Data processing method and device, electronic equipment and storage medium

Abstract

The invention discloses a data processing method and device, electronic equipment and a storage medium, and the data processing method comprises the steps: obtaining a data matrix of main data, and the data matrix comprises data attributes and operation types; determining a data source of the main data according to the data matrix; configuring a main data physical model according to the data source; and performing data service configuration on the master data according to the data source and the master data physical model to obtain target master data, the data service configuration including data input parameter configuration, data output parameter configuration and filtering condition configuration. The technical problem that in the prior art, the processing efficiency of the main data processing process is low is solved.

Description

Technical Field

[0001] This invention relates to the field of automation control technology, and more specifically, to a data processing method, apparatus, electronic device, and storage medium. Background Technology

[0002] As enterprises accelerate their digital transformation, the demand for high-quality, efficient master data processing is becoming increasingly urgent. Business departments expect to quickly obtain accurate and consistent master data information to support scientific decision-making and precise operations, while IT departments need to simplify data integration processes, reduce maintenance costs, and improve the efficiency of collaboration between systems.

[0003] However, in today's data-driven business environment, enterprise master data governance faces a series of challenges and shortcomings, particularly in terms of processing efficiency. On the one hand, traditional master data management often relies on manual or semi-automatic methods, resulting in time-consuming and error-prone data entry, review, and update processes, making it difficult to adapt to rapidly changing business needs. On the other hand, the numerous internal systems within enterprises create obstacles to the integration and sharing of master data across different systems, increasing data redundancy and conflicts, further slowing down the governance process. Furthermore, the lack of unified data quality standards and evaluation systems leads to frequent data errors and inconsistencies, directly impacting business decision-making and operational efficiency.

[0004] Therefore, there is an urgent need for an innovative data processing method to make up for the shortcomings of existing technologies. Summary of the Invention

[0005] The present invention provides a data processing method, apparatus, electronic device, and storage medium to at least solve the technical problem of low processing efficiency in the master data processing process in the prior art.

[0006] According to one embodiment of the present invention, a data processing method is provided, comprising: obtaining a data matrix of master data, wherein the data matrix includes data attributes and operation types; determining the data source of the master data based on the data matrix; configuring a physical model of the master data based on the data source; and configuring data services for the master data based on the data source and the physical model of the master data to obtain target master data, wherein the data service configuration includes data input parameter configuration, data output parameter configuration, and filtering condition configuration.

[0007] Optionally, the data processing method may further include: obtaining master data processing requirements; generating master data processing tasks based on the master data maturity assessment model and master data processing requirements; and determining a data matrix based on the master data processing tasks.

[0008] Optionally, the data processing method further includes: determining the output data items of the master data based on the data matrix; and determining the data source based on the output data items and the data logic model.

[0009] Optionally, the data processing method further includes: determining the data attributes of the master data based on the data source and the master data identification model; and configuring the master data physical model based on the data attributes.

[0010] Optionally, the data processing method also includes: generating a data service interface for master data based on the data source and the master data physical model.

[0011] Optionally, the data processing method further includes: obtaining multiple data type fields of the target master data; obtaining multiple data validation rules for the multiple data type fields, wherein the multiple data type fields and the multiple data validation rules correspond one-to-one; performing data validation on the multiple data type fields according to the multiple data validation rules, and obtaining a validation report.

[0012] Optionally, the data processing method further includes: obtaining data identification rules for the target master data; determining a problem list for the target master data based on the data identification rules, wherein the problem list includes multiple master data problems; obtaining multiple solutions for the multiple master data problems, wherein the multiple master data problems and multiple solutions correspond one-to-one; and processing the multiple master data problems according to the multiple solutions.

[0013] According to one embodiment of the present invention, a data processing apparatus is also provided, comprising: a first acquisition module for acquiring a data matrix of master data, wherein the data matrix includes data attributes and operation types; a first determination module for determining the data source of the master data based on the data matrix; a first configuration module for configuring a physical model of the master data based on the data source; and a second configuration module for configuring data services on the master data based on the data source and the physical model of the master data to obtain target master data, wherein the data service configuration includes data input parameter configuration, data output parameter configuration, and filtering condition configuration.

