Multi-source heterogeneous data synchronization system

Abstract

The invention discloses a multi-source heterogeneous data synchronization system, and relates to the field of computer information management application. The system fuses key technologies such as flow dynamic regulation and control, self-adaptive load balancing and intelligent semantic mapping. Through a real-time network state monitoring and self-adaptive flow regulation and control algorithm, the data transmission rate is dynamically adjusted according to the server resource utilization rate, the data priority and the network bandwidth, data backlog and system overload are avoided, and the robustness and the real-time performance of a synchronization task are remarkably improved. The intelligent field mapping engine is based on semantic analysis, word vector representation and a machine learning model, automatically recognizes field semantic consistency of heterogeneous data sources, generates an optimal mapping rule and dynamically optimizes a mapping result, the manual configuration cost is reduced, and the mapping accuracy rate reaches 98% or above. The system has high expandability and self-learning ability, is suitable for a large-scale multi-source heterogeneous data integration scene, and provides an efficient, intelligent and self-adaptive full-stack solution for a data synchronization task.

Description

Technical Field

[0001] The present invention relates to the field of computer information management applications, and in particular to a multi-source heterogeneous data synchronization system. Background Art

[0002] With the widespread adoption of cloud computing, the Internet of Things, and distributed systems, offline synchronization of multi-source heterogeneous data (such as relational databases, APIs, log files, and sensor data) has become a critical component of enterprise data governance. Efficient and reliable data synchronization technology is crucial for ensuring data consistency, supporting business analysis, and optimizing decision-making. However, existing data synchronization technologies still face the following significant bottlenecks in complex data environments:

[0003] (1) Rigid flow control mechanism: Existing mainstream data synchronization tools (such as Apache Kafka Connect and ETL tools) typically use fixed rates or simple token bucket algorithm-based flow control strategies, which are difficult to adapt to dynamic factors such as network bandwidth fluctuations, server load, and data priority. In high-concurrency, high-traffic scenarios, this mechanism can easily lead to data backlogs and even synchronization task blocking, seriously affecting the real-time performance and stability of the system.

[0004] For example, the order volume of an e-commerce platform surged during a promotion. Because the synchronization tool was unable to dynamically adjust the rate, the order data synchronization was delayed by 30 minutes, seriously affecting the order fulfillment efficiency.

[0005] (2) Field mapping rules rely on manual configuration: Multi-source heterogeneous data has significant differences in field naming, format, and semantics. Existing synchronization tools often require manual definition of field mapping relationships (e.g., mapping the MySQL user_id field to the MongoDB uid field). This process is not only time-consuming and cumbersome, but also susceptible to human factors. Studies have shown that the error rate of manually configured field mapping can reach 15%, posing a serious threat to the accuracy and consistency of data synchronization. Summary of the Invention

[0006] The purpose of the present invention is to address the above shortcomings and propose a highly automated and scalable framework to realize a multi-source heterogeneous data synchronization system that can efficiently and securely synchronize data from data sources of different sources, different structures, and different protocols.

[0007] The present invention specifically adopts the following technical solutions:

[0008] A multi-source heterogeneous data synchronization system, including a multi-source heterogeneous data access and management module, an intelligent task scheduling and execution management module, a workflow automation orchestration and scheduling module, a real-time task monitoring and intelligent alarm recovery module, and a system security and compliance management module;

[0009] The multi-source heterogeneous data access and management module is used for access and configuration management of multi-source heterogeneous data sources, ensuring high availability and cross-platform compatibility of data access. Through a multi-source connection adapter based on the data source abstraction layer, it supports access to a variety of data sources, including relational databases (RDBMS), non-relational databases (NoSQL), distributed storage systems such as HDFS and Cassandra, RESTful API interfaces, and message queues such as Kafka and RabbitMQ. The Unified DataSource Configuration Hub provides centralized data source registration, parameterized configuration, and policy-based access control, supporting horizontal expansion and dynamic resource scheduling. The module integrates an intelligent field mapping engine that automatically matches field names, types, and semantics through semantic analysis and machine learning models.

[0010] The intelligent task scheduling and execution management module is used for efficient data synchronization task creation, scheduling, and instance management, optimizing task execution efficiency and fault tolerance. By introducing an intelligent task orchestration engine based on the Dynamic Directed Acyclic Graph (DAG) scheduling algorithm, it enables parallel task execution, intelligent dependency modeling, automatic retry mechanisms, and priority scheduling. The task instance management center provides execution status monitoring, historical log tracking, and intelligent exception recovery, supporting breakpoint resuming and failure rollback mechanisms.

