Data development management processing method and system and electronic equipment

Abstract

The invention discloses a data development management processing method and device and electronic equipment. The data development management processing method comprises the following steps: taking a dimension correlation model structure definition as a center of communication construction management; a project space is used as a basic unit of management, data architecture is flattened and labeled, and data component content is abstracted into resources and managed in a unified mode; access limitation setting is performed on resources in a project space, preprocessing and post-calculation are performed on data, and multiple forms of data services which are safe, compliant, credible and controllable are provided. According to the technical scheme, a low-threshold, low-cost and easy-to-expand capability is provided for data development management processing, meanwhile, an effective method for simply sharing data is provided, and the data construction efficiency, the data circulation efficiency and the data use efficiency are improved.

Description

Technical Field

[0001] The present invention relates to computer technology, and in particular to a data development, management and processing method, device and electronic equipment. Background Art

[0002] With the rapid growth of data volumes in today's society, effective data development, opening up, and sharing have become crucial issues. Furthermore, finding the right platform and tools for quickly sharing high-value, compliant data has become crucial. Within enterprises, the rapid growth of business data has also created challenges, such as lengthy data development and governance cycles and poor scalability of data tools.

[0003] As data engineering labor becomes increasingly specialized and segmented, effective and efficient collaboration among numerous data professionals and complex data tasks has become a top priority. To successfully implement data development and governance, data-related personnel typically need to handle a range of tasks, including requirements, acquisition, integration, development, quality assurance, security, metadata, master data, data warehouse, business intelligence, lineage management, data compliance, data assetization, and data factorization. This has led to the emergence of numerous data roles, including business personnel, data developers, data analysts, data architects, and data managers. Furthermore, data users often far outnumber technical staff. Therefore, enhancing the business-oriented nature of data platform systems and lowering the barrier to entry for business personnel are becoming increasingly important in data development, management, and governance.

[0004] More specifically, different data roles focus on different data outputs. Business personnel formulate data requirements, create requirements documents, and utilize corresponding requirements management tools. Project managers use project management tools to schedule these requirements. After multiple discussions and scheduling, data developers conduct data collection, integration, and development on the data platform. Modules such as quality and safety metadata created by data architects then come into play. This process also requires the development of data standardization systems, such as the data warehouse. For example, dimensional modeling requires sequential business segmentation, such as business segments, data domains, and business processes, and the establishment of pre-conditions for data development. Only then can data analysts access application-layer data tables, analyze them using business intelligence tools, and provide business personnel with corresponding analysis reports and charts. Business personnel focus on requirements progress and the analysis charts provided by data analysts, while technical R&D focuses on database tables. Technical personnel are also responsible for quality and safety aspects of these tables. Consequently, the focus of different roles is inconsistent, and changes to data requirements can be accompanied by arguments and buck-passing, delaying project completion. Data project development is sequentially dependent on each other, resulting in inefficient progress and lengthy and complex processes.

[0005] On a larger scale, all industries have collected massive amounts of data. How to share compliant data safely and efficiently, how to improve the efficiency of data circulation, and how to retrieve high-value data more quickly have become unavoidable issues. Summary of the Invention

[0006] This application provides a data development management and processing method, system and electronic equipment, which delves into the core of data communication construction management, improves the development management and processing capabilities of data, and effectively improves the circulation, sharing and sharing efficiency of compliant data.

[0007] This application adopts the following technical solution.

[0008] This embodiment of the present application provides a data development management processing method, including: The definition of the dimension-related model structure is the center of communication and construction management; Using project space as the basic unit of management, the data architecture is flattened and labeled, and data component content is abstracted into resources and managed in a unified manner. Set access restrictions on resources within the project space, pre-process and post-calculate data, and provide secure, compliant, reliable, and controllable data services in various forms.

[0009] Preferably, the center is characterized by: The basic components of the dimension association model structure definition include dimension information, data sets and data books; The dimension information is a consistency dimension, which constitutes equivalent consistency between different project spaces; The dataset field classification includes dimensions, indicators and objects, and the datasets are associated through the dimension information; The data book includes data tables, data sources, files, streaming media and access control, and the data tables are mapped to the corresponding data sources; The dimension information and the fields of the data set are dynamically configured and mapped to the data table fields corresponding to the data book; The object classification of the data set is associated to the file or the streaming media of the data set by configuration under access control; The dimension association model structure definition is used as the central component as the focus of data management, and peripheral component governance is carried out around the central component; The peripheral components include requirements, acquisition, integration, development, quality, security, metadata, business intelligence, lineage management and data compliance, and asset elements; The data management includes the central management and peripheral governance. The peripheral governance manages around the central management and performs life cycle management accordingly.

[0010] Preferably, the project space is used as the basic unit of management to flatten and label the data architecture, including: Constructing the association model structure in the space, wherein the data architecture is a business structure and an organizational structure; The project space has more than one organization member, a sole business leader and a sole technical leader; The project space has zero or more project administrators who create groups of organization members within the project space; The business structure includes business segments, data domains, and business processes, which serve as descriptive labels for the dimensional association model; The dimension association model structure provides a custom business description tag, a unique business person in charge and a unique technical person in charge; The dimension association model structure establishes an evaluation system and labels it, so that all accessible users can participate in the evaluation of the dimension association model; The peripheral components associated with the dimension association model structure provide custom business description labels and set corresponding persons in charge.

[0011] Preferably, the data component content is abstracted into resources for unified management, including: The data component content includes the central component instance and the peripheral component instance; The resource exists globally or within the project space or provides a unique identifier; The central component instance includes the dimension information, the data set, the data book and the dimension data; Constructing an association relationship structure logic of the peripheral component instances associated with the central component instance around the central component instance; The corresponding central component instance and the peripheral component instance information can be retrieved through the unique identifier.

[0012] Preferably, access restrictions are set for resources within the project space, data is pre-processed and post-calculated, and various forms of data services that are secure, compliant, reliable, and controllable are provided, including The visibility range of the resource includes public, restricted, and private; the visibility range of the space includes public, restricted, and private; The resource permission rules include the visibility scope of the resource itself, the visibility scope of the project to which the resource belongs, and custom access configuration; Synchronize or elevate data security to the management of the central management component instance resources, and perform security classification and security level management on the resources; Taking the authority rules and security levels as restriction conditions, the data book is queried and pre-processed under the restriction conditions, including extraction, cleaning, conversion, and merging; Based on the definition of the dimension association model and data screening conditions, the results of different data sets are merged and output or merged with dimension information or simple calculations are performed; The data service mode includes a graphical form or an interface form, and the graphical form is a visualization mode of the association between dimension information and data sets.