[0014] Optionally, the first acquisition module includes: an acquisition unit for acquiring master data processing requirements; a generation unit for generating master data processing tasks based on the master data maturity assessment model and the master data processing requirements; and a first determination unit for determining a data matrix based on the master data processing tasks.

[0015] Optionally, the first determining module includes: a second determining unit, used to determine the output data items of the master data based on the data matrix; and a third determining unit, used to determine the data source based on the output data items and the data logic model.

[0016] Optionally, the first configuration module includes: a fourth determining unit, used to determine the data attributes of the master data based on the data source and the master data identification model; and a configuration unit, used to configure the master data physical model based on the data attributes.

[0017] Optionally, the data processing apparatus further includes: a generation module for generating a data service interface for master data based on the data source and the master data physical model.

[0018] Optionally, the data processing device further includes: a second acquisition module for acquiring multiple data type fields of the target master data; a third acquisition module for acquiring multiple data validation rules for the multiple data type fields, wherein the multiple data type fields and the multiple data validation rules correspond one-to-one; and a validation module for performing data validation on the multiple data type fields according to the multiple data validation rules to obtain a validation report.

[0019] Optionally, the data processing device further includes: a fourth acquisition module for acquiring data identification rules for the target master data; a second determination module for determining a problem list for the target master data according to the data identification rules, wherein the problem list includes multiple master data problems; a fifth acquisition module for acquiring multiple solutions for the multiple master data problems, wherein the multiple master data problems and multiple solutions correspond one-to-one; and a processing module for processing the multiple master data problems according to the multiple solutions.

[0020] According to one embodiment of the present invention, an electronic device is also provided, including a memory and a processor, wherein the memory stores a computer program and the processor is configured to run the computer program to perform the data processing method described in any of the preceding claims.

[0021] According to one embodiment of the present invention, a non-volatile storage medium is also provided, wherein a computer program is stored in the non-volatile storage medium, and the computer program is configured to execute the data processing method described in any of the above-mentioned embodiments when running.

[0022] According to one embodiment of the present invention, a computer program product is also provided, which stores a computer program, wherein the computer program, when executed by a processor, implements the steps of the data processing method described in any of the above claims.

[0023] In this embodiment of the invention, a data matrix for acquiring master data is used. The data matrix includes data attributes and operation types. The data source of the master data is determined based on the data matrix, thereby achieving the purpose of configuring the physical model of master data according to the data source. This achieves the technical effect of configuring data services for master data based on the data source and the physical model of master data to obtain the target master data. The data service configuration includes data input parameter configuration, data output parameter configuration, and filtering condition configuration, which can solve the technical problem of low processing efficiency in the master data processing process in the prior art. Attached Figure Description

[0024] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0025] Figure 1 This is a flowchart of a data processing method according to one embodiment of the present invention;

[0026] Figure 2 This is a structural block diagram of a data processing apparatus according to one embodiment of the present invention. Detailed Implementation

[0027] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0028] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such terms can be used interchangeably where appropriate so that embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0029] According to an embodiment of the present invention, an embodiment of a data processing method is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system containing at least one set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0030] This method embodiment can also be performed in an electronic device, similar control device, or electronic device that includes a memory and a processor. Taking an electronic device as an example, the electronic device may include one or more processors and a memory for storing data. Optionally, the aforementioned electronic device may also include a communication device for communication functions and a display device. Those skilled in the art will understand that the above structural description is merely illustrative and does not limit the structure of the aforementioned electronic device. For example, the electronic device may also include more or fewer components than those described above, or have a different configuration than those described above.

[0031] A processor may include one or more processing units. For example, a processor may include a central processing unit (CPU), a graphics processing unit (GPU), a digital signal processing (DSP) chip, a microprocessor, a field-programmable gate array (FPGA), a neural network processing unit (NPU), a tensor processing unit (TPU), or an artificial intelligence (AI) type processor. Different processing units may be independent components or integrated into one or more processors. In some instances, electronic devices may also include one or more processors.