[0011] The workflow automation orchestration and scheduling module uses visual modeling tools and an event-driven workflow scheduling engine to achieve efficient management and automated execution of the data synchronization process;

[0012] The real-time task monitoring and intelligent alarm recovery module uses distributed log collection and streaming data analysis to achieve real-time monitoring of task execution status, throughput, latency, and visualization of abnormal information;

[0013] The system security and compliance management module has a fine-grained permission control model and audit log management function to ensure the security of system resource access and the traceability of data operations.

[0014] Preferably, to create a system data synchronization task, the user first starts the synchronization task through the task creation interface, specifies the task name and configures the field mapping for the task; then the user needs to select the source data source type, which supports options including relational databases, NoSQL databases, API interfaces or message queues, and configure the corresponding data source connection parameters; when processing database type data sources, the system allows users to select existing database connections or create new connections through the dynamic connection management mechanism. In addition, users can customize SQL queries or field filtering logic;

[0015] During the data extraction mode configuration phase, users can choose between full or incremental extraction. In full extraction mode, the system synchronizes the entire dataset once, applying the target table preprocessing rules. If users choose incremental extraction mode, they must specify the incremental identification field and its incremental type, and can choose whether to perform a full initial extraction to ensure effective synchronization of incremental data.

[0016] During the data preview and verification phase, the system performs pre-synchronization data quality verification through the data preview interface to ensure field mapping consistency and content consistency between the source data and the target data. After the source data source configuration is confirmed, the system saves the relevant configuration to the centralized configuration center to ensure centralized management and traceability of data source information. Finally, the user selects the target data source. If the target table already exists, the system automatically performs field mapping and performs synchronization. If the target table does not exist, the user needs to define the target table structure and submit the table creation configuration to complete the final configuration and creation of the data synchronization task.

[0017] Preferably, the system also includes a dynamic traffic adaptive control module, which optimizes the data synchronization rate in real time through a multi-level collaborative strategy to ensure system resource utilization and synchronization task efficiency; the component units include:

[0018] (1) Real-time monitoring unit

[0019] The real-time monitoring unit is deployed on the server node and collects system operating status parameters in real time based on the system API interface or performance monitoring tools. The collected indicators include CPU utilization, memory usage, network bandwidth usage, and disk I / O performance. The sampling period can be adaptively adjusted, supporting both scheduled sampling and event-triggered sampling modes to meet diverse scheduling needs.

[0020] (2) Dispatching Decision Center

[0021] The scheduling decision center dynamically calculates the optimal synchronization rate based on feedback data from real-time monitoring units and the system's preset adaptive scheduling algorithm. Scheduling decisions are based on resource utilization thresholds, historical task performance, and task priority strategies. When resource load exceeds a set threshold, the system can dynamically issue traffic rate limit instructions. When resources are idle, the system automatically adjusts the synchronization rate to fully utilize computing resources.

[0022] (3) Data synchronization execution unit

[0023] The data synchronization execution unit is used to execute specific data transmission tasks. It manages concurrency through a dynamic thread pool and adjusts the number of concurrent connections, data batch size and transmission rate in real time according to scheduling decision instructions. For sensitive tasks, the system can use a rate smoothing algorithm to ensure that the synchronization rate changes smoothly and avoid the impact of sudden resource preemption on the system.

[0024] Optimally, the intelligent field mapping engine uses multi-layer feature representation and machine learning algorithms to achieve semantic matching and automatic mapping of cross-source heterogeneous data fields, significantly improving the intelligence level of data integration tasks, including:

[0025] (1) Field extraction module

[0026] The field extraction module automatically extracts field names, data types, primary and foreign key relationships, and field metadata information based on the data source interface; it supports multiple data source types, including relational databases (MySQL, PostgreSQL), NoSQL databases (MongoDB, Redis), file-based data sources (CSV, JSON), and API interface data;

[0027] (2) Semantic feature generation module

[0028] Use natural language processing (NLP) technology to segment field names and metadata, perform part-of-speech tagging, and generate semantic feature vectors. Use pre-trained word vector models such as Word2Vec or BERT to generate field semantic feature vectors to capture the semantic information of field names. For multilingual environments, the system can use the multilingual embedding model Multilingual BERT to improve cross-language semantic matching capabilities.

