A multi-source heterogeneous data migration system based on field mapping configuration

Abstract

The application discloses a kind of based on field mapping configuration's multi-source heterogeneous data migration system, comprising: data source adaptation layer, constructs unified data access benchmark, is connected to different types of source data by built-in driver pool, and the physical connection management is carried out to source data, and source data is carried out format packaging;Metadata depth perception layer, the structure analysis and sample image of encapsulated source data are carried out;Intelligent field mapping configuration layer, based on semantic similarity algorithm and business context reasoning, establish the mapping configuration file of source and target end;Dynamic conversion engine layer, using the architecture design of operator, according to mapping configuration file, in the migration process, using memory level streaming processing technology, executes real-time logic processing;Distributed migration execution layer, using master-slave collaborative distributed architecture, by sharding parallel processing and memory level streaming conversion, execute data migration;Full-link quality monitoring layer provides multidimensional audit and management of full life cycle for migration process.

Description

Technical Field

[0001] This invention belongs to the field of data processing and integration, and specifically relates to a multi-source heterogeneous data migration system based on field mapping configuration. Background Technology

[0002] In the process of enterprise informatization and digital transformation, cross-platform data integration and consolidation are the core foundation for realizing business analysis and decision support. With the increasing complexity of information systems, data exhibits high fragmentation and heterogeneity, distributed across relational databases, non-relational databases, and various file systems. To achieve unified management and flow of data assets, data migration technology has become a key means to connect different business systems, build data lake warehouses, and achieve smooth system upgrades. Its core lies in ensuring that data can be transmitted accurately, efficiently, and completely between the source and destination ends.

[0003] Among these technologies, multi-source heterogeneous data migration technology focuses on solving the interoperability challenges between different storage media, protocol standards, and data structures. The core logic of this technology lies in establishing logical relationships between source and target data fields—that is, field mapping configuration—to guide data flow and complete necessary format conversions. A mature migration system not only needs broad data source compatibility but also requires flexible mapping rule definition capabilities to support complex and ever-changing business logic and multi-dimensional data synchronization needs.

[0004] Existing technologies face significant challenges in handling massive amounts of multi-source heterogeneous data. Traditional data synchronization solutions typically employ customized development or hard-coding to achieve field interfacing. This approach lacks the necessary flexibility when facing frequent changes in source systems or the integration of new heterogeneous sources, resulting in high system maintenance costs and lengthy response cycles. Due to significant differences in type constraints, encoding formats, and semantic definitions between different data sources, existing mapping configuration mechanisms often lack automated calibration and deep semantic association capabilities, easily leading to data distortion or type overflow risks during migration. Furthermore, for large-scale concurrent migration scenarios, existing systems are significantly deficient in mapping logic parsing efficiency, visualization monitoring of the migration process, and abnormal retry mechanisms. This makes them prone to performance bottlenecks and data inconsistencies during large-scale heterogeneous data exchange, failing to meet the business requirements of real-time performance and high reliability. Therefore, a multi-source heterogeneous data migration system based on field mapping configuration is urgently needed. Summary of the Invention

[0005] To address the technical problems of existing technologies in multi-source heterogeneous data migration, such as poor system flexibility, low migration accuracy, weak semantic association capabilities, performance bottlenecks, and data inconsistencies in large-scale concurrent scenarios due to the diverse types of data sources, large differences in data structures, and hard-coded migration logic, this invention aims to overcome these technical deficiencies and provide a multi-source heterogeneous data migration system based on field mapping configuration.

[0006] In view of this, the present invention provides a multi-source heterogeneous data migration system based on field mapping configuration, comprising: The data source adaptation layer is used to build a unified data access benchmark. Through the built-in driver pool, it realizes physical connection management for different types of source data and is also used to encapsulate source data in a unified format. The metadata deep perception layer is used to perform structural parsing and sample profiling on the source data after format encapsulation. The intelligent field mapping configuration layer is used to establish a mapping configuration file between the source end and the migrated target end based on semantic similarity algorithms and business context reasoning; The dynamic transformation engine layer is designed with an operator-based architecture. Based on the mapping configuration file, it uses memory-level streaming processing technology to perform real-time logic processing during data migration. The distributed migration execution layer is used to perform data migration using a master-slave collaborative distributed architecture, through sharded parallel processing and in-memory streaming transformation; and The end-to-end quality monitoring layer is used to provide multi-dimensional auditing and governance throughout the entire data migration process.

[0007] As an improvement to the above system, the built-in driver pool includes: a relational database driver, a non-relational database protocol stack, a distributed file system access interface, a structured document parser, and a data stream access driver for real-time message queues.

[0008] As an improvement to the above system, the physical connection management for different types of source data specifically includes: All active physical connections are maintained through a global connection pool manager. When the system initiates a migration request, the connection pool manager identifies the target type based on the Uniform Resource Locator (URL) of the source or target end and dynamically loads the corresponding communication protocol stack from the driver pool. It also performs strict lifecycle management, including connection pre-allocation, health heartbeat detection, idle timeout recycling, and automatic reconnection after abnormal interruption.

[0009] As an improvement to the above system, for each active physical connection, the data source adaptation layer performs in-depth optimization of communication parameters, including: automatically adjusting the socket buffer size for high-latency network environments and configuring a large number of prefetched records for high throughput requirements.