[0013] The present invention provides a data development management processing device, including: The central module is used to define the dimension-related model structure as the center of communication construction management; The space module uses the project space as the basic unit of management, flattens the data architecture and labels it, abstracts the data component content into resources, and manages them in a unified manner. The service module sets access restrictions on resources within the project space, performs pre-processing and post-computation on data, and provides various forms of data services that are secure, compliant, reliable, and controllable.

[0014] Preferably, the central module is characterized by: Define the basic component content. The dimension association model structure defines the basic components including dimension information, data sets and data books. The dimension information is a consistency dimension, which constitutes equivalent consistency between different project spaces; Construct a dimension association model, the dataset field classification includes dimensions, indicators and objects, and the datasets are associated through the dimension information. The data book includes data tables, data sources, files, streaming media and access control, and the data tables are mapped to the corresponding data sources. The dimension information and the fields of the data set are dynamically configured and mapped to the data table fields corresponding to the data book; The object classification of the data set is associated to the file or the streaming media of the data set by configuration under access control; Conduct data management governance, take the dimension association model structure definition as the central component as the focus of data management, and conduct peripheral component governance around the central component. The peripheral components include demand, acquisition, integration, development, quality, security, metadata, business intelligence, lineage management and data compliance, and asset elements. The data management includes the central management and peripheral governance. The peripheral governance manages around the central management and performs life cycle management accordingly.

[0015] Preferably, the space module is characterized by: A labeled data architecture is used to construct the associated model structure within the space, wherein the data architecture is a business structure and an organizational structure; The project space has more than one organization member, a sole business leader and a sole technical leader; The project space has zero or more project administrators who create groups of organization members within the project space; The business structure includes business segments, data domains, and business processes, which serve as descriptive labels for the dimensional association model; The dimension association model structure provides a custom business description tag, a unique business person in charge and a unique technical person in charge; The dimension association model structure establishes an evaluation system and labels it, so that all accessible users can participate in the evaluation of the dimension association model; The peripheral components associated with the dimension association model structure are provided with custom business description labels and corresponding persons in charge are set for them; Resource data components, wherein the data component content includes the central component instance and the peripheral component instance; The resource exists globally or within the project space or provides a unique identifier; The central component instance includes the dimension information, the data set, the data book and the dimension data; Constructing an association relationship structure logic of the peripheral component instances associated with the central component instance around the central component instance; The corresponding central component instance and the peripheral component instance information can be retrieved through the unique identifier.

[0016] Preferably, the service module is used to set access restrictions on resources within the project space, pre-process and post-calculate data, and provide various forms of data services that are secure, compliant, reliable, and controllable, including Visibility control: the visibility of resources includes public, restricted, and private; the visibility of project spaces includes public, restricted, and private; Permission rule management: the permission rules for resources include the resource's own visibility, the resource's project visibility, and custom configurations; Data security and compliance: the central management component instance resource data security and data compliance management, security classification and security level management of the resources, Taking the authority rules and security levels as restriction conditions, the data book is queried and pre-processed under the restriction conditions, including extraction, cleaning, conversion, and merging; Data processing and calculation: Based on the definition of dimension association model and data screening conditions, the results of different data sets are merged and output or merged with dimension information or simple calculations are performed; Data service mode, the data service mode includes a graphical form or an interface form, and the graphical form is a visualization mode of the association between dimension information and data sets.

[0017] The present application provides an electronic device for data development, management and processing, including: a memory and a processor; characterized in that: The memory is used to store a program for data development management processing, and when the program for data development management processing is read and executed by the processor, the following operations are performed: The definition of the dimension-related model structure is the center of communication and construction management; Using project space as the basic unit of management, the data architecture is flattened and labeled, and data component content is abstracted into resources and managed in a unified manner. Set access restrictions on resources within the project space, pre-process and post-calculate data, and provide secure, compliant, reliable, and controllable data services in various forms.

[0018] Compared with the prior art, this application has the following advantages: At least one embodiment of the present application focuses on the definition of the dimension-related model structure in data communication and construction management; uses the project space as the basic unit of management, flattens and labels the data architecture, abstracts the data component content into resources and manages them in a unified manner; sets access restrictions on resources within the project space, pre-processes and post-calculates data, and provides various forms of data services that are secure, compliant, reliable, and controllable.

[0019] The definition of the dimensional association model structure serves as the focal point for all stakeholders involved in requirements, acquisition, integration, development, quality, security, metadata, business intelligence, lineage management, data compliance, asset elements, and data sharing. All parties are held accountable for defining the association model structure, reducing buck-passing and ultimately accelerating project progress. Data books, datasets, and dimensional information are reduced to a few core concepts, minimizing the need for more technical concepts. Data preprocessing within the data book, especially when merging data from different sources with the same structure (which can be partially identical, with the same meaning), reduces cluster and region restrictions. Data book preprocessing and association of datasets reduce the need to build more large and wide tables, improving data governance and circulation efficiency. This reduces the number of jobs and physical tables, conserving computing and storage resources.

[0020] In terms of collaboration, all parties involved in data development minimize upstream and downstream interdependence. Business personnel, data analysts, and others can pre-define dimensional association model structures without the presence of real business data, while also providing simulated data in the data sheet. This allows for pilot testing of applications such as business intelligence, which can then be used by the application side to construct preliminary charts, preview results, and gain a deeper understanding of business needs. This reduces early misunderstandings of requirements, thereby minimizing unnecessary changes. Simultaneously, back-end technical staff can develop corresponding data tables, data operations, and other data in parallel, without interfering with each other. Furthermore, if technical staff need to iterate on technical solutions, such as optimizing storage and computing, they can do so with minimal or no impact on the application side, making work progress much easier.

[0021] In terms of circulation and sharing, a similar form of code management is adopted, with the dimension-related model structure definition as the center, providing corresponding management operations and sharing capabilities. Various component instances, especially central components, are abstracted as resources and divided into public, restricted, and private. In conjunction with project space management and data security compliance, users only need to perform equivalent and consistent operations on dimension information. At this time, for compliant data, especially those that are easy to disclose and share, dimension-related data sets will become easier to share and circulate. The dimension-related model structure definition is assisted in operation by interfaces or commands. The standardized data generated in this way becomes high-value content for data circulation, data assetization, and data factorization, which also provides a way for different organizations to collaborate on data efficiently.

[0022] Regarding data security, the database maintains data security levels and classifications, enabling control from the data export perspective. Furthermore, comprehensive data management and control are implemented, requiring users to comply with permission-based rules to access data resources. All data component content can be abstracted into resources, such as business intelligence chart cards and platform system page navigation, action buttons, and filter components. This allows for granular control of resource permissions.