[0032] The memory can be used to store computer programs, such as the computer program corresponding to the data processing method in the embodiments of the present invention. The processor implements the above-described data processing method by running the computer program stored in the memory. The memory may include high-speed random access memory and non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include memory remotely located relative to the processor, and these remote memories can be connected to the electronic device via a grid. Examples of such grids include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0033] The communication device is used to receive or transmit data via a grid. Specific examples of the aforementioned grid may include a wireless grid provided by the mobile terminal's communication provider. In one example, the communication device includes a network interface controller (NIC), which can connect to other grid devices via a base station to communicate with the Internet. In another example, the communication device may be a radio frequency (RF) module used for wireless communication with the Internet. In some embodiments of this solution, the communication device is used to connect to mobile devices such as mobile phones and tablets, enabling the mobile device to send commands to the electronic device.

[0034] The display device can be a touchscreen liquid crystal display (LCD) or a touch display (also referred to as a "touchscreen" or "touch screen"). The LCD allows the user to interact with the user interface of the in-vehicle terminal. In some embodiments, the electronic device has a graphical user interface (GUI), allowing the user to interact with the GUI through finger contact and / or gestures on a touch-sensitive surface. This human-machine interaction function may include a vehicle gear shifting function. Executable instructions for performing the aforementioned human-machine interaction function are configured / stored in one or more processor-executable computer program products or readable storage media.

[0035] Figure 1 This is a flowchart of a data processing method according to one embodiment of the present invention, such as... Figure 1 As shown, the method includes the following steps:

[0036] Step S102: Obtain the data matrix of the master data, wherein the data matrix includes data attributes and operation types.

[0037] Optionally, the execution subject in this embodiment is a data processing system. It should be noted that other electronic devices and processors can also serve as the execution subject, and no further limitations are imposed here.

[0038] In the technical solution provided by step S102 of the present invention, firstly, through in-depth research on business activities, all business units involved in master data management are identified, including data creators, users, and maintainers. Simultaneously, the enterprise's information systems, especially those application systems that store and process master data, are analyzed to determine the data flow path. The initial research results can be organized into a set of data attributes, covering key information such as the name, type, definition, and source of the master data.

[0039] Next, define the operation types, which are the operations that business units can perform on master data, including but not limited to Create, Read, Update, and Delete. Each operation is matched with a specific business scenario and system function to ensure the correctness and legality of data processing.

[0040] Based on the above survey results, a data matrix was generated, where each row represents a master data attribute, each column represents an operation type, and each element in the matrix represents the permission level of a business unit for a specific attribute. For example, a business unit may have CRUD (Create, Read, Update, Delete) permissions for employee IDs, but only R (Read) permissions for bank account information.

[0041] Specifically, data attributes refer to the basic components that make up master data, which usually include the field name, data type, definition and description, as well as details such as whether null values ​​are allowed.

[0042] Specifically, operation type refers to the specific operation performed by a business unit on data attributes, such as Create, Read, Update, and Delete, abbreviated as CRUD. These are the four most common basic operations in data management.

[0043] As an alternative implementation, automation tools can be used to perform deep learning on data flows and business processes within information systems, automatically identifying data attributes and operation types related to master data. Through pre-trained algorithm models, the tool can scan existing business processes and information systems, quickly extract data attributes, and automatically label the operational permissions of business units for these attributes. This approach not only improves the speed of data matrix construction but also enhances the accuracy of data attribute identification and the rationality of operation types by reducing human intervention.

[0044] It is worth noting that clearly defining the operational permissions of each business unit for specific data attributes effectively controls data access and enhances data security. By recording the time, operator, and operation type of each data operation, a basis can be provided for data auditing and problem tracing, thereby improving the transparency of data governance.

[0045] Furthermore, complex master data operations are simplified into standardized data matrices, reducing the complexity of data management and improving data processing efficiency. In addition, ensuring all business units adhere to unified operating procedures promotes cross-departmental collaboration while complying with corporate data management policies, guaranteeing business continuity and compliance.

[0046] Step S104: Determine the data source of the master data based on the data matrix.

[0047] In the technical solution provided in step S104 of the present invention, the main sources of data are identified by analyzing the operation types of data attributes by each business unit in the data matrix, especially creation and update operations. Generally, business units that create and frequently update data can be considered potential data sources.

[0048] Furthermore, by reviewing the enterprise information architecture, it's possible to determine which systems are configured as data input points. By comparing system configurations with business requirements, the authenticity and authority of the data source are confirmed. If multiple systems claim update permissions for the same data, further evaluation is needed to determine which system's data is more comprehensive and updated more frequently, thus identifying the final data source. Data quality testing is then conducted on the selected candidate data sources, including completeness, consistency, accuracy, and timeliness. The system with the highest data quality will be confirmed as the primary data source.

