A data incremental updating method, device and medium

Abstract

The application discloses a data incremental updating method, device and medium, the method comprising: generating a data dictionary template of an ERP system according to a preset multi-layer JSON structure; acquiring system data of different data sources, performing format conversion on the system data, filling the converted system data into the data dictionary template, and obtaining a data dictionary of the ERP system; deploying the data dictionary to different application systems; acquiring updated system data of different data sources within a preset period; performing format conversion on the updated system data, filling the converted updated system data into an updated data dictionary template, and obtaining an updated data dictionary of the ERP system; comparing the updated data dictionary with the data dictionary to obtain differential data; and updating the data dictionary of each application system according to the differential data. The data incremental updating efficiency is improved.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to a data incremental update method, device and medium. Background Technology

[0002] To align with enterprise development plans and digital transformation, the business modules within ERP systems serving these enterprises are expanding and diversifying accordingly, leading to increasingly frequent data sharing and synchronization between these modules. For example, data such as administrative divisions, tax rates, and exchange rates must ensure consistency and timeliness across different business modules. Otherwise, data discrepancies will arise, potentially causing business interruptions. Therefore, proactive data comparison is necessary to identify discrepancies and update them promptly.

[0003] However, the method of manually comparing and updating synchronized data is inefficient and lacks accuracy. Furthermore, the source data exists in various formats, including tables, database data, and data returned by software interfaces.

[0004] Based on this, after comparing the existing data of the application system with the source data, the differences are distributed to different business modules and updated in application systems using different databases. However, during the implementation of a system with multiple users, it's impossible to guarantee that all users use the same database application system. Therefore, as the number of database types in the application system increases, the process of distributing and synchronizing data to multiple databases becomes overly complex, leading to low efficiency in incremental data updates. Thus, it is necessary to efficiently integrate the comparison and synchronization of incremental updates across multiple database types. Summary of the Invention

[0005] This application provides a data incremental update method, device, and medium to solve the problem of low efficiency in data incremental updates.

[0006] The embodiments of this application adopt the following technical solutions:

[0007] On one hand, this application provides a data incremental update method, which includes: generating a data dictionary template for an ERP system based on a pre-defined multi-layer JSON structure; acquiring system data from different data sources, converting the format of the system data, and filling the converted system data into the data dictionary template to obtain the data dictionary of the ERP system; deploying the data dictionary to different application systems; acquiring updated system data from different data sources within a preset period; converting the format of the updated system data, and filling the converted updated system data into the updated data dictionary template to obtain the updated data dictionary of the ERP system; comparing the updated data dictionary with the data dictionary to obtain differential data; and updating the data dictionary of each application system based on the differential data.

[0008] In one example, before generating the data dictionary template for the ERP system based on a pre-defined multi-layered JSON structure, the method further includes: determining the outer layer of the multi-layered JSON structure, wherein the outer layer is used to record the dictionary name, dictionary field names, and business modules affected by the data dictionary; and determining the inner layer of the multi-layered JSON structure, wherein the inner layer is used to record a piece of data in the data dictionary, wherein a piece of data includes a field name and the field value corresponding to the field name.

[0009] In one example, after filling the converted system data into a data dictionary template to obtain the data dictionary of the ERP system, the method further includes: determining the business type of the system data; matching the business type in a pre-built identifier mapping table to obtain a unique data identifier for the business type; and marking the data dictionary with the unique data identifier.

[0010] In one example, comparing the updated data dictionary with the data dictionary to obtain the difference data specifically includes: in the updated data dictionary, determining multiple field values ​​for unique data identifiers, and multiple data entries corresponding to a single field value; in the data dictionary, matching multiple data entries with the same single field value; and comparing the multiple data entries in the updated data dictionary with the multiple data entries in the data dictionary to obtain the difference data.

[0011] In one example, deploying the data dictionary to different application systems specifically includes: determining the configuration information for each application system; the configuration information includes database type, database connection address, database name, database table name, and database table structure; and connecting to the database of each application system according to the configuration information to deploy the data dictionary to different application systems.

[0012] In one example, updating the data dictionary of each application system based on the difference data specifically includes: connecting to the database of each application system according to the configuration information; determining whether the data dictionary exists in the database of each application system; if so, matching the difference data in the data dictionary to obtain target replacement data; replacing the target replacement data with the difference data, and updating the data dictionary of each application system.

[0013] In one example, the method further includes: if there is a target application system that does not have the data dictionary, then providing feedback to the management terminal of the target application system on whether to redeploy the data dictionary; if so, then deploying the updated data dictionary to the target application system.

