An automated data governance method integrating multidimensional matching and LLM agent collaborative reasoning

Abstract

This invention proposes an automated data governance method that integrates multidimensional matching and LLM Agent collaborative reasoning. This method is fully automated and executed by a unified LLM Agent architecture, requiring no manual intervention. It can quickly sort and govern messy and disordered data, automatically identifying Chinese definitions of fields, relationships between fields, and relationships between tables. Specifically, it includes the following steps: (1) multi-source metadata collection and knowledge graph construction; (2) multi-agent intelligent reasoning control based on LangChain; (3) multi-dimensional lineage matching engine; (4) graph neural network relationship prediction; (5) reinforcement learning-driven matching strategy optimization; (6) temporal evolution analysis of data structures; (7) multi-strategy fusion and confidence assessment; and (8) incremental update and continuous learning mechanism. This invention utilizes multi-dimensional fusion analysis: comprehensively employing multiple techniques such as data value matching, name similarity, semantic vectors, LLM reasoning, graph neural networks, reinforcement learning, and temporal analysis to significantly improve matching accuracy.

Description

Technical Field

[0001] This invention relates to the field of data governance technology, and in particular to an automated data governance method that integrates multidimensional matching and LLM Agent collaborative reasoning. Background Technology

[0002] As enterprises deepen their digital transformation, data centers have become core assets. However, in complex data environments such as data lakes and data warehouses, the following technical challenges exist: 1. Missing data lineage: Many data tables lack clear metadata information, and the meaning of fields is unclear, making the data difficult to understand and use effectively; 2. Difficulty in cross-system data mapping: Systems built by different vendors and at different times use different naming conventions. The same business concept may have completely different field names in different systems, making it difficult to establish relationships. 3. High data governance costs: Traditional manual annotation methods are inefficient. When dealing with tens of thousands or even hundreds of thousands of fields, manual governance is costly and prone to errors. 4. Limitations of a single method: Existing data lineage analysis methods mostly use single technical means (such as name matching or rules-based methods only), which have low accuracy when facing complex scenarios; 5. Difficulty in tracking dynamic evolution: The data table structure changes over time, and it is difficult to automatically identify and associate newly added or modified fields; Therefore, there is an urgent need for a method that can comprehensively utilize multiple advanced technologies to achieve automated and intelligent data kinship analysis. Summary of the Invention

[0003] The technical problem to be solved by this invention is to overcome the defects of the existing technology. This invention proposes an automatic data governance method that integrates multidimensional matching and LLM Agent collaborative reasoning.

[0004] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: An automated data governance method integrating multidimensional matching and LLM Agent collaborative reasoning is proposed. This method is fully automatically scheduled and executed by a unified LLM Agent architecture without manual intervention. It can quickly sort and govern messy and disordered data, and automatically identify Chinese definitions of fields, relationships between fields, and relationships between tables. Specifically, it includes the following steps: (1) Multi-source metadata collection and knowledge graph construction; (2) Multi-Agent Intelligent Reasoning Control Based on LangChain; (3) Multi-dimensional bloodline matching engine; (4) Graph neural network relationship prediction; (5) Optimization of matching strategies driven by reinforcement learning; (6) Temporal evolution analysis of data structures; (7) Multi-strategy fusion and confidence assessment; (8) Incremental update and continuous learning mechanism; All sub-modules are coordinated by the master agent, achieving end-to-end automation through prompting engineering, inference chains, and automatic task distribution.

[0005] Preferably, the multi-source metadata collection and knowledge graph construction includes: automatically extracting structured and unstructured metadata from relational database backups, data lakes, and business documents; standardizing field names, data types, comments, and sample values, including case unification, type mapping, Chinese and English extraction, and value format normalization; constructing a lineage knowledge graph in the Neo4j graph database containing databases, tables, fields, and semantic nodes, where node attributes include field meaning, sample hash, and confidence, and relationship attributes include similarity, matching type, and timestamp; and automatically collecting 5,000–10,000 non-empty sample values ​​for each field, storing them in object storage, and generating a hash index for subsequent value matching.

[0006] Preferably, the LangChain-based multi-Agent intelligent reasoning control includes: constructing a collaborative system consisting of a master agent, a data value matching agent, a semantic understanding agent, an annotation generation agent, and a verification agent; the master agent automatically selects the execution path based on the characteristics of the input field, distributes tasks, and coordinates the parallel or serial execution of each specialized agent; a chain-of-thought reasoning chain is used to automate the entire process of problem understanding → knowledge retrieval → multidimensional analysis → decision generation → result verification; the verification agent integrates the outputs of each agent to generate the final matching result and confidence level, without requiring manual intervention throughout the entire process.

[0007] Preferably, the multi-dimensional bloodline matching engine integrates the following four matching mechanisms: Exact data value matching: Fast overlap calculation is achieved based on Bloom filter and hash index, combined with Jaccard, distribution similarity and cross-validation; Field name similarity matching: integrating Levenshtein distance, initial letter mapping of pinyin, and domain thesaurus; Semantic vector similarity matching: BERT / GPT is used to generate field embeddings, and approximate nearest neighbor search is performed through a vector database (such as Milvus); Contextual association analysis: Identify implicit lineage by combining co-occurrence of fields in the same table, table name semantics, association rule mining, and graph structure path analysis.

[0008] Preferably, the graph neural network relationship prediction models kinship identification as a graph link prediction problem, including: extracting multi-dimensional features of nodes from the knowledge graph and encoding them using MLP; using GCN to aggregate heterogeneous neighbor information and distinguish relationship types such as inclusion, similarity, and matching; introducing GAT to dynamically learn the importance weights of neighbors; calculating the matching score between fields based on node representations and simultaneously predicting table-level business associations.

[0009] Preferably, the graph neural network relationship prediction models kinship identification as a graph link prediction problem, including: extracting multi-dimensional features of nodes from the knowledge graph and encoding them using MLP; using GCN to aggregate heterogeneous neighbor information and distinguish relationship types such as inclusion, similarity, and matching; introducing GAT to dynamically learn the importance weights of neighbors; calculating the matching score between fields based on node representations and simultaneously predicting table-level business associations.

[0010] Preferably, the reinforcement learning-driven matching strategy optimization is automatically constructed by the master agent to build a reinforcement learning environment, including: a state space covering field features, graph context and historical matching records; an action space including strategy selection and candidate field decision; a reward function that integrates accuracy, confidence, efficiency and consistency; and the agent automatically selects DQN or policy gradient algorithm (such as PPO) to train the strategy and continuously optimizes the matching path through A / B testing.

[0011] Preferably, the data structure temporal evolution analysis enables dynamic tracking and prediction of lineage relationships, including: automatically recording snapshots of table / field structure changes and constructing a version evolution graph; identifying typical evolution patterns such as renaming, type changes, and table splitting / merging; predicting future field semantics and lineage relationships based on LSTM or Transformer models; automatically evaluating the impact of structural changes on existing matching relationships and triggering graph updates.

[0012] Preferably, the multi-strategy fusion and confidence assessment mechanism includes: integrating multi-dimensional matching results using weighted fusion, voting, and hierarchical integration strategies; confidence is calculated jointly by data value overlap, name similarity, semantic vector similarity, contextual consistency, and historical accuracy; decision-making is based on confidence level classification: high confidence is automatically confirmed, medium confidence is marked for review, and low confidence is suspended; an anomaly detection module is integrated to provide risk warnings for inconsistent or low-quality matches.

[0013] Preferably, the incremental update and continuous learning mechanism is used to support the long-term autonomous operation of the system, including: triggering incremental matching based on change detection to dynamically update the knowledge graph; automatically optimizing model parameters and agent strategies through user feedback or verification results; supporting model version management, rollback and A / B testing; applicable to cross-industry data centers such as medical, financial, manufacturing, and retail, to achieve automated lineage governance across systems and platforms.

[0014] Compared with the prior art, the beneficial effects of the present invention include: 1. Multi-dimensional fusion analysis: By comprehensively utilizing various techniques such as data value matching, name similarity, semantic vectors, LLM inference, graph neural networks, reinforcement learning, and time series analysis, the matching accuracy is significantly improved.

[0015] 2. High-precision matching: The core strategy is to match data values ​​precisely, with an accuracy rate of over 95%, unaffected by field naming.

[0016] 3. High level of intelligence: It uses a large language model for semantic understanding and reasoning, and can handle complex business scenarios.

[0017] 4. Adaptive optimization: The matching strategy is continuously optimized through reinforcement learning, and the system performance is continuously improved.

[0018] 5. High scalability: Supports rapid matching and analysis of large-scale data (millions of fields).

[0019] 6. Industry applicability: The method is not limited to a specific industry and can be applied to various industries such as healthcare, finance, manufacturing, and retail.

[0020] 7. Fully automated: All matching and analysis tasks are performed automatically by the LLM Agent without any human intervention, reducing data governance costs by more than 60% and enabling 24 / 7 unattended operation.

[0021] 8. Dynamic Evolution Tracking: It can track the temporal changes of data structures and adapt to the dynamic evolution of the data environment. Attached Figure Description

[0022] The disclosure of this invention is illustrated with reference to the accompanying drawings. It should be understood that the drawings are for illustrative purposes only and are not intended to limit the scope of protection of this invention. In the drawings, the same reference numerals are used to refer to the same parts. Wherein: Figure 1 This is a diagram illustrating the overall system architecture of an automated data governance method that integrates multidimensional matching and LLM Agent collaborative reasoning, as proposed in this invention. Figure 2 The flowchart of the multi-dimensional lineage matching engine is a method for automatic data governance that integrates multi-dimensional matching and LLM Agent collaborative reasoning proposed in this invention. Figure 3 This is a flowchart of the reinforcement learning strategy optimization process for an automated data governance method that integrates multidimensional matching and LLM Agent collaborative reasoning, as proposed in this invention. Figure 4This is a flowchart of the automated reasoning process of an automated data governance method that integrates multidimensional matching and LLM Agent collaborative reasoning proposed in this invention. Figure 5 This is a closed-loop diagram of incremental update and continuous learning for an automated data governance method that integrates multidimensional matching and LLM Agent collaborative reasoning proposed in this invention. Detailed Implementation

[0023] It is readily understood that, based on the technical solution of this invention, those skilled in the art can propose various interchangeable structural methods and implementations without altering the essential spirit of the invention. Therefore, the following detailed embodiments and accompanying drawings are merely illustrative examples of the technical solution of this invention and should not be considered as the entirety of the invention or as limitations or restrictions on the technical solution of this invention.

[0024] According to one embodiment of the present invention, Figures 1-5 As shown.

[0025] An automated data governance method integrating multidimensional matching and LLM Agent collaborative reasoning is proposed. This method unifies and executes all matching and analysis tasks through an LLM Agent architecture, achieving full automation without any manual intervention. It can quickly organize and manage messy and disordered data, automatically identifying Chinese definitions of fields, relationships between fields, and relationships between tables, achieving high-precision and automated identification and analysis of data lineage. The entire system adopts an intelligent agent architecture based on a large language model. All technical modules (data value matching, name similarity matching, semantic vector matching, graph neural networks, reinforcement learning, etc.) are uniformly scheduled, decided, and executed by the LLM Agent, ensuring full automation from data collection and matching analysis to result confirmation. Specifically, it includes the following steps: Step 1: Multi-source metadata collection and knowledge graph construction 1.1 Metadata Acquisition Module Metadata information is collected from multiple data sources, including: Extract metadata such as table structure, field definition, data type, constraints, and comments from the backup databases of various vendors; Extract table structure information, field types, partition information, storage format, etc. from the data lake; Extract field meanings, business rules, and data standards from data dictionaries, API documentation, and business documentation; Collect sample data values ​​for each field (sample 5000-10000 non-empty values ​​for each field, for subsequent data value matching); 1.2 Data Standardization and Cleaning Standardize the collected metadata: Field name standardization: remove spaces, standardize case, and handle special characters; Data type mapping: unifying the mapping of data types from different databases to a standard type system; Extracting Chinese and English Field Names: Using regular expressions and natural language processing techniques to extract Chinese and English names from comments. Data value standardization: unifying date formats, numerical precision, string encoding, etc.; 1.3 Knowledge Graph Construction Use graph databases (such as Neo4j) to build data lineage knowledge graphs: Node types: database node, table node, field node, field meaning node, vendor node, data source node; Relationship types: containment relationship (database → table → field), meaning relationship (field → meaning), similarity relationship (field → field), matching relationship (data lake field → backup database field), lineage relationship (table → table); Node attributes: store field name, data type, meaning, sample data hash value, sample data storage path, confidence level, etc.; Relationship attributes: store similarity score, match confidence, overlap, match type, timestamp, etc.

[0026] Step 2: Multi-Agent Intelligent Inference Control Based on LangChain (Core Architecture) 2.1 LLM Agent Architecture (Unified Scheduling Center) A multi-agent collaborative system is built using the LangChain framework. This system serves as the core scheduling center of the entire methodology, responsible for the unified coordination and execution of all matching and analysis tasks. The system adopts a layered architecture design, including an agent management layer, a task scheduling layer, an execution engine layer, and a result fusion layer. 2.1.1 Data value matching Agent (fully automated execution) The data value matching agent is a specialized agent dedicated to performing precise data value matching tasks. It operates fully automatically without human intervention. Core functions: Automatically perform data value matching tasks and automatically select the optimal matching strategy based on field characteristics; The system automatically analyzes the reliability of matching results and calculates confidence levels using a multi-factor evaluation model. Automatically generate matching reports and suggestions, including matching details, confidence analysis, risk warnings, etc. Intelligent strategy selection: Automatically selects sampling strategies, overlap calculation methods, and verification mechanisms based on data characteristics (data type, data volume, distribution characteristics, etc.).

