Enterprise data asset mandatory governance system and method

Abstract

The application provides an enterprise-level data asset forced management system and method, relates to the technical field of data asset management, and comprises an access control module for intercepting offline number module design requests (carrying original number module design data) sent by a client to a business system; a metadata modeling module connected with the access control module, used for performing full attribute filling or standard attribute reference on fields contained in the original number module design data based on a pre-set structured template, and generating number module design data with attribute labels; an intelligent verification engine connected with the metadata modeling module and an enterprise-level general number module library, used for real-time matching of the general number module library to perform compliance verification on the number module design data with attribute labels, and outputting a release signal when passing; and an asset synchronization module for writing the number module design data with attribute labels that pass the verification into an enterprise data asset directory as target number module data. The application significantly improves the efficiency and quality of enterprise data management.

Description

Technical Field

[0001] This application relates to the field of data asset governance technology, and in particular to an enterprise-level mandatory data asset governance system and method. Background Technology

[0002] In the digital transformation of large group enterprises (such as steel manufacturing), the systems of each branch plant and business domain (MES, ERP, logistics, etc.) are mostly planned and built independently. Due to the lack of unified digital model design standards and mandatory control measures, significant differences exist in the naming, coding rules, data types, and business meanings of core fields (material numbers, codes) for the same business entity (such as steel coils and spare parts) in different systems. For example, different branch plants within a group define the length and format of steel coil material numbers differently, making it impossible to directly share and associate data across systems, resulting in numerous data silos. This inconsistency in data definitions severely hinders the integration, analysis, and value mining of enterprise-level data. Summary of the Invention

[0003] The purpose of this application is to provide an enterprise-level mandatory governance system and method for data assets, so as to alleviate the aforementioned technical problems existing in the prior art.

[0004] In a first aspect, the present invention provides an enterprise-level mandatory data asset governance system, comprising: The access control module is used to intercept offline digital model design requests sent by the client to the business system, wherein the offline digital model design requests carry the original digital model design data; The metadata modeling module, connected to the access control module, is used to perform full attribute filling or standard attribute referencing on the fields contained in the original digital model design data based on a preset structured template, and generate digital model design data with attribute tags. The intelligent verification engine is connected to the metadata modeling module and the enterprise-level general-purpose digital model library respectively. It is used to match the general-purpose digital model library in real time to perform compliance verification on the digital model design data with attribute tags, and output a release signal when the verification is passed. The asset synchronization module is used to respond to the release signal by writing the verified digital model design data with attribute tags into the enterprise data asset catalog as the target digital model data.

[0005] In an optional implementation, the access control module includes a traffic sniffing unit and a proxy forwarding unit; The traffic sniffing unit is used to monitor the communication port between the client and the business system and identify the characteristic identifiers of the offline digital model design request; The proxy forwarding unit is used to block the sending path of the offline digital model design request to the business system after recognizing the feature identifier, and redirect the offline digital model design request to the metadata modeling module.

[0006] In an optional implementation, the metadata modeling module is specifically used for: Parse the original digital model design data and extract the field names of the fields to be designed; Calculate the semantic similarity between the field name and the standard field name in the enterprise-level general mathematical modeling library; If the semantic similarity is greater than a preset threshold, the standard attribute corresponding to the standard field name will be automatically referenced as the full attribute filling content of the field. If the semantic similarity is less than or equal to a preset threshold, then attribute completion suggestions are generated, and custom attribute content based on the structured template input is received.

[0007] In an optional implementation, the intelligent verification engine includes a syntax verification unit and a consistency verification unit; The syntax verification unit is used to verify whether the field naming conventions, data types, and lengths in the mathematical model design data with attribute tags conform to preset syntax rules. The consistency verification unit is used to verify whether the business scope definition in the digital model design data with attribute tags is consistent with the definition of registered assets in the enterprise-level general digital model library.

[0008] In an optional implementation, the intelligent verification engine is further used for: If the numerical model design data with attribute tags fails the compliance verification, a verification failure report containing error field identifiers and correction suggestions will be generated. The verification failure report is fed back to the client, and the access control module is controlled to maintain the interception state of the offline digital model design request until the corrected digital model design data is received.

[0009] In an optional implementation, the asset synchronization module is specifically used for: Convert the target mathematical model data into a database definition language script; The database definition language script is sent to the target database corresponding to the business system for execution, so as to create physical tables in the target database; After the physical table is successfully created, the target digital model data is registered to the enterprise data asset catalog, and a mapping relationship between the physical table and the registered data is established.

[0010] In an optional implementation, the structured template includes business attribute fields, management attribute fields, and technical attribute fields; The business attribute field is used to define the business meaning and calculation method of the field; The management attribute field is used to define the security level, owner department, and retention policy of the field. The technical attribute field is used to define the physical name, data type, and precision of the field.

