle platform, which includes: obtaining initial master data information, integrating and standardizing the initial master data information through the data middle platform to obtain master data information; establishing a data quality management mechanism, performing quality management on the master data information, and obtaining standard master data information; performing feature extraction and semantic analysis on the standard master data information, and classifying the standard master data information; performing attribute evaluation on the standard master data information according to the semantic analysis result information and the standard master data feature information, and obtaining attribute evaluation result information; establishing a permission management mechanism, setting permissions and encrypting information for the standard master data information; establishing a data monitoring and governance mechanism, and performing real-time monitoring and analysis on the master data information. However, this patent is only an implementation process of data governance, and does not provide a systematic working method for data governance and data development of the data middle platform, which is not suitable for guiding large-scale, multi-team synchronous industrial data governance and development for the data middle platform.

[0005] To sum up, in response to the above-mentioned problems of the existing technologies, studying an industrial data governance and development synchronization method and system for the data middle platform has become a key task that needs to be solved urgently. Summary of the invention

[0006] In view of the defects in the prior art, the purpose of the present invention is to provide an industrial data governance and development synchronization method and system for a data middle platform.

[0007] According to the present invention, a method for synchronizing industrial data governance and development for a data middle platform is provided, comprising the following steps:

[0008] Step S1: Build the data architecture of the data center according to the different needs of the enterprise;

[0009] Step S2: Establish a collaborative mechanism for data governance and development of the data center based on the data architecture;

[0010] Step S3: Based on the data architecture and collaboration mechanism, formulate the construction method of data governance and development of the data center;

[0011] Step S4: Based on the data architecture, collaboration mechanism and construction method, formulate the work specifications for data governance and development of the data center.

[0012] Preferably, in step S1, data is managed in layers according to different needs of the enterprise for unified data management, building public data assets and constructing personalized data applications, and a three-layer logical data architecture is constructed, including a source data layer, a public data layer and an application data layer.

[0013] Preferably, step S1 includes the following sub-steps:

[0014] Step S1.1, constructing data of the source data layer;

[0015] Step S1.2, constructing the data of the public data layer based on the data of the source data layer;

[0016] Step S1.3, constructing the data of the application data layer based on the data of the public data layer.

[0017] Preferably, in step S2, various elements of data governance and development are integrated through measures such as establishing a data middle platform team, constructing data governance elements, standardizing the data development process, and establishing a collaborative mechanism matrix.

[0018] Preferably, step S2 includes the following sub-steps:

[0019] Step 2.1, build a data center team;

[0020] Step 2.2, build data governance elements: including: data standard governance, data quality governance, data model governance, data security governance, data life cycle governance and metadata governance;

[0021] Step 2.3, standardize the data development process: including: project planning stage, demand analysis stage, basic design stage, development and debugging stage, and online release stage;

[0022] Step 2.4, establish a coordination mechanism matrix consisting of data governance elements, data development process and data middle platform team.

[0023] Preferably, in step S3, the data domain is used as a unit, and the data directory is used as a way to organize the data content.

[0024] Preferably, step S3 includes the following sub-steps:

[0025] Step S3.1, data domain construction method: divide the public data model of the CDM layer of the data middle platform according to certain rules, and then store it centrally logically or physically to form a data asset collection;

[0026] Step S3.2, data directory construction method: divide the public data model of the data center CDM according to certain rules, and then store it centrally logically or physically to form a data asset collection.

[0027] Preferably, in step S4, the data middle platform is governed in layers and the regulatory requirements for data development at each layer are clarified to ensure that data governance and data development can be effectively implemented simultaneously.

[0028] Preferably, step S4 includes the following sub-steps:

[0029] Step S4.1, paste source data layer governance specifications;

[0030] Step S4.2, public data layer governance specification;

[0031] Step S4.3, apply data layer governance specifications.

