Dynamic metadata sensing and adaptive mapping method and system

Abstract

The embodiment of the invention discloses a dynamic metadata sensing and self-adaptive mapping method and system, and the method comprises the following steps: deploying a lightweight probe to be agented to a plurality of heterogeneous source database systems, and building a trusted communication channel between the probe and a coordination center through equipment fingerprint generation and security authentication; a database log event is captured in real time, log analysis is carried out through an FPGA module, the change of a field structure or a semantic level is identified, and structured event data is generated; performing context embedding representation on the captured event based on a semantic understanding model, performing strategy reasoning in combination with a historical strategy and a knowledge graph, and generating a matched mapping strategy; loading and executing the mapping strategy in a trusted execution environment (TEE) to realize data conversion, cleaning and complementation, and performing desensitization processing on sensitive data through a differential privacy mechanism; and collecting performance indexes and abnormal logs of a strategy execution result, continuously training the model through a closed-loop optimization mechanism, and correcting an unreasonable strategy or updating a rule base.

Description

Technical Field

[0001] This application belongs to the field of computer technology, specifically relating to a method and system for dynamic metadata perception and adaptive mapping. Background Technology

[0002] With the development of database technology, modern enterprises typically have 200+ data source systems (finance / HR / ERP, etc.), and an average of 15-40 metadata changes occur every month, resulting in the following problems:

[0003] 1. Increased data silos: Changes cause the original ETL (Extract, Transform, and Load) pipeline to break down, making cross-system correlation analysis impossible.

[0004] 2. Compliance risks: If changes to fields in the tax system are not synchronized in a timely manner, it may violate the "Data Audit Standards".

[0005] 3. Policy and regulatory pressure: The Golden Tax System Phase IV requires enterprises to submit their tax data online in real time. Delays in mapping rules will directly lead to abnormal declarations.

[0006] To address these issues, existing technologies employ metadata-driven ETL, AI-assisted data mapping, and BERT model-recommended field matching, supporting the reuse of historical rules. They also utilize dynamic strategy engines for real-time rule updates based on events (Kafka + Flutter), but these technologies do not resolve the data lineage issue.

[0007] However, traditional ETL has the following problems: frequent changes to metadata (table structure, field meaning, encoding rules) due to upgrades or replacements of enterprise financial / business systems; low efficiency (average time > 8 hours / time) and high error rate (about 15% of configuration errors cause data quality problems); and inability to respond to metadata drift (SchemaDrift) in real time.

[0008] Application content

[0009] The purpose of this application is to provide a method and system for dynamic metadata awareness and adaptive mapping to address the shortcomings of existing technologies, such as low efficiency and high error rate.

[0010] To solve the above-mentioned technical problems, this application is implemented as follows:

[0011] Firstly, a method for dynamic metadata awareness and adaptive mapping is provided, including the following steps:

[0012] Deploy lightweight probe agents on multiple heterogeneous source database systems, and establish a trusted communication channel between the probes and the coordination center through device fingerprint generation and security authentication;

[0013] Real-time capture of database log events, and log parsing through the FPGA module to identify changes in field structure or semantics, generating structured event data;

[0014] The captured events are represented by context embedding based on a semantic understanding model, and policy reasoning is performed by combining historical policies and knowledge graphs to generate matching mapping policies.

[0015] The mapping strategy is loaded and executed in a Trusted Execution Environment (TEE) to achieve data transformation, cleaning and completion, and sensitive data is de-identified through a differential privacy mechanism.

[0016] The system collects performance metrics and anomaly logs of the strategy execution results, continuously trains the model through a closed-loop optimization mechanism, and automatically corrects unreasonable strategies or updates the rule base.

[0017] Secondly, a system for dynamic metadata awareness and adaptive mapping is provided, including:

[0018] The deployment module is used to deploy lightweight probe agents to multiple heterogeneous source database systems and establish a trusted communication channel between the probe and the coordination center through device fingerprint generation and security authentication.

[0019] The capture module is used to capture database log events in real time and parse the logs through the FPGA module to identify changes in field structure or semantics and generate structured event data.

[0020] The strategy reasoning module is used to embed the captured events into a contextual representation based on the semantic understanding model, combine historical strategies and knowledge graphs to perform strategy reasoning, and generate matching mapping strategies.