[0011] In an optional implementation, a version lineage management module is also included, which is connected to the metadata modeling module and the asset synchronization module, respectively. The version lineage management module is used to record the version number, change time, and operator information of the target model data when the target model data is written into the enterprise data asset catalog. Based on the mapping relationship between the original digital model design data and the target digital model data, a traceability link is constructed from the physical tables of the business system to the standard assets of the enterprise-level general digital model library.

[0012] In an optional implementation, the enterprise-level general-purpose numerical model library includes a dynamic rule configuration center; The dynamic rule configuration center is used to receive verification rule update instructions input by the administrator. The verification rule update instructions include adding naming conventions or modifying attribute mapping relationships. The intelligent verification engine and the metadata modeling module maintain a heartbeat connection with the dynamic rule configuration center to load updated verification rules and attribute mapping relationships in real time.

[0013] Secondly, the present invention provides a mandatory governance method for enterprise-level data assets, applied to an enterprise-level mandatory governance system for data assets as described in any of the foregoing embodiments, the method comprising: Intercept offline digital model design requests sent by the client to the business system, which carry the original digital model design data; Based on a pre-set structured template, the fields contained in the original numerical model design data are filled with all attributes or referenced with standard attributes to generate numerical model design data with attribute labels. The system performs real-time matching with an enterprise-level general-purpose mathematical model library to verify the compliance of the mathematical model design data with attribute tags, and outputs a release signal when the verification is successful. In response to the release signal, the verified numerical model design data with attribute tags is written into the enterprise data asset catalog as the target numerical model data.

[0014] The enterprise-level mandatory data asset governance system and method provided in this application intercepts offline digital model design requests through an access control module, blocking the flow of non-standard designs into business systems at the source, thus solving the problems of scattered designs and inconsistent standards in the traditional model. The metadata modeling module performs full attribute filling or standard attribute referencing on fields based on structured templates, forcibly completing metadata such as business meaning and management affiliation, making the definition of each field unambiguous and effectively eliminating data silos across systems. The intelligent verification engine performs real-time compliance verification against an enterprise-level general digital model library, ensuring that table naming, field types, and relationships are consistent with group standards, avoiding duplicate definitions and coding conflicts. The asset synchronization module automatically writes verified designs into the enterprise data asset catalog, making standardized digital models searchable and reusable assets across the entire group, significantly reducing the cleaning and mapping costs of cross-system data fusion. Overall, this system realizes a shift from "post-event governance" to "pre-event mandatory control," ensuring data consistency and assetization from the source, and significantly improving the efficiency and quality of enterprise data governance. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the specific embodiments of this application or the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0016] Figure 1 A structural diagram of an enterprise-level mandatory data asset governance system provided in this application embodiment; Figure 2 A structural diagram of another enterprise-level mandatory data asset governance system provided in this application embodiment; Figure 3 A flowchart illustrating a mandatory governance method for enterprise-level data assets provided in this application embodiment; Figure 4 This is a flowchart illustrating a specific enterprise-level mandatory governance method for data assets, provided as an embodiment of this application. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0018] Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0019] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0020] like Figure 1 As shown, this application provides an enterprise-level mandatory data asset governance system, the core of which lies in achieving centralized control over the digital model design of the entire group's business systems through "mandatory unified modeling". The system mainly includes an access control module, a metadata modeling module, an intelligent verification engine, an asset synchronization module, and an enterprise-level general-purpose digital model library that works in conjunction with it.

[0021] The access control module acts as the "master gate" for all digital modeling design requests within an enterprise. Deployed on the communication link between the client and the business system, it intercepts any offline digital modeling design requests attempting to bypass the platform and send them directly to the business system. "Offline digital modeling design requests" refer to digital modeling design files independently completed by designers locally using traditional tools (such as PowerDesigner, Excel, and Word) or directly written data definition language (DDL) scripts. The module's responsibility is to block these unapproved design channels and force design activities to be directed to the platform.

[0022] The metadata modeling module, connected to the access control module, is responsible for converting the intercepted raw digital model design data into a standardized model that conforms to the group's standards. Based on a pre-set structured template, it performs either "full attribute filling" or "standard attribute referencing" on each field in the raw data. "Full attribute filling" means that designers cannot simply fill in the field name and type; they must complete the entire set of metadata, including business meaning, management affiliation, and technical specifications, according to the template requirements. "Standard attribute referencing" means that if a field matches an existing standard field, the system automatically imports the standard attributes, eliminating the need for designers to fill them in repeatedly. After processing by this module, the raw design data is tagged with structured attribute labels, forming "digital model design data with attribute labels."

[0023] The intelligent verification engine connects to both the metadata modeling module and the enterprise-level general-purpose digital model library. Its task is to compare and verify the currently designed data against the standard assets already registered in the enterprise-level general-purpose digital model library in real time, either during or after modeling. Verification includes checking for standardized field naming, compliant data types, duplicate definitions, and consistency of business definitions. Once verification passes, the engine outputs a pass signal; otherwise, it rejects the verification and returns an error message.