[0023] Data governance is highly scalable. Based on collaborative data project spaces, different business teams can build and manage distinct project spaces, easily expanding from a small data volume to a large-scale data service platform. Dimensional association structures (data books, dimensional information, and datasets) within different project spaces can be reviewed and authorized as needed, reducing data duplication and improving data utilization efficiency. Alternatively, different organizations can build their own system platforms based on this, allowing data output from one organization to become data input to another, with data flowing in one direction, for example, from a lower-level organization to a higher-level organization.

[0024] Of course, any product implementing the present application does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention but do not constitute a limitation of the present invention.

[0026] Figure 1 The flowchart of a data development management processing method according to an embodiment of the present invention is schematically shown.

[0027] Figure 2 The following schematically shows a data development management processing system according to an embodiment of the present invention.

[0028] Figure 3 Schematic diagram of the star structure in dimensional modeling. Figure 4 The following schematically shows a diagram of dimension association structure definition according to an embodiment of the present invention.

[0029] Figure 5 The diagram schematically shows a dimension association structure definition as a center according to an embodiment of the present invention. Figure 6 The figure schematically shows the relationship between dimension-related data sets according to an embodiment of the present invention.

[0030] Figure 7 The diagram schematically shows one of the visualization forms when providing data services according to an embodiment of the present invention.

[0031] Figure 8 Another schematic diagram of a visualization form when providing data services according to an embodiment of the present invention is schematically shown. DETAILED DESCRIPTION

[0032] The following detailed description of the embodiments of the present invention is provided in conjunction with the accompanying drawings and examples, thereby providing a full understanding and implementation of how the present invention applies technical means to solve technical problems and achieve technical effects. It should be noted that, as long as no conflict exists, the various embodiments of the present invention and the various features within each embodiment may be combined with each other, and the resulting technical solutions are all within the scope of protection of the present invention.

[0033] Meanwhile, in the following description, for the purpose of explanation, many specific details are set forth to provide a thorough understanding of the embodiments of the present invention. However, it will be apparent to those skilled in the art that the present invention may be implemented without using the specific details herein or the particular manner described.

[0034] In order to better understand the present invention, a brief introduction to the concepts involved is given.

[0035] First, a brief introduction to the relevant theories of dimensional modeling is given.

[0036] The following introduces some basic knowledge about dimensional modeling, including fact tables, dimension tables, dimensions, measures, multidimensional datasets, etc.

[0037] A fact table is a table that stores a large amount of business measurement data. The measurement values ​​in a fact table are generally called facts. Generally, the most useful facts are numeric and additive. The granularity of the fact table determines the level of detail of the data in the data warehouse. Dimension tables are windows through which users analyze data, such as year, city, and department. Dimension tables contain attributes of records in the fact table. Some attributes provide descriptive information, while others specify how to summarize fact table data to provide useful information to analysts.

[0038] There are two basic forms of dimensional modeling: the star schema and the snowflake schema. When all dimension tables are directly connected to the fact table, the diagram resembles a star, which is called a star schema. When multiple dimension tables are not directly connected to the fact table but are indirectly connected to it through other dimension tables, the diagram resembles a snowflake, hence the name snowflake schema. Figure 3 Schematic diagram of the star structure in dimensional modeling.

[0039] What is a multidimensional dataset? A dataset is a data model consisting of one or more tables, serving as the foundation for visual query analysis and data dashboards. A multidimensional dataset typically contains numerous fields, some of which are dimensions (perspectives on data) and some are measures (specific quantitative values).

[0040] On this basis, the dataset (or data model, or model) in this article has its field classification expanded to dimensions, indicators, and objects (with the object classification being newly added), and has been optimized and upgraded to a dimension-association model structure definition. The basic components of the dimension-association model structure definition include dimension information, datasets, and data volumes. Data volumes include data tables, data sources, files, streaming media, and access control. Data tables are mapped to corresponding data sources. The dimension information and dataset fields are dynamically configured and mapped to the corresponding data table fields in the data volume. The dataset object classification is configured and associated with the files or streaming media in the data volume under access control.

[0041] It's important to note that the term "data book" in this article is presented as a concept close to the business. Many data items are put together like a book, with data representing the pages within the book. Images, audio, video, and other files, as well as streaming media, are also managed within the book. For example, a regional dimension might include not only numerical indicators like GDP but also images, audio, video, and other files within the same region, as well as live streaming. This richer form of data description exists, and data dashboards can be a significant application of this.

[0042] Secondly, a brief introduction to domain modeling and code hosting platform is given.

[0043] Regarding domain modeling. Domain-Driven Design (DDD) is a way to address core software complexity, allowing software modules to be more flexibly upgraded to adapt to business changes. Models are defined using a ubiquitous language (such as UML). In practice, a ubiquitous language strengthens communication and solidarity among all stakeholders. When every team member uses the same terminology, understanding, communication, and cohesion improve. When non-technical experts also use the ubiquitous language, communication between them and with developers becomes more effective.

[0044] GitHub is a hosting platform for open source and proprietary software projects. It's named GitHub because it exclusively supports Git as the repository format. Repositories are the most fundamental element of GitHub. Repositories store code, files, and each file's revision history. Repositories can have multiple collaborators and can be public, internal, or private.

[0045] Domain modeling and GitHub have become paradigms for software collaboration and code sharing, respectively. So, in the data field, how can we find a core terminology that facilitates collaboration among all parties and enables faster data sharing? Defining the structure of a dimension-related model is the central concept of this article. Combining this with resource-based management and permission control within a space can achieve this effect.

[0046] Again, assuming that existing data stakeholders (whose data is compliant and easily publicly shareable) do not delve into the business rationality of data analysis, each data stakeholder, after a certain period of communication, forms the following data requirement description: Global dimension information: year GDI_YEAR (year), city GDI_CITY (city, province).

[0047] Group of Interest O1: Organization personnel, business personnel USER_B1, developer USER_D1, data analyst USER_A1, data manager USER_M1, and other users; Data content T101, city, year, number of tourists, tourism income; Data content T102, city, year, population, per capita income; City data CITY1, city, province.

[0048] Group of Interest O2: Organization personnel: Business personnel USER_B2, developer USER_D2, data analyst USER_A2, data manager USER_M2, and other users; Data content T201, city, year, product, department, sales quantity, sales amount; City data CITY2, city, province; Department data DEPART, department, branch; Product data PRODUCT, product, category; Groups of Interest O3: Organization personnel, business personnel USER_B3, developers USER_D3, data analysts USER_A3, data managers USER_M3, and other users; Data content T301, city, year, promotional atlas; City data CITY3, city, province.

[0049] When organizations O1, O2, and O3 belong to different entities, such as different companies, data is often isolated and managed on different platforms, rather than being shared on a unified platform. Within the same organization, when there are a large number of people involved, data management and collaboration become extremely complex. Data flow between different teams becomes cumbersome, leading to duplication of work.