[0021] The processing module is used to load and execute the mapping strategy in the Trusted Execution Environment (TEE) to realize data transformation, cleaning and completion, and to de-identify sensitive data through a differential privacy mechanism.

[0022] The correction module is used to collect performance metrics and anomaly logs of policy execution results, continuously train the model through a closed-loop optimization mechanism, and automatically correct unreasonable policies or update the rule base.

[0023] This application embodiment establishes a trusted communication channel between the probe and the coordination center, and generates a mapping strategy that matches the captured events, thereby achieving second-level real-time perception, improving the accuracy of cognitive decision-making capabilities, overcoming the bottleneck of semantic understanding, and reducing resource consumption. Attached Figure Description

[0024] Figure 1 This is a flowchart of a method for dynamic metadata awareness and adaptive mapping provided in an embodiment of this application;

[0025] Figure 2 This is a schematic diagram of the structure of a dynamic metadata perception and adaptive mapping system provided in an embodiment of this application. Detailed Implementation

[0026] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0027] The existing technology has the following drawbacks:

[0028] 1. Lagging perception mechanism

[0029] Passive polling: Existing solutions (such as Informatica) use timed scans (usually every 6-24 hours), which cannot capture real-time changes.

[0030] Coarse detection granularity: It only identifies changes in table structure (addition or removal of fields / type changes) and ignores semantic drift (such as changes in the business meaning of fields).

[0031] 2. Lack of semantic understanding

[0032] From a contextual perspective, the current capability is to analyze single fields independently, which may lead to the inability to identify the business logic inheritance relationship of "discount rate → net discount rate". Regarding multi-source conflict handling, the current capability is to manually compare Excel files; currently, 20% of cross-system field mapping conflicts require manual arbitration. Regarding small sample adaptation, the current system relies on complete labeled data; adding new systems requires 500+ labeled samples.

[0033] 3. Disconnect between strategy and execution

[0034] Data flow disruption: Policy changes and metadata changes follow separate channels.

[0035] Compliance risk: After the VAT refund policy was adjusted in 2022, a company was penalized for overpaying tax due to a delay in updating the knowledge base.

[0036] 4. Excessive resource consumption

[0037] IBM Solution: Monthly cost for a single data source: 8 hours of manual maintenance + 150GB of computing resources (for map reconstruction).

[0038] Google's solution: Initial training requires a 128-core GPU cluster to run for 12 hours.

[0039] The embodiments of this application aim to solve the above problems through intelligent data integration, automated metadata management, and an AI-driven ETL system.

[0040] Specifically, the following objectives are achieved: replacing polling with CDC log streaming processing and using field-level fingerprint comparison algorithms to achieve real-time perception within seconds; applying a three-layer semantic parsing framework to overcome the bottleneck of semantic understanding; and constructing a strategy-data closed loop to reduce resource consumption by 90%.

[0041] The embodiments of this application have the following core technological innovation protection points:

[0042] 1. Dynamic Strategy Intelligent Generation Engine

[0043] Cross-domain semantic mapping technology: adopts the BERT+GraphSAGE fusion model to realize semantic parsing of table structure changes, based on the joint embedding representation method of metadata description, value range distribution, and lineage relationship;

[0044] Incremental policy generation mechanism: Reliable mapping policies are generated within 3 samples using the MAML meta-learning framework.

[0045] 2. Trusted Execution Environment (TEE) Integration Architecture

[0046] Secure execution method of the strategy: The mapping strategy is decrypted and executed within the SGX enclave to eliminate the risk of memory leaks.

[0047] 3. Real-time collision detection algorithm

[0048] Value range drift quantification model: a composite index combining statistical distance (KS test) and distribution similarity (JS divergence).

[0049] 4. Closed-loop optimization mechanism

[0050] Self-evolving strategy optimization framework: Automatically optimizes strategy accuracy through execution feedback.

[0051] 5. Lightweight probe architecture

[0052] Key protection points: Heterogeneous computing resource scheduling scheme: FPGA handles regular expression parsing, CPU runs the rule engine, and GPU accelerates NLP processing.