[0024] The asset synchronization module, responding to the release signal issued by the intelligent verification engine, writes the verified digital model design data with attribute tags as "target digital model data" into the enterprise data asset catalog. After writing, the digital model design becomes an officially recognized asset of the group, which can be retrieved and reused by other business systems throughout the group.

[0025] Through the mandatory closed loop of the above four modules, the design results of any newly built or modified business system will be automatically deposited into the enterprise's standardized assets after the completion of the digital model design, eliminating data definition ambiguity and silo problems from the source.

[0026] For ease of understanding, the implementation details of the embodiments of this application are described in detail below.

[0027] The aforementioned access control module includes a traffic sniffing unit and a proxy forwarding unit.

[0028] The traffic sniffing unit is used to monitor the communication ports between the client and the business system. In actual deployment, this unit can access the network as a bypass mirror or a serial gateway. It has a built-in feature recognition library containing common characteristics of mathematical model design requests. For example, for relational databases, keywords of DDL statements such as CREATE TABLE, ALTER TABLE, and DROP TABLE are listed as characteristics; for mathematical model design files uploaded via HTTP, the unit checks the file name extension (such as .sql, .pdm, .xlsx) and MIME type. Once the traffic content matches these characteristics, the sniffing unit determines that the current request is an offline mathematical model design request.

[0029] The proxy forwarding unit acts immediately upon detecting a distinctive feature identified by the sniffing unit. First, it blocks the original request's path to the business system, preventing it from reaching the target database. Then, the proxy forwarding unit repackages the intercepted request content (including the original mathematical modeling design data) and redirects it to the interface address of the metadata modeling module. Simultaneously, it returns an HTTP redirection response or a custom protocol message to the client, informing the designer that "the current design request has been taken over; please continue modeling in the enterprise-level data asset governance platform." Through this "intercept-block-redirect" mechanism, the system ensures that designers cannot bypass the platform to directly manipulate the production database.

[0030] Furthermore, regarding the automatic referencing of standard attributes based on semantic matching in the metadata modeling module, in practice, the metadata modeling module first parses the original mathematical model design data forwarded by the access control module. The parsing process includes extracting the table name, field name, field type, comments, and other content to be designed. Then, for each field to be designed, the module extracts its field name (e.g., "product identifier," "material code," "coil number," etc.) and calculates its semantic similarity with the standard field names registered in the enterprise-level general mathematical model library.

[0031] Similarity calculation can employ word embedding methods from natural language processing (such as Word2Vec and BERT Lite) or edit distance weighted algorithms. For example, the semantic similarity between "material number" and "material code" might reach 85%, and the similarity between "coil ID" and "coil serial number" might reach 90%. The system pre-sets a similarity threshold (e.g., 80%). When the calculated similarity is greater than or equal to this threshold, the module automatically determines that the current field to be designed and the standard field are essentially the same business entity. At this point, the module automatically references the complete set of attributes (including business attributes, management attributes, and technical attributes) corresponding to the standard field and directly fills them into the current field, without requiring any additional input from the designer.

[0032] When the similarity is less than a threshold, the module determines that the current field is a new, unstandardized field. At this point, the module generates an "attribute completion hint" interface, requiring the designer to fill in the custom attributes item by item according to the system's pre-set structured template. After all required fields are filled in, the module receives and stores these custom attribute contents. This "forced completion" design ensures that even for new fields, no attributes will be missing.

[0033] In one implementation, the intelligent verification engine may further include a syntax verification unit and a consistency verification unit.

[0034] The syntax validation unit is responsible for performing basic syntax checks on the mathematical model design data with attribute tags. It has a built-in enterprise data standard rule library. For example, it checks whether table names / field names conform to the naming convention of "business domain_entity name_attribute name," prohibiting the use of special characters (such as @#$%) and meaningless abbreviations (such as a1, col2). Simultaneously, it validates whether the data type (such as VARCHAR, INT, DECIMAL) and length / precision of fields are consistent with enterprise standards. For example, if the standard specifies that the "material number" length is 18 characters, but the designer designs it to be 20 characters, the syntax validation unit will report an error.

[0035] The consistency verification unit focuses on semantic and business logic consistency. It compares whether the business definition of the field to be designed (e.g., whether "coil weight" refers to gross weight or net weight) matches the definition of similar assets already registered in the enterprise-level general-purpose numerical model library. If the current field claims to be "coil weight," but its business description does not clearly distinguish between gross weight and net weight, while the standard asset clearly states "net weight (excluding packaging)," then the consistency verification unit will determine that it is inconsistent.

