Customization system realized through field mapping

Abstract

The invention provides a customization system realized through field mapping, which comprises the following steps of: determining the type of a data source, and extracting all related field data from the data source; identifying a field of the target data model according to requirements of a target system, and determining a target field; converting fields in the source data into fields in target data one by one according to a defined mapping rule; for fields possibly missing in the source data, complementing the fields through the configuration file; verifying the converted data; outputting the verified data to a target system through an interface or a data import tool; a micro-service architecture is designed, and a field mapping function is realized as an independent micro-service. The method has the beneficial effects that the consistency of the source data and the target data is ensured by defining the data mapping rule; creating a unified data dictionary, and eliminating naming ambiguity among different data sources; and a flexible field mapping completion mechanism is designed, and rapid change of service requirements is supported.

Description

Technical Field

[0001] The present invention belongs to the field of information technology, and in particular relates to a customized system implemented through field mapping. Background Art

[0002] With the development of information technology, enterprises are facing an increasing demand for data integration. Traditional hard-coding methods are difficult to adapt to rapidly changing business needs and have high maintenance costs. Especially when integrating data between different data sources, the process of manually writing code for mapping and conversion is complex, inefficient, and prone to errors. Therefore, a system development method that can quickly respond to business changes and is easy to maintain and expand is needed.

[0003] At present, many existing technologies rely on manual configuration or hard coding to achieve data field mapping and conversion, resulting in poor flexibility in data integration and requiring a lot of manual modifications during system upgrades or maintenance. Therefore, developing a customized system development method based on field mapping can effectively improve the efficiency and accuracy of data integration and reduce subsequent maintenance costs, which has become an urgent problem to be solved in the current technological development. Summary of the invention

[0004] In view of this, the present invention aims to propose a customized system implemented through field mapping, so as to efficiently handle field mapping and data conversion problems between different data sources.

[0005] To achieve the above object, the technical solution of the present invention is achieved as follows: A customized system implemented through field mapping, including: Determine the type of data source and extract all relevant field data from the data source; According to the requirements of the target system, identify the fields of the target data model and determine the target fields; Convert the fields in the source data into the fields in the target data one by one according to the defined mapping rules; For fields that may be missing in the source data, complete them through the configuration file; Verify the converted data; Output the verified data to the target system through the interface or data import tool; Design a microservice architecture and implement the field mapping function as a separate microservice.

[0006] Furthermore, in the step of determining the type of data source and extracting all relevant field data from the data source, the data source includes a relational database, a NoSQL database, a file system, etc. When extracting fields, the developer identifies all available fields from the data source through query statements or ETL tools to ensure data integrity.

[0007] Furthermore, in the step of identifying the fields of the target data model and determining the target fields according to the requirements of the target system, the developer defines a mapping relationship between the source data fields and the target data fields, clarifies the correspondence between the source fields and the target fields through a mapping table or a configuration file, and forms specific mapping rules.

[0008] Furthermore, mapping rules are defined in the following manner: a developer creates a mapping table or configuration file, the mapping table lists the correspondence between source fields and target fields, and defines conversion rules for each pair of source and target fields, and the mapping rules are configured by directly editing the mapping table.

[0009] Furthermore, data validation and testing are performed in the following ways: developers write automated test scripts and use JUnit and TestNG frameworks to validate the converted data. The validation content includes the correctness of data types, field mapping accuracy, and whether missing fields are correctly completed. All validation steps are performed through automated testing tools.

[0010] Furthermore, the present solution discloses an electronic device, including a processor and a memory that is communicatively connected to the processor and is used to store executable instructions of the processor, wherein the processor is used to execute a customized system implemented by field mapping.

[0011] Furthermore, the present solution discloses a server comprising at least one processor and a memory communicatively connected to the processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the processor so that the at least one processor executes a customized system implemented through field mapping.

[0012] Furthermore, the present solution discloses a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, a customized system is implemented through field mapping.