[0027] Technical Implementation: Task reception: Receive matching tasks from the master agent, including information on the fields to be matched, data source information, constraints, etc. Data sampling: Automatically sample data values ​​from the data lake and standby database. Sampling strategies include: Random sampling: suitable for uniformly distributed data; Stratified sampling: suitable for data with obvious stratification; Quantile sampling: suitable for numerical fields, ensuring the representativeness of the distribution; Diversity sampling: Applicable to string fields, covering different lengths and patterns; Matching calculation: Automatically performs data value overlap calculation, supporting multiple algorithms: Jaccard similarity: J(A,B)=|A∩B| / |A∪B|; ExactMatch(A,B) = |A∩B| / max(|A|,|B|); Intersection ratio: IntersectionRatio(A,B)=|A∩B| / min(|A|,|B|); Result evaluation: Match confidence is automatically calculated, taking into account factors including: Data value overlap (weight 0.4); Sample size sufficiency (weight 0.2); Data distribution consistency (weight 0.2); Cross-validation results (weight 0.2); 2.1.2 Semantic Understanding Agent (Fully Automated Execution) The semantic understanding agent is a specialized agent responsible for understanding the business meaning of fields and performing semantic reasoning. It executes this process fully automatically without human intervention. Core functions: Automatically understand the business meaning of fields and use a large language model for deep semantic analysis; Automatically retrieve relevant candidate meanings from the knowledge graph using graph traversal algorithms and vector retrieval techniques; It automatically performs semantic reasoning and decision-making, combining domain knowledge and contextual information for reasoning; Module invocation: Automatically invokes modules such as name similarity matching, semantic vector matching, and context analysis; Technical Implementation: Semantic analysis: Using pre-trained large language models (such as GPT-4, Claude, etc.) for field semantic understanding: Input: field name, data type, context field, table name, table comment, etc.; Output: Business meaning of the field, possible candidate matches, and confidence score; Knowledge graph retrieval: Use the Cypher query language to retrieve relevant fields from Neo4j; Use vector retrieval to retrieve semantically similar fields from a vector database; Combine graph traversal algorithms (such as random walk and PageRank) to discover potential relationships; Multi-module coordination: Automatically invoke the name similarity matching module (Section 3.2); Automatically invoke the semantic vector matching module (Section 3.3); Automatically invoke the context analysis module (Section 3.4); The results from each module are automatically merged to generate a comprehensive semantic understanding result. 2.1.3 Generate Agent by Labeling (Fully Automated) The annotation generation agent is a professional agent responsible for automatically generating field annotation information. It is fully automated and requires no manual coding. Core functions: Automatically generate suggested Chinese and English names for fields, generating standardized names based on matching results and domain knowledge; Automatically generate explanations of the business meaning of fields, including field purpose, value range, business rules, etc. Automatically generates field tags and categories, supporting multi-level classification systems; Standards followed: Labels are automatically generated based on domain knowledge and conform to industry standards; Technical Implementation: Naming generation: Chinese naming: Generate Chinese names based on field meanings and business scenarios; English naming conventions: Follow camelCase, underscore, and other naming conventions; Naming validation: Checks whether the naming conforms to the specifications and avoids conflicts; Meaning explanation generated: Use LLM to generate explanations of the business meaning of fields; It includes the field's purpose, data type, value range, constraints, and business rules; Supports multilingual instructions (Chinese and English); Tag Classification: Automatically identify the business domain of fields (such as customer domain, order domain, product domain, etc.); Automatically identify the data category of a field (such as identifier, attribute, measure, etc.); Automatically identify the sensitivity level of fields (such as public, internal, sensitive, confidential, etc.); Standardized inspection: Check whether the naming conforms to industry standards (such as HL7 for medical, ISO20022 for financial, etc.). Check the completeness of the annotations to ensure that all necessary information has been generated; Check the consistency of annotations to ensure that the annotation style for similar fields is consistent. 2.1.4 Verify Agent (fully automated) The verification agent is a specialized agent responsible for verifying the quality and consistency of matching results. This process is fully automated and requires no manual review. Core functions: Automatically cross-validate the results of multiple agents to detect consistency and conflicts between the results; The consistency and reasonableness of the automatic detection results are assessed using a rule engine and machine learning model. Confidence scores are automatically generated, taking into account multiple factors. Anomaly Detection: Automatically identifies abnormal results and triggers a re-analysis process; Technical Implementation: Cross-validation mechanism: Compare the matching results of different agents to detect consistency; Using a voting mechanism: Multiple agents vote on the matching results for the same field; Use weighted fusion: perform weighted fusion based on the historical accuracy of each agent; Conflict detection: Identifies conflicts between results from different agents, triggering in-depth analysis; Reasonableness check: Business rule validation: Check whether the matching results conform to the business rules; Data type validation: Checks whether the data types of the matched fields are compatible; Contextual consistency: Checks whether the matching result is consistent with the context field; Historical pattern matching: Checks whether the matching results match historical matching patterns; Confidence calculation: Multifactor model: Confidence = f(overlap_score, similarity_score, context_score, history_score); Dynamic weight adjustment: The weights of each factor are dynamically adjusted based on historical accuracy. Confidence level classification: High (>0.9), Medium (0.7-0.9), Low (<0.7); Exception handling: Anomaly detection: Use statistical methods (such as Z-score, IQR) to detect abnormal results; Anomaly marking: Automatically marks abnormal results and records the reasons for the anomalies; Reanalysis: Automatically triggers a reanalysis process for abnormal results; Manual review marking: Mark abnormal results that cannot be processed automatically as requiring manual review; 2.1.5 Agent Coordination Mechanism (Fully Automated Process) The agent coordination mechanism is the core of the LLM agent architecture, responsible for the unified scheduling and management of all agents, and it is fully automated without human intervention. Orchestrator Agent: Responsibilities: Responsible for overall process scheduling, automatically selecting the optimal Agent combination and execution order based on the characteristics of the fields to be analyzed; Decision-making mechanism: Use reinforcement learning models to learn the optimal agent combination strategy; Select the agent combination based on field characteristics (data type, data volume, complexity, etc.); The execution order is dynamically adjusted, prioritizing the execution of high-confidence matching strategies; State Management: Maintain task status (pending, in progress, completed, failed); Track the execution status and resource usage of each agent; Record task execution history and performance metrics; Task distribution mechanism: Task queues: Use message queues (such as RabbitMQ and Kafka) to manage task distribution; Load balancing: Dynamically allocate tasks based on the load of each agent; Priority scheduling: scheduling tasks based on task priority and dependencies; Parallel execution: Supports multiple agents executing different tasks in parallel, improving efficiency and enabling result fusion mechanisms. Results collection: Automatically collects the execution results of each agent, including matching results, confidence level, execution time, etc. Intelligent integration: Weighted fusion: Weighting based on the historical accuracy of each agent; Voting fusion: Multiple agents vote on the same result; Hierarchical fusion: first fast strategy, then precise strategy, and finally inference strategy; Conflict resolution: Detect conflicts between results from different agents; Conflicts are resolved using rule engines and machine learning models; Unresolvable conflicts are marked as pending manual review; Quality control mechanism: Quality Inspection: Automatically performs quality inspections, including: Confidence check: Check whether the confidence level of the matching results reaches the threshold; Integrity check: Checks whether the matching results contain the necessary information; —Consistency check: Checks whether the matching results are consistent with historical results; Supplementary analysis: Automatic supplementary analysis is triggered for low-confidence results. Increase the number of samples; Use more matching strategies; Call LLM for deep inference; Pending Processing Mark: Result that cannot be processed automatically is marked as pending processing, and processing suggestions are recorded. Exception Handling Mechanism: Anomaly detection: Automatically detects abnormal situations, including: Data missing: data source connection failure, data not found, etc. Computational anomalies: algorithm execution failure, timeout, etc.; System errors: insufficient memory, network failure, etc.; Abnormal recovery: Automatic retry: Automatically retry for temporary exceptions; Degradation measures: Use alternative strategies or simplified algorithms; Error logging: Records exception information and processing results; Alarm notification: Automatically send alarm notifications (such as emails, SMS messages, etc.) for serious anomalies; 2.2 Chain-of-Thought Reasoning (Fully Automated Reasoning) The design incorporates a multi-step reasoning chain, automating the entire reasoning process without human intervention. The reasoning chain employs an iterative optimization mechanism, automatically adjusting the reasoning strategy based on intermediate results to achieve fully autonomous intelligent reasoning.

[0028] Detailed explanation of the inference chain steps: Problem understanding (automatic): The agent automatically parses the context information of the field to be analyzed, including: Field name, data type, data sample; The table name, table comments, and other field information of the table in question; Data source information, business domain information; The agent automatically extracts key features: Field naming patterns (such as camelCase, underscore, etc.); Data type characteristics (such as numeric, string, date, etc.); Data distribution characteristics (such as the number of unique values, the rate of missing values, etc.); The agent automatically identifies the problem type: Exact match problem: Both field names and data values ​​are known; Semantic matching issue: It requires understanding the business meaning of the fields; Contextual inference problems: inferences require combining contextual information; Knowledge retrieval (automatic): The agent automatically retrieves relevant candidate matches from the knowledge graph: Use Cypher to query: Query based on field names, data types, and other conditions; Using vector search: Retrieve semantically similar fields based on field name vectors; Using graph traversal: Discovering potential related fields through relational paths; The agent automatically retrieves from the vector database: Use Approximate Nearest Neighbor (ANN) search to retrieve similar fields; Supports large-scale retrieval (million-level field library); Return the Top-K candidate results (K is usually 10-50); The agent automatically filters candidate results: Filter incompatible candidates based on data type; Filter irrelevant candidates based on business domain; Filter low-probability candidates based on historical matching patterns; Multidimensional analysis (automatic): The agent automatically analyzes data from multiple dimensions: Data value dimension: Match data values ​​with the Agent and calculate the overlap of data values; Name dimension: Call the name similarity matching module to calculate name similarity; Semantic dimension: Call the semantic vector matching module to calculate semantic similarity; Context dimension: Invoke the context analysis module to analyze context consistency; The agent automatically calculates the confidence level for each dimension: Data value matching confidence: calculated based on overlap; Name matching confidence: calculated based on edit distance, synonyms, etc. Semantic matching confidence: calculated based on vector similarity; Context matching confidence: calculated based on context consistency; The agent automatically generates multi-dimensional analysis reports: Matching results and confidence levels for each dimension; Consistency analysis across all dimensions; Anomaly dimension identification; Reasoning and decision-making (automatic): The agent automatically uses LLM for deep inference: Inputs: Field information, candidate matches, multi-dimensional analysis results, and domain knowledge; Reasoning process: Using Chain-of-Thought hints to guide LLM step-by step reasoning; Output: Final matching result, confidence level, and reasoning basis; The agent automatically performs multiple rounds of reasoning (if needed): Round 1: Rapid reasoning to generate preliminary results; Round Two: In-depth reasoning, involving in-depth analysis of uncertain results; The third round: verifying the reasoning and the reasonableness of the results; Agent; Automatically generates reasoning reports: Record of reasoning process; Explanation of the reasoning basis; Uncertainty analysis; Result verification (automatic): The agent automatically verifies the reasonableness of the results: Business rule validation: Check whether the results comply with the business rules; Data type validation: Checks whether data types are compatible; Context consistency verification: Checks whether the result is consistent with the context; Historical pattern verification: Check whether the results match historical patterns; The agent automatically verifies the consistency of the results: Cross-Agent Consistency: Check whether the results from different agents are consistent; Cross-dimensional consistency: Checking whether results are consistent across different dimensions; Consistency across time: Check whether the results are consistent with historical results; The agent automatically generates a verification report: Summary of verification results; Explanation of abnormal situations; Recommendations for handling this matter; Adaptive inference mechanism: Strategy Adjustment: Automatically adjust the inference strategy based on intermediate results. If the data value matches with a high confidence level (>0.9), the data value matching result should be used first. If the semantic matching confidence score is high (>0.8), the semantic matching result should be used first. If the confidence scores for all dimensions are low (<0.6), trigger deep LLM inference; Iterative optimization: Iterative optimization of low-confidence results: Increase the number of samples and use more matching strategies; Expand the scope of knowledge retrieval; Perform multiple rounds of LLM inference; Learning mechanism: Learning from historical reasoning: Record the reasoning process and results; Analyze and reason about patterns of success and failure; Optimize inference strategies and parameters.