[0050] It's important to note that the data within the three organizations represents the internal needs of each organization. This data can be existing physical data or a summary of pending business needs. The naming conventions used in this article are for illustrative purposes only and are not intended to be limiting. Organization names begin with "O," business data tables begin with "T," users begin with "USER," data books begin with "BOOK," and dimension information begins with "DI" or "GDI." Dimension tables are named according to their meaning. Some names have different numerical suffixes for clarity and ease of explanation.

[0051] In order to make the technical solution of the present invention clearer and more understandable, the present invention will be described in detail below with reference to the accompanying drawings and in combination with specific embodiments.

[0052] Example 1 like Figure 1 As shown, a data development management processing method includes: Step S301: Using the dimension-related model structure definition as the center of communication construction management; Step S302: Using the project space as the basic unit of management, the data architecture is flattened and labeled, and the data component content is abstracted into resources and managed uniformly; Step S303: Set access restrictions on resources within the project space, pre-process and post-calculate data, and provide secure, compliant, reliable, and controllable data services in various forms.

[0053] First, in step S301, the dimensional association model structure definition serves as the center of communication and construction management. Business personnel USER_B1, developer USER_D1, data analyst USER_A1, and data manager USER_M1 within organization O1 use the dimensional association model structure definition as the center of communication and construction management. This includes all aspects of communication, conclusions, progress, development, and evaluation related to achieving data requirements and delivering data value.

[0054] Based on the data requirements, configure data book BOOK101 and provide a direct mapping from data table T101 to storage within data book BOOK101. Also, provide a direct mapping from city data table CITY1 to storage within data book BOOK101 and a direct mapping from year data table YEAR1 to storage within data book BOOK101.

[0055] Create a dataset SET101, which includes dimensions of city and year, and indicators of number of tourists and tourism revenue.

[0056] Create city dimension information DI_CITY1, which includes city and province, and map it to the CITY1 field in the city data table in the data book;

[0057] Create the year dimension information DI_YEAR1, which contains the year, and map it to the YEAR1 field in the data book year data table;

[0058] Dataset SET101 city dimension associated with city dimension information DI_CITY1;

[0059] The year dimension of the dataset SET101 is associated with the year dimension information DI_YEAR1;

[0060] The fields of dataset SET101 are mapped to the fields of table T101 in data book. If the indicator has an aggregation function, the aggregation calculation expression is used.

[0061] In this way, within organization O1, the dimensional association model structure definition for dataset SET101, dimension information DI_CITY1, and table T101 in the corresponding data book BOOK101 serves as the central focus for communication and management, achieving consensus on dimensional indicators and object definitions. When a virtual data table T101' is created within data book BOOK101, business person USER_B1 can modify the data mapping relationship from T101 to T101'. Based on this data, business person USER_B1 or data analyst USER_A1 can create examples for chart cards. Forecast data can also be provided in this way. Simultaneously, data developers develop data based on the definition of table T101 in data book BOOK101.

[0062] Data components such as demand, collection, integration, development, quality, security, metadata, business intelligence, lineage management, data compliance, and asset elements are defined around this dimension and associated model structure, and lifecycle management is carried out accordingly. Data development and other departments are responsible for the data quality, compliance, and security of the data set. All data stakeholders take this as the center, such as Figure 5 As shown in the figure, when transforming data into assets and elements, the association model structure definition serves as the fundamental unit. For example, managing the progress of requirements can be evolved into managing the completion of the association model structure definition, and managing the quality of data tables can be evolved into managing the data quality of the association model structure definition. During the completion or refinement of the dimensional association model structure definition, other users within organization O1 who are stakeholders in the data can also participate.

[0063] Based on the requested data content T102 , the data book BOOK102 is configured, and a direct mapping of the data table T102 to the storage is provided within the data BOOK102 .

[0064] Create the dataset SET102, which includes dimensions of city and year, and indicators of population and per capita income. Data set SET102 city dimension associated city dimension information DI_CITY1; The year dimension of the dataset SET102 is associated with the year dimension information DI_YEAR1; The fields of dataset SET102 are mapped to the fields of data book T102. If the indicator has an aggregation function, the aggregation calculation expression is used.

[0065] The corresponding diagram can be found in Figure 4 .

[0066] Similarly, within organization O2, business personnel USER_B2, developer USER_D2, data analyst USER_A2, and data manager USER_M2 use the dimension association model structure definition as the central point for communication and management. Based on data requirements, they configure data book BOOK201 and provide a direct mapping from table T201 to storage within BOOK201. Within BOOK201, they also provide a direct mapping from table CITY2 to storage, table YEAR2 to storage, table DEPAT to storage, and table PRODUCT to storage.

[0067] Create a dataset SET201 with dimensions of city, year, product, and department, and indicators of sales quantity and sales amount.

[0068] Create city dimension information DI_CITY2, which includes city and province, and map it to the CITY2 field in the city data table in the data book; Create the year dimension information DI_YEAR2, which contains the year, and map it to the YEAR2 field in the data book year data table; Create department dimension information DI_DEPART, including departments and branches, and map it to the DEPART field in the department data table in the data book; Create product dimension information DI_PRODUCT, including product and category, and map it to the PRODUCT field in the product data table of the data book; Dataset SET201 city dimension associated with city dimension information DI_CITY2; The year dimension of the dataset SET201 is associated with the year dimension information DI_YEAR2; Data set SET201 department dimension associated with department dimension information DI_DEPART; Data set SET201 product dimension associated with product dimension information DI_PRODUCT; The fields of dataset SET201 are mapped to the fields of table T201 in data book. If the indicator has an aggregation function, the aggregation calculation expression is used.

[0069] Similarly, within organization O3, business personnel USER_B3, developer USER_D3, data analyst USER_A3, and data manager USER_M3 use the dimension association model structure definition as the center of communication and management. Based on data requirements, they configure data book BOOK301 and provide a direct mapping from the CITY3 table to storage within BOOK301. This also provides a direct mapping from the CITY3 table to storage within BOOK301.

[0070] Create a dataset SET301 with dimensions of city and year and an object field of promotional atlas. Create city dimension information DI_CITY3, which includes city and province, and map it to the CITY3 field in the city data table in the data book; Dataset SET301 city dimension associated with city dimension information DI_CITY3; The fields of dataset SET301 are mapped to the fields of data book T301 table, which enables access to the promotional atlas through authorization.

[0071] The promotional atlas field of the dataset SET301 exists in the form of a picture object, which is an effective supplement to the existing dataset field classification.

[0072] It's important to note that data tables T101, T102, T201, T301, CITY1, CITY2, and CITY3 within a data book can be stored in databases, data lakes, or provide remote access. This means there are no restrictions on data sources or storage methods (accessibility is adapted within the data book). Furthermore, when actual business data doesn't exist, it can be defined first. Data books offer flexible usage. For example, dimension tables can be allocated to a separate data book, such as BOOKDI, specifically for managing dimension tables. When mapping datasets or dimension information to data book tables, calculation expressions, such as functions, can be used in addition to direct field mapping.