[0010] As an improvement to the above system, the processing procedure of the metadata deep perception layer specifically includes: Access the source database's system metadata database, data dictionary, and tablespace definition files through a recursive scanning mechanism; extract table names and field names, data type definitions, numerical precision and scaling, primary and foreign key constraints, unique indexes, field default values, NOT NULL constraints, and the encoding format and physical storage order at the storage level; A Bernoulli sampling-based strategy is adopted to extract representative feature sample data from the source according to a set sampling ratio, perform statistical analysis, and calculate the actual value distribution range, dispersion, average length, maximum and minimum values ​​and null value rate of each field to obtain a sample profile. When the data source is unstructured or semi-structured, a heuristic pattern recognition algorithm is used to scan the data content in multiple rounds. By identifying repeated label structures or delimiter logic, the implicit hierarchical structure is automatically inferred and converted into a flattened set of virtual fields.

[0011] As an improvement to the above system, the intelligent field mapping configuration layer includes a semantic parsing unit based on deep learning. The processing procedure of the semantic parsing unit is as follows: The field names and annotation text extracted from the metadata deep perception layer are preprocessed, including: removing invalid characters, identifying and converting industry abbreviations, and semantically segmenting compound words; Using a pre-built industrial-grade semantic knowledge base, the processed terms are mapped to a high-dimensional feature vector space; Perform semantic similarity calculation; Based on the preset two-level judgment threshold, the association status between the source field vector and the target field vector is determined by semantic similarity.

[0012] As an improvement to the aforementioned system, the intelligent field mapping configuration layer includes a mapping logic orchestrator and a conflict detection unit, wherein... The mapping logic orchestrator supports one-to-one direct mapping, many-to-one aggregation mapping, and one-to-many split mapping. The conflict detection unit is used to perform pre-simulation verification of the physical constraints of the target database, specifically including the following verifications: By comparing whether the actual value range of the source field exceeds the length limit defined on the target, we can check whether the null value distribution on the source violates the non-null constraint on the target. Verify whether the mapping logic causes a primary key conflict; If any of the above verifications fails, a detailed remediation suggestion report will be generated, and the migration can only be started after the correction is completed; if all of the above verifications are completed, a mapping configuration file will be established between the source end and the target end after migration.

[0013] As an improvement to the above system, the semantic similarity The calculation satisfies the following formula:

[0014] in, and They represent the first The source field vector and the first A vector of target fields, Indicates the total number of source field vectors; when When the value exceeds 0.85, the source field vector and the target field vector are determined to be strongly correlated. when When the value is between 0.6 and 0.85, a potential correlation is identified, and the operator is notified to make a manual decision. when If the value is less than 0.6, it is determined that there is no correlation.

[0015] As an improvement to the above system, the dynamic conversion engine layer adopts an operator-based architecture design, wherein the operators include at least: an automatic transcoding operator across character sets, a dynamic numerical precision alignment operator, a unified conversion operator for multiple date formats, and a data cleaning operator based on regular expressions; The aforementioned use of memory-level streaming processing technology specifically includes: When reading data from the source, it flows directly in the memory of the execution node in the form of an object stream. For sensitive fields, irreversible encryption or masking is performed. When the memory usage reaches the preset warning level, the overflow disk caching policy is automatically activated. The sensitive fields include at least mobile phone numbers, ID card numbers, and bank card numbers. The disk caching policy is to use sequential writing to dump the intermediate processing state to a temporary high-speed storage device.

[0016] As an improvement to the above system, the master-slave collaborative distributed architecture includes: a scheduling module as the master node and multiple execution nodes; The sharding strategy of the distributed migration execution layer includes: When the source table has a primary key or unique index, the scheduling module uses the numerical range or value distribution of the primary key to perform uniform partitioning. When the data source has no primary key or the index distribution is extremely uneven, the scheduling module adopts a random sharding strategy based on the consistent hashing algorithm. The segmented parallel processing specifically includes: The scheduling module dynamically distributes shards to active execution nodes in the cluster. Each execution node adopts a multi-threaded concurrency model and is independently responsible for reading the corresponding shard, calling the transformation engine, and writing to the target end.

[0017] As an improvement to the above system, the distributed migration execution layer introduces a comprehensive fault tolerance mechanism, specifically including: During execution, the scheduling module maintains a distributed state checkpoint library. Whenever a shard is successfully written to the target and receives confirmation feedback, the execution node updates the processing progress of the current shard to the state checkpoint library. In the event of network jitter, server crash, or target deadlock, the migration task will automatically be suspended. Once the fault is cleared, the scheduling module, based on the records in the checkpoint database, instructs the execution node to continue execution from the position of the last successful submission, thus enabling breakpoint resumption. When migrating critical business systems, the distributed migration execution layer also supports a two-phase commit protocol to ensure that the status flags on the source end and the data insertion on the target end logically constitute an atomic transaction.

[0018] As an improvement to the aforementioned system, the end-to-end quality monitoring layer constructs a real-time, visualized monitoring dashboard through embedded sampling and real-time log collection. The content displayed on the monitoring dashboard includes: Standard metrics should include at least the current migration rate, completion percentage, and number of active threads. Heatmap: Includes the load status of each execution node; The consistency verification results are shown at different stages of the migration process. In the first stage, the total number of records between the source and target ends is quickly compared. In the second stage, distributed summation and mean verification are performed on key numerical fields marked in the mapping configuration file. For financial or medical data with extremely high security requirements, the third stage of sampling mirror comparison is initiated, which compares the full field values ​​byte by byte by randomly selecting a set proportion of records.

[0019] Compared with the prior art, the advantages of the present invention are: 1. This invention fundamentally solves the problem of lack of flexibility in migrating multi-source heterogeneous data by constructing a metadata deep-aware layer and an intelligent field mapping configuration layer. Utilizing semantic similarity algorithms and business context reasoning, the system can automatically identify field relationships across systems, significantly reducing the complexity and error rate of manual configuration. This intelligent mapping mechanism enables the system to maintain extremely high data mapping accuracy even when facing complex business semantic differences, effectively avoiding the drawbacks of high maintenance costs and slow response to changes in traditional hard-coded models.