[0029] The technical scope of the present invention is not limited to the content described above. Those skilled in the art can make various modifications and variations to the above embodiments without departing from the technical concept of the present invention, and all such modifications and variations should fall within the protection scope of the present invention. 2.3 Prompt Engineering Professionally designed prompt templates are automatically generated and optimized by the Agent, eliminating the need for manual coding. Prompt template structure: System role definition: Define the roles and responsibilities of the Agent; For example: "You are a data governance expert, specifically responsible for identifying and labeling the meaning of data fields"; Domain knowledge background: includes domain-related knowledge; Medical field: including medical terminology, medical business processes, etc. Financial sector: Includes financial terminology, financial business rules, etc. Manufacturing sector: Includes manufacturing terminology, process flow, etc.; Task Description: Clearly define the task objectives and requirements; Input information: field name, data type, context, etc.; Output requirements: matching results, confidence level, reasoning basis, etc. Examples and reference cases: Examples are provided to aid understanding; Positive example: Showing the correct matching case; Counterexamples: Show incorrect matching cases and the reasons; Boundary Case: Demonstrates how to handle boundary situations; Output format requirements: Clearly define the output format; JSON format: Structured output, easy to parse; Includes fields such as: matching results, confidence level, reasoning basis, and uncertainty. Confidence assessment requirements: Output the confidence level; Confidence range: Floating-point numbers between 0 and 1; Confidence level explanation: Explains the basis for calculating the confidence level; Uncertainty analysis: Explaining the sources of uncertainty in the results; Dynamic prompt generation: Context awareness: Dynamically generate suggestions based on the field context; If the field is in the order table, the prompt will include order-related domain knowledge; If the field is a date type, the prompt will include date format requirements; Historical learning: Learn from historical matching and optimize suggestion templates; Analyze the patterns of successful historical matches; Analyze the issues raised by historical matching failure prompts; Automatically optimize suggestion templates to improve matching accuracy; Multilingual support: Supports both Chinese and English prompts; Automatically select the prompt language based on the field language; Supports mixed Chinese and English prompts; Optimization mechanism suggestion: A / B testing: Perform A / B testing on different prompt templates; Compare the matching accuracy of different prompt templates; Choose the optimal prompt template; Continuous optimization: Continuously optimize prompts based on matching results; Record the prompt template and matching results; Analyze the impact of the suggested templates on the matching results; Automatically generate optimized prompt templates; Step 3: Multi-dimensional lineage matching engine (LLM Agent unified scheduling) All matching algorithms in this step are uniformly scheduled and executed by the LLM Agent, requiring no manual intervention; The LLM Agent intelligently selects the optimal combination of matching strategies based on the characteristics of the fields to be matched, automatically executes the matching process, and generates the final match by combining the results of each strategy. 3.1 Data Value Precision Matching Module (Core Strategy, Automatically Executed by LLM Agent) A field identification method based on exact data value matching, which is entirely executed automatically by a data value matching agent, includes: 3.1.1 Intelligent Sampling Strategy (Agent Automatic Selection); The agent automatically selects the optimal sampling strategy based on field characteristics, executing the process fully automatically without manual configuration. Sampling strategy type: Random sampling: suitable for uniformly distributed data; Method: Randomly select N samples from the dataset; Applicable scenarios: Data is evenly distributed and has no obvious pattern; Parameter: Number of samples N (usually 5000-10000); Stratified sampling: suitable for data with obvious stratification; Method: Stratify the data according to a certain feature, and sample each layer proportionally; Applicable scenarios: Data is clearly stratified (e.g., stratified by region, time, etc.); Parameters: Layered features, sampling ratio for each layer; Quantile sampling: suitable for numerical fields, ensuring the representativeness of the distribution; Method: Sampling is performed according to quantiles (e.g., 0, 25%, 50%, 75%, 100%); Applicable scenarios: Numerical fields where it is necessary to ensure the representativeness of the distribution; Parameter: quantile points (usually [0, 0.25, 0.5, 0.75, 1.0]); Formula: Q(p) = F^(-1)(p), where F is the cumulative distribution function; Diversity sampling: Applicable to string fields, covering different lengths and patterns; Method: Prioritize sampling strings of different lengths and patterns; Applicable scenarios: String fields that need to cover diversity; Parameters: Length range, mode type; Sampling optimization mechanism: Adaptive sampling quantity: Automatically adjusts the sampling quantity based on data characteristics; Small data volume (<1000): Full sampling; Medium data volume (1000-100000): Sample 5000-10000 records; For large datasets (>100,000): sample 10,000 data points using stratified or quantile sampling; Deduplication optimization: Prioritize sampling of deduplicated values ​​to improve matching efficiency; Calculate the number of unique values ​​in a field; If the number of unique values ​​is less than the number of samples, sample all unique values. If the number of unique values ​​is greater than the number of samples, sample from the unique values; Non-empty values ​​first: Non-empty values ​​are sampled first to avoid interference from empty values ​​in the matching process; Calculate the null value rate of a field; If the null value rate is less than 10%, filter out null values ​​during sampling. If the null value rate > 10%, sample null and non-null values proportionally; 3.1.2 Data Standardization and Normalization (Automatically Executed by Agent) The Agent automatically standardizes and normalizes the data to ensure correct matching of different data formats: Unify date formats: Identify date formats: Automatically identify various date formats; Standard formats: YYYY-MM-DD, YYYY-MM-DD HH:MM:SS; Compact formats: YYYYMMDD, YYYYMMDDHHMMSS; Delimiter formats: YYYY / MM / DD, YYYY.MM.DD; Chinese format: YYYY year MM month DD day; Format conversion: Unify all date formats into the standard format (YYYY-MM-DD HH:MM:SS); Use regular expressions to identify date formats; Use a date parsing library (such as Python's datetime) to parse dates; Convert to the standard format; Time zone handling: Unify time zones to avoid matching failures caused by time zone differences; Identify time zone information; Convert to a unified time zone (such as UTC); Record time zone conversion information; String normalization: Remove spaces: Remove leading and trailing spaces and extra spaces in the middle of the string; Methods: Use trim() to remove leading and trailing spaces, and regular expressions to remove extra spaces; Unify case: Uniformly convert to lowercase or uppercase; Methods: toLowerCase() or toUpperCase(); Strategy: Usually convert to lowercase, unless there are special requirements for the field name; Remove special characters: Remove special characters that do not affect matching; Keep: Letters, numbers, common punctuation marks (such as underscores, hyphens); Remove: Control characters, invisible characters, etc.; Encoding unification: Unify the character encoding to UTF-8; Detect the original encoding (such as GBK, GB2312, etc.); Convert to UTF-8 encoding; Handle encoding errors (such as using error handling strategies); Numerical precision uniformity: Precision recognition: Automatically identifies the precision of numerical values; Integer: No decimal part; Floating-point numbers: have a decimal part, and can identify the number of decimal places; Precision unification: unifying the precision of floating-point numbers; Method: round(value, precision), where precision is usually 2-4 bits; Objective: To avoid matching failures caused by differences in precision; Note: Maintain sufficient precision to avoid information loss; Scientific notation processing: Standardize the scientific notation format; Identify scientific notation (e.g., 1.23E+10); Convert to standard numeric format; Alternatively, it can be uniformly converted to scientific notation format; Unified encoding: Character encoding detection: Automatically detects the character encoding of data; Use encoding detection libraries (such as Python's chardet); Detects common encodings (UTF-8, GBK, GB2312, ISO-8859-1, etc.); Encoding conversion: Convert to UTF-8 encoding uniformly; Use an encoding conversion library to perform the conversion; Handle conversion errors (e.g., using error handling strategies); BOM processing: Processing the byte order mark (BOM); Detect BOM (such as UTF-8 BOM, UTF-16 BOM, etc.); Remove the BOM to avoid matching interference; 3.1.3 Overlap Calculation Algorithm (Automatic Agent Selection) The agent automatically selects the optimal overlap calculation algorithm, supporting multiple algorithms and automatically choosing based on data characteristics: Jaccard similarity: Formula: J(A,B)=|A∩B| / |A∪B|; Meaning: Calculates the ratio of the intersection to the union of two datasets; Applicable scenarios: Suitable for collection data, where the order of elements is not important; Computational complexity: O(n+m), where n and m are the sizes of the two sets; Perfect match rate: Formula: ExactMatch(A,B)=|A∩B| / max(|A|,|B|); Meaning: The percentage of data values ​​that are exactly the same; Applicable scenarios: Suitable for scenarios requiring precise matching; Features: Insensitive to partial matches, only considers complete matches; Intersection ratio of sets: Formula: IntersectionRatio(A,B)=|A∩B| / min(|A|,|B|); Meaning: The proportion of the intersection to the smaller set (for handling partial matching scenarios); Applicable scenarios: Suitable for situations where one set is a subset of another set; Features: Sensitive to partial matching, suitable for handling subset relationships; Distribution similarity: Applicable scenarios: For numeric fields, calculate the similarity of data distributions; Calculation method: Mean similarity: |mean(A)-mean(B)| / max(|mean(A)|,|mean(B)|); Variance similarity: |var(A)-var(B)| / max(|var(A)|,|var(B)|); Quantile similarity: Calculate the similarity of multiple quantiles and take the average value; Quantile points: typically [0.25, 0.5, 0.75]; Similarity calculation: 1 - |Q_A(p) - Q_B(p)| / max(|Q_A(p)|, |Q_B(p)|); Overall score: DistributionSimilarity = (mean_sim + var_sim + quantile_sim) / 3; Algorithm selection strategy: Data type determination: Numerical data: Prioritize distribution similarity, combined with Jaccard similarity; For string-based data: Jaccard similarity is preferred, combined with perfect match rate; Date type: Convert to numerical value and then use distribution similarity; Data volume assessment: Small datasets (<1000): Use precise algorithms (Jaccard, perfect match rate); Large datasets (>10000): Use approximation algorithms (MinHash, LSH); Matching scenario judgment: In a scenario where the match is perfect, use the perfect match rate. Partial matching scenarios: using set intersection rate; Distribution matching scenario: using distribution similarity; 3.1.4 Multi-level fast filtering mechanism (automatically executed by the agent) The agent automatically performs multi-level fast filtering, significantly reducing computation and improving matching efficiency. First stage: Bloom filter for rapid filtration; Bloom filter principle: using a bit array and multiple hash functions; Map elements to multiple positions in a bit array; During the query, check if all positions are equal to 1; If all positions are 1, the element may exist (there may be false positives). If a position is 0, the element definitely does not exist (no false negatives); Implementation method: Create a Bloom filter for each backup database field; Insert all sample values ​​of the backup database field into a Bloom filter; When querying, sample values ​​of the data lake fields are retrieved from the Bloom filter; If the query result is "may exist", proceed to the next level of filtering; If the query result is "definitely does not exist", filter it out directly; Parameter settings: False alarm rate: usually set to 0.001 (0.1%). Capacity: Set according to the number of samples, usually 1.5-2 times the number of samples; Number of hash functions: Calculated based on false positive rate and capacity, typically 3-5; Performance optimization: Using parallel processing: Bloom filters with multiple fields are queried in parallel; Use caching: Cache commonly used Bloom filters; Using compression: Compress and store the Bloom filter; Level 2: Exact hash value matching; Hash value calculation: Calculate the hash value (MD5 or SHA-256) for each sample value; Store hash values ​​in a collection (such as Python's set); Hash values ​​are also calculated for sample values ​​of data lake fields; Quickly determine whether data is completely consistent using a set of hash values; Implementation method: Establish a hash value set for each backup database field; During the query, the hash value of the data lake field sample values ​​is calculated; Check if the hash value is in the hash value set of the standby database field; If the hash values ​​match, proceed to the exact match calculation; If the hash value does not match, filter it out directly; Performance optimization: Using set data structures: O(1) query complexity; Use memory mapping: Use memory mapping for large collections of hash values; Filtration effect: Reduced computation: Through two-level filtering, more than 90% of mismatched field pairs can be filtered out; Matching efficiency improved: Matching time was reduced from O(n×m) to O(n×m×0.1), an improvement of more than 10 times; Accuracy Guarantee: Bloom filters may have false alarms, but no false alarms, ensuring no match is missed; 3.1.5 Cross-validation mechanism (automatically executed by the agent) The agent automatically performs cross-validation, improving the reliability and accuracy of matching results: Same table field validation: Validation principle: Verify the reasonableness of the current matching result by checking other matched fields in the same table; Verification method: Retrieve other matching fields from the data lake table; Check if these fields also match the same backup table; If multiple fields match the same backup table, the overall confidence level is improved. If different backup database tables are matched, check if there are any business relationships. Confidence adjustment: If more than 3 fields in the same table match the same standby table, the confidence level is increased by 0.1; If a field in the same table matches a related backup table (such as the order table and the order details table), the confidence level increases by 0.05. If a field in the same table matches an unrelated standby table, the confidence level is -0.1. Table-level matching validation: Verification principle: If multiple fields in the data lake table match the same standby table, the overall confidence level is improved; Verification method: Analyze the number of fields in the statistical lake table that match each standby database table; If the number of matching fields in a certain backup table exceeds the threshold (e.g., 3), the table-level matching is considered successful. Calculate table-level matching score: matched_fields_count / total_fields_count; Confidence adjustment: Table-level match score > 0.5: Increase the confidence score of all fields by 0.15; Table-level match score > 0.3: Increase the confidence score of all fields by 0.1; Table-level match score < 0.2: Confidence score of all fields -0.1; Business rule verification: Verification principle: Verify the business rationality of the matching results based on domain knowledge; Verification method: Data type validation: Checks whether the data types of the matched fields are compatible; If the data types are incompatible (e.g., a string matches a numeric value), the confidence level is -0.2. If the data types are compatible, increase the confidence level by 0.05. Value range validation: Check whether the value range of the matched field is reasonable; If the value of a field in the data lake exceeds the range of values ​​for a field in the standby database, the confidence level is -0.15. If the value of a data lake field is within the range of values ​​for a standby database field, the confidence level increases by 0.1. Business logic validation: Check whether the matching results conform to the business logic; If the matching result matches the business logic (e.g., the order ID matches the order table), the confidence level increases by 0.1; If the matching result does not conform to the business logic (e.g., the order ID matches the user table), the confidence level is -0.2; Domain knowledge base: In the medical field: Patient IDs should be in the patient table, not the drug table; In the financial sector: Account numbers should be in the account table, not the transaction table; In manufacturing: Material codes should be in the material list, not the order list; 3.2 Field Name Similarity Matching Module (Automatically executed by LLM Agent); This module is automatically invoked and executed by the semantic understanding agent, which automatically selects the optimal combination of similarity algorithms based on the field name features; 3.2.1 String similarity algorithm (Agent automatically selected); The agent automatically selects the optimal string similarity algorithm based on field name characteristics, supporting multiple algorithms and automatically choosing the optimal combination: Edit distance algorithm: Levenshtein distance: Definition: The minimum number of single-character edit operations required to convert one string to another; Operation types: Insert, Delete, Replace; Formula: lev(a,b)=min(lev(a[1:],b)+1,lev(a,b[1:])+1,lev(a[1:],b[1:])+cost); Where cost = 0 if a[0] == b[0] else 1; Similarity calculation: similarity = 1 - lev(a,b) / max(len(a), len(b)); Time complexity: O(m×n), where m and n are the lengths of the two strings; Suitable for short strings (length < 20) that are sensitive to spelling errors; Implementation: Use dynamic programming algorithm, or an optimized version (such as using a rolling array); Distance between Damerau and Levenshtein: Definition: An extension of the Levenshtein distance, which adds swap operations; Operation types: Insert, Delete, Replace, Swap (adjacent characters); Advantages: More sensitive to errors in swapping adjacent characters (such as "ab" and "ba"); Applicable scenarios: Suitable for scenarios that may contain errors involving the swapping of adjacent characters; Implementation: Add swap operation processing to the Levenshtein distance; Jaccard similarity (based on n-gram): n-gram principle: Decompose a string into a substring of n consecutive characters; For example, the 2-gram of "ORDER_ID" is: ["OR","RD","DE","ER","R_","I","ID"]; Calculation method: Convert both strings into n-gram sets; Calculate the Jaccard similarity between two n-gram sets; Formula: J(A,B)=|A_n-gram∩B_n-gram| / |A_n-gram∪B_n-gram|; Parameter selection: n=2 (bigram): Suitable for short strings; n=3 (trigram): Suitable for strings of medium length; n=4 (4-gram): Suitable for long strings; Applicable scenarios: Suitable for scenarios where character order needs to be considered but an exact match is not required; Cosine similarity (based on character vectors): Character vector construction: Convert a string into a character frequency vector; For example, the character vector of "ORDER_ID" is: {'O':1,'R':2,'D':2,'E':1,'':1,'I':1}; Calculation method: Calculate the cosine similarity between two character vectors; Formula: cos(θ)=(A·B) / (||A||×||B||); Where A·B is the vector dot product, and ||A|| and ||B|| are the vector magnitudes; Applicable scenarios: Suitable for scenarios where character frequency needs to be considered but character order does not; Longest Common Subsequence (LCS): Definition: The length of the longest common subsequence of two strings; Calculation method: Dynamic programming algorithm is used; Formula: LCS(i,j)=LCS(i-1,j-1)+1ifa[i]==b[j]; LCS(i,j)=max(LCS(i-1,j), LCS(i,j-1)) otherwise; Similarity calculation: similarity=LCS(a,b) / max(len(a),len(b)); Applicable scenarios: Suitable for scenarios where character order needs to be considered but character omissions are acceptable; Time complexity: O(m×n); Implementation: Using dynamic programming, a space-optimized version can be used; Algorithm selection strategy: String length: Short strings (<10): Edit distance is preferred; Medium length (10-30): Use Jaccard similarity or cosine similarity; Long strings (>30): Use n-gram similarity; Matching scenarios: Exact match: using edit distance; Partial matching: using LCS; Order-independent: Use cosine similarity; Overall score: The agent automatically uses multiple algorithms and takes a weighted average; The weights are dynamically adjusted based on historical accuracy; formula: FinalScore=Σ(Algorithm_i×Weight_i); 3.2.2 Pinyin initial letter matching (Agent automatic recognition) The Agent automatically recognizes the first-letter abbreviations of Chinese pinyin, supports multiple pinyin abbreviation modes, and automatically establishes mapping relationships: Recognition of the first letters of pinyin: Recognition method: Use a pinyin library (such as pypinyin) to convert Chinese characters into pinyin; Extract the first letters of the pinyin; for example: "患者" → "huan zhe" → "HZ"; Construction of the mapping dictionary: Automatically extract the Chinese names of fields from the knowledge graph; Convert the Chinese name into the first letters of pinyin; Establish a mapping dictionary from the first letters of pinyin to the Chinese meanings; For example: {"HZ": "患者", "YS": "医生", "KS": "科室"}; Recognition of multi-letter combinations: Support 2-letter combinations: such as "HZ" (患者), "YS" (医生); Support 3-letter combinations: such as "WDSCSJ" (未到手术时间); Support combinations of 4 letters and above: such as "SPMC" (商品名称); Fuzzy matching: Support partial matching: such as "HZ_ID" can match "患者ID"; Support order independence: such as "HZ_ID" and "ID_HZ" can both match "患者ID"; Matching algorithm: Exact matching: Directly search the pinyin first-letter mapping dictionary; Fuzzy matching: Use the edit distance to match the first letters of pinyin; Combined matching: Decompose the field name into multiple parts and match the first letters of pinyin respectively; 3.2.3 Thesaurus matching (automatically constructed and used by the Agent) The Agent automatically constructs and uses a domain thesaurus, supports multi-language thesaurus matching, and automatically updates the thesaurus: Construction of the thesaurus: Automatic construction: Extract the Chinese and English names of fields from the knowledge graph; Identify synonymous relationships (such as "客户" and "Customer", "CUST"); Use the LLM to identify synonyms: Input the field name, and the LLM outputs a list of synonyms; Use vector similarity to identify synonyms: Calculate the similarity of the field name vectors; Domain Knowledge Base: Healthcare Domain: {"Patient": ["Patient", "PAT", "HZ"], "Doctor": ["Doctor", "DOC", "YS"]}; In the financial sector: {"Customer":["Customer","CUST","KH"],"Account":["Account","ACC","ZH"]}; Manufacturing sector: {"Materials":["Material","MAT","WL"],"Orders":["Order","ORD","DD"]}; Multilingual support: Chinese and English synonyms: {"Patient ID":["Patient_ID","PATIENT_ID","HZ_ID"]}; Supports abbreviations: {"Patient":["PAT","HZ","Patient"]}; Supports both full names and abbreviations: {"Customer Relationship Management":["CRM","Customer Relationship Management"]}; Matching algorithm: Direct matching: Directly search for field names in the thesaurus; Partial matching: If the field name contains words from the thesaurus, partial matching is performed; Combination matching: Decompose the field name into multiple words and search for each word in the thesaurus; Similarity matching: Uses vector similarity to match synonyms; Thesaurus Update: Automatic updates: Learn synonyms from new matching results; If two fields match successfully but have different names, they may be synonyms. Use LLM to verify if a word is a synonym; If confirmed, add it to the thesaurus; Human feedback: Learn synonyms from human review feedback; Record manually annotated synonym relationships; update the thesaurus; Version management: Manages different versions of the thesaurus and supports rollback; 3.3 Semantic Vector Similarity Matching Module (Automatically executed by LLM Agent) This module is executed automatically by the semantic understanding agent, which automatically selects the optimal embedding model and similarity calculation method. 3.3.1 Text embedding (Agent automatic selection model); The agent automatically selects the optimal embedding model, supports multiple pre-trained models, and automatically selects based on field features: Pre-trained model selection: BERT series models: Model: bert-base-chinese, bert-base-multilingual; Features: Two-way encoding, understands context, suitable for mixed Chinese and English text; Applicable scenarios: Scenarios where understanding the semantics of field names is required; Vector dimension: 768 dimensions; Usage: Use vectors marked with [CLS] as vector representations of field names; GPT series models: Model: text-embedding-3-large, text-embedding-ada-002; Features: Large-scale pre-training, strong semantic understanding capability; Applicable scenarios: Scenarios requiring high-quality semantic representation; Vector dimension: 1536 dimensions (text-embedding-3-large); Usage: Directly call the API to obtain the vector representation; Domain-specific models: Healthcare: Using BERT models pre-trained for the healthcare field; Financial Sector: Using BERT models pre-trained in the financial sector; Manufacturing domain: Using BERT models pre-trained for the manufacturing domain; Advantages: More accurate understanding of domain terminology; Mixed Chinese and English text processing: Text preprocessing: Recognizing Chinese and English parts; Chinese section: Use Chinese word segmentation (e.g., jieba) to retain keywords; English part: Convert to lowercase and remove stop words; Mixed processing: Maintaining the original order of Chinese and English; Embedding generation: Processing mixed text using multilingual models (such as bert-base-multilingual); Alternatively, vectors can be generated separately for the Chinese and English parts, and then merged. Fusion methods: weighted average, splicing, attention fusion, etc.; Context-enhanced embedding: Contextual information extraction: Other field names in the same table: Extracts the names of other fields in the same table; Table name and table comments: Extract table name and table comment information; Business domain information: Extracts the business domain information to which the field belongs; Context fusion: Method 1: Concatenation and fusion: Concatenate field names and context information and then embed them; For example: "ORDER_ID[CONTEXT:ORDER_INFO,ORDER_AMOUNT,ORDER_STATUS]"; Method 2: Separate embedding followed by fusion: Generate vectors for field names and context information separately; Fusion using an attention mechanism: Attention(field_embed, context_embed); Method 3: Hierarchical embedding: First level: Field name embedding; The second layer: enhanced embedding by incorporating contextual information; 3.3.2 Vector Similarity Calculation (Automatic Agent Selection) The agent automatically selects the optimal similarity calculation method, supports multiple similarity metrics, and automatically performs a comprehensive scoring. Cosine similarity: Formula: cos(θ)=(A·B) / (||A||×||B||); Where A·B is the vector dot product, and ||A|| and ||B|| are the vector magnitudes; Features: Value range: [-1, 1], usually normalized to [0, 1]; It is not sensitive to vector length, only considering direction; Suitable for high-dimensional sparse vectors; Applicable scenarios: Standard methods for calculating text vector similarity; Euclidean distance: Formula: d(A,B)=√(Σ(A_i-B_i)²); Similarity conversion: similarity = 1 / (1 + d(A, B)); Features: Considers the absolute distance between vectors; Sensitive to outliers; Suitable for low-dimensional dense vectors; Applicable scenarios: As a supplementary indicator to cosine similarity; Manhattan distance: Formula: d(A,B)=Σ|A_i-B_i|; Similarity conversion: similarity = 1 / (1 + d(A, B)); Features: Considers L1 distance between vectors; Insensitive to outliers; Fast calculation speed; Applicable scenarios: As a rapid similarity metric; Overall rating: Multi-indicator fusion: Cosine similarity (weight 0.6): Key indicators; Euclidean distance similarity (weight 0.2): Supplementary indicator; Manhattan distance similarity (weight 0.2): a quick indicator; Formula: FinalScore = 0.6 × cos_sim + 0.2 × euclidean_sim + 0.2 × manhattan_sim; Dynamic weight adjustment: The weights of each indicator are dynamically adjusted based on historical accuracy. 3.3.3 Vector Database Retrieval (Automatic Agent Management) The agent automatically manages the vector database, supports fast retrieval of large-scale field libraries, and automatically optimizes retrieval performance. Vector database selection: ChromaDB: Features: Lightweight, easy to deploy, supports persistence; Applicable scenarios: Small to medium-sized field libraries (<1 million fields); Retrieval Algorithm: Approximate Nearest Neighbor Search Based on FAISS; Advantages: Simple and easy to use, supports metadata filtering; Milvus: Features: High performance, supports distributed systems, and is highly scalable; Applicable scenarios: Large-scale field databases (>1 million fields); Search algorithms: Supports multiple indexes (IVF_FLAT, HNSW, ANNOY, etc.); Advantages: High performance, supports real-time retrieval; Pinecone: Features: Cloud service, no maintenance required, automatically expandable; Applicable scenarios: Scenarios that require cloud services; Retrieval Algorithm: Optimized Approximate Nearest Neighbor Search; Advantages: No maintenance required, automatically expands; Approximate Nearest Neighbor Search (ANN): HNSW Algorithm (Hierarchical Navigable Small World): Principle: Construct a multi-layered graph structure, start the search from the top layer, and refine it layer by layer; Time complexity: O(logn), where n is the number of vectors; Applicable scenarios: High-dimensional vectors, large-scale data; Parameters: M (number of connections per node, usually 16-32), ef_construction (search range during construction); IVF algorithm (Inverted File Index): Principle: Divide the vector space into multiple clusters, and search only in relevant clusters; Time complexity: O(k+n / k), where k is the number of clusters; Applicable scenarios: Large-scale data, scenarios where high precision is not required; Parameter: nlist (number of clusters, usually 1000-10000); LSH algorithm (Locality Sensitive Hashing): Principle: A hash function is used to map similar vectors into the same bucket; Time complexity: O(1) (average case); Applicable scenarios: Scenarios with extremely high speed requirements; Parameters: hash_num (number of hash functions), bucket_num (number of buckets); Search optimization: Index building: Automatically selects the optimal index type (HNSW, IVF, etc.). Automatically adjust index parameters based on data size; Supports incremental index updates; Search parameter optimization: ef_search: The number of candidates during the search, affecting accuracy and speed; top_k: Returns the number of nearest neighbors, usually 10-50; Automatically adjust ef_search to balance accuracy and speed; Parallel retrieval: Supports batch retrieval: retrieves multiple query vectors at once; Supports parallel retrieval: multiple query vectors can be retrieved in parallel; Use GPU acceleration (if supported); Caching mechanism: Cache frequently used query results; Use the LRU (Least Recently Used) policy to manage the cache; Cache hit rate is typically >80%; Large-scale search support: Sharded storage: Stores a large-scale field library in shards, supporting horizontal scaling; Distributed retrieval: Supports parallel retrieval across multiple nodes, improving retrieval speed; Incremental Update: Supports incremental addition of new field vectors without rebuilding indexes; Performance metrics: Search speed: <10ms (million-field database); Search accuracy: >95% (Top-10 accuracy); Supported scale: >10 million fields; 3.4 Contextual Association Analysis Module (Automatically executed by LLM Agent) This module is executed automatically by the semantic understanding agent, which automatically analyzes the field context and extracts related information; 3.4.1 Table-level context analysis (Agent automatic analysis); The agent automatically analyzes table-level context information and infers the meaning of fields using table structure, table relationships, and other information. Analysis of fields in the same table: Field meaning extraction: Extract the meaning of other fields in the same table from the knowledge graph; Analyze the meaning distribution of fields (such as order-related, user-related, etc.); Identify the subject of a field (such as identifier, attribute, measure, etc.); Field type analysis: Analyze the data type distribution of fields in the same table; Identify common data type combinations (such as ID + name + date); Infer the data type of an unknown field; Field naming pattern analysis: Identify the naming patterns of fields (such as prefixes, suffixes, delimiters, etc.); Analyze naming patterns (such as ORDER_ID, ORDER_NAME, ORDER_AMOUNT); Inferring the meaning of unknown fields based on naming patterns; Analysis of table names and table comments: Table name analysis: Parse the naming pattern of table names (such as ORDER_INFO, ORDER_DETAIL); Extract keywords from table names (such as ORDER, INFO, DETAIL); Inferring the business meaning of a table based on its name; Table annotation analysis: Extracting business meaning from table annotations; Use LLM to parse table annotations and extract structured information; Identify the business domain of the table (such as order domain, user domain, product domain, etc.); Table type identification: Identify the type of table (such as fact table, dimension table, log table, etc.); Inferring the meaning of fields based on table type (e.g., fact tables typically contain measure fields); Identifying inter-table business relationships: Relationship type identification: Master-detail relationship: such as order table and order details table; Relationships: such as between the order table and the user table (linked by user ID); Inheritance relationships: such as base tables and extended tables; Relationship discovery methods: Field matching: If multiple fields of two tables match, a relationship may exist; Foreign key based: Identifying foreign key relationships; Based on business logic: Use LLM to analyze business logic relationships; Relationship application: If the data lake table and the standby table A have a master-slave relationship, and the fields of table A match successfully, then the data lake table may also be related to table A; If a field in a data lake table matches a field in a related table, the match confidence is increased. 3.4.2 Field Combination Pattern Recognition (Automatic Agent Recognition) The agent automatically identifies common field combination patterns and infers the meaning of unknown fields based on these patterns. Composite pattern type: Identifier + Name Pattern: Pattern: ID field + NAME field (e.g., ORDER_ID + ORDER_NAME); Identification: If a table has an ID field, it usually also has a corresponding NAME field; Application: If an unknown field is of type NAME and the table has an ID field, infer that it is the NAME field corresponding to the ID; Primary key + foreign key mode: Pattern: Primary key field + foreign key field (e.g., ORDER_ID + USER_ID); Identify: Identify the relationship between primary keys and foreign keys; Application: If an unknown field is a foreign key type, it is inferred to be the primary key of the related table; Time series patterns: Mode: CREATE_TIME+UPDATE_TIME+DELETE_TIME; Recognition: Recognize the combination patterns of time fields; Application: If the unknown field is of time type, infer the corresponding time field; Amount-related models: Mode: AMOUNT+CURRENCY+EXCHANGE_RATE; Identification: Identify combinations of monetary-related fields; Application: If the unknown field is of a monetary type, infer the corresponding monetary field; Association rule mining (Apriori algorithm): Algorithm principle: Identify frequent itemsets: Support >= minimum support threshold; Generate association rules: Confidence level >= minimum confidence threshold; Support: Support(A→B) = P(A∪B); Confidence level: Confidence(A→B) = P(B|A) = Support(A→B) / Support(A); Application scenario: Discovering relationships between fields (such as ORDER_ID and ORDER_AMOUNT often appearing together); Inferring the meaning of unknown fields based on association relationships; Pattern matching algorithm: Pattern library construction: Extracting field combination patterns from historical matching results; Use LLM to identify common field combination patterns; Build a pattern library, including pattern descriptions, matching rules, application scenarios, etc. Pattern matching: Matches the combination of fields from the table containing the unknown field with patterns in the pattern library; Calculate the match score: number of matched fields / number of fields in the pattern; If the matching degree is greater than the threshold (e.g., 0.7), the meaning of the field is inferred using the pattern. 3.4.3 Graph Structure Analysis (Automatic Execution by Agent) The agent automatically utilizes the structural information of the knowledge graph for analysis, and discovers potential relationships between fields through graph algorithms: Relationship path analysis: Path type: Direct relationship: Field A is directly related to field B; Indirect relationship: Field A is related to field B through an intermediate node (e.g., A→Table→B); Multi-hop relationship: Field A is associated with field B through multiple intermediate nodes; Path discovery: Using graph traversal algorithms (such as BFS, DFS) to discover relational paths; Limit the path length (usually 2-4 hops); Filter irrelevant paths (such as paths containing unrelated nodes); Path application: If an unknown field A has a path relationship with a known field B, and the meaning of B is known, the meaning of A can be inferred. The shorter the path length, the stronger the correlation; The higher the path weight, the stronger the correlation; Random walk algorithm: Algorithm principle: Starting from the initial node, randomly select neighboring nodes to move to; Repeated movements form a walking path; Analyze the nodes visited along the traversal path to discover potential connections; Application scenario: Discovering potential relationships between fields (even if there is no direct relationship); Find similar fields (fields that navigate to the same node may be similar); Parameter settings: Walking distance: usually 10-50 steps; Number of walks: usually 100-1000; Restart probability: to prevent the roaming from deviating too far; PageRank algorithm: Algorithm principle: Calculate the importance of nodes (based on in-degree and out-degree); Important nodes are more likely to be associated with other important nodes; Formula: PR(A)=(1-d)+ d×Σ(PR(T_i) / C(T_i)); Where d is the damping coefficient (usually 0.85), T_i is the node pointing to A, and C(T_i) is the out-degree of T_i; Application scenario: Identifying important fields (such as primary key fields and core business fields); Inferring the meaning of a field based on its importance (important fields usually have a clear meaning); Implementation: Calculate PageRank values ​​using a graph computation library (such as NetworkX); Graph Neural Network (GNN) learning: Node representation learning: Learn the vector representation of field nodes using GCN or GAT; Consider the characteristics of the node (field name, data type, etc.) and the information of the neighboring nodes; The generated vector representation contains graph structure information; Similarity calculation: The similarity between fields is calculated using the learned vector representation; Similarity = cosine_similarity(GNN_embed(field1), GNN_embed(field2)); Advantages: It considers graph structure information and discovers indirect relationships; The learned representations contain rich semantic information; Suitable for large-scale graph data; Implementation: Train the GNN model using a graph neural network framework (such as PyTorch Geometric, DGL); Step 4: Graph Neural Network Relationship Prediction (Automatically invoked by LLM Agent) This module is automatically invoked by the LLM Agent based on analysis requirements and is used to handle complex graph relationship prediction tasks; The agent automatically decides when to use the GNN model, automatically selects model parameters, and automatically interprets prediction results; 4.1 Graph Neural Network Model (Automatic Agent Training and Invocation) LLMAgent automatically learns graph representations of fields and tables using graph neural networks (GNNs), and the model's training, invocation, and optimization are all handled by LLMAgent. 4.1.1 Node Feature Encoding Field node features: field name vector, data type, statistical features (null value rate, number of unique values, etc.); Table node characteristics: table name vector, number of fields, table type, etc.; Features are encoded into vectors using a multilayer perceptron (MLP); 4.1.2 Graph Convolutional Networks (GCN) GCN is used to aggregate information about neighboring nodes; Deep representations of nodes are learned through multi-layer GCN; Consider using different weights for different types of edges (containment relationships, similarity relationships, matching relationships, etc.); 4.1.3 Graph Attention Network (GAT); Use attention mechanisms to learn the importance of neighboring nodes; Dynamically adjust the weights of different relationship types; Increase the model's focus on important relationships; 4.2 Relationship prediction task; 4.2.1 Field matching relationship prediction; Transform the field matching problem into a link prediction problem; Use GNNs to learn the graphical representation of fields; Predict matching relationships between fields using vector similarity; 4.2.2 Prediction of table-level relationships; Business relationships between forecast tables (such as master-slave relationships, association relationships); Relationship classification based on table-level graph representation; 4.3 Model Training and Optimization; Use labeled field matching relationships as training data; Negative samples are generated using a negative sampling strategy; Training is performed using the cross-entropy loss function; Optimize model parameters through backpropagation; Step 5: Optimize the matching strategy driven by reinforcement learning (automatically optimized by the LLM Agent architecture) This module is automatically managed by the LLM Agent architecture built in step two. The master agent automatically designs the reinforcement learning environment, automatically trains and optimizes the strategy, and automatically applies the optimized strategy to the actual matching task. 5.1 Reinforcement Learning Environment Design (Agent Automated Design) The data lineage analysis problem is modeled as a reinforcement learning problem, and the environment design, state space definition, action space design, and reward function design are all automatically completed by the LLM Agent. 