[0014] In one example, before obtaining system data from different data sources, the method further includes: determining that the data sources include system database data, downloaded or exported Excel standard data, and data returned from a data request interface; obtaining the original system data from different data sources; mapping the fields of the Excel standard data to the fields of the data dictionary to obtain corresponding standard data; parsing the returned data according to its data type to obtain parsed returned data; and obtaining system data from different data sources based on the corresponding standard data, the parsed returned data, and the system database data.

[0015] On the other hand, embodiments of this application provide a data incremental update device, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to: generate a data dictionary template for an ERP system according to a pre-defined multi-layer JSON structure; acquire system data from different data sources, perform format conversion on the system data, and fill the converted system data into the data dictionary template to obtain the data dictionary for the ERP system; deploy the data dictionary to different application systems; acquire updated system data from different data sources within a preset period; perform format conversion on the updated system data, and fill the converted updated system data into the updated data dictionary template to obtain the updated data dictionary for the ERP system; compare the updated data dictionary with the data dictionary to obtain differential data; and update the data dictionary for each application system according to the differential data.

[0016] On the other hand, embodiments of this application provide a non-volatile computer storage medium for incremental data updates, storing computer-executable instructions. These computer-executable instructions are configured to: generate a data dictionary template for an ERP system based on a pre-defined multi-layered JSON structure; acquire system data from different data sources, perform format conversion on the system data, and fill the converted system data into the data dictionary template to obtain the data dictionary for the ERP system; deploy the data dictionary to different application systems; acquire updated system data from different data sources within a preset period; perform format conversion on the updated system data, and fill the converted updated system data into the updated data dictionary template to obtain the updated data dictionary for the ERP system; compare the updated data dictionary with the data dictionary to obtain differential data; and update the data dictionary for each application system based on the differential data.

[0017] The above-described technical solutions adopted in the embodiments of this application can achieve the following beneficial effects:

[0018] It can store data dictionaries using JSON format data that is easy to deploy on computers and make differential comparisons with computer software, and generate data dictionary templates. This alleviates the differences in operations caused by different dictionaries having different JSONPATH depths, simplifies the parsing steps, improves data stability, and can support the generation of differential data and updated full files. It can be used for the full deployment of dictionaries in new application systems and the incremental updates of dictionaries in application systems, simplifying the incremental data update process and improving the efficiency of incremental data updates. Attached Figure Description

[0019] To more clearly illustrate the technical solution of this application, some embodiments of this application will be described in detail below with reference to the accompanying drawings, in which:

[0020] Figure 1 A flowchart illustrating a data incremental update method provided in an embodiment of this application;

[0021] Figure 2 This is a schematic diagram of the structure of a data incremental update device provided in an embodiment of this application. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0023] Some embodiments of this application will now be described in detail with reference to the accompanying drawings.

[0024] Maintaining and using a comprehensive data dictionary is both time-consuming and cumbersome for ERP systems, but it is essential for a usable system. For example, recording a company's address or type in the data dictionary and providing optional information can prevent customers from entering incorrect data and maintain data consistency. However, as business expands over time, the data dictionary will need to be expanded and updated. Methods are required to update the existing data dictionary and deploy it to different users' ERP systems.

[0025] Different data dictionaries have different data sources.

[0026] Source 1: Office software storage

[0027] Some users maintain their data dictionaries using office software, with data in tabular format. This type of data is structured data, organized and represented according to user-defined formats and rules, facilitating data analysis and calculation. While this data is intuitive, highly readable, and easy to maintain manually, deployment and application to the system require software or scripts for conversion and storage. Furthermore, most updates are full or incremental, making differentiated deployment and updates difficult.

[0028] Source 2: Data returned by API interface

[0029] API returns data primarily in JSON and XML formats. This type of data is easy for developers to parse and process using software, allowing for more flexible data manipulation and facilitating comparisons and updates of data with different structures. It can be converted to a unified format for comparison and output, masking differences between data structures and focusing on comparisons of values ​​within the same dimension. However, columns unrelated to dictionaries, such as status codes and return messages, in the API return data need to be filtered and removed.

[0030] Source 3: Data within the database

[0031] The system's data dictionary is stored in a database. The database can support storing various types of data, such as numeric, character, and date data, and has powerful data access and storage performance. However, for ERP system users, it is not possible to require users to deploy the same type of database. Different types of databases have different data operations, and it is not possible to directly synchronize data between multiple databases.

[0032] Based on this, this application uses JSON format data, which is convenient for computer deployment and computer software to make differentiated comparisons, to store the data dictionary and build a data dictionary template. This allows for format conversion of system data and filling of the data dictionary template, which can alleviate the differentiated operations caused by different data dictionaries having different JSONPath depths.