[0073] For the city dimension, if DI_CITY1, DI_CITY2, DI_CITY3 and GDI_CITY are to establish equivalent consistency, in the design implementation, the global dimension GDI_CITY provides mapping information to the physical table, DI_CITY1, DI_CITY2, DI_CITY3 can only establish field mapping with GDI_CITY, omitting CITY1, CITY2, CITY3 in their respective data books; or directly use the global dimensions GDI_CITY and GDI_YEAR, that is, SET101, SET201, SET301 city dimensions are associated with the global city dimension information GDI_CITY, and the year dimension is associated with the global year dimension information GDI_YEAR, such as Figure 6 shown.

[0074] In short, with minimal technical knowledge, all data professionals have a unified understanding of the structural definition of the dimensional association model, which comprises data books, dimensional information, and datasets. This is similar to the role of domain-driven models in software engineering. In data management, the management of the dimensional association model structural definition serves as central management, while the governance of related peripheral components serves as peripheral governance, with peripheral governance centered around the central management.

[0075] Next, step S302: using the project space as the basic unit of management, the data architecture is flattened and labeled, and the data component content is abstracted into resources and managed in a unified manner.

[0076] Constructing the association model structure in the space, wherein the data architecture is a business structure and an organizational structure; The project space has more than one organization member, a sole business leader and a sole technical leader; The project space has zero or more project administrators who create groups of organization members within the project space; The business structure includes business segments, data domains, and business processes, which serve as descriptive labels for the dimensional association model; The dimension association model structure provides a custom business description tag, a unique business person in charge and a unique technical person in charge; The dimension association model structure establishes an evaluation system and labels it, so that all accessible users can participate in the evaluation of the dimension association model; The peripheral components associated with the dimension association model structure provide custom business description labels and set corresponding persons in charge.

[0077] The data books, dimension information, and datasets in step S301 above need to be constructed within a project space. Organizations O1, O2, and O3, or their personnel, can create project spaces SPACE1, SPACE2, and SPACE3, respectively. The resulting business and technical leaders can be members of the organization. For example, within project space SPACE1 of organization O1, business person USER_B1 can be the business leader of SPACE1, developer USER_D1 can be the technical leader of SPACE1, and other O1 personnel can serve as managers or ordinary members of SPACE1.

[0078] Unlike existing data warehouse construction, which prioritizes business structure definitions such as business segments, data domains, and business processes, sequentially selecting these segments, data domains, and business processes can lead to significant dependencies as the business rapidly changes. Labeling these segments as descriptive labels within the dimension-related model structure allows standardized data development while reducing dependencies. For example, datasets SET101, SET201, and SET301 can be labeled with the business structure labels business segment, data domain, and business process. The corresponding spaces, such as SPACE1, with business leader USER_B1 and technical leader USER_D1, can serve as organizational labels for dataset SET101. Business constraint classification is centered around the dimension-related model structure and no longer imposes constraints on data warehouse development or metadata technology. Post-standardization of the business and organizational structures eliminates obstacles to technical data development. Within a project space, groups of organizational members can be created to facilitate authorization operations across the project as a whole.

[0079] An evaluation system is established and labeled for dimension-related model structures, such as collection, star-rating, copying, etc. Users can easily select high-value data sets through the evaluation system, which facilitates data circulation, sharing, and other activities.

[0080] When different business personnel have access to dataset SET101, they can add custom business descriptive tags. Similarly, peripheral components associated with dataset SET101, such as requirements, can also have custom business tags, with corresponding owners assigned based on their business or technical nature. For example, setting a security owner on a security component can be a good example.

[0081] The data component content is abstracted into resources for unified management, including: The data component content includes the central component instance and the peripheral component instance; The resource exists globally or within the project space or provides a unique identifier; The central component instance includes the dimension information, the data set, the data book and the dimension data; Constructing an association relationship structure logic of the peripheral component instances associated with the central component instance around the central component instance; The corresponding central component instance and the peripheral component instance information can be retrieved through the unique identifier.

[0082] For example, consider the dimension association structure defined by associating dataset SET101 with DI_CITY1 and its mapped data book BOOK101. The DI_CITY1 information, the SET101 dataset and its dimensions and index fields, the T101 table corresponding to BOOK101, and the city data of DI_CITY1 are abstracted as resources, providing unique identifiers within project space SPACE1. This means that SET101, DI_CITY1, and BOOK101 are unique within SPACE1, and can also be globally unique. (It should be emphasized that the naming conventions for unique identifiers are provided for illustration only and are not intended to be limiting.)

[0083] In this way, through the data of DI_CITY1, such as "Beijing", you can obtain the dimension-related data structure of the dataset SET101. Similarly, you can query the unique identifiers of surrounding components such as the requirement number (resourced and unique code or identifier of the requirement) and the corresponding component information.

[0084] In step S303, access restrictions are set for resources within the project space, and data is pre-processed and post-calculated to provide secure, compliant, reliable, and controllable data services in various forms.

[0085] Visibility scope refers to whether a resource is visible to a user. Resources exist in a project space, and users exist in a project space. Visibility scopes include "Public," "Restricted," and "Private." In terms of scope size, "Public" is larger than "Restricted," which in turn is larger than "Private." This is used to omit authorization in some situations, making it easier to share compliant data.

[0086] To illustrate one implementation, when designing visibility scopes, if a resource's visibility scope is unset, it can inherit the visibility scope of the space where the resource resides. However, when setting its own visibility scope (only a more restrictive visibility scope can be set; for example, if the space is restricted, its resource visibility scope cannot be set to "Public," but can only be set to a smaller visibility scope of "Private" or remain "Restricted"), the resource's visibility scope takes precedence. Furthermore, in design implementation, dimension information within a project is generally visible within the project scope, with the minimum visibility scope set to "Restricted," as shown in step 302: "Dataset SET102 city dimension is associated with city dimension information DI_CITY1." The technical or business lead for a project space has access to all resources within that space and has permission to modify their visibility scopes.

[0087] Still taking the dimension association structure defined by associating SPACE1 and data set SET101 with GDI_CITY and the mapped data book BOOK101 in step S302 as an example: If the visibility level of space SPACE1 is set to private, the visibility level of the abstract resources corresponding to datasets SET101, GDI_CITY, and BOOK101 is set to "private" by default. Ordinary users in SPACE1 can access these resources but must obtain authorization. The abstract resources corresponding to SET101, GDI_CITY, and BOOK101 do not need to, and cannot, have a wider visibility level.