[0049] Specifically, the aforementioned data source refers to the business unit or information system responsible for generating, maintaining, and providing the original records of master data. The determination of the data source can be used to ensure data consistency.

[0050] As an alternative implementation, artificial intelligence algorithms, such as machine learning models, are used to automatically analyze data flow patterns within the enterprise. By identifying nodes where frequent data exchanges occur, the location of data sources can be inferred. Furthermore, the AI ​​model intelligently weighs the operation types in the data matrix, system configuration information, and historical data operation records to determine single or multiple data sources. This model can take into account the timeliness of data and the credibility of business units, thereby making more accurate decisions.

[0051] It is worth noting that by identifying the data source of the master data based on the data matrix, a unique and reliable source of data can be clearly identified, reducing data conflicts and inconsistencies. Furthermore, data integration based on the identified data source avoids the complexity and potential errors caused by acquiring data from multiple sources. Moreover, the selection of data sources helps promote the standardization of data formats and definitions, providing a standardized benchmark for subsequent data governance.

[0052] Step S106: Configure the master data physical model according to the data source.

[0053] In the technical solution provided in step S106 of the present invention, the data attributes in the data source are analyzed, including field names, data types, lengths, default values, etc., to ensure that the physical model covers all necessary attributes. Based on relational database design principles, a physical model is designed. The specific design process may include determining the table structure, primary keys, foreign keys, and indexes to ensure data uniqueness, relevance, and query efficiency.

[0054] Furthermore, the three normal forms of database design (first normal form, second normal form, and third normal form) are applied to eliminate data redundancy, ensure data independence, and reduce storage space requirements. Finally, data insertion, updating, and querying are tested in the completed physical model to verify the model's integrity and effectiveness, ensuring it meets business requirements.

[0055] Specifically, the three normal forms of relational database design can be used to ensure the normalization of database design and reduce data redundancy and dependencies. These include the first normal form (atomicity), the second normal form (full dependency), and the third normal form (non-transitive dependency).

[0056] As an alternative implementation, a suitable physical model template can be selected based on the characteristics of the data source. The attribute mapping function of the low-code platform can then be used to match the attributes in the data source with the fields in the template, ensuring model compatibility with the data source. The platform automatically generates the physical model based on the attribute matching results and preset design rules, including the creation of forms, fields, and relationships. Furthermore, the data processing capabilities of the physical model are tested in real time, and necessary adjustments are made based on the test results until the model can accurately and efficiently process the data from the data source.

[0057] It's worth noting that a well-designed physical model ensures optimized data structure during storage, improving readability and maintainability. Furthermore, adopting the third normal form reduces data redundancy, saves storage space, and improves data query efficiency. In addition, the close connection between the physical model and the data source effectively guarantees data consistency across different systems and applications.

[0058] Step S108: Configure data services for the master data based on the data source and the master data physical model to obtain the target master data. The data service configuration includes data input parameter configuration, data output parameter configuration, and filtering condition configuration.

[0059] In the technical solution provided in step S108 of this invention, the input requirements of the data source are analyzed, the list of parameters required for service calls is determined, such as user ID and timestamp, and the input parameter format is designed, including parameter type, length, and default value, to ensure the legality and completeness of the request. Furthermore, based on the physical model definition, the data fields and structure returned in the service response are determined, such as the ID, name, and type of the master data, and the output parameter format is standardized, including data type and encoding method, to ensure the consistency and availability of the returned data. Next, filtering rules are set during service execution, such as filtering data by department or date range, and conditional expressions are configured to ensure that the data service can provide data subsets as needed, improving data access efficiency and privacy protection.

[0060] Specifically, data input parameter configuration refers to defining the parameters required for the data service to receive requests, including parameter names, types, and formats, to ensure that the service receives correct data input.

[0061] Specifically, data output parameter configuration refers to defining the data structure and content returned after the data service is completed, including the definition and data type of the returned fields, to ensure that the service output meets the requirements.

[0062] Specifically, filtering condition configuration refers to setting the conditions used to filter data during the execution of data services, and using logical expressions to achieve on-demand data extraction, thereby improving the flexibility and efficiency of data services.