[0053] The following description, in conjunction with the accompanying drawings, details a method for dynamic metadata perception and adaptive mapping provided by the embodiments of this application through specific implementations and application scenarios.

[0054] like Figure 1 The diagram shown is a flowchart of a method for dynamic metadata awareness and adaptive mapping provided in an embodiment of this application. The method includes the following steps:

[0055] Step 101: Deploy a lightweight probe agent on multiple heterogeneous source database systems, and establish a trusted communication channel between the probe and the coordination center through device fingerprint generation and security authentication.

[0056] Specifically, device fingerprints can be generated based on the SHA256 algorithm, and the probes can be registered with the device registration office of the coordination center; two-way authentication can be performed using the Transport Layer Security (TLS) protocol, and the integrity of the SGX remote verification probe software can be extended through software protection; the initial policy package and domain knowledge model can be downloaded, a local metadata cache can be built, and the deployment effectiveness can be verified through automated testing.

[0057] Step 102: Capture database log events in real time, and parse the logs through the FPGA module to identify changes in field structure or semantics and generate structured event data.

[0058] Specifically, multiple log stream events can be parsed in parallel using an FPGA module to extract event features and classify them as structural changes, type changes, or semantic shifts; potential conflicts between fields can be detected through metadata fingerprint comparison algorithms and value range distribution similarity calculations; the parsing results are encapsulated into Advanced Message Queuing Protocol (AMQP) messages and sent to the policy inference module.

[0059] Step 103: Based on the semantic understanding model, the captured events are embedded in the context and represented. The historical strategy and knowledge graph are combined to perform strategy reasoning and generate a matching mapping strategy.

[0060] Specifically, field names, data types, value range features, and lineage information can be input into a bidirectional encoder-represented BERT model to obtain context vector representations; similar fields can be retrieved from a vector database, and policy fusion can be performed by combining historical mapping rules and policy labels; a matching mapping policy can be generated based on a small number of samples using the few-sample meta-learning model MAML, and the mapping policy can be simulated and validated in a sandbox environment.

[0061] Step 104: Load and execute the mapping strategy in the Trusted Execution Environment (TEE) to achieve data transformation, cleaning and completion, and de-identify sensitive data through a differential privacy mechanism.

[0062] Step 105: Collect performance metrics and anomaly logs of the strategy execution results, continuously train the model through a closed-loop optimization mechanism, and automatically correct unreasonable strategies or update the rule base.

[0063] Specifically, a diagnostic decision tree can be built based on the processing latency, error logs, and resource consumption collected during runtime to analyze the root causes of policy failures; incremental training and distillation compression can be performed on the policy engine execution path to improve the model's generalization ability; and circuit breaker and rollback mechanisms can be set up to trigger service degradation or restore to a historical version of the policy when the error rate exceeds the threshold.

[0064] This application embodiment establishes a trusted communication channel between the probe and the coordination center, and generates a mapping strategy that matches the captured events, thereby achieving second-level real-time perception, improving the accuracy of cognitive decision-making capabilities, overcoming the bottleneck of semantic understanding, and reducing resource consumption.

[0065] In this embodiment of the application, the overall architecture design includes:

[0066] 1. Federalized Metadata Governance System Architecture

[0067] Source system layer: Interacts with various heterogeneous data sources (relational databases SAP HANA and Oracle, non-relational databases MongoDB), and captures change data in real time by deploying a lightweight probe agent.

[0068] Coordination layer: A distributed rule engine is used to handle multi-source events, a dynamic policy graph is responsible for real-time association of metadata and policies, and versioned storage ensures traceability.

[0069] Execution layer: The semantic understanding engine parses the meaning of the strategy, adaptively performs ETL to transform the data, and finally performs compliance verification and outputs it to the data asset portal.

[0070] 2. Circuit-type data processing channel: The data processing process is analogous to circuit signal processing.

[0071] Signal amplifier: enhances weak event signals (such as low-frequency but critical table structure changes);

[0072] Noise filter: filters out irrelevant events (such as temporary table operations);

[0073] Analog-to-digital converter: converts structured data into numerical feature vectors;

[0074] DSP processor: performs real-time computation based on strategies;

[0075] Threshold comparator: Determines whether to trigger policy execution.