5.1.1 State Space (Automatically constructed by the Agent) The agent automatically constructs a multidimensional state representation, which includes the following information: Field feature status: Field name characteristics: field name vector, field name length, naming pattern (camel case, underscore, etc.); Data type characteristics: data type encoding, data length, precision, etc.; Statistical characteristics: null value rate, number of unique values, data distribution characteristics (mean, variance, quantiles, etc.); Sample data characteristics: sample data hash value, data value distribution pattern, etc.; Knowledge graph status: Graph structure characteristics: field's degree (number of connections), centrality, PageRank value, etc. in the knowledge graph; Neighbor node characteristics: characteristics of other fields in the same table, characteristics of related tables, etc.; Relationship characteristics: existing matching relationships, similarity relationships, blood relations, etc.; Path features: shortest path from the current field to the candidate field, path length, path weight, etc. Historical Matching Status: Matching History: This field shows the historical matching records, matching success rate, and matching strategy usage history, etc. Similar field matching: Matching results for similar fields, selection of matching strategies, etc.; Table-level matching: Matching status with other fields in the same table, table-level matching degree, etc. Context state: Table-level context: table name, table comments, table type, number of table fields, etc.; Business domain context: business domain information, domain knowledge, business rules, etc.; Data source context: data source type, data source version, data source metadata, etc.; Status coding method: Numerical characteristics: directly use or normalize to the [0,1] interval; Categorical features: using one-hot encoding or embedding vectors; Graph features: Graph representation vectors extracted using GNN; Text features: Text vectors extracted using models such as BERT; State fusion: Using multi-layer perceptron (MLP) or attention mechanisms to fuse multi-dimensional states; 5.1.2 Action Space (Agent Automated Design) The agent automatically designs the action space, supporting multi-level decision-making: Strategy selection action: Data value matching strategy: Select whether to use data value matching, and select the sampling strategy (random, stratified, quantile, etc.); Name matching strategy: Select a name similarity algorithm (edit distance, Jaccard, cosine similarity, etc.); Semantic matching strategy: Select an embedding model (BERT, GPT, etc.) and a similarity calculation method; Contextual analysis strategy: Select the scope of contextual analysis (same table, same domain, full graph, etc.); LLM inference strategy: Choose whether to invoke LLM, and choose the inference depth (fast, standard, deep). GNN prediction strategy: Choose whether to use GNN, and choose the GNN model type (GCN, GAT, etc.). Candidate field selection action: Candidate Quantity Selection: Select the number of candidate fields to retrieve (Top-5, Top-10, Top-20, etc.); Candidate filtering strategy: Select filtering conditions (data type, business domain, confidence threshold, etc.); Candidate ranking strategy: Select a ranking method (similarity, confidence, comprehensive score, etc.); Decision-making actions: Accept Match: Accepts the current match result; the confidence threshold is adjustable. Reject Match: Reject the current match result and trigger a new match; Request more information: Request more contextual information or use a more complex strategy; Marked as pending: This indicates that the item requires manual review or further processing. Action coding method: Discrete actions: using one-hot encoding or embedding vectors; Continuous actions: represented using continuous values ​​(such as confidence thresholds, weights, etc.); Mixed motion space: Combining discrete and continuous actions; 5.1.3 Reward Function (Automatic Agent Design) The agent automatically designs multi-level reward functions to guide it in learning the optimal strategy. Accuracy Bonus: Correct Match Reward: If the match is correct, a positive reward (+1.0) is given. Mismatch penalty: If the match result is incorrect, a negative reward (-1.0) is given. Partially correct reward: If the matching result is partially correct (e.g., a relevant field is matched), a partial reward (+0.5) will be given. Confidence reward: High confidence bonus: If the match confidence is >0.9, an additional bonus (+0.3) is given. Medium confidence bonus: If the match confidence is between 0.7 and 0.9, a standard bonus (+0.1) is given. Low confidence penalty: If the match confidence is <0.7, a penalty of -0.2 is applied. Efficiency Rewards: Quick Match Bonus: If the match time is short, an efficiency bonus (+0.1) will be given. Strategy selection reward: If the selected strategy combination is efficient, a reward of (+0.2) will be given. Resource consumption penalty: If too much computing resources are consumed, a penalty of -0.1 will be imposed. Consistency Rewards: Cross-policy consistency: If multiple policies yield consistent results, a consistency reward (+0.2) is given. Historical consistency: If the matching result matches the historical match, a reward (+0.1) is given. Context consistency: If the matching result is consistent with the context, a reward (+0.15) is given. Exploration Rewards: New Strategy Trial Reward: Try new strategy combinations and receive an exploration reward (+0.05). Diversity rewards: Maintain diversity in strategy selection and avoid over-reliance on a single strategy; Reward function formula: Total reward: R_tota = R_accuracy + α × R_confidence + β × R_efficiency + γ × R_consistency + δ × R_exploration; Weight adjustment: The agent automatically adjusts the weights α, β, γ, and δ according to the characteristics of the task; 5.2 Reinforcement Learning Algorithm (Agent Automatic Selection and Training) The agent automatically selects the optimal reinforcement learning algorithm and automatically trains and optimizes the model. 5.2.1 Deep Q-Network (DQN); Algorithm principle: Q-function approximation: The Q-function Q(s,a) is approximated using a deep neural network, with input state s and action a, and output Q-value; Objective function: Minimize the TD error, L(θ)=E[(r+γ×maxQ(s',a';θ')-Q(s,a;θ))²]; Where θ represents the current network parameters, θ' represents the target network parameters, and γ represents the discount factor; Experience replay: Use the experience replay buffer to store historical experiences (s,a,r,s'), and randomly sample for training; Target network: Use the target network for stable training and update the target network parameters regularly; Network architecture: Input layer: State feature vectors (dimensions determined by the state space); Hidden layers: Multiple fully connected layers, each with 256-512 neurons, using the ReLU activation function; Output layer: Q-value vector (dimension equal to the size of the action space); Dropout: Use Dropout to prevent overfitting; the dropout rate is typically 0.2-0.3. Training strategy: Exploration strategy: Use an ε-greedy strategy, with an initial ε=1.0, which gradually decreases to 0.1; Learning rate: Initial learning rate 0.001, with learning rate decay. Batch size: 32-128; Update frequency: The network is updated every N steps (N is usually 4-10); 5.2.2 Policy Gradient Method Algorithm principle: Policy function: The policy is represented by a neural network π(a|s;θ), which outputs the probability distribution of actions. Objective function: Maximize expected cumulative reward, J(θ) = E[Ʃγ^t × r_t]; Gradient Ascent: The gradient is calculated using the policy gradient theorem, ∇J(θ)=E[∇logπ(a|s;θ)×Q(s,a)]; Dominance function: Use the dominance function A(s,a) = Q(s,a) - V(s) to reduce variance; Actor-Critic architecture: Actor network: outputs the probability distribution of actions π(a|s); Critic network: outputs state value function V(s) or Q-value function Q(s,a); Shared feature extraction: Actors and Critics share the same underlying feature extraction network; Independent output layers: Actor and Critic have independent output layers; Algorithm variants: A2C (Advantage Actor-Critic): Uses the advantage function A(s,a)=Q(s,a)-V(s); A3C (Asynchronous Advantage Actor-Critic): Asynchronously trains multiple agents; PPO (Proximal Policy Optimization): Uses a pruning mechanism to stabilize training; SAC (Soft Actor-Critic): Supports continuous action space and uses entropy regularization; 5.2.3 Multi-agent reinforcement learning (optional) Application scenario: Multi-agent reinforcement learning can be used when multiple matching tasks are executed in parallel; Multi-agent architecture: Independent learning: Each agent learns independently and does not share experience; Shared experience: Multiple agents share an experience replay buffer; Centralized training and distributed execution: The strategy network is trained centrally and then executed distributedly. Coordination mechanism: Communication mechanism: Agents can communicate with each other and share matching results; Coordination strategy: Use coordination strategies to avoid conflicts and improve overall efficiency; 5.3 Strategy Optimization (Agent Automatic Optimization) The agent automatically optimizes the matching strategy to continuously improve matching performance. 5.3.1 Real-time optimization of online learning: Online updates: The agent updates its strategy in real time during the matching process; Incremental learning: Incrementally update the model using new matching experience; Rapid adaptation: Quickly adapt to new data patterns and business scenarios; 5.3.2 Offline learning batch optimization: Regular retraining: Regularly retrain the model using historical data; Model evaluation: Evaluate model performance using a validation set; Model selection: Choose the model version with the best performance; 5.3.3 Transfer Learning Across Scenarios: Pre-trained model: A model pre-trained on a general dataset; Fine-tuning: Fine-tuning the model for specific business scenarios; Knowledge transfer: Transferring knowledge learned in one scenario to a new scenario; 5.3.4 Yuan Learning for Quick Adaptation: Learning how to learn: How agents learn to quickly adapt to new tasks; Few-shot learning: quickly learning new scenarios using a small number of samples; Task generalization: Improve the model's ability to generalize across different tasks; 5.3.5 Adaptive Parameter Adjustment (Dynamic Adjustment): Threshold Adaptive: Automatically adjusts the confidence threshold based on historical accuracy; Adaptive weighting: Automatically adjusts strategy weights based on strategy performance; Hyperparameter optimization: Automatically optimize hyperparameters using methods such as Bayesian optimization; 5.4 Policy Application and Monitoring (Automatic Agent Execution) 5.4.1 Strategy deployment; A / B testing: The new strategy and the old strategy are run in parallel to compare performance; Gray-scale release: Gradually applying the new strategy to more tasks; Rollback mechanism: If the performance of the new strategy degrades, it will automatically roll back to the old strategy; 5.4.2 Performance monitoring; Real-time monitoring: Real-time monitoring of matching accuracy, efficiency and other indicators; Anomaly detection: Automatically detects performance anomalies and triggers alarms; Performance analysis: Analyze the performance of the strategy in different scenarios; 5.4.3 Continuous optimization; Feedback Loop: Establish a feedback loop from matching results to strategy optimization; Automatic tuning: Automatically adjusts strategy parameters based on performance metrics; Version management: Strategies for managing different versions, supporting version comparison and rollback; Step Six: Data Structure Temporal Evolution Analysis (Automatically tracked by the LLM Agent architecture) This module is executed automatically by the LLM Agent architecture built in step two. The master agent automatically tracks changes in data structure, automatically identifies evolution patterns, and automatically predicts future changes. 6.1 Data Version Management (Automatic Agent Management) The agent automatically manages version information for data structures and establishes a complete version history. Version 6.1.1 snapshot mechanism; Automatic snapshot: Periodic snapshots: The agent automatically creates snapshots of the data structure periodically (e.g., daily, weekly); Change-triggered snapshot: A snapshot is automatically created when a structural change is detected; Incremental snapshots: only record the changed parts, saving storage space; Version metadata: Version number: Use semantic version number (such as v1.0.0, v1.1.0) or timestamp version number; Version Time: Records the snapshot creation time; Version description: Automatically generate version change descriptions (e.g., "3 new fields added, 1 field deleted"). Version tags: Supports adding tags to important versions (such as "major changes", "business launch", etc.); 6.1.2 Field Change Tracking; Field lifecycle tracking: Creation time: The time when the record field first appears; Modification history: Records all modifications to fields (renaming, type changes, constraint changes, etc.); Deletion Time: The time the record field was deleted (if it has already been deleted); Related changes: Track changes in field relationships (such as changes in foreign key relationships); Change type identification: Adding a field: Creating a new field, recording the field name, type, constraints, etc.; Field deletion: Deleting a field and recording the reason for deletion (such as business shutdown, data migration, etc.); Field renaming: Changing the field name and establishing a mapping relationship between the old and new names; Type change: Changes in data type (such as VARCHAR→TEXT, INT→BIGINT, etc.); Constraint changes: Changes to constraints (such as NULL → NOT NULL, adding a unique constraint, etc.); Default value change: Changes to the default value; Comment changes: Changes to field comments; 6.1.3 Table structure change tracking; Table-level changes: Adding a table: Creating a new table, recording the table name, field list, indexes, etc.; Deleting a table: When deleting a table, record the reason for deletion; Table renaming: Changing the table name and establishing a mapping relationship between the old and new names; Table merging: Merging multiple tables, recording the merging rules; Table splitting: Splitting a table, recording the splitting rules; Relationship change tracking: Foreign key relationships: creation, deletion, and modification of foreign key relationships; Primary key change: Changes to the primary key field; Index changes: creation, deletion, and modification of indexes; Inter-table relationships: Changes in business relationships (such as master-slave relationships, association relationships, etc.); Version 6.1.4 Difference Analysis Difference detection algorithm: Structure Comparison: Compare the differences in table structure between the two versions; Field comparison: Compare differences at the field level (addition, deletion, modification); Relationship comparison: Compare the differences in relationships between tables; Data comparison: Compare the differences between sample data (optional); Difference report generation: Change summary: Automatically generate change summaries (e.g., "5 new fields added, 2 fields deleted, 3 fields modified"). Detailed Change List: Lists detailed information on all changes; Impact Analysis: Analyze the impact of the changes on existing matching relationships; Migration recommendations: Provide suggestions for data migration and matching updates; 6.2 Evolutionary Pattern Recognition (Automatic Agent Recognition) The agent automatically identifies the evolution patterns of data structures and discovers evolutionary rules: 6.2.1 Field Change Pattern Recognition Rename pattern: Direct renaming: The field name is changed directly (e.g., ORDER_ID → ORDER_NO); Prefix / suffix change: Change of field name prefix or suffix (e.g., ORDER_ID → SALE_ORDER_ID); Naming convention change: unification of naming conventions (e.g., camel case → underscore); Chinese-English conversion: Conversion of Chinese and English field names (e.g., "Order ID" → ORDER_ID); Type evolution pattern: Precision improvement: Improvement of the precision of numeric types (e.g., DECIMAL(10,2)→DECIMAL(15,4)); Length extension: String type length extension (e.g., VARCHAR(50) → VARCHAR(200)); Type upgrade: Type upgrade (e.g., INT → BIGINT, VARCHAR → TEXT); Type conversion: Type conversion (e.g., string → date, number → string); Split / Merge Mode: Field splitting: splitting a field into multiple fields (e.g., splitting "name" into "last name" and "first name"); Field merging: Multiple fields are merged into one field (e.g., "province" + "city" + "district" are merged into "address"); Field combination: Combining multiple fields into a new field (e.g., combining "year" + "month" + "day" into "date"); 6.2.2 Recognition of Table Structure Evolution Patterns Performative mode: Table splitting mode: A large table is split into multiple smaller tables (e.g., an order table is split into a main order table and an order detail table). Table merging mode: Multiple tables are merged into one table (e.g., multiple business tables are merged into a unified table). Table extension mode: The table is continuously expanded by adding fields (such as continuously adding new attribute fields to a user table). Table normalization mode: Normalization of a table (e.g., extracting redundant fields into a new table). Relationship evolution pattern: Adding new relationships: Adding relationships between tables (such as adding foreign key relationships); Deleting a relationship: Deleting relationships between tables (such as deleting a foreign key relationship); Changes in relation type: Changes in relation type (e.g., one-to-one → one-to-many); Change of relationship direction: A change in the direction of a relationship; 6.2.3 Business Evolution Pattern Identification Business logic evolution: Business process changes: Changes in business processes lead to changes in data structure; Business rule changes: Changes to business rules lead to changes in constraints; Business domain expansion: The expansion of the business domain results in the creation of new tables and fields; Business integration: Business integration leads to the merging and unification of table structures; Evolutionary cycle pattern: Periodic evolution: Identifying periodic structural changes (e.g., monthly, quarterly); Phased evolution: Identify phased structural changes (such as project launch, system upgrade); Incremental evolution: Identifying incremental structural changes (such as adding fields step by step); 6.3 Time Series Prediction (Agent-Automatic Prediction) The agent automatically uses time series analysis methods to predict future changes in data structures: 6.3.1 Time Series Modeling Time series feature extraction: Trend characteristics: Extract the trend of structural changes (such as the growth trend of the number of fields). Periodic features: Extract periodic patterns (such as adding fields periodically); Seasonal features: Extracting seasonal patterns (such as changes over a specific time period); Anomaly characteristics: Identify abnormal change patterns; Predictive model: ARIMA model: Autoregressive integral moving average model, suitable for stationary time series; LSTM model: Long Short-Term Memory network, suitable for long-term dependencies; Prophet model: A time-series forecasting model developed by Facebook, suitable for trending and seasonal data; Transformer model: a time-series prediction model that uses an attention mechanism; 6.3.2 Field Evolution Prediction Field creation prediction: Predicting new fields: Predicting potentially new fields based on historical patterns; Predicted Field Type: The data type of the new field to be predicted; Predicting field meaning: Predicting the business meaning of new fields based on context; Field change prediction: Predict field renaming: Predict fields that may be renamed; Predicting type change: Predicting fields that may change type; Predict field deletion: Predict fields that may be deleted; 6.3.3 Table Structure Evolution Prediction Table Change Prediction: Predicting new tables: Predicting the tables that may be added based on business development trends; Predicting table merging: Predicting tables that may be merged; Predict table splits: Predict the tables that may be split. Relationship evolution prediction: Predicting new relationships: Predicting possible new inter-table relationships; Predicting Relationship Changes: Predicting the types of relationships that may change; 6.3.4 Blood Relationship Prediction Predictions based on evolutionary patterns: Historical pattern matching: predicting new kinship relationships based on historical evolution patterns; Similar scene inference: Inferring the future evolution of the current scene based on the historical evolution of similar scenes; Business logic inference: Inferring possible data lineage relationships based on business logic; Early identification: Potential relationship identification: Identifying potential blood relationships in advance; Relationship strength prediction: Predicting the strength of blood relations (confidence level); Relationship Timing Prediction: Predicting the timing of the establishment of blood relations; 6.4 Evolutionary Impact Analysis (Agent-Automated Analysis) 6.4.1 Analysis of the Scope of Impact Impact of Matching Relationships: Analyze the impact of structural changes on existing matching relationships; Impact of Dependency Relationships: Analyzing the impact of structural changes on data dependencies; Business impact: Analyze the impact of structural changes on business systems; 6.4.2 Automatic Update Mechanism Matching relationship update: Automatically update affected matching relationships; Knowledge graph update: Automatically update nodes and relationships in the knowledge graph; Strategy Adjustment: Automatically adjust the matching strategy based on the evolution pattern; 6.5 Evolutionary Visualization (Automatic Agent Generation) 6.5.1 Evolutionary Timeline Timeline view: Visualize how data structures change over time; Change hotspots: Identify time periods and areas where changes occur frequently; Evolutionary Trends: Demonstrating the trends in structural evolution; 6.5.2 Evolutionary Report Evolutionary Summary: Generates a summary report of the structural evolution; Change statistics: Statistics on the number and frequency of various changes; Prediction Report: Generates prediction reports on future evolution; Step 7: Multi-strategy fusion and confidence assessment (automatic fusion by the verification agent) This module is automatically executed by the verification agent defined in step two. The agent automatically merges the results of multiple matching strategies in step three, automatically calculates the confidence level, and automatically makes the final decision. 7.1 Multi-strategy fusion algorithm (automatic agent fusion) The agent automatically merges the results of multiple matching strategies to generate the optimal final match: 7.1.1 Weighted Fusion (Agent automatically adjusts weights) Weighting mechanism: Initial weights: Assign initial weights based on the theoretical accuracy of the strategy; Data value matching: 0.4 (core strategy, highest accuracy); Semantic matching: 0.2 (Important strategy, understanding semantics); Contextual analysis: 0.2 (Important strategy, leveraging context); LLM inference: 0.15 (supplementary strategies, deep understanding); Name matching: 0.05 (auxiliary strategy, fast filtering); Dynamic weight adjustment: Historical accuracy: Weights are dynamically adjusted based on the historical accuracy of each strategy; Strategies with higher accuracy receive increased weight; Reduce the weight of strategies with low accuracy; Scene adaptability: Adjust weights based on the characteristics of the current scene; If the data values ​​are of high quality, increase the matching weight of the data values; If the field names are standardized, increase the weight of name matching; If the context is rich, increase the weight of context analysis; Real-time feedback: Adjust weights based on real-time matching results; If a strategy performs well in the current task, temporarily increase its weight; If a strategy performs poorly, temporarily reduce its weight. Weight update formula: Exponential moving average: w_i(t+1)=α×w_i(t)+(1-α)×accuracy_i(t); Where α is the smoothing factor (usually 0.9), and accuracy_i(t) is the accuracy of strategy i at time t; Normalization: Ensures that the sum of all weights is 1; Fusion computing: Weighted summation: FinalScore = Σ(w_i × score_i); Where w_i is the weight of strategy i, and score_i is the matching score of strategy i; Weighted average: FinalScore = Σ(w_i×score_i) / Σw_i; Weighted geometric mean: FinalScore = (Π(score_i^w_i))^(1 / Σw_i); 7.1.2 Voting Mechanism (Automatically executed by the Agent) Voting strategy: Majority voting: Multiple strategies vote on candidate fields, and the candidate with the most votes is selected; Weighted voting: Weighted voting is performed based on the strategy weights; Threshold voting: Only strategies with a confidence level exceeding a threshold can be voted on; Voting rules: One vote per candidate: Each strategy can only vote for one candidate field; Multiple votes for multiple candidates: Each strategy can vote for multiple candidate fields (Top-K); Confidence-weighted: Voting weights are determined based on the confidence level of the strategy; Conflict resolution: Tie-breaking: If multiple candidates receive the same number of votes, select the one with the highest confidence level; Confidence comparison: Compare the overall confidence levels of different candidates; Contextual validation: Use contextual information to validate voting results; 7.1.3 Layered Fusion (Automatic Agent Execution) The agent automatically employs a hierarchical fusion strategy, filtering and verifying layer by layer: First layer: Fast strategy (fast filtering); Objective: To quickly filter out obviously mismatched candidates and reduce computational load; Strategies: Name matching, hash matching, Bloom filter; Threshold: Use a lower threshold (e.g., similarity > 0.3) to retain more candidates; Output: Preliminary candidate list (usually retaining the Top-50); Second layer: Precise strategy (precise matching); Objective: To perform precise matching on the initial candidates and improve accuracy; Strategies: Data value matching, semantic vector matching, context analysis; Threshold: Use a medium threshold (e.g., similarity > 0.6) to filter high-quality candidates; Output: A precise list of candidates (usually keeping the Top-10); The third layer: Reasoning strategies (deep reasoning); Objective: To perform deep reasoning on precise candidates to determine the final match; Strategies: LLM inference, GNN prediction, reinforcement learning strategy selection; Threshold: Use a higher threshold (e.g., confidence > 0.8) to determine the final match; Output: Final matching results (usually retaining Top-1 or Top-3); Inter-level transfer: Candidate passing: The output of the previous layer is used as the input of the next layer; Confidence propagation: The confidence level of the previous layer serves as prior information for the next layer; 7.2 Confidence Assessment (Automatically Calculated by the Agent) The agent automatically calculates the confidence level of the matching results and makes a final decision based on the confidence level: 7.2.1 Confidence Calculation Method Baseline confidence: The baseline confidence is calculated based on the original matching scores of each strategy; Data value matching score × weight + semantic matching score × weight + ... + name matching score × weight; Adjustment factor: Adjust confidence based on context, historical matching, conflict detection, etc. Context consistency adjustment: If the matching result is consistent with the context, increase the confidence (+0.1). Historical match consistency adjustment: If the matching result is consistent with the historical match, increase the confidence level (+0.05). Conflict adjustment: If the results of multiple strategies conflict, reduce the confidence level (-0.15). Final confidence level: base confidence level + adjustment factor; 7.2.2 Confidence Level Decision Making High confidence level (≥0.9): Decision: Automatically confirm matching results; Action: Establish a matching relationship in the knowledge graph and mark it as "confirmed"; Application scenario: When multiple strategies yield consistent results with high confidence levels; Medium confidence level (0.7-0.9): Decision: Manual review is recommended, but pre-confirmation is acceptable; Action: Establish matching relationships in the knowledge graph, but mark them as "pending review"; Marked as "Pending Review" to provide review suggestions and risk warnings; Application scenario: When multiple factors are moderate, but the overall confidence level reaches the threshold; Low confidence level (<0.7): Decision: Mark as pending, requiring more information or human intervention; Action: Do not establish a matching relationship; record the reason for pending processing. Tagging: Mark as "Pending Processing" and record processing suggestions (such as increasing sampling, using more strategies, etc.); Application scenario: When all factors are low or conflicting; Confidence adjustment mechanism: Cross-validation adjustment: If multiple strategies yield consistent results, increase the confidence level (+0.1). Conflict detection adjustment: If multiple strategy results conflict, reduce the confidence level (-0.15). Historical validation adjustment: If the matching result is consistent with the historical pattern, increase the confidence level (+0.05). 7.2.3 Uncertainty Quantification (Agent-Automatic Assessment) Sources of uncertainty: Data quality uncertainty: Uncertainty caused by low data value quality; Strategy uncertainty: Uncertainty caused by inconsistent results from different strategies; Contextual uncertainty: Uncertainty caused by insufficient contextual information; Model uncertainty: The uncertainty of model predictions; Uncertainty calculation: Variance calculation: Calculate the variance of the results of multiple strategies. A large variance indicates high uncertainty. Entropy calculation: Calculate the entropy of the matching result; a large entropy indicates high uncertainty. Confidence interval: The confidence interval used to calculate the confidence level; a wider interval indicates higher uncertainty. Uncertainty handling: High uncertainty: If the uncertainty is high, lower the confidence level and mark it as needing more information; Low uncertainty: Lower uncertainty can increase confidence. Uncertainty Report: Reports uncertainty information in the matching results; 7.3 Anomaly Detection and Quality Control (Automatically Executed by Agent) 7.3.1 Abnormal Pattern Detection Exception type: Abnormal confidence levels: a sudden drop or abnormally high confidence level; Strategy conflict anomaly: Multiple strategy results are in serious conflict; Historical pattern error: The matching result does not match the historical pattern; Context error: The matching result is inconsistent with the context; Anomaly detection methods: Statistical methods: Use methods such as Z-score and IQR to detect statistical anomalies; Machine learning methods: using anomaly detection models (such as Isolation Forest) to detect anomalies; Rule-based approach: Use business rules to detect anomalies; Exception handling: Anomaly marking: Automatically marks abnormal matching results; Anomaly Analysis: Analyze the causes of anomalies and generate anomaly reports; Rematch: Triggers a rematch process for abnormal results; Manual review: Exceptions that cannot be automatically processed are marked as requiring manual review; 7.3.2 Quality Score (Automatically Calculated by Agent) Quality Dimension: Accuracy: The accuracy of matching results (based on historical validation); Consistency: Consistency of matching results (across strategies and across time); Completeness: The completeness of the matching results (whether they contain the necessary information); Reliability: The reliability of the matching results (confidence level, uncertainty); Quality score calculation: Multi-dimensional scoring: Calculates quality scores from multiple dimensions; Weighted synthesis: performing a weighted synthesis across multiple dimensions; Grading evaluation: The quality is divided into four levels: excellent, good, average, and poor. Quality Report: Quality Summary: Generates a quality score summary; Quality Analysis: Analyzing quality problems and proposing improvement suggestions; Quality Trends: Tracking trends in quality changes; 7.3.3 Risk Warning (Automatically generated by Agent); Risk type: High confidence level risk: Although the confidence level is high, there are potential risks (such as data quality issues). Low confidence risk: Low confidence level, unreliable matching results; Conflict risk: Multiple strategies may result in conflicting outcomes, creating uncertainty. Historical risks: Inconsistent with historical patterns, anomalies may exist; Risk warning content: Risk Level: Indicates the risk level (high, medium, low); Reasons for the risk: Explain the reasons for the risk; Risk Impact: Explain the potential impact of the risk; Recommendations: Provide risk management suggestions; Risk Management: Automatic processing: Automatic processing for low-risk cases (such as adjusting confidence levels); Manual review: High-risk items are marked as requiring manual review; Risk tracking: Tracking the results of risk management; Step 8: Incremental Update and Continuous Learning Mechanism (Automatically learned by the LLM Agent Architecture) This module is automatically managed by the LLM Agent architecture built in step two. The master agent automatically detects data changes, automatically triggers incremental matching (calling the matching engine in step three), and automatically learns and optimizes from feedback. 8.1 Incremental matching mechanism (automatically triggered by the agent) The agent automatically detects data changes and triggers incremental matching to achieve real-time updates. 8.1.1 Change Detection (Automatic Agent Detection) Detection method: Regular scanning: The agent automatically scans the data source periodically to detect structural changes; Event-driven: Listen for data source change events (such as database DDL events) and detect changes in real time; Version Comparison: Compare the current version with previous versions to identify changes; Difference analysis: Use the time series evolution analysis module in step six to identify changes; Test content: New field detection: Detects newly added fields; Field change detection: Detects modifications to fields (renaming, type changes, etc.); Field deletion detection: Detects deleted fields; Table Change Detection: Detects table-level changes (additions, deletions, modifications); Change notification: Change events: Generate change events, including change type, changed object, change time, etc.; Change Priority: Set the priority (high, medium, low) based on the impact of the change; Change queue: Adds change events to the processing queue; 8.1.2 Incremental Matching Triggered (Agent Automatically Triggered) Triggering conditions: New field trigger: Automatically triggers matching when a new field is detected; Field change trigger: Automatically triggers rematching when a field change is detected; Table change trigger: Automatically triggers table-level matching when a table change is detected; Manual triggering: Supports manually triggering incremental matching (such as batch updates); Matching strategy selection: Quick Match: Apply a quick matching strategy (name matching, hash matching) to the new field; Full match: Use the full match strategy (all strategies) on important fields; Incremental matching: Matches only the changed parts, utilizing existing matching results; Matching range: Single field matching: Matches only the single field that has changed; Table-level matching: Matches all fields in the entire table; Batch matching: Matches multiple changed fields in batches; 8.1.3 Incremental update of the knowledge graph (automatic agent update) Node update: Add a node: Create a new node for the new field; Update Node: Updates the node attributes of the changed field; Delete node: Deletes the node with the deleted field (optional, usually marked as deleted); Relationship Update: Add Relationship: Creates a relationship for a new match result; Update Relationship: Updates and changes the matched relationship attributes; Delete relationship: Delete invalid matching relationships; Version control: Version tagging: A version number is assigned to each update; Version history: Retains historical versions and supports version rollback; Version comparison: Supports comparative analysis between versions; 8.1.4 Incremental Matching Optimization Caching mechanism: Result caching: Caches already matched results to avoid duplicate calculations; Intermediate result caching: caching intermediate calculation results (such as vectors, hash values, etc.); Cache invalidation: Automatically invalidate the relevant cache when the data changes; Parallel processing: Parallel matching: Matching multiple fields in parallel improves efficiency; Task scheduling: Use a task queue to schedule and match tasks; Resource management: Managing computing resources and avoiding resource conflicts; Incremental calculation: Incremental sampling: Samples only new or changed data; Incremental calculation: Only the changed parts are calculated, and existing calculation results are reused; Incremental indexes: incremental updates to vector indexes, hash indexes, etc.; 8.2 Feedback Learning Mechanism (Agent Automatic Learning) The agent automatically learns from feedback and continuously optimizes matching performance: 8.2.1 Feedback Data Collection (Automatic Collection by Agent) Feedback source: Human review feedback: Collect feedback from human reviewers (correct, incorrect, partially correct); Automated validation feedback: Collect feedback from automated validation (such as cross-validation, business rule validation); Business feedback: Collect feedback from business usage (such as data usage and problem feedback); Performance feedback: Collect feedback from performance monitoring (such as matching accuracy, efficiency, etc.); Feedback type: Positive feedback: Feedback indicating a correct matching result; Negative feedback: Feedback indicating an incorrect matching result; Partial feedback: Feedback indicating that some of the matching results were correct; Uncertainty feedback: Feedback indicating uncertainty in the matching results; Feedback data structure: Matching results: Records the matched field pairs, matching strategy, confidence level, etc. Feedback tags: Record the type of feedback (correct, incorrect, etc.); Feedback time: Record the feedback time; Feedback Source: Record the source of feedback (manual, automated, etc.); 8.2.2 Model Optimization (Agent Automatic Optimization) Supervised learning optimization: Training data construction: Construct the training dataset using the feedback data; Model retraining: Retraining the matching model using new data; Model fine-tuning: Fine-tuning based on the pre-trained model; Model evaluation: Evaluate model performance using a validation set; Reinforcement learning optimization: Experience replay: Storing feedback as experience in the experience replay buffer; Policy update: Use feedback to update the reinforcement learning policy; Reward Adjustment: Adjust the reward function based on feedback; Exploration and Optimization: Optimize exploration strategies to balance exploration and utilization; Transfer learning optimization: Knowledge transfer: Transferring knowledge learned in one scenario to a new scenario; Domain Adaptation: Adapting to new business domains; Few-shot learning: Adapting quickly using a small amount of feedback data; 8.2.3 Parameter Adaptive Adjustment (Agent Automatic Adjustment) Threshold adaptation: Confidence threshold: The confidence threshold is automatically adjusted based on feedback; Similarity threshold: The similarity threshold is automatically adjusted based on feedback; Sampling threshold: The sampling quantity threshold is automatically adjusted based on feedback; Weight adaptive: Strategy weights: The weights are automatically adjusted based on the feedback from each strategy; Factor weights: The confidence scores are automatically adjusted to calculate the weights based on the feedback from each factor. Blending weights: Automatically adjust blending weights based on feedback on the blending effect; Hyperparameter optimization: Grid search: Optimize hyperparameters using grid search; Bayesian optimization: Automatically search for optimal hyperparameters using Bayesian optimization; Reinforcement learning optimization: Using reinforcement learning to optimize hyperparameters; 8.2.4 Continuous Learning Strategies Online learning: Real-time updates: Update the model in real time using new feedback; Incremental learning: Incrementally update the model without retraining the entire model; Rapid adaptation: Quickly adapt to new data patterns and business scenarios; Offline learning: Regular retraining: Regularly retrain the model using accumulated feedback data; Batch update: Process feedback data in batches to improve efficiency; Model version management: Manage different versions of the model; Meta-learning: Learning how to learn: Learning how to quickly adapt to new tasks; Few-shot learning: learning quickly using a small amount of feedback data; Task generalization: Improve the model's ability to generalize across different tasks; 8.3 Model Version Management (Automatic Agent Management) The agent automatically manages model versions, supporting version comparison, rollback, and A / B testing. Version control 8.3.1 Version identifier: Version number: Use semantic version number (such as v1.0.0, v1.1.0) or timestamp version number; Version tags: Add tags to important versions (such as "production version", "test version"); Version Description: Records descriptions of version changes and performance improvements; Version storage: Model file: Stores model files (weights, structure, etc.); Configuration file: Storage model configuration (hyperparameters, strategy parameters, etc.); Metadata: Stores version metadata (creation time, performance metrics, etc.); Version history: Version list: Maintains a list of all versions; Version relationships: Records the inheritance relationships between versions; Version comparison: Supports comparative analysis between versions; 8.3.2 Model Rollback (Automatically Supported by Agent) Rollback Triggered: Performance degradation: If the new version experiences performance degradation, it will automatically roll back to the old version; Anomaly detection: If an anomaly is detected, the rollback will be performed automatically; Manual rollback: Supports manual triggering of rollback; Rollback strategy: Rollback immediately: Switch to the old version immediately; Gray-scale rollback: Rollback is done gradually, starting with a portion of the traffic. Rollback Verification: Verify performance after rollback to confirm successful rollback; Rollback records: Rollback log: Records rollback operations and reasons; Rollback impact: Analyze the scope of the rollback impact; Rollback recovery: Supports recovery from rollback; 8.3.3 A / B Testing (Automatically executed by the Agent) Test Design: Test group division: Divide the matching tasks into test groups (Group A, Group B); Traffic allocation: Distribute traffic to different groups (e.g., 50% to group A, 50% to group B). Test metrics: Define test metrics (accuracy, efficiency, cost, etc.); Test execution: Parallel execution: Group A and Group B run in parallel; Data collection: Collect performance data for each group; Statistical analysis: Analyze the test results using statistical methods; Test Decision: Significance test: Use statistical tests to determine whether the difference is significant; Performance comparison: Compare the performance metrics of each group; Optimal choice: Choose the version with the best performance; 8.3.4 Performance Monitoring and Tracking (Agent-based Automated Monitoring) Performance metrics: Accuracy: Match accuracy (precision, recall, F1 score); Efficiency: Matching efficiency (processing time, throughput); Cost: Computational costs (CPU, memory, API call costs); Stability: System stability (error rate, anomaly rate); Monitoring methods: Real-time monitoring: Monitor performance metrics in real time; Regular Reports: Generate performance reports periodically; Abnormal alarm: Automatic alarm when performance is abnormal; Performance Tracing: Performance Trends: Tracking performance change trends; Performance Comparison: Compare the performance of different versions; Performance analysis: Analyze performance issues and identify areas for improvement; 8.4 Knowledge Accumulation and Reuse (Automatic Agent Accumulation) 8.4.1 Knowledge Base Construction Matching Pattern Library: Accumulates successful matching patterns for later reuse; Domain knowledge base: Accumulate domain knowledge (terminology, rules, etc.); Strategy knowledge base: Accumulate effective strategy combinations; 8.4.2 Knowledge Reuse Pattern matching: Uses historical matching patterns to guide new matches; Knowledge retrieval: Retrieving relevant knowledge from a knowledge base; Knowledge-based reasoning: reasoning based on accumulated knowledge; 8.4.3 Knowledge Update Knowledge validation: Verifying the validity of knowledge; Knowledge obsolescence: Eliminating outdated knowledge; Knowledge expansion: Expand the knowledge base by adding new knowledge.