[0063] As an alternative implementation, physical model and data source information can be imported as a basic template for service configuration. The platform automatically parses the model structure and data source characteristics, preparing the starting point for service configuration. Furthermore, utilizing the platform's pre-built rule engine, it intelligently recommends input and output parameter configurations, reducing errors and workload from manual input. Simultaneously, it automatically populates the parameter list and format based on common service call requirements. In addition, the platform automatically generates potential filtering options based on data source attributes and physical model fields. Users only need to select applicable filtering conditions, and the platform automatically writes the conditional expressions, simplifying the service customization process.

[0064] It's worth noting that ensuring the consistency and standardization of data service interfaces facilitates seamless integration between different systems. Furthermore, setting filtering conditions allows for the rapid retrieval and extraction of data subsets, reducing unnecessary data transfer and improving data access speed. In addition, strict configuration of input and output parameters restricts the service's invocation methods and returned content, enhancing the security of the data service.

[0065] From steps S102 to S108 above, it can be seen that in this invention, a data matrix for obtaining master data is used, wherein the data matrix includes data attributes and operation types, and the data source of the master data is determined according to the data matrix, thereby achieving the purpose of configuring the physical model of master data according to the data source. This achieves the technical effect of configuring data services for master data according to the data source and the physical model of master data to obtain the target master data. The data service configuration includes data input parameter configuration, data output parameter configuration, and filtering condition configuration, which can solve the technical problem of low processing efficiency in the master data processing process in the prior art.

[0066] The method described in this embodiment will now be described in further detail.

[0067] Step S1021: Obtain master data processing requirements;

[0068] Step S1022: Generate master data processing tasks based on the master data maturity assessment model and master data processing requirements;

[0069] Step S1023: Determine the data matrix based on the master data processing task.

[0070] In this embodiment, a business survey is first conducted, involving interviews with department heads to understand their specific needs for master data, including data attributes, data usage scenarios, and data access permissions. For example, the finance department might need access to employee salary information, while the sales department is more concerned with customer data. These needs are collected and documented through the survey, providing a basis for the next step of task generation.

[0071] Furthermore, the collected requirements are input into an existing master data maturity assessment model. This model quantifies the importance and urgency of data processing requirements based on the enterprise's data management standards and objectives, and then generates corresponding processing tasks. Optionally, the model may prioritize generating data processing tasks with a high impact on the business, such as fixing high-priority data errors and ensuring the accuracy of critical master data, based on the impact of the requirements and the integrity of the data.

[0072] Ultimately, the master data processing tasks are broken down into data attributes and operation types, forming a data matrix. Each task corresponds to one or more entries in the data matrix, which detail which business units can perform what operations on which data attributes. For example, if the "Finance Department" needs to perform "read" and "update" operations on the "Employee Salaries" item, this will be marked at the corresponding location in the data matrix, ensuring the transparency and accuracy of permission allocation.

[0073] Specifically, the Master Data Maturity Assessment Model is a framework for evaluating an enterprise's master data management level. It typically includes multiple dimensions such as data quality, data integrity, data compliance, and data utilization, helping enterprises identify the strengths and areas for improvement in master data management.

[0074] Specifically, a data matrix is ​​a table that records the permissions of business units to operate on data attributes. It shows in matrix form which data attributes different business units can read, write, update and delete, and is an important tool for permission allocation and monitoring in master data governance.

[0075] As an optional implementation, after receiving the master data planning task, data managers systematically conduct three research tasks: First, they research the business processes and units involved in the entire lifecycle of master data within the system. Second, they research the "information chain" of the information architecture center to identify the entire process of master data creation, updating, reading, and deletion in business activities. Finally, they research the "data flow" of the information architecture center to identify the various application systems involved in master data management.

[0076] Furthermore, based on the aforementioned survey results, the main data center integrates business units with complete CRUD (Create, Read, Update, Delete) management responsibilities with application systems that achieve 100% digital twin functionality. Through information chains and data flows, it visually displays the role of each business unit in master data management, as well as the business status (e.g., design, development, operation) and operation types (e.g., read, create, update, delete) of each business unit for various master data attributes. Once this matrix is ​​defined, the system automatically closes the corresponding governance plan tasks.