[0029] (3) Similarity calculation module

[0030] Calculates semantic similarity scores between field feature vectors based on cosine similarity, Jaccard similarity, and the deep semantic matching network Siamese Network. Supports multi-feature weighted fusion, and comprehensively considers field name, field description, and data type information for similarity measurement.

[0031] (4) Rule Inference Engine

[0032] The rule inference engine optimizes and filters the preliminary matching results based on predefined business rules, supports custom configuration of rules, and has a built-in common data mapping rule library;

[0033] (5) Machine Learning Matching Module

[0034] Use supervised learning models or deep learning models to train historical mapping data and dynamically learn field matching features. Support online model learning and incremental updates to gradually improve matching accuracy.

[0035] (6) Mapping generation and verification module

[0036] Generate field mapping relationships based on matching results, supporting manual interactive verification and result adjustment; mapping relationships can be exported as mapping configuration files and can be edited and reviewed through a visual interface.

[0037] The present invention has the following beneficial effects:

[0038] The system has designed a dynamic synchronization rate adjustment mechanism based on Real-time Network Sensing (RNS) and System Load Monitoring (SLM), combined with a Priority Adaptive Scheduling (PAS) strategy to achieve refined control of synchronization traffic. This mechanism can sense network bandwidth fluctuations and server load in real time, dynamically adjusting the data transmission rate to avoid data accumulation and system overload, thereby improving the robustness and real-time performance of synchronization tasks.

[0039] Based on natural language processing (NLP) and semantic similarity calculation (SSC) technologies, we built an Automatic Field Semantic Matching Model (AFSM) to automatically identify and map fields from heterogeneous data sources. By introducing contextual semantic augmentation (CSA) and mapping confidence evaluation (MCE) mechanisms, we significantly reduced manual intervention costs and improved the accuracy and consistency of field mapping. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 This is the system architecture diagram.

[0041] Figure 2 Create a flowchart for data synchronization tasks. DETAILED DESCRIPTION

[0042] The specific implementation of the present invention will be further described below with reference to the accompanying drawings and specific embodiments:

[0043] Combine Figure 1 A multi-source heterogeneous data synchronization system includes a multi-source heterogeneous data access and management module, an intelligent task scheduling and execution management module, a workflow automation orchestration and scheduling module, a real-time task monitoring and intelligent alarm recovery module, and a system security and compliance management module.

[0044] The Multi-Source Heterogeneous Data Access and Management module is responsible for accessing and configuring multiple heterogeneous data sources, ensuring high availability and cross-platform compatibility. Through multi-source connection adapters based on the data source abstraction layer, the module supports access to a variety of data sources, including relational databases (RDBMS), non-relational databases (NoSQL), distributed storage systems (such as HDFS and Cassandra), RESTful APIs, and message queues (such as Kafka and RabbitMQ). Combined with dynamic connection pool management, automatic reconnection mechanisms, and load balancing strategies, it improves system robustness and performance in high-concurrency scenarios. Furthermore, the Unified Data Source Configuration Hub provides centralized data source registration, parameterized configuration, and policy-based access control, supporting horizontal expansion and dynamic resource scheduling, ensuring the elastic scalability and high performance of large-scale distributed data synchronization systems. The module integrates an intelligent field mapping engine that automatically matches field names, types, and semantics through semantic analysis and machine learning models, reducing manual configuration costs and improving mapping accuracy.

[0045] The intelligent task scheduling and execution management module is responsible for efficient data synchronization task creation, scheduling, and instance management, optimizing task execution efficiency and fault tolerance. By introducing an intelligent task orchestration engine based on the dynamic graph scheduling algorithm (Dynamic Directed Acyclic Graph, DAG), it realizes the parallel execution of tasks, intelligent modeling of dependencies, automatic retry mechanism, and priority scheduling, significantly improving the real-time performance and flexibility of task scheduling. The task instance management center provides execution status monitoring, historical log tracking, and intelligent exception recovery functions, supports breakpoint resumption and failure rollback mechanisms, significantly enhancing the reliability and fault tolerance of task scheduling, and ensuring the high availability and fault isolation of the system in a distributed environment. The module adopts dynamic traffic adaptive control technology to dynamically adjust the synchronization rate based on real-time network status, server load, and data priority to ensure data transmission efficiency and system stability in high-concurrency scenarios.