[0088] If the visibility level of space SPACE1 is set to Restricted, the visibility level of the abstract resources corresponding to datasets SET101, GDI_CITY, and BOOK101 is set to Restricted by default. Ordinary users in SPACE1 can access these resources without authorization. The visibility level of the abstract resources corresponding to SET101, GDI_CITY, and BOOK101 can be set to Private, which means that ordinary users in SPACE1 need authorization to access these resources. The visibility level of the abstract resources corresponding to SET101, GDI_CITY, and BOOK101 cannot be set to Public.

[0089] If the visibility level of space SPACE1 is set to "Public," the visibility level of the abstract resources corresponding to datasets SET101, GDI_CITY, and BOOK101 is set to "Public" by default, allowing users in SPACE1 to access them without additional authorization. The visibility level of the abstract resources corresponding to SET101, GDI_CITY, and BOOK101 can be set to "Private," requiring authorization for access by ordinary users in SPACE1. The visibility level of the abstract resources corresponding to SET101, GDI_CITY, and BOOK101 can be set to "Restricted," allowing ordinary users in SPACE1 to access them without additional authorization.

[0090] When working across project spaces, for the abstract resource corresponding to dataset SET101 in project space SPACE1, if the user of SPACE2 is not in space SPACE1: When the visibility is "public", users in other spaces such as SPACE2 can directly access the space without authorization. When the visibility range is "limited", users in other spaces such as SPACE2 can directly access it with authorization; When the visibility range is "Private", users in other spaces such as SPACE2 cannot access it and cannot access it through authorization.

[0091] In step S302, the visibility scope of the abstract resources SET101, GDI_CITY, DATA_B1, and GDI_CITY data (e.g., "Beijing"), as well as related demand resources, can be configured. When designing permission rules, resource groups (or roles) can be created to configure resource permissions and grant them to users. This visibility scope, combined with resource permission restrictions, can contribute to secure and controllable resource access. The reason for abstracting dimensional information data as a resource is that, as in the example, a user may only have permission to data with "City = Beijing." In this case, abstracting the city dimension data as a resource and using it for permission management greatly improves convenience.

[0092] Data security and compliance management is performed on the data book BOOK101, with data security classification and security level management, such as public, restricted, private, semi-public, and highest security levels. These levels take priority over the visible range.

[0093] Using permission rules and security levels as restrictive conditions, data book BOOK101 performs query preprocessing under these restrictive conditions, including extraction, cleaning, conversion, and merging. In particular, when homogeneous data tables within the data book are distributed in different regions or clusters, the resulting data is queried and merged in different clusters separately, subject to compliance. This effectively reduces the complexity of data processing and can effectively reduce additional operations such as data migration and replication.

[0094] Based on the dimensional association model definition and data screening conditions, the results of different data sets are merged and output or merged with the dimensional information or simple calculations are performed; the data service method includes a graphical form or an interface form, and the graphical form is a visual method of the association between dimensional information and data sets.

[0095] When data resources are visible between different projects, for example, under compliance conditions, the data resources of SPACE1, SPACE2, and SPACE3 are all public and visible to each other. Then, when providing data to upper-level applications, the following situations can be possible: First, the data output of SET101, SET201, and SET301 under the common dimensions GDI_YEAR and GDI_CITY is Result data content: city, year, number of tourists, tourism revenue, sales quantity, sales amount, promotional pictures; Second, when adding the "province" content of city information, Result data content: city, province, year, number of tourists, tourism revenue, sales quantity, sales amount, promotional pictures; Third, in other cases, the non-common dimensions of one of the three datasets, such as "Product" and "Department" of SET201, can be used as selectable output fields, that is, Result data content: city, year, product, department, number of tourists, tourism revenue, sales quantity, sales amount, promotional pictures; Finally, the merge scenario, i.e. Result data content: city, province, year, product, department, number of tourists, tourism revenue, sales quantity, sales amount, and promotional picture album.

[0096] These scenarios are just a few examples of data output sets and are not intended to be limiting. The resulting content can be a collection of fewer fields. Clearly, these scenarios all indicate that an additional dataset and the associated data development tasks are not necessary at this point. In this case, the business and technical leaders of the various project spaces are responsible for the quality and security of the datasets involved. There's no need to produce a new dataset, thus reducing unnecessary workload.

[0097] For simple calculations, based on the dimensional association model structure definition, for example, SET101 and SET201, the result data content includes: city, year, product, sales quantity / number of tourists. SQL pseudocode parsing is provided, such as "Select SET101.city, SET101.year, SET201.product, SET201.sales quantity / SET101.number of tourists from SETS where SET101.city = 'Beijing'" and so on.

[0098] When providing data services to the upper layer, it can be in the form of interfaces under permission restrictions, such as dataset lists, indicator lists, dimension lists, and lists of dimension-related datasets, etc.; it can also be the visualization of dimension-related datasets under permission restrictions, forming an optional relationship diagram, such as Figure 7 In the , three classification dimensions, indicators, and objects are displayed separately, and relationship lines and multiple selection boxes are provided; Figure 8 In the data set, indicators or objects appear in the form of indicator combinations or object combinations, and are associated with dimensions, and relationship lines and optional items are also provided. It should be noted that the multiple-choice boxes and graphical content are for illustration only and are not restrictions. More metadata information content can be provided in the display, and the ultimate effect is the visualization of the association between dimension information and data sets. Within the scope of authority restrictions, users can obtain the required dimension information and associated graphics of the data set through screening, and select the dimensions, indicators, and objects of interest, and then perform the next calculation step on this basis, such as setting filter conditions. Taking business intelligence as an example, based on the above process, choose appropriate charts to display the analysis. The visualization form can greatly improve the ease of use and lower the threshold for business use.

[0099] This approach makes data construction as simple as playing cards. The structure of dimension-related models can be defined as a card model, facilitating communication. Building a data open collaboration platform with this approach would allow thousands of users to share thousands of compliant datasets, greatly improving data circulation efficiency. Much like how code collaboration platforms have done in the code world, this approach can fully tap into the potential value of data.

[0100] Example 2 like Figure 2 As shown, a data development management processing system includes: The central module 401 uses the dimension-related model structure definition as the center of communication construction management; The space module 402 is used to flatten and label the data architecture using the project space as the basic unit of management, and to abstract the data component content into resources and manage them in a unified manner; Service module 403 sets access restrictions on resources within the project space, performs pre-processing and post-computation on data, and provides various forms of data services that are secure, compliant, reliable, and controllable.