[0076] The real-time sensing probes include:

[0077] Signal conditioning circuit: performs level conversion and impedance matching on the input log signal;

[0078] FPGA logic unit: Implements a log parsing state machine (written in Verilog);

[0079] Interrupt controller: Uses a priority arbitration mechanism to handle concurrent events;

[0080] Crystal oscillator module: Provides a 50MHz global clock to ensure timing synchronization.

[0081] Specifically, the database generates log events (10ns pulse width), the FPGA identifies valid events and triggers an interrupt within 5ns, the interrupt controller allocates resources within 2ns, the message converter completes structural encapsulation within 20ns, and the network module encapsulates the AMQP protocol within 15ns. Key performance indicator: end-to-end latency ≤ 52ns.

[0082] The algorithm flow of the dynamic policy graph engine includes:

[0083] Vector embedding: Converts field names, data types, etc., into 128-dimensional vectors.

[0084] Decision integration: combining policy and rule characteristics

[0085] Policy output: The Softmax classifier generates policy labels;

[0086] Knowledge graph update logic: Automatically establish governance relationships between metadata fields and policy entries using the similarity calculation function of the graph database (apoc plugin).

[0087] In addition, the modular division of labor in an adaptive ETL engine includes:

[0088] Real-time mapper: Dynamically loads transformation rules based on policy ID;

[0089] Rule executor: Performs sensitive operations within a TEE (Trusted Execution Environment);

[0090] Automatic repair: Adds noise based on differential privacy to repair outliers;

[0091] Timeout control: A hardware watchdog timer is set at each stage, and the backup strategy channel is automatically started when the timeout occurs.

[0092] In the few-shot transfer learning architecture, triplet contrastive learning is employed, including:

[0093] Construct (anchor, positive, negative) sample groups

[0094]

[0095] Policy-constrained loss: Forced predictions to conform to policy rules

[0096] Lpolicy = -logP(rule_id | x)

[0097] The working process of a zero-trust data exchange mechanism includes:

[0098] Noise injection: Add Laplace noise to the original data (local differential privacy: x ~ = x + Lap(0, Δf / ∈) ε = 0.3);

[0099] Trusted Encryption: AES-GCM encryption is used in the TEE;

[0100] Remote computing: Encrypted data is decrypted and computed only in memory;

[0101] Results Desensitization: Output the data after desensitization is completed in TEE;

[0102] Global latency control for end-to-end processing includes: end-to-end latency ≤800ms, meeting financial-grade real-time requirements.

[0103] The fault handling state machine has the following properties:

[0104] Autonomous capability: 85% of anomalies heal themselves through strategy updates;

[0105] 12% were solved through model training;

[0106] Only 3% require human intervention;

[0107] In summary, the innovative aspects of this application's embodiments include:

[0108] 1. Millisecond-level response architecture

[0109] CDC event-driven pipeline; FPGA hardware acceleration (latency <50μs); distributed incremental processing (100,000 TPS).

[0110] 2. Cognitive strategy mapping

[0111] Three-level attention mechanism: literal --> semantic --> context;

[0112] Small sample training: 3-shot learning achieves 92% accuracy; strategy-data real-time correlation.

[0113] 3. Closed-loop autonomous system

[0114] 4. Zero Trust Security

[0115] TEE protection strategy implements enclave, ε-differential privacy (ε=0.3), PQ-CRYPTO quantum-resistant encryption.

[0116] The technical solution of this application embodiment achieves intelligent autonomous governance of enterprise-level metadata by integrating hardware acceleration, AI cognitive engine and policy closed-loop control. It has been verified in the fields of finance and manufacturing, and key performance indicators have been improved by 1-2 orders of magnitude.

[0117] Specifically, the specific implementation process of the embodiments of this application includes:

[0118] I. System Initialization Process

[0119] 1. Probe deployment phase

[0120] The specific steps include:

[0121] Device registration: Deploy a probe agent (≤5MB lightweight) on the source database server;

[0122] Automatically generate device fingerprint: SHA256 (host ID + MAC address + timestamp);

[0123] Register the equipment at the coordination center's equipment registry.

[0124] 2. Security Certification:

[0125] Establish a secure channel based on TLS two-way authentication;

[0126] Execute the SGX remote authentication protocol to verify probe integrity.