[0030] The present invention will be described in detail below with reference to specific embodiments; the method of the present invention can be widely applied to data governance scenarios in data centers of various industries such as finance, retail, manufacturing, and healthcare; Example 1: Data lineage analysis of retail e-commerce data centers Scene Description A large e-commerce company's data center contains backup databases for multiple business systems (such as order systems, inventory systems, payment systems, logistics systems, membership systems, etc.) and a data lake. The data lake contains a large number of unlabeled fields that need to be established with the known fields in the backup databases to support data analysis and business decisions. Implementation steps Step 1: Metadata collection (automatically executed by the LLM Agent); The main control agent automatically schedules metadata collection tasks: Collect table structure information from the order system backup database, including fields such as ORDER_ID, ORDER_NO, USER_ID, ORDER_AMOUNT, and ORDER_STATUS. Collect the following table structure information from the inventory system: product table, inventory table, warehouse table, etc., including fields such as PRODUCT_ID, SKU_CODE, STOCK_QTY, WAREHOUSE_ID, etc. Collect data from the payment system's backup database, including payment record tables, refund tables, etc., containing fields such as PAYMENT_ID, PAY_AMOUNT, PAY_METHOD, etc. Extract the following from the data lake: structural information of all tables, including table names, field names, data types, etc. Sample data collection: The agent automatically samples 5,000-10,000 non-empty sample data values ​​for each field, stores them in object storage, and calculates the hash value; Step 2: Knowledge Graph Construction (Automatically executed by LLM Agent) The Agent automatically uses Neo4j to build a knowledge graph: Create a table node: (Table:Metadata{name:"ORDER_INFO",name_chinese:"Order Information Table", source:"ORDER_SYSTEM"}); Create a field node: (Field:Metadata{name:"ORDER_ID",name_chinese:"Order ID",data_type:"VARCHAR"}); Create a relationship: (Table) - [:HAS_FIELD] -> (Field); Store sample data path and hash value: Field.sample_storage_path = "s3: / / bucket / fields / ORDER_ID / sample.csv", Field.sample_hash = "abc123..."; Step 3: Multi-dimensional matching (automatic scheduling and execution by LLM Agent) The master agent identifies the unknown field "UNKNOWN_FIELD_001" in the data lake and automatically schedules various specialized agents to perform matching. Data value matching (automatic execution by the data value matching agent): The agent automatically samples 5000 data values ​​for this field; the agent automatically calculates the overlap with sample data from all fields in the backup database; the agent automatically finds that the data value overlap with the "ORDER_ID" field reaches 94%; the agent automatically calculates the confidence level: 0.94; Name similarity matching (automatically executed by the semantic understanding agent): The agent automatically calculates the edit distance between "UNKNOWN_FIELD_001" and the field names of each backup database; the agent automatically discovers that the similarity with "ORDER_ID" is low (0.25) because the field names are completely different; the agent automatically calculates the confidence score: 0.25; Semantic vector matching (automatic execution by the semantic understanding agent): The agent automatically generates field name vectors using the BERT model; the agent automatically calculates the cosine similarity with each backup database field; the agent automatically finds that the semantic similarity with "ORDER_ID" is 0.68; the agent automatically calculates the confidence score: 0.68. Contextual analysis (automatic execution by the semantic understanding agent): The agent automatically analyzes other fields in the table containing "UNKNOWN_FIELD_001"; the agent automatically discovers fields such as "ORDER_AMOUNT", "ORDER_STATUS", and "USER_ID" in the same table; the agent automatically infers that the table is an order-related table; the agent automatically calculates the confidence level: 0.82; LLM inference (automatic execution by the semantic understanding agent): The agent automatically inputs field information and candidate matches into GPT-4; the agent automatically performs LLM analysis, and determines that the field is "order ID" by combining context fields (ORDER_AMOUNT, ORDER_STATUS, etc.); the agent automatically calculates the confidence score: 0.91; Step 4: Multi-strategy fusion (verification agent automatically executes); the verification agent automatically collects the execution results of each agent and automatically fuses them: The agent automatically assigns weights: data value matching weight 0.4 (core strategy), semantic matching weight 0.2, context analysis weight 0.2, and LLM inference weight 0.2. The agent automatically calculates the final confidence level as follows: 0.94 × 0.4 + 0.68 × 0.2 + 0.82 × 0.2 + 0.91 × 0.2 = 0.858. Step 5: Result Confirmation (Verification Agent Automated Decision-Making) The verification agent automatically determines the final confidence level > 0.8, automatically confirms the matching result, and automatically establishes the relationship in the knowledge graph without manual review; the relationship type is MATCHED_BY_DATA, which includes attributes such as confidence level, overlap, matching type, execution agent, and timestamp; Step 6: Continuous optimization (Agent automatic learning); The agent automatically records matching results and feedback (if any); The agent automatically uses feedback data to optimize the weights of each strategy; The agent automatically adjusts its matching strategy selection through reinforcement learning; The entire process is fully automated, requiring no manual parameter tuning or intervention; Technical effect This embodiment verifies that, in the retail e-commerce industry scenario: The data value matching accuracy reaches 94%, which is the core matching strategy; Contextual analysis played a crucial role (confidence level 0.82) because order-related fields typically appear in the same table; LLM inference provides a high-confidence semantic understanding (confidence 0.91); The final fusion confidence level was 0.858, exceeding the threshold of 0.8, so the system automatically confirmed it without manual review.