[0032] The present invention also provides an industrial data governance and development synchronization system for a data middle platform, including:

[0033] Model M1 builds the data architecture of the data center according to the different needs of enterprises;

[0034] Model M2, based on the data architecture, establishes a collaborative mechanism for data governance and development in the data center;

[0035] Model M3, based on data architecture and collaboration mechanism, formulates the construction method of data governance and development of the data center;

[0036] Model M4, based on data architecture, collaboration mechanism and construction method, formulates the working specifications for data governance and development of the data middle platform.

[0037] Compared with the prior art, the present invention has the following beneficial effects:

[0038] 1. The present invention includes the data architecture design of the data middle platform, the collaborative mechanism, construction method, work specifications, etc. of governance and development. Among them, by designing the three-layer logical data architecture of the data middle platform, it can meet the different needs of enterprises for unified data management, construction of public data assets, construction of personalized data applications, etc.

[0039] 2. In terms of establishing a collaborative mechanism, the present invention sets up a data middle-office team, constructs data governance elements, standardizes the data development process, establishes a collaborative mechanism matrix, etc., so as to effectively integrate various elements of data governance and development, thereby realizing the simultaneous implementation of data governance and data development.

[0040] 3. In terms of the formulation of the construction method, the present invention constructs data based on data domains and uses data directories as a way to organize data content, thereby realizing the data standardization construction and classified management of the data center.

[0041] 4. In terms of work specification formulation, the present invention ensures that various tasks are carried out in an orderly manner by standardizing the hierarchical management of the data center and the development of data at each layer.

[0042] 5. The present invention provides a complete solution for data construction in the data center, helping enterprises to build high-quality data assets quickly, orderly and efficiently. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings:

[0044] Figure 1 This is a flow chart of an industrial data governance and development synchronization method for a data middle platform in an embodiment of the present invention;

[0045] Figure 2 The data architecture design of the data middle platform in the embodiment of the present invention;

[0046] Figure 3 The data governance and development collaboration mechanism matrix of the data middle platform in the embodiment of the present invention;

[0047] Figure 4 It is the data directory architecture design in the embodiment of the present invention. DETAILED DESCRIPTION

[0048] The present invention is described in detail below in conjunction with specific embodiments. The following embodiments will help those skilled in the art to further understand the present invention, but are not intended to limit the present invention in any form. It should be noted that, for those of ordinary skill in the art, several changes and improvements can also be made without departing from the concept of the present invention. These all belong to the protection scope of the present invention.

[0049] Embodiment 1:

[0050] Figure 1 This is a flow chart of an industrial data governance and development synchronization method for a data middle platform in an embodiment of the present invention.

[0051] like Figure 1 As shown, this embodiment provides an industrial data governance and development synchronization method for a data middle station, including the following steps:

[0052] Step S1: Build the data architecture of the data center according to the different needs of the enterprise.

[0053] Figure 2 The data architecture design of the data middle platform in the embodiment of the present invention.

[0054] like Figure 2 As shown, in this embodiment, according to the different needs of the enterprise such as unified data management, construction of public data assets and construction of personalized data applications, data is managed in layers, and a three-layer logical data architecture is constructed including the operational data layer (Operational Data Store, ODS), the common data layer (Common Data Model, CDM) and the application data layer (Application Data Service, ADS), which constitutes the enterprise data capability base for the continuous accumulation of high-quality data assets.

[0055] Specifically, step S1 includes the following sub-steps:

[0056] Step S1.1, constructing the data of the ODS layer.

[0057] Specifically, the functional positioning of the ODS layer is similar to that of the ODS layer of a traditional data warehouse. It is located at the bottom of the three-layer logical architecture and is used to store the original data of the enterprise, providing raw materials for further data integration and data empowerment. The structure and content of the ODS layer data are consistent with the original data of the source business system.

[0058] When constructing the ODS layer data, various types of original business data generated by various business activities of the enterprise are collected and connected to the data middle platform. Through data collection, logical aggregation of various types of business data is achieved, laying the foundation for further breaking down data silos, realizing data integration and providing data sharing services.