[0046] The workflow automation orchestration and scheduling module utilizes visual modeling tools and an event-driven workflow scheduling engine to enable efficient management and automated execution of the data synchronization process. Through a drag-and-drop graphical interface, users can intuitively configure task nodes, branch control, and data flow, simplifying the design and configuration of complex workflows. The workflow scheduling engine utilizes time scheduling and conditional triggering mechanisms to support scheduled tasks, conditionally dependent tasks, and event-triggered task scheduling. This enhances the flexibility and automated execution capabilities of task scheduling, ensuring that data synchronization tasks can be executed accurately and efficiently in highly dynamic production environments.

[0047] The real-time task monitoring and intelligent alarm recovery module utilizes visual modeling tools and an event-driven workflow scheduling engine to enable efficient management and automated execution of the data synchronization process. Through a drag-and-drop graphical interface, users can intuitively configure task nodes, branch control, and data flow, simplifying the design and configuration of complex workflows. The workflow scheduling engine utilizes time scheduling and conditional triggering mechanisms to support scheduled tasks, conditionally dependent tasks, and event-triggered task scheduling. This enhances task orchestration flexibility and automated execution capabilities, ensuring accurate and efficient execution of data synchronization tasks in highly dynamic production environments.

[0048] System Security and Compliance Management Module This module ensures the security of system resource access and the traceability of data operations through a fine-grained permission control model and audit log management functions. Combining role-based access control (RBAC) and attribute-based access control (ABAC) strategies, it provides refined access control to system resources. The module records operation logs and detects abnormal behavior through an audit-based compliance management framework (Audit-Based Compliance Framework) to meet enterprise-level security audit and compliance requirements. Through security event monitoring and behavioral anomaly analysis, it supports compliance backtracking and operational security audits, ensuring that the system has a high level of security protection capabilities and compliance verifiability when facing external attacks and internal threats.

[0049] Combine Figure 2 The configuration process involves several key links. First, the user starts the synchronization task through the task creation interface, and specifies the task name and configures the field mapping for the task. Next, the user needs to select the source data source type. Supported options include relational databases, NoSQL databases, API interfaces, or message queues, and configure the corresponding data source connection parameters. When processing database-type data sources, the system allows users to select existing database connections or create new connections through the dynamic connection management mechanism. In addition, users can customize SQL queries or field filtering logic to precisely define data extraction rules.

[0050] During the data extraction mode configuration phase, users can choose between full or incremental extraction. In full extraction mode, the system performs a one-time synchronization of the entire dataset, applying target table preprocessing rules (such as data cleansing and deduplication) during the process. If users choose incremental extraction mode, they must specify the incremental identification field and its incremental type (such as timestamp or unique identifier), and can choose whether to perform a full initial extraction to ensure effective synchronization of incremental data.

[0051] During the data preview and verification phase, the system performs pre-synchronization data quality verification through the data preview interface to ensure field mapping and content consistency between the source and target data. After confirming the source data source configuration, the system saves the relevant configuration to the centralized configuration center to ensure centralized management and traceability of data source information. Finally, the user selects the target data source. If the target table already exists, the system automatically performs field mapping and performs synchronization. If the target table does not exist, the user must define the target table structure and submit the table creation configuration to complete the final configuration and creation of the data synchronization task.

[0052] The system also includes a dynamic traffic adaptive control module, which optimizes the data synchronization rate in real time through a multi-level collaborative strategy to ensure system resource utilization and synchronization task efficiency. The components include:

[0053] (1) Real-time monitoring unit

[0054] The real-time monitoring unit is deployed on the server node and collects system operation status parameters in real time based on the system API interface or performance monitoring tools. The collected indicators include CPU utilization, memory occupancy, network bandwidth utilization, and disk I / O performance. The sampling period can be adaptively adjusted and supports both scheduled sampling and event-triggered sampling modes to meet diverse scheduling needs.

[0055] (2) Dispatching Decision Center

[0056] The scheduling decision center dynamically calculates the optimal synchronization rate based on feedback from real-time monitoring units and the system's pre-set adaptive scheduling algorithm. Scheduling decisions are based on resource utilization thresholds, historical task performance, and task priority policies. When resource load exceeds a set threshold, the system dynamically issues traffic rate limits. When resources are idle, the system automatically adjusts the synchronization rate to fully utilize computing resources.

[0057] (3) Data synchronization execution unit

[0058] The data synchronization execution unit is used to execute specific data transmission tasks. It manages concurrency through a dynamic thread pool and adjusts the number of concurrent connections, data batch size and transmission rate in real time according to scheduling decision instructions. For sensitive tasks, the system can use a rate smoothing algorithm to ensure that the synchronization rate changes smoothly and avoid the impact of sudden resource preemption on the system.