[0013] Compared with the prior art, the customized system implemented by field mapping described in the present invention has the following beneficial effects: The customized system implemented by field mapping described in the present invention ensures the consistency of source data and target data by defining data mapping rules; creates a unified data dictionary to eliminate naming ambiguity between different data sources; designs a flexible field mapping completion mechanism to support rapid changes in business needs; and designs the system based on a microservice architecture to ensure the scalability and ease of maintenance of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The accompanying drawings constituting a part of the present invention are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the accompanying drawings: Figure 1 The figure is a schematic diagram of the principle of a customized system implemented through field mapping according to an embodiment of the present invention. DETAILED DESCRIPTION

[0015] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments may be combined with each other.

[0016] The present invention will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments.

[0017] The present invention provides a customized system development method based on field mapping, comprising the following steps: Define data mapping rules and clarify the correspondence between source database fields and target database fields through mapping tables, configuration files or data integration tools.

[0018] Create a unified data dictionary to standardize field naming and definitions across different data sources, eliminating confusion and ambiguity.

[0019] Data source analysis: Detailed analysis of the structure and content of source data to ensure the accuracy of field mapping.

[0020] Data conversion: convert data formats as needed, including date format, numerical units, and string processing.

[0021] Design a field mapping completion mechanism to adjust the field mapping relationship through configuration rather than coding to adapt to business changes.

[0022] Using the microservice architecture, the field customization service is designed as an independent service, which interacts with the business service through an interface.

[0023] Leverage existing tools and frameworks (such as MyBatis Plus, MapStruct, etc.) to simplify the field mapping process.

[0024] The specific implementation steps are as follows: 1. Data source identification First, determine the data source, that is, determine the system or database where the data comes from. The data source can be a relational database, NoSQL database, file, or other different types of storage.

[0025] 2. Extract data fields Extract all available fields from the data source and use the data query interface or ETL tool to extract the source data fields as the basis for subsequent field mapping.

[0026] 3. Target data model identification According to the structure of the target system or database, identify the target data model and define the mapping relationship. The target model should take into account the data requirements and storage format of the target system to ensure seamless access.

[0027] 4. Application of mapping relationship rules By formulating field mapping rules, source data fields are mapped to fields in the target data model. The formulation of mapping rules can rely on mapping tables, configuration files, or be implemented through data integration tools.

[0028] 5. Data conversion and completion In the data mapping process, in addition to field conversion, data type conversion may also be required, such as date format, numerical unit conversion, string encoding conversion, etc. Developers can implement these conversions through custom conversion logic.

[0029] 6. Data verification, test verification Through automated testing or manual verification, ensure that the results of data conversion meet expectations. The verification step is a key step to ensure the accuracy and integrity of data.

[0030] 7. Mapping result output Output the verified data results to the target system to ensure that the target system can correctly receive and process the data.

[0031] 8. Microservice Architecture Design Through the microservice architecture, the field mapping service is used as an independent module, providing a standardized interface for business system calls. The microservice architecture helps improve the maintainability and scalability of the system.

[0032] At the same time, in today's software development practice, customized system development is an important issue, especially for enterprises that need to process large amounts of heterogeneous data. Field mapping, as a key technology, can help developers achieve seamless integration and customized development between different data sources. The following is a detailed introduction to a customized system development method achieved through field mapping.

[0033] 1. Understand the importance of field mapping Field mapping is the core of data integration, which involves mapping the fields of the source database to the fields of the target database. This process requires developers to formulate clear field mapping rules, which may be implemented through mapping tables, configuration files, or professional data integration tools. Field mapping not only helps ensure that data is correctly mapped, but also achieves data standardization and consistency.

[0034] / / Source database entity class @Entity public class SourceEntity { @Id private Long id; private String sourceField1; private String sourceField2; / / getters and setters } / / Target database entity class @Entity public class TargetEntity { @Id private Long id; private String targetField1; private String targetField2; / / getters and setters } 2. Define data mapping rules Before starting field mapping, developers need to define the rules for data mapping. This includes identifying the fields in the source and target databases and establishing the relationship between them. Developers can define which source fields should be mapped to which target fields and how to transform the data to fit the structure of the target database by creating a mapping table.