[0059] The data collection requirement supports four technical means, namely batch integration, synchronous replication, streaming integration and message writing. Among them, batch integration is suitable for scenarios with large data volume, complex data and need for preprocessing. Synchronous replication is suitable for scenarios with high availability and high timeliness; streaming integration is suitable for scenarios with real-time processing and real-time integration; message integration is suitable for scenarios where the source actively sends data, but is not suitable for scenarios with large amounts of data.

[0060] At the same time, data collection also requires support for structured data, unstructured data, and semi-structured data. Structured data refers to data stored in a two-dimensional table structure. This type of data strictly adheres to data format and length specifications and is mainly stored and managed through relational databases. Unstructured data mainly includes various heterogeneous format files such as unformatted text, formatted documents, images, audio, and video. Generally, it can be directly stored in the unstructured data storage area of ​​the data center when it is first generated, and its descriptive attributes are extracted and stored in the ODS layer. Semi-structured data is a data type between structured data and unstructured data. For example, time series data at industrial production sites are generally aligned at the edge data node first, converted into structured data, and then connected to the ODS layer of the data center.

[0061] Step S1.2, constructing the data of the CDM layer based on the data of the ODS layer.

[0062] Specifically, the functional positioning of the CDM layer is the core layer of data asset construction, capability output, data businessization and standardization of the data center. The CDM layer uses the data of the ODS layer to process, integrate and govern data in accordance with the requirements of the enterprise's unified data asset catalog and standard specification system, realize cross-business, cross-organization and cross-cycle data integration, and form a unified data model for the enterprise.

[0063] In terms of CDM layer data construction, the CDM layer can generally be divided into the detail data layer (DataWarehouseDetail, DWD) and the summary data layer (DataWarehouseSummary, DWS). The detail data layer is designed for business facts in a "bottom-up" manner, aiming to present objective business facts completely, accurately and consistently, thereby establishing basic data assets; the summary data layer is designed for business analysis in a "top-down" manner. It is based on the data in the detail data layer, conducts in-depth mining and fusion extraction of the data, and provides data support for business analysis decisions.

[0064] Business data modeling is the core work of CDM layer development. It is the fundamental guarantee for the data center to efficiently provide data services to the outside world and effectively accumulate data service capabilities internally. Specifically, business data modeling is based on the ODS layer data, through data cleaning and processing conversion, to reorganize and store business data, so as to achieve a unified data view of various business areas (such as: traditional sales, procurement, manufacturing, finance, equipment, etc.) and data integration and connection between different business areas (for example: sales, manufacturing, and cost).

[0065] Step S1.3, constructing the data of the ADS layer based on the CDM data.

[0066] Specifically, the ADS layer is the logical layer where the data center directly realizes the value of data, and it describes the personalized business characteristics of the enterprise. Generally, based on the data of the CDM layer, data is processed and services are provided for specific business (data) demand scenarios or personalized needs, including application data models, algorithm models, label portraits, etc. for specific application scenarios, which are mainly built on the application data layer.

[0067] When constructing ADS layer data, in order to meet the processing data requirements of specific business (data) demand scenarios, different business semantics are required for different business needs, and based on the data processed by the CDM layer, it mainly includes business indicator modeling, label portrait modeling, and algorithm analysis modeling and other activities.

[0068] Step S2: Establish a collaborative mechanism for data governance and development of the data center based on the data architecture;

[0069] Specifically, various elements of data governance and development are integrated through measures such as establishing a data middle-office team, building data governance elements, standardizing the data development process, and establishing a collaborative mechanism matrix.

[0070] More specifically, step S2 includes the following sub-steps:

[0071] Step 2.1, build a data center team.

[0072] Specifically, the data center team includes:

[0073] (1) Management Party: As the department responsible for enterprise data management, it is responsible for formulating the enterprise's data strategy and data management system, taking the lead in organizing and carrying out various data management tasks, and coordinating the planning, construction and operation of data management.