[0036] When validation fails, the intelligent validation engine generates a validation failure report. This report contains at least three elements: an error field identifier (clearly indicating which table and field are incorrect), the type of violation (e.g., "inconsistent naming," "type length conflict," "inconsistent business definitions"), and corrective suggestions (e.g., "Please change the field length to 18" or "Please supplement the business definition description"). This report is sent to the client, and the access control module maintains the blocking status of the offline digital model design request. In other words, if the design data fails validation, the system will not allow it to proceed, and the designer cannot publish the design to the production environment. Only after the designer modifies the data according to the corrective suggestions, resubmits it, and passes validation again can it proceed to the next stage.

[0037] Regarding the asset synchronization module, when converting target digital model data into executable physical tables and registering them as assets, the module first automatically generates a database definition language script based on the validated target digital model data. This script is generated according to internal enterprise standards (such as dialect specifications for MySQL, Oracle, and SQL Server) and the database type required by the target business system. For example, for a table containing fields such as "material number," "specification," and "weight," the system-generated MySQL table creation statement will include the corresponding field definitions, primary key constraints, and index rules.

[0038] Then, the asset synchronization module sends the DDL script to the target database corresponding to the business system for execution, creating the physical table in the database. This step is crucial for implementing the design, ensuring that the business system can directly use the table structure. After the physical table is successfully created, the module registers the target data model (including its complete three-attribute metadata) to the enterprise data asset catalog. During registration, the system establishes a mapping relationship between the physical table and the registered data, for example, recording "physical table logistics_steel_coil corresponds to asset IDASSET_001". Subsequently, when other business systems within the group retrieve this asset, they can trace back to the specific physical table location and structure.

[0039] Furthermore, the "structured template" used in the metadata modeling module was defined by domain. This template contains three core attribute domains.

[0040] Business attribute fields are used to define the business meaning and calculation method of a field. For example, the business attributes of the field "steel coil weight" might include: "Business meaning: net weight of steel coil, in kilograms; Calculation method: measurement value from weighing equipment minus the weight of packaging materials; Statistical dimension: summarized by day, by production line; Business responsible person: Zhang XX from the Production Management Department; Business application scenario: inventory counting, logistics billing." This information transforms the data from an isolated field into a understandable business term.

[0041] Management attribute fields are used to define the management-related information of fields. Examples include: data security level (Level 1 for public, Level 2 for internal, Level 3 for sensitive), privacy level (whether it contains personally identifiable information), responsible department (e.g., "Production Management Department"), sharing scope (production domain / sales domain / logistics domain / entire group), and data lifecycle (e.g., "retain for 10 years"). These attributes provide a basis for compliant data use and access control.

[0042] The technical attribute field defines the physical implementation details of the field. Examples include: physical name (the actual column name in the database, such as `coil_material_no`), data type (VARCHAR, INT), length / precision (18, (10,2)), whether it is NOT NULL, primary key / foreign key constraints, and indexing rules (BTREE, HASH). In the steel manufacturing industry, the technical attributes of the material number field are mandated to be defined as a specific physical name, data type, and NOT NULL constraint to ensure consistent coding rules across the entire group.

[0043] This three-attribute template makes each field a "living asset," capable of carrying rich business semantics and management information, rather than just a technical definition.

[0044] The version lineage management module described above is used to record the entire lifecycle information of the digital model design. In specific implementation, the version lineage management module is connected to both the metadata modeling module and the asset synchronization module. Whenever the target digital model data is written to the enterprise data asset catalog, this module automatically records a version record, including: version number (incrementing from 1.0), change time (accurate to milliseconds), operator (obtained from the currently logged-in user session), and change type (new, modified, obsolete, reuse), etc. Simultaneously, the module constructs a traceability chain based on the mapping relationship between the original digital model design data and the final target digital model data (e.g., the original field prod_id is mapped to the standard field coil_material_no).

[0045] This link can start from the physical tables of the business system and trace all the way to the standard assets in the enterprise-level general digital model library, forming a complete "physical table → design model → standard asset" lineage. Group data administrators can use this module to perform source tracing queries by field, by business system, and by time dimension. They can also generate impact analysis reports on digital model changes, such as "If the length of the standard field 'steel coil material number' is modified, which physical tables of existing systems will be affected?"

[0046] The dynamic rule configuration center provides a visual management interface for administrators. Administrators can use this interface to input validation rule update commands online, including adding naming conventions (e.g., adding a table name prefix rule for "New Energy Production Line"), modifying attribute mapping relationships (e.g., adjusting the matching weight between the "Product Code" field and the standard "Material Number" field), and deactivating outdated rules. The configuration center stores these rules in structured data (such as JSON and XML) and provides a RESTful API or message queue interface.

[0047] The intelligent verification engine and metadata modeling module maintain a heartbeat connection with the dynamic rule configuration center (e.g., sending a request every 30 seconds). When a rule is updated, the configuration center broadcasts a "rule version number increment" notification. Upon receiving the notification, each module immediately pulls the latest rule set from the configuration center and loads it into local memory, achieving "hot updates" without requiring a service restart. This design ensures that enterprise data standards can evolve flexibly, while all ongoing data modeling designs are subject to the constraints of new rules in real time.