[0020] 2. The distributed migration execution layer and dynamic conversion engine layer designed in this invention significantly improve the performance and reliability of large-scale data exchange. Through sharded parallel processing and memory-level streaming conversion, the system can fully exploit the computing potential of the cluster, eliminate single-point bottlenecks, and achieve efficient transfer at the TB or even PB level. Simultaneously, the introduction of breakpoint resumption, heterogeneous transaction consistency protocols, and breakpoint retry mechanisms ensures the atomicity and integrity of data migration in extremely complex and unstable network and hardware environments, greatly improving the system's robustness in production environments.

[0021] 3. This invention constructs a transparent and quantifiable data migration governance system through a full-link quality monitoring layer. The system not only achieves real-time monitoring and intelligent anomaly diagnosis of the migration process, but also provides hard constraints on data quality through multi-dimensional consistency verification before and after migration. Furthermore, the self-learning capability and version management function of the mapping rules enable the system to have self-evolution and rapid backtracking capabilities, continuously optimizing as the enterprise's data scale grows and business logic evolves, providing a high-performance, highly reliable, and highly intelligent data integration foundation for enterprise digital transformation.

[0022] 4. This invention offers significant advantages in terms of security and compatibility. Through its built-in masking and desensitization component, the system achieves native protection of privacy data during migration, meeting increasingly stringent data security compliance requirements. Furthermore, its plug-in-based driver management architecture provides the system with strong cross-platform compatibility and scalability, enabling low-cost access to various new heterogeneous data sources and ensuring the forward-looking nature and longevity of the technical architecture. Attached Figure Description

[0023] Figure 1 This is a schematic diagram of the overall technical solution architecture of the multi-source heterogeneous data migration system based on field mapping configuration of the present invention; Figure 2 This is a schematic diagram of the core principle framework of the intelligent field mapping configuration layer in this invention; Figure 3 This is a logical flow diagram of the metadata deep perception layer in this invention; Figure 4 This is a logical flow diagram of the distributed migration execution layer in this invention; Figure 5 This is a schematic diagram of the data flow between the end-to-end quality monitoring layer and each execution node in this invention. Detailed Implementation

[0024] The technical solution of the present invention is to provide a multi-source heterogeneous data migration system based on field mapping configuration. The system is logically divided into a data source adaptation layer, a metadata deep perception layer, an intelligent field mapping configuration layer, a dynamic conversion engine layer, a distributed migration execution layer, and a full-link quality monitoring layer.

[0025] 1. Data source adaptation layer: This system is used to build a unified data access benchmark. Through built-in drivers for various protocols, it manages physical connections to relational databases, non-relational databases, distributed file systems, structured documents, and real-time data stream access points. The data source adaptation layer is responsible for maintaining the lifecycle of connection pools for different data sources and encapsulating various underlying communication protocols, thereby providing standardized raw data reading and writing interfaces for subsequent layers. As one embodiment of this invention, the data source adaptation layer supports dynamic expansion. When enterprises introduce new distributed storage systems or cloud-native databases, developers only need to implement a very small number of core interface methods based on the standardized driver development framework provided by the system to seamlessly integrate the new data source into the migration system without any changes to the core architecture of the existing system.

[0026] 2. Metadata Deep Awareness Layer This layer is used for comprehensive analysis of data assets at both the source and target ends. It recursively scans the system tables, data dictionary, and table structure definitions of the data source to extract full metadata, including table names, field names, data types, precision, scale, constraints, encoding formats, and comment text. Furthermore, the metadata deep-aware layer includes a data sample sniffing module to extract a certain proportion of feature samples from the source end. By analyzing the value range, dispersion, and null value rate of the actual stored values, it constructs a more accurate dynamic metadata profile than static definitions. Further, as one embodiment of the invention, when processing unstructured or semi-structured data sources, the metadata deep-aware layer uses a preset pattern recognition algorithm to heuristically scan the data stream, automatically inferring the implicit hierarchical structure and flattening it into virtual fields recognizable by the mapping layer, thereby achieving standardized access to schema-less data sources.

[0027] 3. Smart Field Mapping Configuration Layer: This is the core of the invention, used to establish the logical relationship between the source and target ends. This layer does not rely on simple name equivalence matching, but instead employs a mapping mechanism based on semantic similarity calculation and business context reasoning. The intelligent field mapping configuration layer includes a semantic parsing unit, which performs word segmentation on the extracted field names and annotations, and uses a pre-set industrial-grade semantic knowledge base to calculate the correlation coefficient between the source and target fields in terms of business semantics. As one embodiment of the invention, when the system identifies two fields with inconsistent names but highly related business meanings, it automatically recommends mapping suggestions. Furthermore, this layer provides a graphical configuration interface, supporting user-defined complex mapping logic, including but not limited to one-to-one mapping, many-to-one aggregation mapping, and one-to-many splitting mapping, and persists all mapping relationships as a structured mapping configuration file. Further, as one embodiment of the invention, the intelligent field mapping configuration layer also includes a conflict detection unit. Before saving the mapping configuration, this unit performs pre-validation of the constraints on the target end, including primary key uniqueness constraints, non-null constraints, and foreign key association constraints. If the mapping configuration is found to cause write failures on the target end, the system will automatically trigger an alarm and provide repair suggestions. As one embodiment of the invention, the mapping configuration file supports version management. Whenever a mapping rule is changed and takes effect, the system automatically creates a new version snapshot. When a new configuration is found to cause a deterioration in the data quality on the target end, the system supports one-click rollback to any previous stable version, greatly enhancing fault tolerance and operational security in the production environment.