[0031] Example 2: Data Lineage Analysis in the Financial Industry Scene Description A large commercial bank's data center includes backup databases for multiple business systems such as the core banking system, credit management system, risk control system, anti-money laundering system, and customer relationship management system, as well as a data lake. The data lake stores historical transaction data, customer data, risk data, etc. from various systems, and contains a large number of unlabeled fields. It is necessary to establish a complete data lineage to support risk analysis, compliance auditing, and business decision-making. Implementation steps Step 1: Metadata Collection (Automatically executed by LLM Agent) The master agent automatically schedules metadata collection tasks, optimized for the characteristics of the financial industry: Data collected from the core banking system's backup database: Account table (ACCOUNT_INFO): Fields include account number, account type, account opening date, account status, etc. Transaction table (TRANSACTION_RECORD): Fields include transaction number, transaction amount, transaction type, and transaction time; Customer table (CUSTOMER_INFO): Fields include customer ID, customer name, ID number, customer level, etc. Collected from the credit system's backup database: Loan application form (LOAN_APPLICATION): Fields include application number, loan amount, loan term, interest rate, etc. Loan Contract Form (LOAN_CONTRACT): Fields include contract number, loan disbursement date, repayment method, etc. Repayment Record Table (REPAYMENT_RECORD): Fields include repayment serial number, repayment amount, and repayment date; Data collected from the risk control system's backup database: Risk Scoring Table (RISK_SCORE): Fields such as customer risk score, credit rating, and risk level; Blacklist table: fields include blacklist ID, reason for inclusion, and date of inclusion; Extract the following from the data lake: structural information of all tables, including table name, field name, data type, partition information, etc. Sample data collection: The agent automatically samples 5,000-10,000 sample data values ​​for each field, and automatically de-identifies sensitive fields (such as ID card number and account number) and stores them in encrypted object storage; Step 2: Knowledge Graph Construction (Automatically executed by LLM Agent) The Agent automatically uses Neo4j to build a financial data knowledge graph: Create a table node: (Table:Metadata{name:"ACCOUNT_INFO",name_chinese:"Account Information Table", source:"CORE_BANKING",data_classification:"SENSITIVE"}); Create a field node: (Field:Metadata{name:"ACCOUNT_NO",name_chinese:"Account Number",data_type:"VARCHAR",is_pii:true,encryption:true}); Create a relationship: (Table) - [:HAS_FIELD] -> (Field); Establishing a financial business relationship: (ACCOUNT_INFO)-[:RELATED_TO{relation_type: "OWNED_BY"}]->(CUSTOMER_INFO); Step 3: Multi-dimensional matching (automatic scheduling and execution by LLM Agent); The master agent identifies the unknown field "UNKNOWN_FIELD_002" in the data lake and automatically schedules various professional agents to perform matching: Data value matching (automatic execution by the data value matching agent): The agent automatically samples 5000 data values ​​(already anonymized) for this field; The Agent automatically calculates the overlap between the sample data and all fields in the backup database; The agent automatically discovered that the data values ​​of the "ACCOUNT_NO" field overlapped by 96%. The agent automatically calculates the confidence level as 0.96. Name similarity matching (automatically executed by the semantic understanding agent): The agent automatically uses a thesaurus of financial terms ("account", "account number", "ACCOUNT", "ACC", etc.). The Agent automatically calculates the edit distance and synonym matching between "UNKNOWN_FIELD_002" and the field names of each backup database; The agent automatically found a similarity of 0.35 with "ACCOUNT_NO" (the field names are different, but the matching is done through a thesaurus). The agent automatically calculates the confidence level as 0.35. Semantic vector matching (automatic execution by the semantic understanding agent): The agent automatically generates field name vectors using a pre-trained BERT model in the financial domain; The Agent automatically calculates the cosine similarity with each backup database field; The agent automatically found that the semantic similarity with "ACCOUNT_NO" was 0.72; The agent automatically calculates the confidence level as 0.72. Contextual analysis (automatic execution by the semantic understanding agent): The agent automatically analyzes other fields in the table containing "UNKNOWN_FIELD_002"; The agent automatically detects fields such as "ACCOUNT_TYPE", "ACCOUNT_BALANCE", and "OPEN_DATE" in the same table; the agent automatically infers that the table is an account-related table. The agent automatically calculates the confidence level as 0.88. Business rule verification (automatic execution by the verification agent): The agent automatically verifies whether the field values ​​conform to the account number format rules (such as length, character set, etc.). The agent automatically verifies whether the field value is within the range of valid account numbers; The agent automatically calculates the confidence level for business rule verification: 0.95; LLM inference (automatic execution by the semantic understanding agent): The agent automatically inputs field information, context fields, and candidate matches into GPT-4; The Agent automatically combines financial knowledge to reason and determine that the field is "account number"; The agent automatically calculates the confidence level as 0.93. Step 4: Multi-strategy fusion (verification agent executes automatically); The verification agent automatically collects the execution results of each agent and automatically merges them: The agent automatically assigns weights: data value matching weight 0.4 (core strategy), business rule verification weight 0.2, context analysis weight 0.15, semantic matching weight 0.15, and LLM inference weight 0.1. The agent automatically calculates the final confidence level as follows: 0.96×0.4 + 0.95×0.2 + 0.88×0.15 + 0.72×0.15 + 0.93×0.1 = 0.927. Step 5: Compliance Check (Verification Agent executed automatically) The agent automatically performs compliance checks. The agent automatically checks whether the data lineage complies with the requirements of the Personal Information Protection Law; The agent automatically checks the access permissions and encryption status of sensitive fields; The agent automatically generates compliance reports; The agent automatically labels data with classification levels (e.g., sensitive, confidential, internal, etc.). Step 6: Result Confirmation (Verifying Agent's Automated Decision-Making) The verification agent automatically determines the final confidence level > 0.9, automatically confirms the matching result, automatically establishes relationships in the knowledge graph, and automatically marks compliance information; Relationships include attributes such as confidence level, overlap, business rule verification score, compliance status, data classification, and encryption requirements; Step 7: Continuous optimization (Agent automatic learning); The agent automatically records matching results and compliance check results; The agent learns automatically from regulatory feedback (if any); The agent automatically optimizes the validation logic for financial business rules. The Agent automatically updates the thesaurus of financial terms. The entire process is fully automated and complies with financial industry compliance requirements; Technical effect This embodiment verifies that, in a financial industry scenario: The data value matching accuracy reaches 96%, which is the core matching strategy; Business rule verification played a crucial role (confidence level 0.95) in ensuring that the matching results complied with financial business regulations. Compliance checks are completed automatically, requiring no manual review. The final fusion confidence score was 0.927, exceeding the threshold of 0.9, and the system automatically confirmed it. Sensitive data is automatically anonymized and encrypted, meeting data security requirements.