[0048] Based on the above system architecture, as a specific embodiment, the management platform of this application is configured as the sole digital model design entry point for all new or modified business systems of the enterprise. See also Figure 2As shown, the management platform includes a visual design workbench, a unified modeling process engine, a three-attribute metadata template library, an intelligent real-time verification engine, an enterprise-level general-purpose digital model library, and supporting modules. Each module establishes a data transmission channel in sequence, forming a complete link from design to assetization.

[0049] The visual design workbench provides designers with visual editing capabilities for table structures and field definitions, and explicitly does not support any offline digital model design imports, eliminating non-standard designs from the outset. The unified modeling workflow engine incorporates a standardized process for digital model design, validation, review, and script generation, forcing all designs to proceed sequentially. The three-attribute metadata template library pre-sets structured business, management, and technical attribute templates. Business attributes cover the field's business meaning, calculation method, statistical dimension, business owner, and application scenario; management attributes include data security level (levels 1-3), privacy level, responsible department, sharing scope (e.g., production domain / sales domain / logistics domain / entire group), and data lifecycle; technical attributes define the field's physical name, data type, length / precision, whether it is nullable, primary key / foreign key constraints, and indexing rules. In the steel manufacturing field, the technical attributes of the material number field are forcibly specified as physical name, data type, and non-null constraints to ensure unified coding across the entire group. Designers must configure required attributes or reuse standard attributes for all fields; otherwise, the fields cannot be saved.

[0050] The intelligent real-time validation engine incorporates an enterprise-level data standard rule library, with several validation rules customized for the steel manufacturing industry: Naming compliance checks whether table / field names conform to the "business domain_entity name_attribute name" specification, prohibiting special characters and meaningless abbreviations; Type and length compliance checks whether field types and lengths are consistent with industry / enterprise standards; Duplicate definition conflict detection checks whether the proposed field has semantic duplication or encoding conflicts with existing fields in the enterprise-level general-purpose numerical model library; Association integrity requires that foreign keys in newly created numerical models must reference standard codes in the enterprise standard code library. The engine also supports adding, modifying, and deactivating rules, enabling flexible upgrades to governance capabilities as business evolves.

[0051] The enterprise-level general-purpose numerical model library pre-loads a unified core data model across the entire group, including master data for steel coil materials, spare parts and components codes, energy consumption metering data, and organizational structure codes. The steel coil material number is an 18-digit code, following a rule of 2 digits for the plant, 2 digits for the production line, 8 digits for the date, and 6 digits for the serial number. This numerical model library also supports intelligent real-time recommendation for reuse; when a designer inputs a field to be added, the system automatically retrieves matching standard fields and pushes reuse suggestions.

[0052] The supporting modules include an automatic DDL script generation module and an enterprise data asset catalog module. The platform automatically generates compliant DDL scripts for data model designs that have passed full-process verification, eliminating the need for manual writing. Simultaneously, new data models are automatically synchronized to the data asset catalog, enabling them to be searchable and reused across the entire group. Furthermore, the management platform features data model version management and traceability functions, comprehensively recording the entire lifecycle information of data models—design, modification, reuse, and obsolescence. It supports traceability queries by field, business system, and time dimension, and can generate impact analysis reports on data model changes, providing strong support for the long-term governance of data assets.

[0053] This specific implementation fully embodies the core ideas of mandatory unified modeling, full-attribute metadata control, intelligent verification, and automatic assetization, and is applicable to data governance scenarios of large group enterprises such as steel manufacturing.

[0054] like Figure 3 As shown, this application also provides a mandatory governance method for enterprise-level data assets, which is applied to the aforementioned system. The method includes the following steps.

[0055] S310 intercepts offline digital model design requests sent by the client to the business system, which carry the original digital model design data.

[0056] Specifically, the access control module deployed on the network listens to the port in real time. Once it detects features such as SQL script uploads or DDL statement sending, it immediately blocks and redirects the data to the platform.

[0057] S320, based on a preset structured template, performs full attribute filling or standard attribute referencing on the fields contained in the original digital model design data to generate digital model design data with attribute labels.

[0058] The metadata modeling module parses the raw data and performs semantic matching on the fields to be designed. If the matching degree exceeds the threshold, the three attributes of the standard field are automatically referenced; if it is below the threshold, the designer is forced to complete all required attributes according to the template.

[0059] The S330 performs real-time matching with enterprise-level general-purpose numerical modeling libraries to verify the compliance of numerical modeling design data with attribute tags, and outputs a release signal when the verification is successful.

[0060] The intelligent validation engine performs comprehensive validation on field naming, type length, duplicate definitions, and business definitions. If a validation fails, an error report is returned and the process remains blocked; if it passes, a release signal is output.

