Heterogeneous metadata programmable formalized unified modeling method

Abstract

The invention discloses a heterogeneous metadata programmable, formalized and unified modeling method. The method comprises the steps of 1, feature decoupling and unified representation to form a standardized feature library, 2, programmable mode definition, 3, formalized semantic mapping and unified metadata model generation, and 4, model verification and evolution. According to the method, a heterogeneous metadata adaptation scene is improved through user-defined matching, conversion and constraint rules, an underlying framework does not need to be modified, the logic of a model is ensured and business rules are met by converting semantic description into a formalized language and combining model detection and theorem proof, the risk of data quality after integration is reduced, and the efficiency of data integration is improved. Through online incremental updating and version management, the method adapts to rapid changes of business requirements, avoids the cost problem caused by shutdown reconstruction of traditional modeling, automatically solves term conflicts through fusion of ontology reasoning and semantic embedding, and improves the efficiency of large-scale data integration.

Description

Technical Field

[0001] This invention relates to the field of data management technology, and in particular to a programmable formal unified modeling method for heterogeneous metadata. Background Technology

[0002] Traditional methods rely on predefined fixed patterns, which are difficult to adapt to the personalized element requirements of emerging fields. Existing semantic conflicts of heterogeneous metadata are usually resolved by manual mapping tables, which is inefficient and prone to subjective errors, and cannot meet the real-time requirements of large-scale data integration. At the same time, existing modeling results lack formal verification, leading to frequent data quality problems after integration, and lack dynamic evolution capabilities, which cannot support online evolution and posture adjustment of metadata. Therefore, this invention proposes a programmable formal unified modeling method for heterogeneous metadata to solve the problems existing in the prior art. Summary of the Invention

[0003] To address the aforementioned problems, the present invention aims to propose a programmable formal unified modeling method for heterogeneous metadata. This method achieves automated unified modeling of multi-source heterogeneous metadata through programmable schema definition, formal semantic mapping, and dynamic model evolution mechanism, thereby improving the flexibility, semantic consistency, and verifiability of the model.

[0004] To achieve the objectives of this invention, the invention is implemented through the following technical solution: a programmable formal unified modeling method for heterogeneous metadata, comprising the following steps:

[0005] Step 1: Decouple and enhance the multi-dimensional features of multi-source heterogeneous metadata, extract its structural features, semantic features and management features, and map the domain knowledge graph of the three elements to a unified feature space to form a standardized feature library;

[0006] Step 2: The programmable pattern definition platform accepts domain-specific language scripts or visual orchestration instructions input by users, and combines them with a machine learning recommendation engine to generate pattern templates that include matching rules, transformation logic, and constraint enhancement.

[0007] Step 3: Based on the standardized feature library and pattern template, perform hybrid-driven semantic formalization processing and multi-strategy cross-domain semantic alignment operations to generate an executable unified metadata model;

[0008] Step 4: Perform hierarchical formal verification of the unified metadata model and support a dynamic evolution mechanism based on change impact analysis to adapt to dynamic changes in business requirements.

[0009] Further improvements are made in the following aspects: the structural features in step one include data types, nesting relationships, and field-level constraints; the semantic features include terminology definitions and contextual associations; and the management features include version information, access permissions, and lifecycle.

[0010] The domain knowledge graph comprises an entity layer, a relation layer, and a rule layer. The entity layer extracts core entities and their attributes from the metadata; the relation layer defines semantic and management relationships between entities; and the rule layer consists of storage domain-specific constraints, formally expressed through OWL axioms.

[0011] The further improvement lies in the fact that the multi-dimensional feature decoupling in step one is implemented using a multimodal deep learning model, specifically:

[0012] Structural feature extraction involves analyzing the nesting hierarchy and field dependencies of metadata using graph neural networks (GNNs) to identify tree structures in JSON / XML and ER graph relationships in database tables.

[0013] Semantic feature extraction utilizes pre-trained language models to semantically encode metadata field annotations and business documents, and combines domain knowledge graphs to complete term definitions and contextual associations;

[0014] Management feature extraction involves parsing version logs and permission descriptions from the metadata dictionary using natural language processing, and then extracting lifecycle policies in conjunction with the configuration management system.

[0015] A further improvement is made in the following step: the construction of the unified feature space in step one introduces a dynamic weighting mechanism, which adjusts the embedding vector weights of structural / semantic / management features according to the needs of the business scenario, thereby optimizing the semantic expressive power of the feature space.