[0028] In one embodiment of the present invention, the semantic similarity calculation employs a bidirectional encoder representation model for vectorization. The system converts field names and business descriptions into feature vectors in a high-dimensional space and evaluates their relevance by calculating cosine similarity. When the similarity score exceeds 0.85, the system automatically establishes a preliminary mapping association; when the score is between 0.6 and 0.85, the system adds the mapping to a pending confirmation list to assist technical personnel in making quick decisions.

[0029] 4. Dynamic conversion engine layer This engine is used to perform real-time format conversion and data cleaning during data flow, based on the definitions in the mapping configuration file. It includes an operator-based logic library capable of handling automatic transcoding across character sets, numerical precision alignment, date format unification, and forced conversion of field types. Furthermore, the engine supports embedding business filtering rules during the mapping process, utilizing predefined cleaning scripts to preprocess redundant, distorted, or non-compliant data, ensuring a high degree of cleanliness for data entering the execution layer. Further, as one embodiment of the invention, the dynamic conversion engine layer employs memory-level streaming processing technology. After data is read from the source, conversion and cleaning are directly completed in the execution node's memory buffer, avoiding the input / output performance overhead caused by intermediate disk writes. For ultra-large-scale data conversion tasks, the engine automatically enables a caching strategy based on overflow disks to ensure system stability under extreme memory pressure. As one embodiment of the invention, the dynamic conversion engine layer also includes a masking and desensitization component. During cross-domain migrations or migrations to test environments, this component encrypts, masks, or replaces sensitive personal information such as phone numbers, ID card numbers, and bank card numbers according to the de-identification policy defined in the mapping configuration. The de-identification process is completed entirely in memory and is irreversible, ensuring the security and compliance of the data migration process.

[0030] 5. Distributed migration execution layer This layer is used to achieve efficient large-scale data transfer. It employs a master-slave collaborative distributed architecture, dividing the overall migration task into multiple data shards that can be executed in parallel. The scheduling module dynamically allocates data shards to multiple execution nodes based on the load status of the source and target ends, utilizing multi-threaded concurrency technology to improve data throughput. During execution, the distributed migration execution layer introduces a breakpoint resumption mechanism and a transaction consistency guarantee protocol to ensure that the migration task can resume from the nearest checkpoint in the event of network fluctuations or hardware failures, preventing data retransmission or loss. Furthermore, as one embodiment of the invention, the distributed migration execution layer supports heterogeneous transaction management. When performing critical business data migration, the system ensures that the state changes of source data and the writing of data to the target end are logically atomic through a two-phase commit protocol or an eventual consistency scheme based on a local message table. As one embodiment of the invention, the distributed migration execution layer supports batch writing and parallel loading when writing data to the target end. The system dynamically adjusts the size of each batch based on the target database's capacity, typically setting it to 1000 to 5000 records per batch to fully utilize the database's batch processing advantages, reduce transaction commit frequency, and thus improve write performance. As one embodiment of the invention, the distributed migration execution layer possesses resource-aware scheduling capabilities. Before a task starts, the scheduler comprehensively evaluates the current load, network bandwidth utilization, and disk I / O latency of all execution nodes. The scheduler prioritizes allocating computationally intensive conversion tasks to nodes with abundant CPU resources, while allocating data-intensive transfer tasks to nodes with ample network bandwidth, achieving optimal allocation of cluster resources.

[0031] As one embodiment of the present invention, the system prioritizes primary key range sharding in its data sharding strategy. When the source table does not have a primary key or unique index, the system automatically uses row identifiers or calculates the hash distribution of field values ​​to evenly divide the entire data into 1024 logical shards, thereby ensuring load balancing among distributed nodes and preventing long-tail tasks from affecting the overall progress.

[0032] 6. End-to-end quality monitoring layer This layer is used for real-time auditing and feedback of the migration process. It initiates data consistency verification logic before and after migration, verifying data integrity and accuracy by comparing the number of records at the source and target ends, and by sampling comparison based on a checksum algorithm. Simultaneously, this layer collects real-time data on the CPU utilization, memory consumption, network bandwidth pressure, and real-time migration rate of each execution node, and feeds this data back to the scheduling module to achieve dynamic traffic smoothing. Furthermore, as one embodiment of the invention, the end-to-end quality monitoring layer has intelligent anomaly diagnosis capabilities. When the system detects a significant drop in migration rate or an error rate exceeding a preset 5% threshold, it automatically invokes a log analysis probe to pinpoint the fault location—whether it's a source query timeout, network link congestion, or target deadlock—and automatically executes the corresponding retry or rate limiting strategy based on the fault type. As another embodiment of the invention, the end-to-end quality monitoring layer provides real-time status display through a visual dashboard. The dashboard displays the number of currently active tasks, the total amount of data migrated cumulatively, the average number of records processed per second, the health score of each execution node, and the estimated remaining completion time. When a critical alarm occurs in the system, the monitoring layer triggers instant messaging, email, or SMS notifications via application programming interfaces.

[0033] This invention also provides an operational flow for a multi-source heterogeneous data migration system based on field mapping configuration. First, the data source adaptation layer completes the handshake connection with the heterogeneous sources. Then, the metadata deep perception layer extracts data structures from multiple dimensions. Next, the intelligent field mapping configuration layer generates mapping logic based on semantic analysis and performs conflict detection. Subsequently, the distributed migration execution layer initiates multi-threaded migration according to a sharding strategy, with data undergoing real-time correction as it flows through the dynamic transformation engine layer. Finally, the end-to-end quality monitoring layer performs closed-loop acceptance of the migration results.

[0034] In one embodiment of the present invention, the data consistency verification logic includes a full row count comparison and a key field summary comparison. In the first stage after the migration task is completed, the system queries the total number of records between the source and target ends. In the second stage, the system selects numerical key fields for summation or averaging operations. If the difference rate between the summary results of the source and target ends is less than 0.01%, it is determined to be logically consistent. For highly sensitive data, the system will initiate a third stage of row-by-row sampling comparison, extracting 1% of the data for a mirror comparison of all field contents.