[0077] As an alternative implementation, the event listener and demand capture module of the digital platform can be used to automatically track and record dynamic operations and feedback related to master data in the business system, collecting real-time master data processing requirements. Optionally, the system can monitor frequent access to employee data in financial applications and automatically generate records of processing requirements for high-frequency access to employee data. Furthermore, the platform employs machine learning algorithms, combined with a master data maturity assessment model, to automatically analyze the collected master data processing requirements and generate a data processing task list. That is, through algorithmic analysis, the system can automatically identify abnormally high employee data update frequencies, potentially indicating data quality issues, and then generate tasks for checking and repairing employee data. Finally, the digital platform can automatically update the data matrix based on the generated master data processing tasks, dynamically adjusting the business units' access permissions to data attributes. For example, when the system detects that the "Finance Department" frequently accesses and updates "Employee Salary" data, it automatically enhances the Finance Department's CRUD permissions for this data in the data matrix and records these operations for subsequent auditing and management.

[0078] It's worth noting that by obtaining a data matrix from the master data, it's possible to ensure that master data processing requirements are accurately identified and recorded, providing a solid foundation for subsequent task generation. Furthermore, this simplifies the task generation process, improves efficiency, reduces human error, and makes the data governance process more agile and responsive. In addition, the determination of the data matrix enables fine-grained access control for master data attribute operations, enhancing data security and compliance.

[0079] Step S1041: Determine the output data items of the master data based on the data matrix;

[0080] Step S1042: Determine the data source based on the output data items and the data logic model.

[0081] In this embodiment, the operation types of business units on data attributes recorded in the data matrix, especially creation and update operations, are analyzed to determine the output data items, which are the master data attributes directly or indirectly generated or updated by the business units. For example, the data matrix shows that the finance department has write operations on "employee salaries" and "expense records," indicating that these two data items may originate from the finance department's business system.

[0082] Furthermore, by combining the data logic model, the flow path of output data items within the entire data ecosystem is analyzed to determine the initial input point of the data. This input point is the data source of the master data. For example, the logic model shows that the "employee salary" data item flows into the finance system after being updated by the human resources system, while "expense records" are generated directly by the finance system. Therefore, the human resources system and the finance system become the data sources for "employee salaries" and "expense records," respectively.

[0083] Specifically, output data items refer to the data attributes identified in the data matrix that are directly output or updated by the business unit, and are key to determining the data source.

[0084] Specifically, a data logic model is a model used to describe the logical relationships between data structures and attributes, and is used to analyze the flow path and source of data in business processes.

[0085] As an optional implementation, after receiving the master data architecture design task, data administrators, based on the defined master data management business units, break down the output data items, define logical entities, and standardize data standards according to enterprise data standards. Further, they design logical data models, clearly describe the relationships between entities, establish a mapping between business metadata and technical metadata, and identify and determine the unique trusted data source for master data based on the "information chain" and "data flow" identified in the first phase. Finally, the data is published to the enterprise data asset catalog.

[0086] As an alternative implementation, specialized automated analysis tools, combined with machine learning algorithms, can be used to automatically identify data sources based on data matrices and logical models. These tools infer data sources based on the frequency of data item generation, flow direction, and operation type. For example, the tool's analysis might show that the "employee salary" data item flows from the human resources system to the finance system at fixed times each month, while "expense records" are automatically generated within the finance system. Based on these analysis results, the automated tool determines that the human resources system and the finance system are the data sources for "employee salaries" and "expense records," respectively.

[0087] It is worth noting that the above technical steps can effectively identify the initial point of origin for master data, ensuring that the data governance process starts from the source and improving governance efficiency. Furthermore, a clearly defined single data source reduces data redundancy and conflicts, enhances the authority and credibility of master data, and facilitates cross-departmental data sharing and consistency maintenance.

[0088] Step S1061: Determine the data attributes of the master data based on the data source and the master data identification model;

[0089] Step S1062: Configure the master data physical model according to the data attributes.

[0090] In this embodiment, fields related to master data in the data source are analyzed. A master data identification model is used to filter and determine which fields should be part of the data attributes in the master data physical model. This specifically involves the classification, formatting, and standardization of data fields. For example, when processing "product" master data, the data source may be the databases of multiple business systems, containing information such as product ID, product name, product category, and production date. The master data identification model determines that product ID, product name, and product category are the key attributes of the master data, while production date and other attributes are secondary attributes.