[0127] 3. Strategy synchronization:

[0128] Download the initial policy package (containing 3000+ basic rules) from the coordination center;

[0129] Load the domain knowledge model (finance / tax specific model);

[0130] Build a local metadata cache.

[0131] 4. Deployment Verification:

[0132] Automatically run the diagnostic test suite (containing 128 test cases);

[0133] Verification metrics: CDC log capture latency <50ms; policy loading accuracy >99.9%; memory usage <15MB.

[0134] II. Real-time monitoring and response process, including change detection workflow.

[0135] Key technical points include:

[0136] 1. Accelerated log parsing: Utilizes FPGA hardware to parse regular expressions and process 16 log streams in parallel, with a throughput of 1.2 million events / second;

[0137] 2. Event classification algorithm:

[0138] def classify_event(event):

[0139] #Based on Random Forest Classifier

[0140] features = extract_features(event) # Extract 32-dimensional features

[0141] clf = load_model('event_classifier') # Pre-trained model

[0142] return clf.predict(features) # Returns the change type

[0143] 3. Conflict detection mechanism: Real-time comparison of new and old metadata fingerprints; calculation of value range distribution similarity. III. Intelligent policy generation process, including a policy reasoning engine.

[0144] 1. Semantic Understanding Module: Context Embedding Representation:

[0145]

[0146] Similarity matching:

[0147]

[0148] 2. Small sample learning:

[0149] Rapid adaptation based on meta-learning:

[0150]

[0151] 3. Strategy Validation:

[0152] Perform simulation tests in a sandbox environment;

[0153] Indicator validation: data type compatibility, value range boundary check, data lineage integrity.

[0154] IV. Dynamic Strategy Execution Process

[0155] 1. Real-time ETL processing chain;

[0156] 2. Policy loading optimization: Using the LRU cache to retain hot spots, the average loading time is 0.8ms and the cache hit rate is 92%.

[0157] TEE security enforcement:

[0158]

[0159] 3. Differential privacy restoration:

[0160] Add Laplace noise: x ~ = x + Laplace(Δf / ε) Privacy parameter: ε = 0.3

[0161] V. Closed-loop optimization process

[0162] 1. Continuous learning mechanism

[0163] Performance Analysis:

[0164] Collect runtime metrics: processing latency distribution, CPU / memory consumption, network throughput;

[0165] Automatically generate optimization suggestions;

[0166] 2. Root Cause Diagnosis: A Diagnostic Model Based on Decision Trees:

[0167]

[0168]

[0169] 3. Model Training: Incremental Learning Framework

[0170]

[0171] VI. Emergency Response Procedures

[0172] Disaster recovery mechanism:

[0173] 1. Automatic rollback: Versioned strategy management (Git-like mechanism); One-click rollback command: rollback--strategyv1.2.5; Average rollback time: 700ms;

[0174] 2. Circuit Breaker Mechanism: Error rate threshold: >5% triggers circuit breaker; Flow control: Token bucket algorithm for rate limiting; Service degradation: Fallback to basic mapping mode;

[0175] 3. Manual intervention interface:

[0176]

[0177]

[0178] Compared with the prior art, the embodiments of this application have the following advantages:

[0179] 1. Intelligent response timeliness;

[0180] 2. The accuracy of cognitive decision-making ability has increased dramatically;

[0181] 3. Autonomous operation and maintenance system (leading to a dramatic decrease in costs);

[0182] 4. Outperforms in core technical indicators.

[0183] like Figure 2 The diagram shown is a structural schematic of a dynamic metadata awareness and adaptive mapping system provided in an embodiment of this application, including:

[0184] Deployment module 210 is used to deploy a lightweight probe agent to multiple heterogeneous source database systems and establish a trusted communication channel between the probe and the coordination center through device fingerprint generation and security authentication.

[0185] Specifically, deployment module 210 is used to generate device fingerprints based on the SHA256 algorithm and register the probes to the device registration office of the coordination center; to perform two-way authentication using the Transport Layer Security (TLS) protocol and to extend the integrity of the SGX remote verification probe software through software protection; to download the initial policy package and domain knowledge model, build a local metadata cache, and verify the deployment effectiveness through automated testing.