[0032] Example 3: Data Lineage Analysis in the Manufacturing Industry Scene Description A large manufacturing enterprise's data center includes backup databases for multiple business systems such as Enterprise Resource Planning (ERP), Manufacturing Execution System (MES), Warehouse Management System (WMS), Quality Management System (QMS), and Equipment Management System (EAM), as well as a data lake. The data lake stores production data, material data, quality data, equipment data, etc. from various systems, and contains a large number of unlabeled fields. It is necessary to establish a complete data lineage to support production traceability, quality analysis, and supply chain optimization. Implementation steps Step 1: Metadata Collection (Automatically executed by LLM Agent) The master agent automatically schedules metadata collection tasks, optimized for the characteristics of the manufacturing industry: Data collected from the ERP system's backup inventory: Material Master Data Table (MATERIAL_MASTER): Fields include material code, material name, material type, and unit; Bill of Materials (BOM): fields include BOM number, parent material code, child material code, and quantity. Production Order Table (PRODUCTION_ORDER): Fields include order number, material code, planned quantity, planned start date, etc. Collected from the MES system backup database: Work Order Table (WORK_ORDER): Fields include work order number, production order number, process code, and planned start time; Production Report (PRODUCTION_REPORT): Fields include report number, report number, actual output, and reporting time; ROUTING: Fields include process route code, process sequence, and standard working hours; Collected from the WMS system backup database: Inventory table (INVENTORY): Fields such as material code, warehouse code, inventory quantity, and storage location code; Inbound Order Table (INBOUND_ORDER): Fields include inbound order number, material code, inbound quantity, and inbound date; Outbound Order Table (OUTBOUND_ORDER): Fields include outbound order number, material code, outbound quantity, outbound date, etc. Data collected from the QMS system backup database: Inspection Order Form (INSPECTION_ORDER): Fields include inspection order number, material code, batch number, and inspection result; Non-conforming Product List (DEFECTIVE_PRODUCT): Fields include non-conforming product number, material code, batch number, reason for non-conformity, etc. Extract the following from the data lake: structural information of all tables, including table names, field names, data types, etc. Sample data collection: The agent automatically samples 5,000-10,000 data points for each field, paying particular attention to key fields such as material code and batch number; Step 2: Knowledge Graph Construction (Automatically executed by LLM Agent) The Agent automatically uses Neo4j to build a knowledge graph of manufacturing data: Create a table node: (Table:Metadata{name:"MATERIAL_MASTER",name_chinese:"Material Master Data Table",source:"ERP",business_domain:"MATERIAL_MANAGEMENT"}); Create a field node: (Field:Metadata{name:"MATERIAL_CODE",name_chinese:"material code", data_type:"VARCHAR",is_key_field:true}); Create a relationship: (Table) - [:HAS_FIELD] -> (Field); Establish manufacturing business relationships: (PRODUCTION_ORDER)-[:USES_MATERIAL]->(MATERIAL_MASTER), (WORK_ORDER)-[:BELONGS_TO]->(PRODUCTION_ORDER); Step 3: Multi-dimensional matching (automatic scheduling and execution by LLM Agent) The master agent identifies the unknown field "UNKNOWN_FIELD_003" in the data lake and automatically schedules various specialized agents to perform matching: Data value matching (automatic execution by the data value matching agent): The agent automatically samples 5000 data values ​​for this field; The agent automatically identifies the data value format: it finds that it is an 8-digit numeric code (such as "12345678"). The Agent automatically calculates the overlap between the sample data and all fields in the backup database; The agent automatically discovered that the data value overlap with the "MATERIAL_CODE" field reached 91%; The agent automatically calculates the confidence level as 0.91. Name similarity matching (automatically executed by the semantic understanding agent): The agent automatically uses a thesaurus of manufacturing industry terms ("material", "material", "MATERIAL", "ITEM", "material code", "part number", etc.). The Agent automatically calculates the edit distance and synonym matching between "UNKNOWN_FIELD_003" and the field names of each backup database; The agent automatically found a similarity of 0.42 to "MATERIAL_CODE" (through the thesaurus matching). The agent automatically calculates the confidence level as 0.42. Semantic vector matching (automatic execution by the semantic understanding agent): The agent automatically generates field name vectors using a pre-trained BERT model in the manufacturing domain; The Agent automatically calculates the cosine similarity with each backup database field; The agent automatically found that the semantic similarity with "MATERIAL_CODE" was 0.75; The agent automatically calculates the confidence level as 0.75. Contextual analysis (automatic execution by the semantic understanding agent): The agent automatically analyzes other fields in the table containing "UNKNOWN_FIELD_003"; The Agent automatically discovers fields such as "MATERIAL_NAME", "MATERIAL_TYPE", and "UNIT" in the same table; The Agent automatically identifies this table as a material-related table. The agent automatically calculates the confidence level as 0.86. Encoding rule verification (automatic execution by the verification agent): The agent automatically verifies whether the field values ​​conform to the material coding rules (such as length, character set, meaning of coding segments, etc.). The agent automatically verifies whether the code is within the range of valid material codes; The agent automatically calculates the confidence level for verifying the coding rules: 0.89; Process flow association analysis (automatic execution by semantic understanding agent): The agent automatically analyzes the position of this field in the process flow; The Agent automatically discovers that this field has a relationship with tables such as production orders, work orders, and inventory. The Agent automatically infers that this field is a material code (the material is the core entity in the production process). The agent automatically calculates the confidence level of the process flow association: 0.87; LLM reasoning (automatic execution by semantic understanding agent): The agent automatically inputs field information, context fields, process flow information, and candidate matches into GPT-4; The Agent automatically combines manufacturing domain knowledge to reason and determine that the field is a "material code"; The agent automatically calculates the confidence level as 0.92. Step 4: Multi-strategy fusion (verification agent executes automatically); The verification agent automatically collects the execution results of each agent and automatically merges them: The agent automatically assigns weights: data value matching weight 0.35 (core strategy), encoding rule verification weight 0.2, process flow association weight 0.15, context analysis weight 0.15, semantic matching weight 0.1, and LLM inference weight 0.05. The agent automatically calculates the final confidence level as follows: 0.91×0.35 + 0.89×0.2 + 0.87×0.15 + 0.86×0.15 + 0.75×0.1 + 0.92×0.05 = 0.884. Step 5: Quality Traceability Association (Verification Agent executed automatically); The verification agent automatically establishes the quality traceability association: The agent automatically identifies the association between this field (material code) and the batch number; Agent automatically establishes a traceability chain from material code to batch number to inspection report to non-conforming products; The Agent automatically establishes traceability relationships in the knowledge graph, including relationship types such as TRACES_TO, HAS_INSPECTION, and MAY_HAVE_DEFECT. Step 6: Result Confirmation (Verifying Agent's Automated Decision-Making) The verification agent automatically determines the final confidence level > 0.85, automatically confirms the matching result, automatically establishes the relationship in the knowledge graph, and automatically establishes the traceability link; The relationship includes attributes such as confidence level, overlap, coding rule verification score, process flow association score, and traceability link establishment flag; Step 7: Continuous optimization (Agent automatic learning); The agent automatically records the matching results and the correlation between the process flow; The agent learns automatically from production feedback (if any); The Agent automatically optimizes the material coding rule verification logic; The Agent automatically updates the thesaurus of manufacturing industry terms; The agent automatically optimizes the quality traceability chain; The entire process is fully automated, supporting the manufacturing industry's needs for production traceability and quality analysis; Technical effect This embodiment verifies that, in a manufacturing industry scenario: The data value matching accuracy reaches 91%, which is the core matching strategy; Encoding rule verification played a crucial role (confidence level 0.89), ensuring that the matching results conformed to the coding standards of the manufacturing industry; The process flow correlation analysis provided important support (confidence level 0.87), helping to understand the role of the fields in the manufacturing process; The quality traceability chain is automatically established, supporting full-process traceability from materials to finished products; The final fusion confidence score was 0.884, exceeding the threshold of 0.85, and the system automatically confirmed this. It supports the material management, production management, and quality management needs of the manufacturing industry.