[0016] Further improvements include: the programmable pattern definition platform in step two integrates a visual orchestration interface and a DSL editor; the domain-specific language supports user customization, and custom matching rules include field name matching based on regular expressions and concept matching based on semantic similarity; custom conversion logic includes type conversion, value mapping, and multi-source field merging; custom constraint enhancement includes injecting business rules and quality rules; the pattern template in step two supports versioned storage, with each version recording modification differences, and supports collaborative editing via Git-like commands for check-in / check-out.

[0017] Further improvements are made in the following: the hybrid-driven semantic formalization process in step three includes formalization based on descriptive logic and constraint modeling based on algebraic norms. Specifically, OWL-DL is used to define core ontology classes and attributes, and business logic is encoded through SWRL rules; CASL is used to describe statistical constraints of numeric fields or format constraints of string fields.

[0018] Further improvements are made in the following aspects: Step 3, the multi-strategy cross-domain semantic alignment, includes hard alignment, soft alignment, and hybrid alignment. Hard alignment specifically detects term conflicts based on the ontology alignment toolchain and resolves conflicts through axiom injection. Soft alignment specifically uses a contrastive learning semantic embedding model to calculate the semantic similarity of cross-domain concepts and optimizes the alignment results by combining the hierarchical relationship of the domain knowledge graph. Hybrid alignment specifically adopts a collaborative strategy of prioritizing hard alignment and supplementing soft alignment for high-conflict domains to balance accuracy and coverage.

[0019] Further improvements are made in the following: the output format of the unified metadata model in step three includes RDF ontology, UML class diagram, XSD schema, or a custom extended format that includes static structure, dynamic behavior, and management strategies.

[0020] When the unified metadata model outputs an RDF ontology, class definitions correspond to metadata entities, object attributes define relationships between entities, data attributes define entity attribute constraints, and ontology annotations store and manage features.

[0021] Further improvements are made in the following aspects: The hierarchical formal verification in step four includes structural consistency verification, semantic completeness verification, and performance reachability verification. Structural consistency verification specifically uses a model checking tool to verify whether the model has contradictory constraints; semantic completeness verification specifically uses a theorem prover to inductively prove the core business rules and generate a highly readable proof trajectory; performance reachability verification specifically uses a random test generator to simulate high-concurrency data scenarios and verify whether the model's query response time under millions of data points meets the service level agreement.

[0022] Further improvements are made in the following aspects: The dynamic evolution mechanism in step four includes change impact analysis, intelligent evolution strategy, and self-healing synchronization. Change impact analysis automatically identifies affected downstream components through dependency graphs and generates impact reports. The intelligent evolution strategy selects the evolution method according to the change type, including triggering partial updates for lightweight changes and incremental deployment after full verification for major changes. Self-healing synchronization records all model change events through event sourcing. When downstream system synchronization fails, it automatically retryes or rolls back to a consistent state based on the event logs.

[0023] The beneficial effects of this invention are as follows: This invention improves the adaptation scenarios of heterogeneous metadata by user-defined matching, transformation and constraint rules without modifying the underlying framework. By converting semantic descriptions into formal language and combining model detection and theorem proof, the logic of the model is guaranteed and it meets business rules, reducing the risk of data quality after integration. Through online incremental updates and version management, it adapts to rapid changes in business needs and avoids the cost problems caused by downtime reconstruction in traditional modeling. By integrating ontology reasoning and semantic embedding, it automatically resolves terminology conflicts and improves the efficiency of large-scale data integration. Attached Figure Description

[0024] Figure 1 This is a flowchart of the modeling method of the present invention. Detailed Implementation

[0025] To enhance understanding of the present invention, the present invention will be further described in detail below with reference to embodiments. These embodiments are only used to explain the present invention and do not constitute a limitation on the scope of protection of the present invention.

[0026] With the deepening of digital transformation, data sources within and across organizations are experiencing explosive growth. As the core catalog of data assets, metadata is facing increasingly prominent heterogeneity issues. Metadata generated by different systems has significant differences in structure, semantics, constraints, and management logic. Existing technologies have many shortcomings in modeling heterogeneous metadata.

[0027] Traditional methods rely on predefined fixed patterns, such as the general metadata model DCAT and ISO 19115, which are difficult to adapt to the personalized metadata needs of emerging fields such as AI training data and blockchain transaction records. Modifying the pattern requires reconstructing the entire model system, which is costly.