[0035] As one embodiment of the present invention, the system also supports an incremental migration mode. In this mode, the system captures data addition, deletion, and modification operations in real time by parsing the change logs of the source database, such as redo logs or binary logs. The change capture module parses the logs into a standard event stream, processes it through the intelligent field mapping configuration layer, and then replays it sequentially on the target end, thereby achieving near real-time synchronization of data between the source and target ends, with the synchronization delay controlled within 3 seconds.

[0036] In one embodiment of the present invention, the system incorporates a self-learning module for mapping rules. This module continuously collects records of manually corrected mapping relationships and continuously optimizes the semantic analysis model through reinforcement learning algorithms. As the number of migration tasks increases, the system's accuracy in recognizing field naming conventions, abbreviation habits, and data conversion patterns in specific business domains will significantly improve, realizing the evolution from manual configuration to automated assisted configuration.

[0037] As one embodiment of the present invention, the system has multi-tenant management capabilities. In large-scale group application scenarios, the system can divide independent namespaces and resource pools for different business departments, ensuring metadata isolation, permission isolation, and computing resource isolation between different migration tasks, and guaranteeing the data migration priority of core businesses.

[0038] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and embodiments.

[0039] Example The system constructed in this embodiment aims to solve the problem of enterprise-level data integration with ultra-large scale and high complexity. By introducing deep metadata awareness and intelligent semantic mapping mechanism, it realizes fully automatic, high-performance and highly reliable data flow from source to target.

[0040] Reference Appendix Figure 1 The system is built with six tightly coupled and functionally distinct layers in its logical architecture: the data source adaptation layer responsible for physical connection and basic driving; the metadata deep perception layer responsible for structure parsing and sample profiling; the intelligent field mapping configuration layer, which serves as the core mapping hub; the dynamic conversion engine layer that performs real-time logical processing; the distributed migration execution layer that supports large-scale concurrent throughput; and the full-link quality monitoring layer that provides full lifecycle auditing.

[0041] The data source adaptation layer forms the foundation of this system, its primary task being to bridge the physical protocol differences between various storage media. This layer integrates a highly modular driver pool, encompassing mainstream relational database drivers, non-relational database protocol stacks, distributed file system access interfaces, structured document parsers, and data stream access drivers for real-time message queues. The data source adaptation layer maintains all active physical connections through a global connection pool manager. When the system initiates a migration request, the connection pool manager identifies the target type based on the Uniform Resource Locator (URL) of the source or target end and dynamically loads the corresponding communication protocol stack from the driver pool. During this process, the data source adaptation layer performs strict lifecycle management, including connection pre-allocation, health heartbeat detection, idle timeout recycling, and automatic reconnection after abnormal interruptions. For each physical connection, this layer deeply optimizes its communication parameters, such as automatically adjusting the socket buffer size for high-latency network environments or configuring a large number of prefetched records for high-throughput requirements. In addition, the data source adaptation layer is also responsible for the initial encapsulation of the underlying raw data stream, converting various specific format byte streams into a unified data frame format within the system, thereby providing standardized field reading and writing interfaces for the upper-layer logic.

[0042] Combined with appendix Figure 3This document details the implementation of the metadata deep-aware layer. Activated during the initialization phase of a data migration task, this layer performs a full analysis of data assets on both the source and target sides. The metadata deep-aware layer accesses the source database's system metadata database, data dictionary, and tablespace definition files via a recursive scanning mechanism. The extracted metadata goes beyond basic table and field names, encompassing precise data type definitions, numerical precision and scaling, primary and foreign key constraints, unique indexes, field default values, NOT NULL constraints, and encoding formats and physical storage order at the storage level. To address the issues of missing or incomplete metadata annotations, a data sample sniffing module is embedded within the metadata deep-aware layer. This module employs a Bernoulli sampling strategy to extract representative feature samples from the source tables, typically with a sampling rate of 0.1% to 1%. Through statistical analysis of these sample data, the sniffing module calculates the actual value distribution range, dispersion, average length, maximum and minimum values, and null value rate for each field. These dynamic attributes, combined with static structural definitions, constitute a more accurate dynamic metadata profile than a traditional data dictionary. When faced with unstructured or semi-structured data sources, the metadata deep-aware layer uses heuristic pattern recognition algorithms to scan the data content multiple times. By identifying recurring tag structures or delimiter logic, it automatically infers the implicit hierarchical structure and converts it into a flattened set of virtual fields. These virtual fields are assigned temporary metadata attributes, allowing the subsequent mapping layer to configure them logically as if they were ordinary database fields.

[0043] Reference Appendix Figure 2 The intelligent field mapping configuration layer is the core control hub of this invention. This layer completely abandons the limitations of traditional methods based on manual hard coding or simple name equivalence matching. The intelligent field mapping configuration layer integrates a deep learning-based semantic parsing unit. This unit first performs refined preprocessing on the field names and annotation text extracted from the metadata deep perception layer, including removing invalid characters, identifying and converting industry abbreviations, and semantically segmenting compound words. Subsequently, the semantic parsing unit uses a pre-set industrial-grade semantic knowledge base to map the processed terms to a high-dimensional feature vector space.

[0044] In this embodiment, the semantic similarity calculation process strictly follows the following mathematical logic. The system uses a bidirectional encoder representation model to vectorize the business semantics of the fields, obtaining source field vectors and target field vectors. To quantitatively evaluate the correlation strength between the two, the system introduces a cosine similarity calculation algorithm, the formula of which is expressed as:

[0045] In the above formula, and They represent the first The source field vector and the first A vector of target fields, The denominator represents the total number of source field vectors; the denominator represents the product of the magnitudes of the source and target field vectors, and the numerator represents the dot product of the two vectors.