[0074] (2) Business parties: They belong to the competent business departments and are responsible for the construction of data applications and data domains within their business areas. They are also responsible for data governance, data classification and grading protection, and personal information protection. They ensure that business data is provided in a timely and accurate manner and coordinate data business innovation activities in this field.

[0075] (3) Technical party: The party that implements the technology is responsible for data architecture design, data technology research and development, data processing and handling, data development, data application and service development, and also provides technical support for data governance and data security protection.

[0076] Step 2.2, build data governance elements.

[0077] Specifically, data governance elements include:

[0078] (1) Data standard governance: It is the data language of the unified data platform. By formulating unified data standards, the accuracy and standardization of data can be managed. The scope of data standards mainly includes business terms, data elements, master data and reference data, indicator data, data classification and grading, naming specifications, etc., involving key activities such as standard formulation, review, registration, reference, revision, and retirement.

[0079] (2) Data quality governance: aims to improve the data quality of the data center and build clean data assets. It manages and controls data quality issues that may arise during data collection, storage, processing, and use, involving key activities such as identification, measurement, monitoring, early warning, and correction.

[0080] (3) Data model governance: It is used to unify the data model definition of the data center, stipulate that the construction of physical tables must be materialized based on the data model, and achieve accurate and standardized management of business objects, behaviors, processes, etc. Key activities include model requirements analysis, model design, model review, model registration, model reference, model maintenance, etc.

[0081] (4) Data security governance: aims to enhance the data security of the data center, mainly including data security, privacy protection and data compliance. Through all-round security management and control of data in various links such as collection, storage, processing, use, exchange, retirement and backtracking, it can achieve the effect of pre-management, controllability during the process and post-investigation. It mainly involves key activities such as data classification and grading, definition of confidentiality level, security rules, data desensitization, security monitoring and security audit.

[0082] (5) Data lifecycle governance: aims to improve the scientificity and standardization of data lifecycle management in the data center. Key activities include data demand (including application, review, development), data archiving, data retirement, etc.

[0083] (6) Metadata governance: It aims to uniformly manage all types of metadata information in the data center, mainly including business metadata, technical metadata, and management metadata. The main function of metadata is to describe data assets, build data asset catalogs, clarify data lineage relationships, build data asset maps, etc. Key activities include meta-model design, metadata collection, metadata registration, metadata application, and metadata management and maintenance.

[0084] In this embodiment, the core idea of ​​data governance in the data middle platform is: from the perspective of governance direction, it includes data standards, data quality, data models, data security, data life cycle, metadata and other contents; from the perspective of governance content, metadata is the basis of data governance, and various data governance activities are based on metadata-driven implementation; from the perspective of governance work, data standards are the core of data governance and the basis for the implementation of various governance work; from the perspective of governance tools, data governance is implemented through data governance tools, and the data standard requirements are conveyed to data development tools, and data development is carried out in accordance with the requirements of the standards.

[0085] Step 2.3, standardize the data development process.

[0086] Specifically, the data development process specifications include:

[0087] (1) The first stage is project planning: a data middle platform team consisting of management, business and technical parties is formed, led by the management party, and jointly with the data domain construction team to formulate a project plan. During this process, the focus is on clarifying the construction scope, the business systems involved, the basic resources and workspace of the data middle platform, etc.; in accordance with the data development governance specifications, the data middle platform data domain construction plan is output.

[0088] (2) The second stage is demand analysis: the business side takes the lead in completing the sorting of data entering the lake and carrying out conceptual model design, completing the sorting of business objects, business processes, business indicators, etc., and forming data element standards.

[0089] (3) The third stage is basic design: the technical party takes the lead in completing the logical data model design, physical model design and ODS layer data access design, etc., citing and improving data element standards, and following data modeling specifications, data access specifications, data quality requirements, data security specifications, etc.