[0101] Define the basic component content. The dimension association model structure defines the basic components including dimension information, data sets and data books. The dimension information is a consistency dimension, which constitutes equivalent consistency between different project spaces; Construct a dimension association model, the dataset field classification includes dimensions, indicators and objects, and the datasets are associated through the dimension information. The data book includes data tables, data sources, files, streaming media and access control, and the data tables are mapped to the corresponding data sources. The dimension information and the fields of the data set are dynamically configured and mapped to the data table fields corresponding to the data book; The object classification of the data set is associated to the file or the streaming media of the data set by configuration under access control; Conduct data management governance, take the dimension association model structure definition as the central component as the focus of data management, and conduct peripheral component governance around the central component. The peripheral components include demand, acquisition, integration, development, quality, security, metadata, business intelligence, lineage management and data compliance, and asset elements. The data management includes the central management and peripheral governance. The peripheral governance manages around the central management and performs life cycle management accordingly.

[0102] In a simplified form, data books can be collapsed into dimensional information and datasets, where dimension fields or dataset fields are directly mapped to data source table fields.

[0103] In this embodiment, the space module is used to flatten and label the data architecture using the project space as the basic unit of management. It mainly includes A labeled data architecture is used to construct the associated model structure within the space, wherein the data architecture is a business structure and an organizational structure; The project space has more than one organization member, a sole business leader and a sole technical leader; The project space has zero or more project administrators who create groups of organization members within the project space; The business structure includes business segments, data domains, and business processes, which serve as descriptive labels for the dimensional association model; The dimension association model structure provides a custom business description tag, a unique business person in charge and a unique technical person in charge; The dimension association model structure establishes an evaluation system and labels it, so that all accessible users can participate in the evaluation of the dimension association model; The peripheral components associated with the dimension association model structure are provided with custom business description labels and corresponding persons in charge are set for them; Resource data components, wherein the data component content includes the central component instance and the peripheral component instance; The resource exists globally or within the project space or provides a unique identifier; The central component instance includes the dimension information, the data set, the data book and the dimension data; Constructing an association relationship structure logic of the peripheral component instances associated with the central component instance around the central component instance; The corresponding central component instance and the peripheral component instance information can be retrieved through the unique identifier.

[0104] In this embodiment, the service module is used to set access restrictions on resources in the project space, pre-process and post-calculate data, and provide various forms of data services that are secure, compliant, reliable, and controllable; including Visibility control: the visibility of resources includes public, restricted, and private; the visibility of project spaces includes public, restricted, and private; Permission rule management: the permission rules for resources include the resource's own visibility, the resource's project visibility, and custom configurations; Data security and compliance: the central management component instance resource data security and data compliance management, security classification and security level management of the resources, Taking the authority rules and security levels as restriction conditions, the data book is queried and pre-processed under the restriction conditions, including extraction, cleaning, conversion, and merging; Data processing and calculation: Based on the definition of dimension association model and data screening conditions, the results of different data sets are merged and output or merged with dimension information or simple calculations are performed; Data service mode, the data service mode includes a graphical form or an interface form, and the graphical form is a visualization mode of the association between dimension information and data sets.

[0105] From a system perspective, dimensional information, datasets, and data books are a few core concepts. The definition of the dimensional association model structure becomes the center of communication, construction, and management, effectively lowering the entry point for business personnel. Data books strengthen the business nature of the data source, while datasets can be as simple as using a spreadsheet, with dimensional information as descriptive information. Regarding security, data books strengthen data security levels, security classification, and access control, which is the first level of restriction. Data sets are combined with visibility, spatial control, and permission rules, which is the second level of restriction. Data book preprocessing and the dimensional association model structure significantly reduce physical limitations such as data clustering and data geography, thereby reducing data tables and data operations, conserving resources, and reducing duplication of work.

[0106] The system uses project space as the basic unit of management. In addition to the central component of the management dimension association model structure, requirements, acquisition, integration, development, quality, security, metadata, business intelligence, lineage management and data compliance, asset elements, etc. can all be included in the management scope; for example, building a business intelligence module in the project space can significantly improve the analysis efficiency of charts and other such elements in business intelligence, expand the scope of dimension indicator object selection, and can adopt Figure 7 、 Figure 8 The visualization method improves business usability. The full use of dimensional information can reduce the use of technical concepts such as data dictionary in business intelligence analysis.

[0107] In addition to building small-scale enterprise data platforms, the system can also realize large-scale platform-level data platforms; specific implementation focuses, such as the dimension-related model structure as the core of sharing, supplemented by an evaluation system for dimension-related data sets, combined with the management and control of the project space and the visible range of abstract resources within the space; this will greatly improve the circulation efficiency of high-value data.

[0108] Example 3 An electronic device for data development management processing, comprising: a memory and a processor; The memory is used to store a program for data development management processing, and when the program for data development management processing is read and executed by the processor, the following operations are performed: The definition of the dimension-related model structure is the center of communication and construction management; Using project space as the basic unit of management, the data architecture is flattened and labeled, and data component content is abstracted into resources and managed in a unified manner. Set access restrictions on resources within the project space, pre-process and post-calculate data, and provide secure, compliant, reliable, and controllable data services in various forms.

[0109] When the program for data development management processing in this embodiment is read and executed by the processor, the operations performed correspond to steps S301 to S303 of the first embodiment; other details of the operations performed by the program can be found in the first embodiment.

[0110] It should be understood that the embodiments disclosed herein are not limited to the specific processing steps or materials disclosed herein, but should extend to equivalent substitutions of these features understood by those skilled in the relevant art. It should also be understood that the terminology used herein is for the purpose of describing specific embodiments only and is not intended to be limiting.

[0111] The "embodiment" mentioned in the specification means that a particular feature or characteristic described in conjunction with the embodiment is included in at least one embodiment of the present invention. Therefore, the phrase "embodiment" appearing in various places throughout the specification does not necessarily refer to the same embodiment.

[0112] It should be understood by those skilled in the art that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art may use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0113] The steps of the method or algorithm described in conjunction with the embodiments disclosed herein can be implemented directly using hardware, a software module executed by a processor, or a combination of the two. The software module can be placed in a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. Although the embodiments disclosed in the present invention are as above, the contents described are only embodiments adopted to facilitate understanding of the present invention and are not intended to limit the present invention. Any person skilled in the art of the present invention may make any modifications and changes in the form and details of implementation without departing from the spirit and scope disclosed in the present invention, but the scope of protection of the present invention shall still be subject to the scope defined in the appended claims.

Claims

1. A data development management processing method, comprising: The definition of the dimension-related model structure is the center of communication and construction management; Using project space as the basic unit of management, the data architecture is flattened and labeled, and data component content is abstracted into resources and managed in a unified manner. Set access restrictions on resources within the project space, pre-process and post-calculate data, and provide secure, compliant, reliable, and controllable data services in various forms.