[0046] The obtained similarity score ranges between 0 and 1. The system presets two levels of judgment thresholds. In this embodiment, when the similarity score exceeds 0.85, the semantic parsing unit determines that the two fields are highly consistent in business meaning, and even if the names are significantly different, it will automatically establish a strong association suggestion in the mapping relationship database. When the score is between 0.6 and 0.85, the system considers there to be a potential association and marks it as pending confirmation, pushing it to the operator for final manual judgment through the graphical configuration interface.

[0047] The intelligent field mapping configuration layer also supports extremely complex mapping logic definitions. Using the built-in mapping logic orchestrator, users can configure one-to-one direct mappings or many-to-one aggregation mappings. For example, the last name and first name fields from the source can be merged into the full name field on the target using a string concatenation operator. It also supports one-to-many split mappings, such as splitting the composite address field from the source into three independent fields: province, city, and district. All mapping rules, transformation operators, and association logic are ultimately serialized into structured mapping configuration files, typically stored in the configuration center in Extensible Markup Language (EXPLAIN) or Object Notation Format (OMF). To ensure the success rate of migration tasks, the intelligent field mapping configuration layer also includes a conflict detection unit. This unit performs pre-simulation verification of the physical constraints of the target database before the configuration file takes effect. It compares whether the actual value range of the source field exceeds the length limit defined on the target, checks whether the null value distribution on the source violates the non-null constraint on the target, and verifies whether the mapping logic will cause primary key conflicts. Once a risk is detected, the conflict detection unit generates a detailed remediation suggestion report, mandating corrections before starting the migration.

[0048] The dynamic transformation engine layer is responsible for executing specific processing instructions during the real-time data migration process. This engine adopts an operator-based architecture and incorporates a large number of standardized logic libraries. These operators include, but are not limited to, automatic transcoding operators across character sets, dynamic numerical precision alignment operators, unified date conversion operators across multiple formats, and data cleaning operators based on regular expressions. The dynamic transformation engine layer receives data blocks from the distributed migration execution layer and, according to the instructions in the mapping configuration file, performs real-time data trimming and transformation in the memory buffer. Furthermore, the engine supports embedding complex business filtering rules during the transformation process. For example, the system can configure a filtering operator to remove inactive data from the source that was created more than 3 years ago and is in a deactivated state.

[0049] The dynamic conversion engine layer employs high-performance memory-level streaming processing technology. After data is read, it flows directly in the memory of the execution node as an object stream, without any form of disk persistence, thus minimizing input / output latency. For tasks with extremely large data volumes and highly complex conversion logic, in this embodiment, when the memory usage reaches a preset 85% warning level, the engine automatically activates an overflow disk caching strategy. This strategy uses sequential writes to dump intermediate processing states to temporary high-speed storage devices, ensuring that the system will not crash due to memory overflow under extreme loads, while also guaranteeing the continuity of the conversion logic. Furthermore, this layer integrates a masking and desensitization component, capable of performing irreversible encryption or masking processing on sensitive fields such as mobile phone numbers, ID card numbers, and bank card numbers in memory according to compliance requirements.

[0050] Combined with appendix Figure 4 The distributed migration execution layer is the high-performance power source of this system. It adopts a master-slave collaborative distributed scheduling architecture. The scheduling module, as the master node, is responsible for logically splitting the overall migration task. The system exhibits a high degree of intelligence in its data sharding strategy. When the source table has a primary key or unique index, the scheduling module uses the numerical range or value distribution of the primary key to perform uniform sharding.

[0051] When dealing with data sources that have no primary key or extremely uneven index distribution, the distributed migration execution layer adopts a random sharding strategy based on the consistent hashing algorithm. The hash sharding calculation process is as follows:

[0052] In this formula, the system performs a high-performance hash operation on the selected reference field value, and then modulo the result with the total number of shards to determine the logical shard number to which the record belongs. Typically, the system divides the entire dataset into 1024 logical shards. These shards are dynamically distributed to multiple active execution nodes in the cluster. Each execution node employs a multi-threaded concurrency model, independently handling the reading of its corresponding shard, invoking the transformation engine, and writing to the target endpoint.

[0053] To ensure extremely high reliability of the migration task, the distributed migration execution layer introduces a comprehensive fault tolerance mechanism. This layer maintains a distributed state checkpoint database during execution. Whenever a data block is successfully written to the target and receives confirmation, the execution node updates the processing progress of the current shard to the state database. In the event of network jitter, server crashes, or target deadlocks, the migration task will automatically enter a suspended state. Once the fault is resolved, the scheduling module instructs the execution node to resume execution from the last successful commit position based on the records in the checkpoint database, achieving true breakpoint resumption and completely avoiding the frequent problems of duplicate fetching or data loss in large-scale data migrations. When migrating critical business systems, the distributed migration execution layer also supports a two-phase commit protocol, ensuring that the state flags on the source end and the data insertion on the target end logically constitute an atomic transaction.

[0054] Reference Appendix Figure 5 The end-to-end quality monitoring layer provides multi-dimensional auditing and governance for the entire migration process. This layer constructs a real-time visual monitoring dashboard through point-of-sampling and real-time log collection. The dashboard displays not only conventional metrics such as the current migration rate, completion percentage, and number of active threads, but also shows the load status of each execution node in the form of a heatmap. The end-to-end quality monitoring layer initiates consistency checks in the pre-, mid-, and post-task execution phases. In the first phase, the system quickly compares the total number of records between the source and target ends. In the second phase, distributed summation and mean checks are performed on key numerical fields marked in the mapping configuration file. In this embodiment, if the summative difference rate between the source and target ends is less than 0.0001, the data is considered logically highly consistent. For financial or medical data with extremely high security requirements, the monitoring layer initiates a third phase of sampling mirror comparison, randomly selecting 1% of the records for byte-by-byte comparison of all field values.