[0090] (4) The fourth stage is development and debugging: the technical party takes the lead in completing the ODS layer data access registration, CDM layer data development and functional testing, etc., following the data development governance specifications to ensure data quality.

[0091] (5) The fifth stage is online release: mainly led by the business side, organizing relevant parties to complete the data domain online and release the data asset catalog.

[0092] Step S2.4, establish a synergy mechanism matrix.

[0093] Figure 3 This is the data governance and development collaboration mechanism matrix of the data middle platform in the embodiment of the present invention.

[0094] like Figure 3 As shown, in this embodiment, a coordination mechanism matrix consisting of data governance elements, data development process, and data middle-office team is established to clarify various work items, corresponding output results, and job responsibilities of relevant personnel through the coordination mechanism matrix.

[0095] Step S3: Based on the data architecture and collaboration mechanism, formulate the construction method of data governance and development of the data middle platform.

[0096] Specifically, when the data middle platform carries out data construction work, it uses data domains as units and adopts data directories as a way to organize data content, so as to realize the data standard construction and classified management of data of the data middle platform.

[0097] More specifically, step S3 includes the following sub-steps:

[0098] Step S3.1, data domain construction method;

[0099] Specifically, data domain construction is to divide the public data model of the CDM layer of the data middle platform according to certain rules, and then centrally store it logically or physically to form a collection of data assets.

[0100] The data domain construction specifications are as follows:

[0101] (1) In the data middle platform environment, when formulating the above division rules, they are generally based on the business organization actually responsible for building the data domain, or the data classification involved, such as the human resources data domain, marketing data domain, ironmaking data domain, etc.

[0102] (2) Each data domain has a clear responsible unit for construction and governance. The responsible unit is generally determined according to the source unit or department of the main data of the data domain.

[0103] (3) When constructing the data domain, the data assets and data governance specifications of the CDM layer should be followed.

[0104] (4) The CDM layer plans the data domain and builds relevant data assets according to the "data classification standard + data management organization" model.

[0105] Step S3.2, data directory construction method.

[0106] Specifically, the public data model of the data middle platform CDM is divided according to certain rules and then stored centrally logically or physically to form a data asset collection.

[0107] Figure 4 It is the data directory architecture design in the embodiment of the present invention.

[0108] like Figure 4 As shown, the data directory construction specifications are as follows:

[0109] (1) Business classification: Data is classified from the perspective of the business. According to the complexity of the business, it can be refined into multiple layers, such as subject domain grouping, subject domain, etc. In view of the complexity of the data scenario, business domain modeling is carried out through clustering and classification methods, and related, similar, and similar businesses are clustered, and vice versa. The advantage of this design is that it improves the versatility and scalability of the data directory architecture.

[0110] (2) Business object: It is the smallest management unit in the business field, usually the important people, things, objects, activities, etc. in the business field, such as equipment, contracts, products, procurement, etc. Business objects are mainly implemented through conceptual modeling.

[0111] (3) Logical data model: It is the smallest information unit that allows business objects to carry and express certain business rules. It is essentially a collection of business object attributes. Logical data models are mainly implemented through logical modeling, and the data model type determines the logical modeling method, such as ER model, dimensional model, object model, etc.

[0112] (4) Attribute: It is the component of the logical data model and the smallest unit of data management. Its essence is the description of the properties and characteristics of the business object.

[0113] Step S4: Based on the data architecture, collaboration mechanism and construction method, formulate the work specifications for data governance and development of the data center.

[0114] Specifically, the data center is governed by standardized layers, and the regulatory requirements for data development at each layer are clarified to ensure that data governance and data development can be effectively implemented simultaneously.

[0115] More specifically, step S4 includes the following sub-steps:

[0116] Step S4.1, ODS layer governance specification.

[0117] Specifically, the key points of data governance at the ODS layer are: data rights confirmation, data source identification, data security, data quality, standardized naming, data registration, etc.