[0061] S340, in response to the release signal, writes the verified numerical model design data with attribute tags as the target numerical model data into the enterprise data asset catalog.

[0062] The asset synchronization module automatically generates DDL scripts and executes them in the target database to create physical tables; at the same time, it registers the digital model design and its three-attribute information to the asset catalog for sharing across the entire group.

[0063] The above steps constitute a mandatory closed loop. If any step is not completed or passed, the next step cannot be taken, thereby ensuring that standardized governance of data assets is achieved from the source.

[0064] Based on the aforementioned system architecture, this application also provides a management method for an enterprise-level data asset governance platform based on mandatory unified modeling. This method ensures that all digital modeling designs are completed and assetized within the platform through a mandatory closed-loop process. (See [link to relevant documentation]). Figure 4 As shown, the method specifically includes the following steps S1 to S7: S1, Management Platform Access Control. This unifies the digital model design permissions for all newly built and modified business systems within the enterprise onto the management platform, closing the channels for offline digital model design and independent script generation. Any design attempts that bypass the platform are blocked.

[0065] S2 is the visual design workbench for digital model visualization. Designers initiate new digital model designs in the visual design workbench of the management platform, enter table names and basic information about the fields to be added, and then enter online editing mode.

[0066] S3's intelligent real-time validation engine performs semantic and business scenario matching on the fields to be added. The engine retrieves standard model fields from the enterprise-level general model library and pushes reuse suggestions to designers, including the three attribute information of the standard field and the number of business systems that have reused the field.

[0067] S4, attribute configuration in the three-attribute metadata template library. Designers choose "Reuse Standard Fields" or "Initiate New Field Request" based on the push results. If reuse is selected, the system automatically imports the standard three-attribute configuration; if a new request is selected, all attribute configurations must be completed according to the three-attribute metadata template.

[0068] S5's intelligent real-time verification engine performs full-dimensional verification based on the enterprise's data standard rule library. It verifies the completed digital model in real time; if verification fails, the system reports an error and marks the violation. The designer then modifies the code and re-verifies until it passes.

[0069] S6, the unified modeling workflow engine, handles review and script generation. Verified mathematical model designs are submitted to the platform for review. Once approved, the DDL script generation module automatically outputs table creation scripts that conform to enterprise standards.

[0070] S7 automatically synchronizes the new digital model and its complete three-attribute information to the enterprise data asset catalog. The synchronized digital model is automatically included in the enterprise-level general digital model library, enabling it to be searchable and reused throughout the entire group.

[0071] The above steps S1 to S7 constitute a mandatory closed-loop process. If any step is not completed or passed, the next step cannot be entered, thus preventing non-standard designs from flowing into the production system from the source.

[0072] In the intelligent reuse recommendation stage (corresponding to S3), the system calculates the semantic matching degree between the proposed field and the standard field. When the matching degree is not less than 80%, the management platform forces the designer to prioritize the reuse of the standard field; when the matching degree is less than 80%, the designer can initiate a new field application, but must submit a business necessity explanation, and can only enter the attribute configuration stage after approval.

[0073] In the real-time mandatory verification phase (corresponding to S5), the verification response time is controlled within 1 second. When verification fails, the platform automatically marks the violation type, the basis for the violation, and modification suggestions, and supports one-click correction of the violation, greatly improving design efficiency.

[0074] The following is a specific application example, taking the digital model design of an intelligent logistics system for a steel manufacturing group as an example. This steel manufacturing group has five production plants, each deployed with a Manufacturing Execution System (MES). The group also has independent business systems such as procurement, sales, and energy management. The digital models of steel coil material numbers in these systems are inconsistent, with differences in code length, naming, and business meaning. The group plans to build a new intelligent logistics system to uniformly manage the warehousing, transportation, and distribution of steel coils across the group. This requires designing a steel coil information storage table to store information such as the material number, specifications, weight, storage location, and transportation status of the steel coils. This embodiment completes the digital model design of this table through the aforementioned governance platform and methods, achieving digital model unification with the group's existing MES, sales, and other systems.

[0075] Step 1: Management Platform Access Control. The group's technical department unified the digital model design permissions of the intelligent logistics system to the enterprise-level data asset governance platform of this invention, closing the channels for offline digital model design and independent writing of table creation scripts. Designers can only complete digital model design through the platform's visual design workbench.

[0076] Step 2: Visual design of the digital model on the visual design workbench. Designers of the intelligent logistics system log into the platform's visual design workbench, initiate a new table design application, and enter the table name "logistics_steel_coil," the business domain "intelligent logistics," and the table purpose "stores logistics information for all steel coils in the group." If the application passes the initial review, the user will enter the visual editing interface.