[0033] Figure 1 This is a flowchart illustrating a data incremental update method provided in an embodiment of this application. This method can be applied to various business areas, such as internet finance, e-commerce, instant messaging, gaming, and government services. Certain input parameters or intermediate results in this process can be manually adjusted to help improve accuracy.

[0034] The analysis method involved in the embodiments of this application can be implemented by a terminal device or a server, and this application does not impose any special limitations on it. For ease of understanding and description, the following embodiments are all described in detail using a server as an example.

[0035] It should be noted that the server can be a single device or a system composed of multiple devices, i.e., a distributed server. This application does not make any specific limitations on this.

[0036] Figure 1 The process includes the following steps:

[0037] S101: Generate the data dictionary template for the ERP system based on the pre-defined multi-layered JSON structure.

[0038] In some embodiments of this application, a multi-layered JSON structure is used to record the data dictionary in the ERP system, with the following structure:

[0039] On one hand, the outer layer of the multi-layered JSON structure is determined. This outer layer is used to record dictionary names, dictionary field names, and business modules affected by the data dictionary. For example, the funds and finance business modules would have dictionaries for banks, exchange rates, and company information, while the supply chain business module related to product manufacturing would have dictionaries for materials.

[0040] It should be noted that the business modules affected by the data dictionary can meet the different customization needs of different business modules.

[0041] On the other hand, the inner layer of the multi-layered JSON structure is determined. The inner layer is used to record a piece of data in the data dictionary. A piece of data includes a field name and the field value corresponding to the field name.

[0042] It should be noted that the field values ​​can support storing data structures supported by the database, including character data, numerical data, time data, etc.

[0043] One JSON data item can be used as a data unit to record one piece of data in the data dictionary, and multiple data units make up the complete data dictionary.

[0044] As shown in the structure below, the Dbo data points to the dictionary name, dictionary field names, and the business modules affected by the data dictionary. The Data field records the field names and values ​​in a single data entry.

[0045]

[0046] S102: Obtain system data from different data sources, perform format conversion on the system data, and fill the converted system data into the data dictionary template to obtain the data dictionary of the ERP system.

[0047] In some embodiments of this application, the sources of the data are compared, including different systems, different business segments, and specific methods of data acquisition. Different data sources include system database data, downloaded or exported standard Excel data, and data returned by data request interfaces, requiring conversion of the original system data from different data sources.

[0048] Specifically, it involves acquiring raw system data from different data sources.

[0049] Then, the fields of the standard Excel data are mapped to the fields of the data dictionary to obtain the corresponding standard data. For example, for Excel tabular data, the column headers in the table need to be mapped to the dictionary field names.

[0050] Furthermore, the returned data is parsed according to its data type to obtain the parsed returned data. For example, for API return data, the structure of the data is parsed according to the different data types to obtain the field values ​​of the corresponding fields, while ignoring irrelevant levels and fields.

[0051] Finally, based on the corresponding standard data, the parsed returned data, and the system database data, system data from different data sources are obtained.

[0052] In some embodiments of this application, different business types have different types of data dictionaries. For example, the business type may be a bank data dictionary or a material data dictionary.

[0053] Based on this, firstly, the business categories of the system data are determined. Then, the business categories are matched against a pre-built identifier mapping table to obtain unique data identifiers for each business category. Finally, these unique data identifiers are used to mark the data dictionary.

[0054] It should be noted that the identifier mapping table includes unique data identifiers corresponding to different business types.

[0055] For example, the interbank code in the bank's data dictionary serves as a unique data identifier.

[0056] For example, when updating bank information, the bank's interbank code is used as a unique data identifier, and the data of at least one field under the same identifier is compared.

[0057] S103: Deploy the data dictionary to different application systems.

[0058] In some embodiments of this application, it is necessary to connect to the application system.

[0059] Based on this, the first step is to determine the configuration information for each application system. This configuration information includes the database type, database connection address, database name, database table name, and database table structure.

[0060] Then, based on the configuration information, the database of each application system is connected to deploy the data dictionary to different application systems.

[0061] S104: Within a preset period, acquire updated system data from different data sources.

[0062] In other words, system data from different data sources is acquired periodically.

[0063] S105: Convert the format of the updated system data, fill the converted updated system data into the updated data dictionary template, and obtain the updated data dictionary of the ERP system.

[0064] It should be noted that updating the data dictionary template has the same structure as the data dictionary template; that is, updating the data dictionary template is equivalent to creating a new data dictionary template.

[0065] S106: Compare the updated data dictionary with the data dictionary to obtain the difference data.