[0055] Furthermore, the end-to-end quality monitoring layer possesses intelligent anomaly diagnosis capabilities. When the system detects abnormal fluctuations in the migration rate, or when the return error rate at the target end exceeds the 5% threshold set in this embodiment, it automatically invokes the log analysis probe. This probe can quickly pinpoint the root cause of the fault through in-depth analysis of underlying communication packets and system kernel logs. For example, it can accurately distinguish whether the fault is due to excessively long disk I / O wait times at the source end, limited bandwidth in the intermediate network link, or write performance degradation caused by triggers at the target end. Based on the diagnostic results, the monitoring layer automatically triggers corresponding response plans, including dynamically adjusting the number of concurrent threads, switching to a backup network link, temporarily suspending the task, and sending instant communication alerts to operations and maintenance personnel.

[0056] The system provided in this embodiment also possesses excellent self-learning capabilities for mapping rules. The system incorporates a reinforcement learning-based mapping rule correction model. Whenever technicians modify or confirm the automatically recommended mapping relationships on the graphical interface, these actions are recorded as training samples. As the number of migration tasks increases, the self-learning module continuously corrects the feature weights of the semantic parsing units. This allows the system to gradually adapt to the naming conventions and business logic of specific industries. For example, after multiple data migrations in the power industry, the system automatically recognizes that a specific abbreviation represents the current transformer ratio. This self-evolutionary capability significantly improves the system's mapping accuracy over time, truly achieving a leap from tool-based to intelligent.

[0057] When the execution node writes data to the target database, the distributed migration execution layer dynamically implements batch write optimization based on the target database type and the current system load. The system automatically calculates the optimal batch size, typically fluctuating between 1,000 and 5,000 records, to balance memory overhead and transaction commit frequency. This parallel loading technology significantly reduces the log write pressure on the target database, thereby achieving extremely high write throughput without affecting the normal business operations of the target database.

[0058] For large, multi-departmental parallel application scenarios in large group enterprises, this system also provides multi-tenant management capabilities. By introducing tenant identifiers in the metadata deep awareness layer and the distributed migration execution layer, the system can divide independent virtual migration environments for different business lines. Each tenant has an independent namespace, mapping rule base, and computing resource quota. This ensures that when multiple large-scale migration tasks are executed concurrently, the metadata between tasks will not be confused, and resources can be scheduled in a staggered manner according to business priorities. For example, the monthly settlement data migration task of the financial system can be given the highest priority, thereby obtaining more execution threads and network bandwidth when resources are scarce.

[0059] The driver management framework of this system supports plug-in dynamic expansion. When enterprises introduce new cloud-native databases or self-developed distributed storage systems, developers do not need to make any changes to the core code of the migration system. They only need to follow the driver development specifications provided by the system and implement several standardized interfaces, including connection handshake, metadata extraction, batch data reading, and shard location, to integrate the new data source into the migration system through hot loading. It is worth noting that in the above system embodiments, the modules are divided according to functional logic, but are not limited to this division, as long as the corresponding functions can be achieved; furthermore, the specific names of each functional module are only for easy differentiation and are not intended to limit the scope of protection of this invention.

[0060] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to the embodiments, those skilled in the art should understand that modifications or equivalent substitutions to the technical solutions of the present invention do not depart from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A multi-source heterogeneous data migration system based on field mapping configuration, characterized in that, include: The data source adaptation layer is used to build a unified data access benchmark. Through the built-in driver pool, it realizes physical connection management for different types of source data and is also used to encapsulate source data in a unified format. The metadata deep perception layer is used to perform structural parsing and sample profiling on the source data after format encapsulation. The intelligent field mapping configuration layer is used to establish a mapping configuration file between the source end and the migrated target end based on semantic similarity algorithms and business context reasoning; The dynamic transformation engine layer is designed with an operator-based architecture. Based on the mapping configuration file, it uses memory-level streaming processing technology to perform real-time logic processing during data migration. The distributed migration execution layer is used to perform data migration using a master-slave collaborative distributed architecture through sharded parallel processing and memory-level streaming transformation. and The end-to-end quality monitoring layer is used to provide multi-dimensional auditing and governance throughout the entire data migration process.

2. The multi-source heterogeneous data migration system based on field mapping configuration according to claim 1, characterized in that, The built-in driver pool includes: relational database driver, non-relational database protocol stack, distributed file system access interface, structured document parser, and data stream access driver for real-time message queue. 3.The multi-source heterogeneous data migration system based on field mapping configuration of claim 1, wherein, The physical connection management for different types of source data specifically includes: All active physical connections are maintained through a global connection pool manager. When the system initiates a migration request, the connection pool manager identifies the target type based on the Uniform Resource Locator (URL) of the source or target end and dynamically loads the corresponding communication protocol stack from the driver pool. It also performs strict lifecycle management, including connection pre-allocation, health heartbeat detection, idle timeout recycling, and automatic reconnection after abnormal interruption.

4. The multi-source heterogeneous data migration system based on field mapping configuration according to claim 3, characterized in that, For each active physical connection, the data source adaptation layer performs in-depth optimization of communication parameters, including: automatically adjusting the socket buffer size for high-latency network environments and configuring a large number of prefetched records for high-throughput requirements.