[0028] Heterogeneous metadata semantic conflicts, such as homonyms and synonyms, are usually resolved through manual mapping tables, which is inefficient and prone to subjective errors, and cannot meet the real-time requirements of large-scale data integration.

[0029] Existing modeling results are mostly semi-structured descriptions such as Excel spreadsheets and visual graphs, which cannot be verified for consistency and completeness through formal methods such as model checking and theorem proof, resulting in frequent data quality problems after integration;

[0030] When business needs change, such as adding new data sources or adjusting data standards, model updates require downtime and cannot support online evolution and adaptive adjustment of metadata.

[0031] Based on the above issues, according to Figure 1 As shown, this embodiment provides a programmable formal unified modeling method for heterogeneous metadata, including the following steps:

[0032] Step 1: Perform multi-dimensional feature decoupling and enhanced extraction on multi-source heterogeneous metadata, extracting its structural features, semantic features, and management features, and map the domain knowledge graph of the three feature elements to a unified feature space to form a standardized feature library; the standardized feature library supports incremental expansion, and when a new heterogeneous data source is added, its feature extraction model can be quickly adapted through transfer learning without retraining the full amount of data;

[0033] Structural features include data types, nesting relationships, and field-level constraints. Data types include strings, numbers, and booleans. Nesting relationships include hierarchical structures such as object-attribute-value and array types. Field-level constraints include primary keys, foreign keys, value ranges, and regular expression validation rules.

[0034] Semantic features include term definitions and contextual associations. For example, in the medical field, "user age" refers to the time span from one year after birth to death. Contextual associations include time constraints such as the order creation time being earlier than the payment time.

[0035] Management features include version information, access permissions, and lifecycle. Version information includes timestamps of schema changes, modifiers, and change types. Access permissions include role-permission mapping tables, defining that administrators can modify data and analysts can only read data. Lifecycle features include data retention periods such as "automatic archiving 5 years after creation".

[0036] The domain knowledge graph comprises an entity layer, a relation layer, and a rule layer:

[0037] The entity layer extracts core entities and their attributes from the metadata, such as patients, orders, devices, patient age, and order amount.

[0038] The relation layer defines semantic and management relationships between entities. Semantic relationships include patient → visit → order, equipment → generate → log, etc., while management relationships include field A → dependency → field B, role X → own → permission Y, etc.

[0039] The rule layer stores domain-specific constraints, such as the requirement that the date of birth in medical data must be earlier than the date of admission, and expresses them formally through OWL axioms.

[0040] The unified feature space construction introduces a dynamic weighting mechanism. Based on the needs of business scenarios, such as real-time data processing focusing on structural features and compliance auditing focusing on management features, the embedding vector weights of structural / semantic / management features are adjusted to optimize the semantic expressive power of the feature space.

[0041] Multi-dimensional feature decoupling is achieved using a multimodal deep learning model, specifically:

[0042] Structural feature extraction involves analyzing the nesting hierarchy and field dependencies of metadata using graph neural networks (GNNs) to identify tree structures in JSON / XML and ER graph relationships in database tables.

[0043] Semantic feature extraction utilizes pre-trained language models such as RoBERTa to semantically encode metadata field annotations and business documents, and combines domain knowledge graphs such as SNOMED-CT in the medical field and FIBO in the financial field to complete term definitions and contextual associations.

[0044] Management feature extraction involves parsing version logs and permission descriptions in a metadata dictionary using natural language processing, and then combining this with configurations from management systems such as Ansible to extract lifecycle policies.

[0045] Step 2: The programmable pattern definition platform accepts domain-specific language scripts or visual orchestration instructions input by users, and combines them with a machine learning recommendation engine to generate pattern templates that include matching rules, transformation logic, and constraint enhancement.

[0046] The programmable pattern definition platform integrates a visual orchestration interface and a DSL editor, supporting drag-and-drop field matching and logical combination constraints.

[0047] Drag-and-drop field matching allows users to associate fields from different data sources through a graphical interface, such as binding a MySQL user_id with an HBase uid. The platform automatically generates regular expressions or semantic similarity matching rules.