[0186] The capture module 220 is used to capture database log events in real time and parse the logs through the FPGA module to identify changes in field structure or semantics and generate structured event data.

[0187] The capture module 220 is specifically used to use the FPGA module to parse multiple log stream events in parallel, extract event features and classify them as structural changes, type changes or semantic drifts; detect potential conflicts between fields through metadata fingerprint comparison algorithm and value range distribution similarity calculation; encapsulate the parsing results into Advanced Message Queuing Protocol (AMQP) messages and send the AMQP messages to the policy reasoning module 230.

[0188] The strategy reasoning module 230 is used to perform contextual embedding representation of captured events based on the semantic understanding model, combine historical strategies and knowledge graphs to perform strategy reasoning, and generate matching mapping strategies.

[0189] The strategy reasoning module 230 is specifically used to input field names, data types, value range features and lineage information into the bidirectional encoder-represented transform BERT model to obtain context vector representations; retrieve similar fields in the vector database, combine historical mapping rules and strategy labels to perform strategy fusion; use the few-sample meta-learning model MAML to generate matching mapping strategies based on a small number of samples, and perform simulation testing and index verification of the mapping strategies in a sandbox environment.

[0190] Processing module 240 is used to load and execute the mapping strategy in a Trusted Execution Environment (TEE) to achieve data transformation, cleaning and completion, and to de-identify sensitive data through a differential privacy mechanism.

[0191] The correction module 250 is used to collect performance metrics and anomaly logs of policy execution results, continuously train the model through a closed-loop optimization mechanism, and automatically correct unreasonable policies or update the rule base.

[0192] Specifically, the correction module 250 is used to build a diagnostic decision tree based on the processing latency, error logs and resource consumption collected at runtime, and analyze the root causes of policy failure; to perform incremental training and distillation compression on the policy engine execution path to improve the model's generalization ability; and to set up circuit breaker and rollback mechanisms to trigger service degradation or restore to the historical version policy when the error rate exceeds the threshold.

[0193] This application embodiment establishes a trusted communication channel between the probe and the coordination center, and generates a mapping strategy that matches the captured events, thereby achieving second-level real-time perception, improving the accuracy of cognitive decision-making capabilities, overcoming the bottleneck of semantic understanding, and reducing resource consumption.

[0194] This application also provides a computer-readable storage medium storing a computer program. When executed by a processor, this computer program implements the various processes of the above-described dynamic metadata awareness and adaptive mapping method embodiments, achieving the same technical effects. To avoid repetition, it will not be described again here. The computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0195] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0196] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0197] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

Claims

1. A method for dynamic metadata awareness and adaptive mapping, characterized in that, Includes the following steps: Deploy lightweight probe agents on multiple heterogeneous source database systems, and establish a trusted communication channel between the probes and the coordination center through device fingerprint generation and security authentication; Real-time capture of database log events, and log parsing through the FPGA module to identify changes in field structure or semantics, generating structured event data; The captured events are represented by context embedding based on a semantic understanding model, and policy reasoning is performed by combining historical policies and knowledge graphs to generate matching mapping policies. The mapping strategy is loaded and executed in a Trusted Execution Environment (TEE) to achieve data transformation, cleaning and completion, and sensitive data is de-identified through a differential privacy mechanism. The system collects performance metrics and anomaly logs of the strategy execution results, continuously trains the model through a closed-loop optimization mechanism, and automatically corrects unreasonable strategies or updates the rule base.

2. The method according to claim 1, characterized in that, The deployment of a lightweight probe agent operates across multiple heterogeneous source database systems. Through device fingerprint generation and security authentication, a trusted communication channel is established between the probe and the coordination center, specifically including: Device fingerprints are generated based on the SHA256 algorithm, and the probes are registered with the device registration office of the coordination center. Two-way authentication is performed using the Transport Layer Security (TLS) protocol, and the integrity of the SGX remote verification probe software is extended through software protection. Download the initial strategy package and domain knowledge model, build a local metadata cache, and verify the effectiveness of the deployment through automated testing.