[0035] @Service public class MappingService { @Autowired private SourceRepository sourceRepository; @Autowired private TargetRepository targetRepository; public void mapFields() { / / Get data from the source database SourceEntity sourceEntity = sourceRepository.findById(1L).orElse(null); if (sourceEntity != null) { / / Create the target entity and map the fields TargetEntity targetEntity = new TargetEntity(); / / Assume sourceField1 maps to targetField1 and needs to be converted targetEntity.setTargetField1(convertField1(sourceEntity.getSourceField1())); / / sourceField2 is directly mapped to targetField2 targetEntity.setTargetField2(sourceEntity.getSourceField2()); / / Save to the target database targetRepository.save(targetEntity); } } private String convertField1(String sourceField1) { / / You can add conversion logic here, such as string processing or date format conversion return sourceField1.toUpperCase(); / / Example converted to uppercase } } 3. Create a unified data dictionary To eliminate confusion in field naming and data types, developers can create a unified data dictionary. This data dictionary will standardize the naming and definition of fields in different data sources, ensuring that all parties involved have a clear understanding of the meaning of the fields during the data integration process.

[0036] public interface SourceRepository extends JpaRepository<SourceEntity,Long> { / / You can add custom query methods} public interface TargetRepository extends JpaRepository<TargetEntity,Long> { / / You can add custom query methods } 4. Data source analysis Before doing field mapping, it is crucial to conduct a detailed analysis of the source data. Developers need to understand the meaning and data type of the fields in order to better map them. This step can be assisted by data source analysis tools that can help identify and record the structure and content of the data source.

[0037] @Configuration @EnableTransactionManagement @EnableJpaRepositories(basePackages = "com.example.repositories") public class JpaConfig { @Value("${spring.datasource.url}") private String databaseUrl; / / Other configuration... @Bean public LocalContainerEntityManagerFactoryBean entityManagerFactory(){ / / Configure EntityManagerFactory } @Bean public JpaTransactionManager transactionManager() { / / Configure the transaction manager } } 5. Implement data conversion In addition to simple field mapping, data conversion may also be required during field mapping, including date format conversion, numerical unit conversion, and string processing. Developers can use conversion functions or custom code to handle these conversions to ensure that data retains its original meaning and accuracy when transmitted between different systems.

[0038] @Mapper(componentModel = "spring") public interface EntityMapper { EntityMapper INSTANCE = Mappers.getMapper(EntityMapper.class); TargetEntity toTargetEntity(SourceEntity source); } Use @Mapping annotation: In the mapping method, use @Mapping annotation to specify how the fields are mapped.

[0039] copy @Mapperpublic abstract class EntityMapperImpl implements EntityMapper{@Mapping(target = "targetField1", source = "sourceField1", expression = "java(source.getSourceField1().toUpperCase())")abstract TargetEntitytoTargetEntity(SourceEntity source);} 6. Design a flexible field mapping completion mechanism In actual development, developers may need to face changing business requirements. To cope with this situation, a flexible field mapping completion mechanism can be designed. This mechanism allows developers to adjust field mapping relationships through configuration rather than coding, thereby adapting to changing business scenarios. For example, the mapping relationship between module material field codes and data source field IDs can be used to dynamically complete data.

[0040] Use mapping interface in service: In mapping service, inject mapping interface and use it for field mapping.

[0041] copy @Servicepublic class MappingService {@AutowiredprivateSourceRepository sourceRepository;@Autowiredprivate TargetRepositorytargetRepository;@Autowiredprivate EntityMapper entityMapper; / / Use the mapping interface generated by MapStruct public void mapFields() {SourceEntity sourceEntity =sourceRepository.findById(1L).orElse(null);if (sourceEntity != null){TargetEntity targetEntity = entityMapper.toTargetEntity(sourceEntity);targetRepository.save(targetEntity);}}} Automatically configure Mapper scanning: In order to enable Spring Boot to scan MapStruct's mapping interface, you need to add MapStruct's Spring configuration.