[0077] Step 3: The intelligent real-time validation engine performs semantic and business scenario matching on the proposed field. The designer proposes to add a "Product Identifier" field in the editing interface to uniquely identify the physical steel coil. After entering the field name, the platform's intelligent real-time validation engine immediately performs semantic and business scenario matching on "Product Identifier," retrieves the standard field "Steel Coil Material Number" from the enterprise-level general-purpose digital model library, and pushes reuse suggestions to the designer. "The 'Product Identifier' field you intend to add has a 95% match with the group's standard field (coil material number), and we recommend prioritizing its reuse. The field's technical attributes are: physical name coil_material_no, type VARCHAR(18), not null; business attributes: 18-digit code (2-digit factory code + 2-digit production line code + 8-digit date code + 6-digit serial number), uniquely identifying the physical steel coil within the group, with the business manager being Zhang XX of the Group's Production Management Department; management attributes: security level 1, shared across the entire group, with the primary responsible department being the Group's Production Management Department. This field has already been reused by 12 systems including MES, sales, and purchasing." Because the matching rate is ≥80%, the platform forces designers to prioritize reusing this standard field. Designers click the "Reuse" button to complete the addition of this field.

[0078] Step 4: Three-Attribute Configuration and Other Field Design. After the designer reuses a field, the platform automatically imports the complete configuration of the field's business, management, and technical attributes, requiring no manual editing. The designer then adds fields such as "Steel Coil Specifications," "Steel Coil Weight," "Storage Location," and "Transportation Status." "Steel Coil Specifications" and "Steel Coil Weight" are standard group fields that the designer can reuse directly; "Storage Location" and "Transportation Status" are new fields, and the designer manually configures them according to the three-attribute metadata template. Storage location: The technical attribute is defined as VARCHAR(50), which is not empty; the business attribute is "the specific storage location of the steel coil in the logistics warehouse, and the coding rule is 'warehouse code + shelf code + layer code'", and the business person in charge is Li XX from the Logistics Management Department; the management attribute is security level 1, and the scope of sharing is the entire group.

[0079] Transportation status: The technical attribute is defined as VARCHAR(20), which is not null; the business attribute is "the logistics transportation status of steel coils, including 4 statuses: 'pending shipment,' 'in transit,' 'in storage,' and 'signed for'", and the business person in charge is Li XX from the Logistics Management Department; the management attribute is security level 1, and the scope of sharing is the entire group.

[0080] Step 5: Real-time Forced Validation. After the designer completes the design and attribute configuration of all fields, click the "Save and Validate" button. The intelligent real-time validation engine will then perform full-dimensional real-time validation on the entire table. Table name compliance: The table name conforms to the naming convention of "business domain_entity name", and the verification passed; Field naming / type / length: The physical name, type, and length of all fields conform to the enterprise standard and the validation passes; Duplicate definition conflict: No duplicate fields were defined, and the validation passed. Association integrity: All foreign key associations reference the group's standard code library and have passed verification.

[0081] The verification passed without any violations, and the designer can proceed to the next step. If the designer attempts to change the length of the steel coil material number to 20 digits, the platform will immediately report an error: "The length of the field conflicts with the group's standard definition, violating Article 3.2 of the 'Steel Group Data Model Technical Standard V2.0'. Please do not modify or submit a standard revision application," and will block subsequent operations. The designer must change the length back to 18 digits and re-verify.

[0082] Step 6: Review and DDL Script Generation. Designers submit the validated digital model design to the platform for review, allowing for online verification of table structure, field configuration, and three-attribute information. Upon approval, the platform's automatic DDL script generation module immediately outputs a table creation script that conforms to enterprise standards and MySQL syntax. Designers can directly download this script for database construction in the intelligent logistics system, eliminating the need for manual coding.

[0083] Step 7: Data Asset Synchronization. Approved tables and their complete three-attribute information for all fields are automatically synchronized to the group's enterprise data asset catalog and incorporated into the enterprise-level general-purpose data model library. Other business systems within the group can retrieve and reuse the data model design of this table on the platform.

[0084] Step 8: Validation of Results. After the intelligent logistics system was built based on this digital model, it seamlessly integrated with the steel coil material numbers of the group's existing 12 business systems, including MES, sales, and procurement, without requiring any field mapping or data cleaning. The efficiency of cross-system steel coil logistics data fusion was improved by 75%, and the data definitions were completely unified and unambiguous.

[0085] The above-described method and implementation methods, through mechanisms such as mandatory closed-loop processes, intelligent reuse, real-time verification, and automatic assetization, fundamentally solve the problems of ambiguous data definitions, data silos, and high costs of cross-system integration in large group enterprises, and provide a standardized solution that can be implemented and replicated for data governance in industries such as steel manufacturing.