[0048] Logical combination constraints support AND / OR / NOT condition combinations, such as "field A is empty and field B matches the regular expression", and check for rule conflicts in real time;

[0049] The machine learning recommendation engine trains a classification model based on a historical pattern template library to recommend high-frequency matching rules such as "Patient name" usually corresponding to "Patient.name" in medical data, or conversion logic such as prioritizing the conversion of timestamp fields to ISO8601 format.

[0050] Domain-specific languages ​​support user-defined rules, including field name matching based on regular expressions and concept matching based on semantic similarity. For example, if the cosine similarity between a user ID and a customer ID is ≥80%, they are considered to have the same semantic meaning. Custom conversion logic includes type conversion, value mapping, and multi-source field merging. Value mapping rules include converting male / female to M / F, and multi-source field merging rules include merging the product name of supplier A and the product name of supplier B into a unified model product name. Custom constraint enhancement includes injecting business rules and quality rules. Business rules include requiring drug production batch numbers to conform to a set format, and quality rules include requiring user mobile phone numbers to have 11 digits.

[0051] The template supports versioned storage, with each version recording the differences in modifications, such as adding drug batch number verification rules, and supports collaborative editing via Git-like commands for check-in / check-out.

[0052] Step 3: Based on the standardized feature library and pattern template, perform hybrid-driven semantic formalization processing and multi-strategy cross-domain semantic alignment operations to generate an executable unified metadata model;

[0053] Hybrid-driven semantic formalization includes formalization based on descriptive logic and constraint modeling based on algebraic norms:

[0054] Formalization based on description logic uses OWL-DL to define core ontology classes (such as medical records) and attributes (such as hasDiagnosis), and encodes business logic through SWRL rules, such as marking the record status as incomplete when the diagnosis result is empty.

[0055] Constraint modeling based on algebraic norms uses CASL to describe statistical constraints on numeric fields, such as "Total order amount = Sum of unit price of each product × Quantity", or format constraints on string fields, such as "Email address must contain the @ symbol".

[0056] Multi-strategy cross-domain semantic alignment includes hard alignment, soft alignment, and hybrid alignment:

[0057] Hard alignment specifically uses an ontology alignment toolchain such as LogMap+Pellet to detect term conflicts, such as the overlap between instances of "user" and "customer", and resolves conflicts through axiom injection, such as defining "customer ≡ user ∧ role = 'customer'";

[0058] Soft alignment specifically uses a contrastive learning semantic embedding model such as SimCSE to calculate the semantic similarity of cross-domain concepts, and combines the hierarchical relationship of the domain knowledge graph to optimize the alignment result. The hierarchical relationship is such as "logistics tracking number" being a subclass of "waybill number".

[0059] Hybrid alignment specifically employs a collaborative strategy of prioritizing hard alignment and supplementing it with soft alignment in high-conflict areas such as cross-industry data fusion, balancing accuracy and coverage.

[0060] The output format of the unified metadata model includes RDF ontology, UML class diagram, XSD pattern, or a custom extended format that includes static structure, dynamic behavior, and management strategy. The static structure includes class / property definitions, dynamic behavior includes constraint check triggering conditions and event callback functions, and management strategy includes version control rules and permission inheritance strategy.

[0061] When outputting an RDF ontology from a unified metadata model, class definitions correspond to metadata entities, object attributes define relationships between entities, data attributes define entity attribute constraints, and ontology annotations store and manage features.

[0062] Step 4: Perform hierarchical formal verification of the unified metadata model and support a dynamic evolution mechanism based on change impact analysis to adapt to dynamic changes in business requirements.

[0063] Hierarchical formal verification includes structural consistency verification, semantic completeness verification, and performance reachability verification.

[0064] Structural consistency verification specifically uses model checking tools such as NuSMV to verify whether there are contradictory constraints in the model, such as when the same field is defined as both "required" and "optional".

[0065] Semantic completeness verification specifically uses theorem provers such as Coq and Isabelle to inductively prove core business rules, such as ensuring that all drug batch numbers meet the format requirements, and generates highly readable proof trajectories.

[0066] Performance reachability verification is specifically based on simulating high-concurrency data scenarios using a random test generator such as QuickCheck to verify whether the model's query response time meets the service level agreement under millions of data volumes.

[0067] The dynamic evolution mechanism includes change impact analysis, intelligent evolution strategies, and self-healing synchronization.

[0068] Change impact analysis automatically identifies affected downstream components, such as data lake metadata indexes and ETL tasks, through a dependency graph such as feature nodes → schema templates → unified model, and generates an impact report.