5. The multi-source heterogeneous data migration system based on field mapping configuration according to claim 1, characterized in that, The processing of the metadata deep-aware layer specifically includes: Access the source database's system metadata database, data dictionary, and tablespace definition files through a recursive scanning mechanism; extract table names and field names, data type definitions, numerical precision and scaling, primary and foreign key constraints, unique indexes, field default values, NOT NULL constraints, and the encoding format and physical storage order at the storage level; A Bernoulli sampling-based strategy is adopted to extract representative feature sample data from the source according to a set sampling ratio, perform statistical analysis, and calculate the actual value distribution range, dispersion, average length, maximum and minimum values ​​and null value rate of each field to obtain a sample profile. When the data source is unstructured or semi-structured, a heuristic pattern recognition algorithm is used to scan the data content in multiple rounds. By identifying repeated label structures or delimiter logic, the implicit hierarchical structure is automatically inferred and converted into a flattened set of virtual fields.

6. The multi-source heterogeneous data migration system based on field mapping configuration according to claim 1, characterized in that, The intelligent field mapping configuration layer includes a semantic parsing unit based on deep learning. The processing procedure of the semantic parsing unit is as follows: The field names and annotation text extracted from the metadata deep perception layer are preprocessed, including: removing invalid characters, identifying and converting industry abbreviations, and semantically segmenting compound words; Using a pre-built industrial-grade semantic knowledge base, the processed terms are mapped to a high-dimensional feature vector space; Perform semantic similarity calculation; Based on the preset two-level judgment threshold, the association status between the source field vector and the target field vector is determined by semantic similarity.

7. The multi-source heterogeneous data migration system based on field mapping configuration according to claim 6, characterized in that, The intelligent field mapping configuration layer includes a mapping logic orchestrator and a conflict detection unit, wherein... The mapping logic orchestrator supports one-to-one direct mapping, many-to-one aggregation mapping, and one-to-many split mapping. The conflict detection unit is used to perform pre-simulation verification of the physical constraints of the target database, specifically including the following verifications: By comparing whether the actual value range of the source field exceeds the length limit defined on the target, we can check whether the null value distribution on the source violates the non-null constraint on the target. Verify whether the mapping logic causes a primary key conflict; If any of the above verifications fails, a detailed remediation suggestion report will be generated, and the migration can only be started after the correction is completed; if all of the above verifications are completed, a mapping configuration file will be established between the source end and the target end after migration.

8. The multi-source heterogeneous data migration system based on field mapping configuration according to claim 6, characterized in that, The semantic similarity The calculation of the semantic similarity satisfies the following equation: in, and They represent the first The source field vector and the first A vector of target fields, Indicates the total number of source field vectors; When Above 0.85, the source field vector is determined to be strongly associated with the target field vector. When Between 0.6 and 0.85, a potential association is determined and the operator is notified for a human decision; When Less than 0.6, determine no correlation.

9. The multi-source heterogeneous data migration system based on field mapping configuration according to claim 1, characterized in that, The dynamic conversion engine layer adopts an operator-based architecture design, wherein the operators include at least: automatic transcoding operator across character sets, dynamic numerical precision alignment operator, unified conversion operator for multiple date formats, and data cleaning operator based on regular expressions; The aforementioned use of memory-level streaming processing technology specifically includes: When reading data from the source, it flows directly in the memory of the execution node in the form of an object stream. For sensitive fields, irreversible encryption or masking is performed. When the memory usage reaches the preset warning level, the overflow disk caching policy is automatically activated. The sensitive fields include at least mobile phone numbers, ID card numbers, and bank card numbers. The disk caching policy is to use sequential writing to dump the intermediate processing state to a temporary high-speed storage device.

10. The multi-source heterogeneous data migration system based on field mapping configuration of claim 1, wherein, The master-slave collaborative distributed architecture includes: a scheduling module as the master node and multiple execution nodes; The sharding strategy of the distributed migration execution layer includes: When the source table has a primary key or unique index, the scheduling module uses the numerical range or value distribution of the primary key to perform uniform partitioning. When the data source has no primary key or the index distribution is extremely uneven, the scheduling module adopts a random sharding strategy based on the consistent hashing algorithm. The segmented parallel processing specifically includes: The scheduling module dynamically distributes shards to active execution nodes in the cluster. Each execution node adopts a multi-threaded concurrency model and is independently responsible for reading the corresponding shard, calling the transformation engine, and writing to the target end.

11. The multi-source heterogeneous data migration system based on field mapping configuration according to claim 10, characterized in that, The distributed migration execution layer introduces a comprehensive fault tolerance mechanism, specifically including: During execution, the scheduling module maintains a distributed state checkpoint library. Whenever a shard is successfully written to the target and receives confirmation feedback, the execution node updates the processing progress of the current shard to the state checkpoint library. In the event of network jitter, server crash, or target deadlock, the migration task will automatically be suspended. Once the fault is cleared, the scheduling module, based on the records in the checkpoint database, instructs the execution node to continue execution from the position of the last successful submission, thus enabling breakpoint resumption. When migrating critical business systems, the distributed migration execution layer also supports a two-phase commit protocol to ensure that the status flags on the source end and the data insertion on the target end logically constitute an atomic transaction.

12. The multi-source heterogeneous data migration system based on field mapping configuration according to claim 1, characterized in that, The end-to-end quality monitoring layer constructs a real-time, visualized monitoring dashboard through embedded sampling and real-time log collection. The content displayed on the monitoring dashboard includes: Standard metrics should include at least the current migration rate, completion percentage, and number of active threads. Heatmap: Includes the load status of each execution node; The consistency verification results are shown at different stages of the migration process. In the first stage, the total number of records between the source and target ends is quickly compared. In the second stage, distributed summation and mean verification are performed on key numerical fields marked in the mapping configuration file. For financial or medical data with extremely high security requirements, the third stage of sampling mirror comparison is initiated, which compares the full field values ​​byte by byte by randomly selecting a set proportion of records.

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