[0059] The intelligent field mapping engine uses multi-layer feature representation and machine learning algorithms to achieve semantic matching and automatic mapping of cross-source heterogeneous data fields, significantly improving the intelligence level of data integration tasks, including:

[0060] (1) Field extraction module

[0061] The field extraction module automatically extracts field names, data types, primary and foreign key relationships, and field metadata information based on the data source interface; it supports multiple data source types, including relational databases (MySQL, PostgreSQL), NoSQL databases (MongoDB, Redis), file-based data sources (CSV, JSON), and API interface data.

[0062] (2) Semantic feature generation module

[0063] Natural language processing (NLP) technology is used to segment field names and metadata information, perform part-of-speech tagging, and generate semantic feature vectors. Pre-trained word vector models such as Word2Vec or BERT are used to generate field semantic feature vectors to capture the semantic information of field names. For multilingual environments, the system can use the multilingual embedding model Multilingual BERT to improve cross-language semantic matching capabilities.

[0064] (3) Similarity calculation module

[0065] The semantic similarity score between field feature vectors is calculated based on cosine similarity, Jaccard similarity, and deep semantic matching network Siamese Network. It supports multi-feature weighted fusion and comprehensively considers field name, field description, and data type information for similarity measurement.

[0066] (4) Rule Inference Engine

[0067] The rule inference engine optimizes and filters the preliminary matching results based on predefined business rules, supports custom configuration of rules, and has a built-in common data mapping rule library.

[0068] (5) Machine Learning Matching Module

[0069] Historical mapping data is trained through supervised learning models or deep learning models to dynamically learn field matching features; online model learning and incremental updates are supported to gradually improve matching accuracy.

[0070] (6) Mapping generation and verification module

[0071] Generate field mapping relationships based on matching results, supporting manual interactive verification and result adjustment; mapping relationships can be exported as mapping configuration files and can be edited and reviewed through a visual interface.

[0072] Of course, the above description is not a limitation of the present invention, and the present invention is not limited to the above examples. Changes, modifications, additions or substitutions made by technicians in this technical field within the essential scope of the present invention should also fall within the scope of protection of the present invention.

Claims

1. A multi-source heterogeneous data synchronization system, characterized in that: It includes multi-source heterogeneous data access and management module, intelligent task scheduling and execution management module, workflow automation orchestration and scheduling module, real-time task monitoring and intelligent alarm recovery module, and system security and compliance management module; The multi-source heterogeneous data access and management module is used for access and configuration management of multi-source heterogeneous data sources, ensuring high availability and cross-platform compatibility of data access; Through multi-source connection adapters based on the data source abstraction layer, it supports access to multiple data sources, including relational databases (RDBMS), non-relational databases (NoSQL), distributed storage systems such as HDFS and Cassandra, RESTful API interfaces, and message queues such as Kafka and RabbitMQ. The Unified Data Source Configuration Hub provides centralized data source registration, parameterized configuration, and policy-based access control, supporting horizontal expansion and dynamic resource scheduling. The module integrates an intelligent field mapping engine that automatically matches field names, types, and semantics through semantic analysis and machine learning models. The intelligent task scheduling and execution management module is used for efficient data synchronization task creation, scheduling, and instance management, optimizing task execution efficiency and fault tolerance; By introducing an intelligent task orchestration engine based on the Dynamic Directed Acyclic Graph (DAG) scheduling algorithm, it enables parallel execution of tasks, intelligent modeling of dependencies, automatic retry mechanisms, and priority scheduling. The task instance management center provides execution status monitoring, historical log tracking, and intelligent exception recovery, supporting breakpoint resumption and failure rollback mechanisms. The workflow automation orchestration and scheduling module uses visual modeling tools and an event-driven workflow scheduling engine to achieve efficient management and automated execution of the data synchronization process; The real-time task monitoring and intelligent alarm recovery module uses distributed log collection and streaming data analysis to achieve real-time monitoring of task execution status, throughput, latency, and visualization of abnormal information; The system security and compliance management module has a fine-grained permission control model and audit log management function to ensure the security of system resource access and the traceability of data operations.