[0069] The intelligent evolution strategy selects the evolution method based on the type of change, such as adding fields or modifying constraints. This includes lightweight changes such as renaming fields that trigger partial updates, and major changes such as adjusting the ontology structure that trigger full verification followed by incremental deployment.

[0070] Self-healing synchronization records all model change events through event tracing. When downstream system synchronization fails, it automatically retryes or rolls back to a consistent state based on the event log.

[0071] Example

[0072] This embodiment uses cross-enterprise supply chain data for unified source data modeling. Specifically, an automobile manufacturing company that uses a relational database to store component metadata needs to integrate supplier A, supplier B that uses JSON Schema to store logistics metadata, and its own PLM system's product metadata to achieve unified management of supply chain data.

[0073] Step 1: Feature Decoupling and Unified Representation

[0074] Parse Supplier A's SQL table, such as the "Parts Table," which contains fields: part_id (int), name (varchar), and spec (text). Extract the field types, the structural features of the primary key, the semantic features of defining "part_id" as a "unique identifier for the parts," and the management features of setting daily updates at midnight.

[0075] Parse Supplier B's JSON Schema, such as the "Logistics Order" which contains fields: logistics_id (string), origin (string), destination (string), extract the structural features of nested levels and required fields, define the semantic feature of "origin" as "shipping location", and set the management features that require API authentication access;

[0076] The above features are mapped to a unified graph model. The model nodes include structural features such as field types and semantic features such as term definitions. The edges represent the semantic association between part_id and the unique identifier.

[0077] Step 2: Programmable Mode Definition

[0078] Users define matching rules using a domain-specific language (DSL): if a field name contains id and its type is string / integer, it is marked as a unique identifier;

[0079] Conversion logic: Map supplier B's logistics_id to the logistics tracking number field of the unified model, with the format unified as UUID;

[0080] Enhanced constraints: Tracking numbers must conform to the UUID v4 specification.

[0081] Step 3: Formal Semantic Mapping and Model Generation

[0082] Semantic formalization: Convert the definitions of terms such as unique component identifiers and tracking numbers into OWL ontology, and declare that unique component identifiers and tracking numbers are different data attributes;

[0083] Cross-domain alignment: The semantic similarity between part_id and logistics_id is calculated using the BERT model, with a score of 0.82. Combined with business knowledge, it is confirmed that they are different entities, and a mapping graph is generated.

[0084] Unified Model Generation: Outputs an RDF ontology, including class components and logistics orders, with attributes hasPartID (range: string, format: UUID) and hasLogisticsID (range: string, format: UUID), and injects the constraint that hasPartID must be unique.

[0085] Step 4: Model Validation and Evolution

[0086] Formal verification: Use NuSMV to check if there are contradictory constraints in the model. If there are no duplicate primary keys, use Coq to prove that all logistics tracking numbers conform to the UUID v4 specification.

[0087] Dynamic evolution: When supplier C connects and provides quality inspection metadata in XML format, the user updates the DSL mode, adds matching rules and constraints for the "quality grade" field, and the model is automatically regenerated and synchronized to the data lake without downtime.

[0088] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A programmable formal unified modeling method for heterogeneous metadata, characterized in that, Includes the following steps: Step 1: Decouple and enhance the multi-dimensional features of multi-source heterogeneous metadata, extract its structural features, semantic features and management features, and map the domain knowledge graph of the three elements to a unified feature space to form a standardized feature library; Step 2: The programmable pattern definition platform accepts domain-specific language scripts or visual orchestration instructions input by users, and combines them with a machine learning recommendation engine to generate pattern templates that include matching rules, transformation logic, and constraint enhancement. Step 3: Based on the standardized feature library and pattern template, perform hybrid-driven semantic formalization processing and multi-strategy cross-domain semantic alignment operations to generate an executable unified metadata model; Step 4: Perform hierarchical formal verification of the unified metadata model and support a dynamic evolution mechanism based on change impact analysis to adapt to dynamic changes in business requirements.

2. The heterogeneous metadata programmable formal unified modeling method according to claim 1, characterized in that: The structural features in step one include data types, nesting relationships, and field-level constraints. Semantic features include term definitions and contextual associations; Management features include version information, access permissions, and lifecycle; The domain knowledge graph includes an entity layer, a relation layer, and a rule layer. The entity layer extracts the core entities and their attributes from the metadata. The relation layer defines the semantic and management relationships between entities; the rule layer consists of storage-specific constraints, which are formally expressed through OWL axioms.