[0118] ODS layer governance specifications include:

[0119] (1) Authentication data source: clarify the data types of each access data table (fact data, master data, basic data (code data, reference data), report indicator data, rule data, observation data, etc.), source business system, and key business data description.

[0120] (2) Define data confidentiality: To ensure data security at the ODS layer, the confidentiality level must be defined in accordance with the data classification requirements of the unit where the data owner is located.

[0121] (3) Confirmation of data owner: It is required to define the data owner and be responsible for data quality, data confidentiality, etc.

[0122] (4) Register data assets: Follow unified data naming conventions, register source data assets at the ODS layer, and register the data standards followed by important source data items, but do not require conversion according to data standards.

[0123] (5) Monitor data quality: Focus on monitoring integrity, validity, uniqueness, and consistency. Use data quality management to perform data quality monitoring and evaluation on key business data to promote source business systems to improve data quality.

[0124] Step S4.2, CDM layer governance specifications;

[0125] Specifically, the focus of data governance at the CDM layer is: unified catalog, unified data model, unified data standards (data elements, master data, reference data), unified indicators, etc., as well as data rights confirmation, data security, data quality, standardized naming, data registration, etc.

[0126] CDM layer data governance specifications:

[0127] (1) Public data standards and normalization: Based on the construction of public data models, data standards and normalization are realized, including clarifying the data classification, business objects or business processes, and data element standards to be followed.

[0128] (2) Define data confidentiality levels: To ensure data security at the CDM layer, confidentiality levels must be defined in accordance with the data classification requirements of the data owner's unit, and reasonable desensitization or encryption must be performed in accordance with the security protection requirements of each confidentiality level.

[0129] (3) Confirmation of data owner: It is required to define the data owner and be responsible for data quality, data confidentiality, data standards, external data services, etc.

[0130] (4) Standardize the naming and registration of data assets: Follow unified data naming standards, register data assets at the CDM layer, and open the data asset catalog to the entire company.

[0131] Step S4.3, ADS layer governance specification.

[0132] Specifically, the focus of data governance at the ADS layer is: data rights confirmation, data security, data quality, standardized naming, data registration, etc.

[0133] ADS layer data governance specifications:

[0134] (1) Define data classification: To ensure data security at the ADS layer, the classification must be defined in accordance with the data classification requirements of the data owner’s organization, and reasonable desensitization or encryption must be performed in accordance with the security protection requirements of each classification level.

[0135] (2) Confirmation of data owner: It is required to define the data owner and be responsible for data quality, data confidentiality, data standards, external data services, etc.

[0136] (3) Standardize the naming and registration of data assets: Follow unified data naming standards, register data assets at the ADS layer, and open the data asset catalog to designated applications.

[0137] Embodiment 2:

[0138] The present embodiment provides an industrial data governance and development synchronization system for a data middle station. The industrial data governance and development synchronization system for a data middle station can be implemented by executing the process steps of an industrial data governance and development synchronization method for a data middle station. That is, those skilled in the art can understand the industrial data governance and development synchronization method for a data middle station as a preferred implementation method of the industrial data governance and development synchronization system for a data middle station.

[0139] The industrial data governance and development synchronization system includes:

[0140] Model M1 builds the data architecture of the data center according to the different needs of enterprises;

[0141] Model M2, based on the data architecture, establishes a collaborative mechanism for data governance and development in the data center;

[0142] Model M3, based on data architecture and collaboration mechanism, formulates the construction method of data governance and development of the data center;

[0143] Model M4, based on data architecture, collaboration mechanism and construction method, formulates the working specifications for data governance and development of the data middle platform.

[0144] Those skilled in the art know that, in addition to realizing the system and its various devices, modules, and units provided by the present invention in a purely computer-readable program code, it is entirely possible to realize the same functions in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers by logically programming the method steps. Therefore, the system and its various devices, modules, and units provided by the present invention can be considered as a hardware component, and the devices, modules, and units included therein for realizing various functions can also be regarded as structures within the hardware component; the devices, modules, and units for realizing various functions can also be regarded as both software modules for realizing the method and structures within the hardware component.