2. A multi-source heterogeneous data synchronization system according to claim 1, characterized in that: To create a system data synchronization task, users first initiate the synchronization task through the task creation interface, specify a task name, and configure field mappings. Next, users must select the source data source type, including relational databases, NoSQL databases, API interfaces, or message queues, and configure the corresponding data source connection parameters. When processing database-type data sources, the system allows users to select an existing database connection or create a new connection through the dynamic connection management mechanism. In addition, users can customize SQL queries or field filtering logic. During the data extraction mode configuration phase, users can choose between full extraction mode and incremental extraction mode. In full extraction mode, the system will perform a one-time synchronization of the entire dataset and apply the target table preprocessing rules during the process. If the user selects the incremental extraction mode, they need to specify the incremental identification field and its incremental type, and can choose whether to perform full initialization extraction to ensure effective synchronization of incremental data; During the data preview and verification phase, the system performs pre-synchronization data quality verification through the data preview interface to ensure field mapping consistency and content consistency between the source and target data. After confirming the source data source configuration, the system will save the relevant configuration to the centralized configuration center to ensure centralized management and traceability of data source information. Finally, the user selects the target data source. If the target table already exists, the system will automatically perform field mapping and perform synchronization. If the target table does not exist, the user needs to define the target table structure and submit the table creation configuration to complete the final configuration and creation of the data synchronization task.

3. The multi-source heterogeneous data synchronization system according to claim 1, characterized in that: The system also includes a dynamic traffic adaptive control module, which optimizes the data synchronization rate in real time through a multi-level collaborative strategy to ensure system resource utilization and synchronization task efficiency; The components include: (1) Real-time monitoring unit The real-time monitoring unit is deployed on the server node and collects system operating status parameters in real time based on the system API interface or performance monitoring tools. The collected indicators include CPU utilization, memory usage, network bandwidth usage, and disk I / O performance. The sampling period can be adaptively adjusted, supporting both scheduled sampling and event-triggered sampling modes to meet diverse scheduling needs. (2) Dispatching Decision Center The scheduling decision center dynamically calculates the optimal synchronization rate based on feedback data from real-time monitoring units and the system's preset adaptive scheduling algorithm. Scheduling decisions are based on resource utilization thresholds, historical task performance, and task priority strategies. When resource load exceeds a set threshold, the system can dynamically issue traffic rate limit instructions. When resources are idle, the system automatically adjusts the synchronization rate to fully utilize computing resources. (3) Data synchronization execution unit The data synchronization execution unit is used to execute specific data transmission tasks. It manages concurrency through a dynamic thread pool and adjusts the number of concurrent connections, data batch size and transmission rate in real time according to scheduling decision instructions. For sensitive tasks, the system can use a rate smoothing algorithm to ensure that the synchronization rate changes smoothly and avoid the impact of sudden resource preemption on the system.

4. The multi-source heterogeneous data synchronization system according to claim 1, characterized in that: The intelligent field mapping engine uses multi-layer feature representation and machine learning algorithms to achieve semantic matching and automatic mapping of cross-source heterogeneous data fields, significantly improving the intelligence level of data integration tasks, including: (1) Field extraction module The field extraction module automatically extracts field names, data types, primary and foreign key relationships, and field metadata information based on the data source interface; it supports multiple data source types, including relational databases (MySQL, PostgreSQL), NoSQL databases (MongoDB, Redis), file-based data sources (CSV, JSON), and API interface data; (2) Semantic feature generation module Use natural language processing (NLP) technology to segment field names and metadata, perform part-of-speech tagging, and generate semantic feature vectors. Use pre-trained word vector models such as Word2Vec or BERT to generate field semantic feature vectors to capture the semantic information of field names. For multilingual environments, the system can use the multilingual embedding model Multilingual BERT to improve cross-language semantic matching capabilities. (3) Similarity calculation module Calculates semantic similarity scores between field feature vectors based on cosine similarity, Jaccard similarity, and the deep semantic matching network Siamese Network. Supports multi-feature weighted fusion, and comprehensively considers field name, field description, and data type information for similarity measurement. (4) Rule Inference Engine The rule inference engine optimizes and filters the preliminary matching results based on predefined business rules, supports custom configuration of rules, and has a built-in common data mapping rule library; (5) Machine Learning Matching Module Use supervised learning models or deep learning models to train historical mapping data and dynamically learn field matching features. Support online model learning and incremental updates to gradually improve matching accuracy. (6) Mapping generation and verification module Generate field mapping relationships based on matching results, supporting manual interactive verification and result adjustment; mapping relationships can be exported as mapping configuration files and can be edited and reviewed through a visual interface.

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