[0042] @Configuration@EnableJpaRepositories@EnableTransactionManagement@ComponentScan(basePackages = {"com.example.repositories", "com.example.mappers"})public class AppConfig { / / Other configurations...} 7. Exploration of customizable business system architecture In order to realize a highly customizable business system, developers need to explore different architectural designs. This includes field customization, process customization, form customization, and interface customization. Through the microservice architecture, field customization can be designed as a service independent of the original business, so that the original business can interact with the customized service through the interface, thereby realizing flexible expansion and customization of the business. 8. Leverage existing tools and frameworks When implementing field mapping, developers can use existing tools and frameworks to simplify the development process. For example, MyBatis Plus provides the @TableField annotation to flexibly handle field mapping1. MapStruct is a powerful code generation tool that can automatically generate field mapping code and support custom mapping logic. In addition, there are other data mapping tools that can help developers handle matching fields and data mapping processes.

[0043] Through the above methods, a highly customizable system can be implemented that can adapt to changing business needs while maintaining data consistency and accuracy. Field mapping is a key technology in this process, which requires developers to deeply understand and reasonably apply it to ensure the successful development and deployment of the system.

[0044] Those of ordinary skill in the art will appreciate that the units and method steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0045] In the several embodiments provided in the present application, it should be understood that the disclosed methods and systems can be implemented in other ways. For example, the division of the units described above is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. The above-mentioned units may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiments of the present invention.

[0046] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and specification of the present invention.

[0047] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A customized system implemented through field mapping, characterized in that: include: Determine the type of data source and extract all relevant field data from the data source; According to the requirements of the target system, identify the fields of the target data model and determine the target fields; Convert the fields in the source data into the fields in the target data one by one according to the defined mapping rules; For fields that may be missing in the source data, complete them through the configuration file; Verify the converted data; Output the verified data to the target system through the interface or data import tool; Design a microservice architecture and implement the field mapping function as a separate microservice.

2. A customized system implemented by field mapping according to claim 1, characterized in that: In determining the type of data source and extracting all relevant field data from the data source, the data source includes a relational database, a NoSQL database, a file system, etc. When extracting fields, the developer identifies all available fields from the data source through query statements or ETL tools to ensure data integrity.

3. A customized system implemented by field mapping according to claim 1, characterized in that: In the step of identifying the fields of the target data model and determining the target fields according to the requirements of the target system, the developer defines the mapping relationship between the source data fields and the target data fields, clarifies the corresponding relationship between the source fields and the target fields through a mapping table or a configuration file, and forms specific mapping rules.

4. A customized system implemented by field mapping according to claim 1, characterized in that: Mapping rules are defined in the following way: the developer creates a mapping table or configuration file. The mapping table lists the correspondence between source fields and target fields and defines conversion rules for each pair of source and target fields. The mapping rules are configured by directly editing the mapping table.

5. A customized system implemented by field mapping according to claim 1, characterized in that: Data validation and testing are performed in the following ways: developers write automated test scripts and use JUnit and TestNG frameworks to validate the converted data. The validation content includes the correctness of data types, field mapping accuracy, and whether missing fields are correctly completed. All verification steps are performed through automated testing tools.

6. An electronic device, comprising a processor and a memory connected to the processor for storing instructions executable by the processor, characterized in that: The processor is used to execute a customized system implemented by field mapping as described in any one of claims 1-5 above.

7. A server, characterized in that: It includes at least one processor and a memory communicatively connected to the processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the processor so that the at least one processor executes a customized system implemented by field mapping as described in any one of claims 1-5.

8. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the customized system implemented by field mapping as described in any one of claims 1 to 5 is implemented.

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