[0145] The above describes the specific embodiments of the present invention. It should be understood that the present invention is not limited to the above specific embodiments, and those skilled in the art can make various changes or modifications within the scope of the claims, which does not affect the essence of the present invention. In the absence of conflict, the embodiments of the present application and the features in the embodiments can be combined with each other arbitrarily.

Claims

1. A method for synchronizing industrial data governance and development for a data center, characterized in that: The following steps are involved: Step S1: Build the data architecture of the data center according to the different needs of the enterprise; Step S2: Based on the data architecture, a collaborative mechanism for data governance and development of the data center is established; Step S3, based on the data architecture and the collaboration mechanism, formulate a construction method for data governance and development of the data center; Step S4, based on the data architecture, the collaboration mechanism and the construction method, formulate work specifications for data governance and development of the data center.

2. According to the method of industrial data governance and development synchronization for data middle station according to claim 1, it is characterized in that: In step S1, according to the different needs of the enterprise for unified data management, building public data assets and constructing personalized data applications, data is managed in layers to build a three-layer logical data architecture including a source data layer, a public data layer and an application data layer.

3. According to the method of industrial data governance and development synchronization for data middle platform according to claim 2, it is characterized in that: The step S1 includes the following sub-steps: Step S1.1, constructing data of the source data layer; Step S1.2, constructing data of the public data layer based on the data of the source data layer; Step S1.3, constructing data of the application data layer based on the data of the public data layer.

4. According to the method of industrial data governance and development synchronization for data middle platform according to claim 1, it is characterized in that: In step S2, various elements of data governance and development are integrated by establishing a data middle platform team, building data governance elements, standardizing the data development process and establishing a collaborative mechanism matrix.

5. According to the method of industrial data governance and development synchronization for data middle platform according to claim 4, it is characterized in that: The step S2 includes the following sub-steps: Step 2.1, build a data center team; Step 2.2, build data governance elements, including: data standard governance, data quality governance, data model governance, data security governance, data life cycle governance and metadata governance; Step 2.3, standardize the data development process, including: project planning stage, demand analysis stage, basic design stage, development and debugging stage, and online release stage; Step 2.4, establish a coordination mechanism matrix consisting of the data governance elements, the data development process and the data middle office team.

6. According to the method of industrial data governance and development synchronization for data middle platform according to claim 1, it is characterized in that: In the step S3, the data domain is used as a unit, and the data directory is used as a way to organize the data content.

7. The method for industrial data governance and development synchronization for a data middle station according to claim 6 is characterized in that: The step S3 includes the following sub-steps: Step S3.1, data domain construction method: divide the public data model of the CDM layer of the data middle platform according to certain rules, and then store it centrally logically or physically to form a data asset collection; Step S3.2, data directory construction method: divide the public data model of the data center CDM according to certain rules, and then store it centrally logically or physically to form a data asset collection.

8. The method for industrial data governance and development synchronization for a data middle station according to claim 1 is characterized in that: In step S4, the data center is governed in layers and standardized, and the standard requirements for data development at each layer are clarified to ensure that data governance and data development can be effectively implemented simultaneously.

9. The method for industrial data governance and development synchronization for a data middle station according to claim 8 is characterized in that: The step S4 includes the following sub-steps: Step S4.1, paste source data layer governance specifications; Step S4.2, public data layer governance specification; Step S4.3, apply data layer governance specifications.

10. An industrial data governance and development synchronization system for data middle platform, characterized in that: include: Model M1 builds the data architecture of the data center according to the different needs of enterprises; Model M2, based on the data architecture, establishes a collaborative mechanism for data governance and development in the data center; Model M3, based on the data architecture and the collaboration mechanism, formulates the construction method of data governance and development of the data center; Model M4, based on the data architecture, the collaboration mechanism and the construction method, formulates the working specifications for data governance and development of the data center.

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