Claims

1. An automated data governance method integrating multidimensional matching and LLM agent collaborative reasoning, characterized in that, This method is fully automated and executed by a unified LLM Agent architecture, requiring no manual intervention. It can quickly sort and manage messy and disordered data, automatically identifying Chinese definitions of fields, relationships between fields, and relationships between tables. Specifically, it includes the following steps: (1) Multi-source metadata collection and knowledge graph construction; (2) Multi-Agent Intelligent Reasoning Control Based on LangChain; (3) Multi-dimensional bloodline matching engine; (4) Graph neural network relationship prediction; (5) Optimization of matching strategies driven by reinforcement learning; (6) Temporal evolution analysis of data structures; (7) Multi-strategy fusion and confidence assessment; (8) Incremental update and continuous learning mechanism; All sub-modules are coordinated by the master agent, achieving end-to-end automation through prompting engineering, inference chains, and automatic task distribution.

2. The automatic data governance method integrating multidimensional matching and LLM Agent collaborative reasoning as described in claim 1, characterized in that, The multi-source metadata collection and knowledge graph construction includes: automatically extracting structured and unstructured metadata from relational database backups, data lakes, and business documents; standardizing field names, data types, comments, and sample values, including case unification, type mapping, Chinese and English extraction, and value format normalization; constructing a lineage knowledge graph in the Neo4j graph database, containing databases, tables, fields, and semantic nodes, with node attributes including field meaning, sample hash, and confidence level, and relationship attributes including similarity, matching type, and timestamp; automatically collecting 5,000–10,000 non-empty sample values ​​for each field, storing them in object storage, and generating a hash index for subsequent value matching.

3. The automatic data governance method integrating multidimensional matching and LLM Agent collaborative reasoning according to claim 1, characterized in that, The LangChain-based multi-agent intelligent reasoning control includes: constructing a collaborative system consisting of a master agent, a data value matching agent, a semantic understanding agent, an annotation generation agent, and a verification agent; the master agent automatically selects the execution path based on the characteristics of the input fields, distributes tasks, and coordinates the parallel or serial execution of each specialized agent; a chain-of-thought reasoning chain is used to automate the entire process from problem understanding to knowledge retrieval, multidimensional analysis, decision generation, and result verification; the verification agent integrates the outputs of each agent to generate the final matching result and confidence level, all without human intervention.

4. The automatic data governance method integrating multidimensional matching and LLM Agent collaborative reasoning according to claim 1, characterized in that, The multi-dimensional bloodline matching engine integrates the following four matching mechanisms: Exact data value matching: Fast overlap calculation is achieved based on Bloom filter and hash index, combined with Jaccard, distribution similarity and cross-validation; Field name similarity matching: integrates Levenshtein distance, initial letter mapping of pinyin, and domain thesaurus; Semantic vector similarity matching: BERT / GPT is used to generate field embeddings, and approximate nearest neighbor search is performed through a vector database (such as Milvus); Contextual association analysis: Identify implicit lineage by combining co-occurrence of fields in the same table, table name semantics, association rule mining, and graph structure path analysis.

5. The automatic data governance method integrating multidimensional matching and LLM Agent collaborative reasoning according to claim 1, characterized in that, The graph neural network relationship prediction modeled kinship identification as a graph link prediction problem, including: extracting multi-dimensional features of nodes from the knowledge graph and encoding them using MLP; using GCN to aggregate heterogeneous neighbor information and distinguish relationship types such as inclusion, similarity, and matching; and introducing GAT to dynamically learn the importance weights of neighbors. The system calculates matching scores between fields based on node representations and simultaneously predicts table-level business relationships.

6. The automatic data governance method integrating multidimensional matching and LLM Agent collaborative reasoning according to claim 1, characterized in that, The reinforcement learning-driven matching strategy optimization is automatically constructed by the master agent, including: a state space covering field features, graph context and historical matching records; an action space containing policy selection and candidate field decision-making; a reward function that integrates accuracy, confidence, efficiency and consistency; and the agent automatically selects DQN or policy gradient algorithm (such as PPO) to train the policy and continuously optimizes the matching path through A / B testing.

7. The automatic data governance method integrating multidimensional matching and LLM Agent collaborative reasoning according to claim 1, characterized in that, The data structure temporal evolution analysis enables dynamic tracking and prediction of lineage relationships, including: automatically recording snapshots of table / field structure changes and constructing version evolution graphs; identifying typical evolution patterns such as renaming, type changes, and table splitting / merging; predicting future field semantics and lineage relationships based on LSTM or Transformer models; automatically evaluating the impact of structural changes on existing matching relationships and triggering graph updates.

8. The automated data governance method integrating multidimensional matching and LLM Agent collaborative reasoning according to claim 1, characterized in that, The multi-strategy fusion and confidence assessment mechanism includes: integrating multi-dimensional matching results using weighted fusion, voting, and hierarchical integration strategies; confidence is calculated based on data value overlap, name similarity, semantic vector similarity, contextual consistency, and historical accuracy; decision-making is based on confidence level: high confidence is automatically confirmed, medium confidence is marked for review, and low confidence is suspended; an anomaly detection module is integrated to provide risk warnings for inconsistent or low-quality matches.

9. The automatic data governance method integrating multidimensional matching and LLM Agent collaborative reasoning according to claim 1, characterized in that, The incremental update and continuous learning mechanism is used to support the long-term autonomous operation of the system, including: triggering incremental matching based on change detection to dynamically update the knowledge graph; automatically optimizing model parameters and agent strategies based on user feedback or verification results; supporting model version management, rollback and A / B testing; applicable to cross-industry data centers such as medical, financial, manufacturing, and retail, to achieve automated lineage governance across systems and platforms.

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