3. The programmable formal unified modeling method for heterogeneous metadata according to claim 1, characterized in that: In step one, the multi-dimensional feature decoupling is implemented using a multimodal deep learning model, specifically: Structural feature extraction uses graph neural networks to analyze the nesting hierarchy and field dependencies of metadata, and to identify tree structures in JSON / XML and ER diagram relationships in database tables. Semantic feature extraction utilizes pre-trained language models to semantically encode metadata field annotations and business documents, and combines domain knowledge graphs to complete term definitions and contextual associations; Management feature extraction involves parsing version logs and permission descriptions from the metadata dictionary using natural language processing, and then extracting lifecycle policies in conjunction with the configuration management system.

4. The programmable formal unified modeling method for heterogeneous metadata according to claim 1, characterized in that: The unified feature space construction in step one introduces a dynamic weighting mechanism, which adjusts the embedding vector weights of structural / semantic / management features according to business scenario requirements, thereby optimizing the semantic expressive power of the feature space.

5. The programmable formal unified modeling method for heterogeneous metadata according to claim 1, characterized in that: In step two, the programmable mode definition platform integrates a visual orchestration interface and a DSL editor. Domain-specific languages ​​support user-defined rules, including field name matching based on regular expressions and concept matching based on semantic similarity; custom conversion logic includes type conversion, value mapping, and multi-source field merging. Custom constraint enhancements include injecting business rules and quality rules; in step two, the pattern template supports versioned storage, with each version recording modification differences, and supports collaborative editing via Git-like commands for check-in / check-out.

6. The programmable formal unified modeling method for heterogeneous metadata according to claim 1, characterized in that: The hybrid-driven semantic formalization process in step three includes formalization based on descriptive logic and constraint modeling based on algebraic norms. Specifically, OWL-DL is used to define core ontology classes and attributes, and business logic is encoded through SWRL rules. CASL is used to describe statistical constraints for numeric fields or format constraints for string fields.

7. The programmable formal unified modeling method for heterogeneous metadata according to claim 1, characterized in that: Step three involves multi-strategy cross-domain semantic alignment, which includes hard alignment, soft alignment, and hybrid alignment. Hard alignment specifically detects term conflicts based on an ontology alignment toolchain and resolves conflicts through axiom injection. Soft alignment specifically uses a contrastive learning semantic embedding model to calculate the semantic similarity of cross-domain concepts and optimizes the alignment results by combining the hierarchical relationship of the domain knowledge graph. Hybrid alignment specifically employs a collaborative strategy of prioritizing hard alignment and supplementing it with soft alignment for high-conflict domains to balance accuracy and coverage.

8. The programmable formal unified modeling method for heterogeneous metadata according to claim 1, characterized in that: The output format of the unified metadata model in step three includes RDF ontology, UML class diagram, XSD schema, or a custom extended format that includes static structure, dynamic behavior, and management strategies. When the unified metadata model outputs an RDF ontology, class definitions correspond to metadata entities, object attributes define relationships between entities, data attributes define entity attribute constraints, and ontology annotations store and manage features.

9. The programmable formal unified modeling method for heterogeneous metadata according to claim 1, characterized in that: The hierarchical formal verification in step four includes structural consistency verification, semantic completeness verification, and performance reachability verification. Structural consistency verification specifically uses model checking tools to verify whether the model has contradictory constraints. Semantic completeness verification specifically uses a theorem prover to inductively prove the core business rules and generate highly readable proof trajectories. Performance reachability verification specifically uses a random test generator to simulate high-concurrency data scenarios and verify whether the model's query response time meets the service level agreement under millions of data points.

10. The programmable formal unified modeling method for heterogeneous metadata according to claim 1, characterized in that: The dynamic evolution mechanism in step four includes change impact analysis, intelligent evolution strategy, and self-healing synchronization. Change impact analysis automatically identifies affected downstream components through dependency graphs and generates an impact report. The intelligent evolution strategy selects the evolution method according to the change type, including triggering partial updates for lightweight changes and triggering incremental deployment after full verification for major changes. Self-healing synchronization records all model change events through event tracing. When downstream system synchronization fails, it automatically retryes or rolls back to a consistent state based on the event log.

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