[0066] In some embodiments of this application, the comparison process requires comparing fields, that is, comparing fields with the same meaning from two or more data sources. Furthermore, when setting comparison fields, a comparison identifier for that data entry must be determined. Therefore, an identifier must be provided when setting up the data dictionary; that is, a unique data identifier for the data dictionary.

[0067] Based on this, the data dictionary is updated to identify multiple field values ​​for unique data identifiers, as well as multiple data entries corresponding to a single field value.

[0068] Then, in the data dictionary, match multiple data entries that have the same value as a single field.

[0069] Finally, the updated data entries are compared with the original data entries to obtain the difference data.

[0070] In other words, the field values ​​in the updated data dictionary are compared with the field values ​​in the data dictionary.

[0071] Essentially, the comparison work involves updating existing pre-set data and storing the updated system data through a data dictionary template. This includes the dictionary name of the updated data dictionary template, as well as the complete field names and values ​​under the updated data dictionary template.

[0072] When making comparisons, updating the data dictionary is equivalent to comparing the data source, while the data dictionary acts as a reference data source. First, it is necessary to determine the field names and values ​​to be compared in the data source. Then, using the reference data source as a benchmark, the reference data is parsed and compared differentially to obtain the difference results.

[0073] The reference data must include at least one comparison field, as well as the field names of multiple comparison data in the comparison data source, that is, they have a corresponding relationship. The dictionary data to be compared, i.e., the field value, needs to be determined based on the name identifier.

[0074] S107: Update the data dictionary of each application system based on the difference data.

[0075] In some embodiments of this application, firstly, the database of each application system is connected according to the configuration information.

[0076] Then, within the database of each application system, it is determined whether the data dictionary exists.

[0077] If so, the difference data is matched in the data dictionary to obtain the target replacement data.

[0078] Replace the target replacement data with the difference data, and update the data dictionary for each application system.

[0079] In other words, after connecting to the database, depending on whether the data exists in the database, update or add operation statements are concatenated, the data that needs to be compared or synchronized is traversed, and the data is verified or converted according to the configuration information, so as to write the differential data that needs to be synchronized into the target database.

[0080] In some embodiments of this application, if a target application system does not have a data dictionary, feedback is sent to the management terminal of the target application system to indicate whether the data dictionary should be redeployed.

[0081] If yes, the updated data dictionary will be deployed to the target application system; otherwise, the updated data dictionary will not be deployed to the target application system.

[0082] It should be noted that, although the embodiments in this application are based on... Figure 1 Steps S101 to S107 will be described sequentially, but this does not mean that steps S101 to S107 must be performed in a strict order. The reason this embodiment follows this order is... Figure 1 The order in which steps S101 to S107 are described is provided to facilitate understanding of the technical solutions of the embodiments of this application by those skilled in the art. In other words, in the embodiments of this application, the order of steps S101 to S107 can be appropriately adjusted according to actual needs.

[0083] pass Figure 1 This method uses JSON format data, which is easy to deploy on computers and allows for differentiated comparisons using computer software, to store data dictionaries and generate data dictionary templates. This alleviates the differences in operations caused by different dictionaries having different JSONPATH depths, simplifies parsing steps, and improves data stability. It can support the generation of differential data and updated full files, and can be used for the full deployment of dictionaries in new application systems and incremental updates of dictionaries in application systems, simplifying the incremental data update process and improving the efficiency of incremental data updates.

[0084] Based on the same idea, some embodiments of this application also provide devices and non-volatile computer storage media corresponding to the above methods.

[0085] Figure 2 A schematic diagram of a data incremental update device provided in this application embodiment includes:

[0086] At least one processor; and,

[0087] A memory communicatively connected to the at least one processor; wherein,

[0088] The memory stores instructions executable by the at least one processor, which, when executed by the at least one processor, enable the at least one processor to:

[0089] Generate a data dictionary template for the ERP system based on a pre-defined multi-layered JSON structure;

[0090] Acquire system data from different data sources, convert the format of the system data, and fill the converted system data into the data dictionary template to obtain the data dictionary of the ERP system.

[0091] Deploy the data dictionary to different application systems;

[0092] Within a preset period, acquire updated system data from different data sources;

[0093] The updated system data is format-converted, and the converted updated system data is filled into the updated data dictionary template to obtain the updated data dictionary of the ERP system;

[0094] The updated data dictionary is compared with the data dictionary to obtain the difference data;

[0095] Based on the differential data, the data dictionary of each application system is updated.