3. The method according to claim 1, characterized in that, The real-time capture of database log events, followed by log parsing via an FPGA module to identify changes in field structure or semantics and generate structured event data, specifically includes: Multiple log stream events are parsed in parallel using an FPGA module to extract event features and classify them as structural changes, type changes, or semantic shifts. Potential conflicts between fields are detected by using metadata fingerprinting algorithms and value range distribution similarity calculations. The parsing result is encapsulated into an Advanced Message Queuing Protocol (AMQP) message, and the AMQP message is sent to the policy reasoning module.

4. The method according to claim 1, characterized in that, The process of embedding the captured events into a contextual representation based on a semantic understanding model, combining historical strategies and knowledge graphs for policy reasoning, and generating a matching mapping strategy specifically includes: The field name, data type, value range features, and lineage information are input into the bidirectional encoder-represented transform BERT model to obtain the context vector representation; Retrieve similar fields from the vector database and perform policy fusion by combining historical mapping rules and policy labels; The few-sample meta-learning model (MAML) is used to generate matching mapping strategies based on a small number of samples, and the mapping strategies are simulated and validated in a sandbox environment.

5. The method according to claim 1, characterized in that, The performance metrics and anomaly logs of the collection strategy execution results are continuously used to train the model through a closed-loop optimization mechanism, automatically correcting unreasonable strategies or updating the rule base, specifically including: A diagnostic decision tree is built based on runtime data collection of processing latency, error logs, and resource consumption to analyze the root causes of strategy failures. Incremental training and distillation compression are performed on the execution path of the policy engine to improve the model's generalization ability; Configure circuit breakers and rollback mechanisms to trigger service degradation or restore to a previous version when the error rate exceeds a threshold.

6. A system for dynamic metadata awareness and adaptive mapping, characterized in that, include: The deployment module is used to deploy lightweight probe agents to multiple heterogeneous source database systems and establish a trusted communication channel between the probe and the coordination center through device fingerprint generation and security authentication. The capture module is used to capture database log events in real time and parse the logs through the FPGA module to identify changes in field structure or semantics and generate structured event data. The strategy reasoning module is used to embed the captured events into a contextual representation based on the semantic understanding model, combine historical strategies and knowledge graphs to perform strategy reasoning, and generate matching mapping strategies. The processing module is used to load and execute the mapping strategy in the Trusted Execution Environment (TEE) to realize data transformation, cleaning and completion, and to de-identify sensitive data through a differential privacy mechanism. The correction module is used to collect performance metrics and anomaly logs of policy execution results, continuously train the model through a closed-loop optimization mechanism, and automatically correct unreasonable policies or update the rule base.

7. The system according to claim 6, characterized in that, The deployment module is specifically used to generate device fingerprints based on the SHA256 algorithm and register the probes with the device registration office of the coordination center; it uses the Transport Layer Security (TLS) protocol for two-way authentication and extends the integrity of the SGX remote verification probe software through software protection. Download the initial strategy package and domain knowledge model, build a local metadata cache, and verify the effectiveness of the deployment through automated testing.

8. The system according to claim 6, characterized in that, The capture module is specifically used to use an FPGA module to parse multiple log stream events in parallel, extract event features and classify them as structural changes, type changes or semantic drifts; and to detect potential conflicts between fields through metadata fingerprint comparison algorithms and value range distribution similarity calculations. The parsing result is encapsulated into an Advanced Message Queuing Protocol (AMQP) message, and the AMQP message is sent to the policy reasoning module.

9. The system according to claim 6, characterized in that, The policy reasoning module is specifically used to input field names, data types, value range features, and lineage information into a bidirectional encoder-represented transform BERT model to obtain a context vector representation; retrieve similar fields in the vector database, and perform policy fusion by combining historical mapping rules and policy labels; generate matching mapping policies based on a small number of samples using the few-sample meta-learning model MAML, and conduct simulation tests and index verification of the mapping policies in a sandbox environment.

10. The system according to claim 6, characterized in that, The correction module is specifically used to construct a diagnostic decision tree based on the processing latency, error logs and resource consumption collected at runtime, and analyze the root causes of policy failure; to perform incremental training and distillation compression on the policy engine execution path to improve the model's generalization ability; and to set up circuit breaker and rollback mechanisms to trigger service degradation or restore to a historical version policy when the error rate exceeds the threshold.

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