[0086] In the description of this application, it should also be noted that, unless otherwise expressly specified and limited, the terms "set up," "install," "connect," and "link" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0087] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. An enterprise-level mandatory data asset governance system, characterized in that, include: The access control module is used to intercept offline digital model design requests sent by the client to the business system, wherein the offline digital model design requests carry the original digital model design data; The metadata modeling module, connected to the access control module, is used to perform full attribute filling or standard attribute referencing on the fields contained in the original digital model design data based on a preset structured template, and generate digital model design data with attribute tags. The intelligent verification engine is connected to the metadata modeling module and the enterprise-level general-purpose digital model library respectively. It is used to match the general-purpose digital model library in real time to perform compliance verification on the digital model design data with attribute tags, and output a release signal when the verification is passed. The asset synchronization module is used to respond to the release signal by writing the verified digital model design data with attribute tags into the enterprise data asset catalog as the target digital model data.

2. The system according to claim 1, characterized in that, The access control module includes a traffic sniffing unit and a proxy forwarding unit; The traffic sniffing unit is used to monitor the communication port between the client and the business system and identify the characteristic identifiers of the offline digital model design request; The proxy forwarding unit is used to block the sending path of the offline digital model design request to the business system after recognizing the feature identifier, and redirect the offline digital model design request to the metadata modeling module.

3. The enterprise-level data asset mandatory governance system according to claim 1, characterized in that, The metadata modeling module is specifically used for: Parse the original digital model design data and extract the field names of the fields to be designed; Calculate the semantic similarity between the field name and the standard field name in the enterprise-level general mathematical modeling library; If the semantic similarity is greater than a preset threshold, the standard attribute corresponding to the standard field name will be automatically referenced as the full attribute filling content of the field. If the semantic similarity is less than or equal to a preset threshold, then attribute completion suggestions are generated, and custom attribute content based on the structured template input is received.

4. The enterprise-level data asset mandatory governance system according to claim 1, characterized in that, The intelligent verification engine includes a syntax verification unit and a consistency verification unit; The syntax verification unit is used to verify whether the field naming conventions, data types, and lengths in the mathematical model design data with attribute tags conform to preset syntax rules. The consistency verification unit is used to verify whether the business scope definition in the digital model design data with attribute tags is consistent with the definition of registered assets in the enterprise-level general digital model library.

5. The enterprise-level data asset mandatory governance system according to claim 1, characterized in that, The intelligent verification engine is also used for: If the numerical model design data with attribute tags fails the compliance verification, a verification failure report containing error field identifiers and correction suggestions will be generated. The verification failure report is fed back to the client, and the access control module is controlled to maintain the interception state of the offline digital model design request until the corrected digital model design data is received.

6. The enterprise-level data asset mandatory governance system according to claim 1, characterized in that, The asset synchronization module is specifically used for: Convert the target mathematical model data into a database definition language script; The database definition language script is sent to the target database corresponding to the business system for execution, so as to create physical tables in the target database; After the physical table is successfully created, the target digital model data is registered to the enterprise data asset catalog, and a mapping relationship between the physical table and the registered data is established.

7. The enterprise-level data asset mandatory governance system according to claim 1, characterized in that, The structured template includes business attribute fields, management attribute fields, and technical attribute fields; The business attribute field is used to define the business meaning and calculation method of the field; The management attribute field is used to define the security level, owner department, and retention policy of the field. The technical attribute field is used to define the physical name, data type, and precision of the field.

8. The enterprise-level data asset mandatory governance system according to claim 1, characterized in that, It also includes a version lineage management module, which is connected to the metadata modeling module and the asset synchronization module, respectively; The version lineage management module is used to record the version number, change time, and operator information of the target model data when the target model data is written into the enterprise data asset catalog. Based on the mapping relationship between the original digital model design data and the target digital model data, a traceability link is constructed from the physical tables of the business system to the standard assets of the enterprise-level general digital model library.

9. The enterprise-level data asset mandatory governance system according to claim 1, characterized in that, The enterprise-level general-purpose digital model library includes a dynamic rule configuration center; The dynamic rule configuration center is used to receive verification rule update instructions input by the administrator. The verification rule update instructions include adding naming conventions or modifying attribute mapping relationships. The intelligent verification engine and the metadata modeling module maintain a heartbeat connection with the dynamic rule configuration center to load updated verification rules and attribute mapping relationships in real time.

10. A mandatory governance method for enterprise-level data assets, characterized in that, Applied to the enterprise-level data asset mandatory governance system as described in any one of claims 1 to 9, the method includes: Intercept offline digital model design requests sent by the client to the business system, which carry the original digital model design data; Based on a pre-set structured template, the fields contained in the original numerical model design data are filled with all attributes or referenced with standard attributes to generate numerical model design data with attribute labels. The system performs real-time matching with an enterprise-level general-purpose mathematical model library to verify the compliance of the mathematical model design data with attribute tags, and outputs a release signal when the verification is successful. In response to the release signal, the verified numerical model design data with attribute tags is written into the enterprise data asset catalog as the target numerical model data.

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