[0096] Some embodiments of this application provide a non-volatile computer storage medium for incremental data updates, storing computer-executable instructions, wherein the computer-executable instructions are configured as follows:

[0097] Generate a data dictionary template for the ERP system based on a pre-defined multi-layered JSON structure;

[0098] Acquire system data from different data sources, convert the format of the system data, and fill the converted system data into the data dictionary template to obtain the data dictionary of the ERP system.

[0099] Deploy the data dictionary to different application systems;

[0100] Within a preset period, acquire updated system data from different data sources;

[0101] The updated system data is format-converted, and the converted updated system data is filled into the updated data dictionary template to obtain the updated data dictionary of the ERP system;

[0102] The updated data dictionary is compared with the data dictionary to obtain the difference data;

[0103] Based on the differential data, the data dictionary of each application system is updated.

[0104] The various embodiments in this application are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the device and medium embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the description of the method embodiments.

[0105] The devices and media provided in this application are one-to-one with the methods. Therefore, the devices and media also have similar beneficial technical effects as their corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the devices and media will not be repeated here.

[0106] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0107] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0108] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0109] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable apparatus for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0110] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0111] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0112] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0113] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0114] The above description is merely an embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the technical principles of this application should fall within the protection scope of this application.

Claims

1. A data incremental update method, characterized in that, The method includes: Generate a data dictionary template for the ERP system based on a pre-defined multi-layered JSON structure; Acquire system data from different data sources, convert the format of the system data, and fill the converted system data into the data dictionary template to obtain the data dictionary of the ERP system. Deploy the data dictionary to different application systems; Within a preset period, acquire updated system data from different data sources; The updated system data is format-converted, and the converted updated system data is filled into the updated data dictionary template to obtain the updated data dictionary of the ERP system; The updated data dictionary is compared with the data dictionary to obtain the difference data; Based on the differential data, the data dictionary of each application system is updated; Before generating the data dictionary template for the ERP system based on a pre-defined multi-layered JSON structure, the method further includes: Determine the outer layer of the multi-layered JSON structure, which is used to record dictionary names, dictionary field names, and business modules affected by the data dictionary; The inner layer of a multi-layered JSON structure is determined. The inner layer is used to record a single piece of data in the data dictionary. A single piece of data includes a field name and the field value corresponding to the field name.

2. The method according to claim 1, characterized in that, After filling the converted system data into the data dictionary template to obtain the data dictionary of the ERP system, the method further includes: Determine the service type of the system data; In a pre-built identifier mapping table, the business type is matched to obtain a unique data identifier for the business type; The unique data identifier is used to mark the data dictionary.

3. The method according to claim 2, characterized in that, The step of comparing the updated data dictionary with the data dictionary to obtain the difference data specifically includes: In the updated data dictionary, multiple field values ​​for unique data identifiers are determined, as well as multiple data entries corresponding to a single field value; In the data dictionary, match multiple data entries that have the same value as a single field. The difference data is obtained by comparing multiple data entries in the updated data dictionary with multiple data entries in the data dictionary.

4. The method according to claim 1, characterized in that, Deploying the data dictionary to different application systems specifically includes: Determine the configuration information for each application system; the configuration information includes database type, database connection address, database name, database table name, and database table structure; Based on the configuration information, connect to the database of each application system to deploy the data dictionary to different application systems.

5. The method according to claim 4, characterized in that, The step of updating the data dictionary of each application system based on the difference data specifically includes: Based on the configuration information, connect to the database of each application system; In the database of each application system, determine whether the data dictionary exists; If so, the difference data is matched in the data dictionary to obtain the target replacement data; The difference data is used to replace the target replacement data, and the data dictionary of each application system is updated.

6. The method according to claim 5, characterized in that, The method further includes: If there is a target application system that does not have the data dictionary, then feedback should be sent to the management terminal of the target application system to ask whether to redeploy the data dictionary; If so, the updated data dictionary will be deployed to the target application system.

7. The method according to claim 1, characterized in that, Before acquiring system data from different data sources, the method further includes: The data sources include system database data, downloaded or exported standard Excel data, and data returned from data request interfaces; Obtain raw system data from different data sources; The fields of the Excel standard data are mapped to the fields of the data dictionary to obtain the corresponding standard data; Based on the data type of the returned data, the returned data is parsed to obtain the parsed returned data; Based on the corresponding standard data, the parsed returned data, and the system database data, system data from different data sources are obtained.

8. A data incremental update device, characterized in that, include: At least one processor; as well as, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to: perform a data incremental update method as described in any one of claims 1-7.

9. A non-volatile computer storage medium for incremental data updates, storing computer-executable instructions, characterized in that, The computer-executable instructions are configured to execute a data incremental update method as described in any one of claims 1-7.

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