2. The method according to claim 1, wherein the center is characterized by: The basic components of the dimension association model structure definition include dimension information, data sets and data books; The dimension information is a consistency dimension, which constitutes equivalence consistency between different project spaces; The dataset field classification includes dimensions, indicators, and objects, and the datasets are associated through the dimension information; The data book includes data tables, files, streaming media and data sources, access control, and data tables are mapped to corresponding data sources; The dimension information and the fields of the data set are dynamically configured and mapped to the data table fields corresponding to the data book; The object classification field of the data set is associated with the file or the streaming media of the data volume through configuration under access control; The dimension association model structure definition is used as the central component and the focus of data management, and peripheral component governance is carried out around the central component. The peripheral components include requirements, acquisition, integration, development, quality, security, metadata, business intelligence, lineage management and data compliance, and asset elements; The data management includes the central management and peripheral governance. The peripheral governance is carried out around the central management and life cycle management is carried out accordingly.

3. The method according to claim 1 or 2, characterized in that: The project space is used as the basic unit of management to flatten and label the data architecture, including: Constructing the association model structure in the space, wherein the data architecture is a business structure and an organizational structure; The project space has more than one organization member, a sole business leader and a sole technical leader; The project space has zero or more project administrators who create groups of organization members within the project space; The business structure includes business segments, data domains, and business processes, which serve as descriptive labels for the dimensional association model; The dimension association model structure provides a custom business description tag, a unique business person in charge and a unique technical person in charge; The dimension association model structure establishes an evaluation system and labels it, so that all accessible users can participate in the evaluation of the dimension association model; The peripheral components associated with the dimension association model structure provide custom business description labels and set corresponding persons in charge.

4. The method according to claim 1 or 2, characterized in that: The data component content is abstracted into resources for unified management, including: The data component content includes the central component instance and the peripheral component instance; The resource exists globally or within the project space or provides a unique identifier; The central component instance includes the dimension information, the data set, the data book and the dimension data; Constructing an association relationship structure logic of the peripheral component instances associated with the central component instance around the central component instance; The corresponding central component instance and the peripheral component instance information can be retrieved through the unique identifier.

5. The method according to claim 4, characterized in that: The above mentioned method is to set access restrictions on resources within the project space, perform data pre-processing and post-computation, and provide various forms of data services that are safe, compliant, reliable and controllable, including The visibility range of the resource includes public, restricted, and private; the visibility range of the space includes public, restricted, and private; The resource permission rules include the visibility scope of the resource itself, the visibility scope of the project to which the resource belongs, and custom access configuration; Perform data security and data compliance management on the central management component instance resources, and perform security classification and security level management on the resources; Taking the authority rules and security levels as restriction conditions, the data book is queried and pre-processed under the restriction conditions, including extraction, cleaning, conversion, and merging; Based on the definition of the dimension association model and data screening conditions, the results of different data sets are merged and output or merged with dimension information or simple calculations are performed; The data service mode includes a graphical form or an interface form, and the graphical form is a visualization mode of the association between dimension information and data sets.

6. A data development management processing device, comprising: The central module is used to define the dimension-related model structure as the center of communication construction management; The space module uses the project space as the basic unit of management, flattens the data architecture and labels it, abstracts the data component content into resources, and manages them in a unified manner. The service module is used to set access restrictions for resources within the project space, pre-process and post-calculate data, and provide various forms of data services that are secure, compliant, reliable, and controllable.

7. The data development management processing device according to claim 6, wherein the central module is characterized by: Define the basic component content. The dimension association model structure defines the basic components including dimension information, data sets and data books. The dimension information is a consistency dimension, which constitutes equivalent consistency between different project spaces; Construct a dimension association model, wherein the dataset field classification includes dimensions, indicators, and objects, and the datasets are associated through the dimension information. The data book includes data tables, data sources, files, streaming media and access control, and the data tables are mapped to the corresponding data sources. The dimension information and the fields of the data set are dynamically configured and mapped to the data table fields corresponding to the data book; The object classification of the data set is associated to the file or the streaming media of the data set by configuration under access control; Conduct data management governance, take the dimension association model structure definition as the central component as the focus of data management, and conduct peripheral component governance around the central component. The peripheral components include demand, acquisition, integration, development, quality, security, metadata, business intelligence, lineage management and data compliance, and asset elements. The data management includes the central management and peripheral governance. The peripheral governance manages around the central management and performs life cycle management accordingly.

8. The data development management processing device according to claim 6 or 7, wherein the space module is characterized by: A labeled data architecture is used to construct the associated model structure within the space, wherein the data architecture is a business structure and an organizational structure; The project space has more than one organization member, a sole business leader and a sole technical leader; The project space has zero or more project administrators who create groups of organization members within the project space; The business structure includes business segments, data domains, and business processes, which serve as descriptive labels for the dimensional association model; The dimension association model structure provides a custom business description tag, a unique business person in charge and a unique technical person in charge; The dimension association model structure establishes an evaluation system and labels it, so that all accessible users can participate in the evaluation of the dimension association model; The peripheral components associated with the dimension association model structure are provided with custom business description labels and corresponding persons in charge are set for them; Resource data components, wherein the data component content includes the central component instance and the peripheral component instance; The resource exists globally or within the project space or provides a unique identifier; The central component instance includes the dimension information, the data set, the data book and the dimension data; Constructing an association relationship structure logic of the peripheral component instances associated with the central component instance around the central component instance; The corresponding central component instance and the peripheral component instance information can be retrieved through the unique identifier.

9. The data development management processing device according to claim 8, characterized in that: The service module is used to set access restrictions on resources within the project space, pre-process and post-calculate data, and provide various forms of data services that are secure, compliant, reliable, and controllable, including Visibility control: the visibility of resources includes public, restricted, and private; the visibility of project spaces includes public, restricted, and private; Permission rule management: the permission rules for resources include the visibility scope of the resource itself, the visibility scope of the project to which the resource belongs, and the custom configuration of the resource; Data security and compliance: the central management component instance resources perform data security and data compliance management, resource security classification and security level management, Taking the authority rules and security levels as restriction conditions, the data book is queried and pre-processed under the restriction conditions, including extraction, cleaning, conversion, and merging; Data processing and calculation: Based on the definition of dimension association model and data screening conditions, the results of different data sets are merged and output or merged with dimension information or simple calculations are performed; Data service mode, the data service mode includes a graphical form or an interface form, and the graphical form is a visualization mode of the association between dimension information and data sets.

10. An electronic device for data development management processing, comprising: memory and processor; Its characteristics are: The memory is used to store a program for data development management processing, and the program for data development management processing is used by the processor When reading execution, the following operations are performed: The definition of the dimension-related model structure is the center of communication and construction management; Using project space as the basic unit of management, the data architecture is flattened and labeled, and data component content is abstracted into resources and managed in a unified manner. Set access restrictions on resources within the project space, pre-process and post-calculate data, and provide secure, compliant, reliable, and controllable data services in various forms.

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