[0091] Furthermore, based on the defined data attributes, design and configure the master data physical model, including defining table structures, field types, primary keys, foreign key relationships, and data integrity constraints. Optionally, the model design should follow standardization and best practices to ensure data consistency and integrity.

[0092] For example, in the physical model design of the "product" master data, create a table named PRODUCTS, which contains fields PRODUCT_ID (as the primary key to ensure uniqueness), PRODUCT_NAME, and PRODUCT_CATEGORY. Define the field types (such as text or number) and set data integrity rules, such as PRODUCT_NAME cannot be empty.

[0093] Specifically, the data source is the original point of data generation, which can be a business system database, a file, an external data provider, etc.

[0094] Specifically, a master data identification model is a framework or algorithm for analyzing and determining which fields or information should be considered master data attributes, typically based on business rules and data standards.

[0095] As an optional implementation, based on the published data asset catalog, the main data center first automatically identifies the core and related attributes of the master data through a built-in master data identification model. Subsequently, data administrators strictly adhere to the "first normal form, second normal form, and third normal form" principles of relational database design to design and configure the master data physical model. Through a low-code platform, the corresponding master data management interface, list display interface, and search criteria for complex business requirements are dynamically configured and automatically generated, achieving rapid and integrated construction of the model and interface.

[0096] Optionally, the first normal form (1NF) is the most basic requirement for relational database design. Its core is to ensure that the value of each field in the table is an indivisible "atomic value," guaranteeing data atomicity. The second normal form (2NF), building on the first normal form, further requires that all non-prime attributes must be fully functionally dependent on the entire primary key, and not just a part of it. The third normal form (3NF), building on the second normal form, requires that there be no transitive functional dependencies between all non-prime attributes. That is, no non-prime attribute can depend on any other non-prime attribute.

[0097] It is worth noting that the above technical steps ensure that data collected from different data sources can be uniformly managed and represented in the physical model, reducing data redundancy and inconsistency. Furthermore, by defining primary keys, foreign keys, and data integrity constraints, the physical model can automatically check and maintain data integrity, preventing the entry or storage of erroneous data. In addition, the standardized design of the physical model simplifies the data integration process, making interactions with other systems or data sources smoother and improving data availability and efficiency.

[0098] Step S110: Generate the data service interface for the master data based on the data source and the master data physical model.

[0099] In this embodiment, the storage location and format of the master data in the data source are obtained, compared and mapped with the master data physical model, and it is determined which data fields match the entities and attributes in the model. For example, it is determined that the "Employee ID" in the "Employee Information" data source corresponds to the "employee_id" field in the physical model, so as to ensure that the interface can access and manipulate the data accurately.

[0100] Furthermore, based on the structure of the physical model and the characteristics of the data source, design the specifications of the data service interface, including defining request parameters, response formats, data operation types (such as CRUD operations), and error handling mechanisms. For example, create a RESTful API that allows other systems to read, create, update, and delete employee information using HTTP methods (GET, POST, PUT, DELETE), while ensuring that the interface's response format is consistent with the physical model, such as returning employee data in JSON format.

[0101] Finally, the interface logic is implemented using a suitable programming language and framework, and unit and integration tests are performed to ensure that the interface can stably read data from the data source and operate according to the requirements of the physical model. For example, in a Java environment, the data service interface is implemented using the Spring Boot framework. The test verifies whether the interface can retrieve data for a specific employee from the "employee information" data source without errors and perform update operations without damaging the data structure.

[0102] Specifically, a data service interface refers to a standardized interface that provides access to and manipulation of data. It typically follows the design principles of RESTful APIs and supports HTTP methods such as GET, POST, PUT, and DELETE to enable data reading, creation, updating, and deletion.

[0103] Optionally, the physical model defines how data is organized and stored on physical storage media, including specific fields, data types, indexes, and stored procedures.

[0104] As an optional implementation, developers manually write the code for the data service interface based on the correspondence between the physical model and the data source. This includes data access logic, data conversion rules, and exception handling mechanisms. In other words, developers use SQL query language to extract employee information from the database, and then use an API written in Java to convert this information into JSON format for external systems to call.

[0105] As an alternative implementation, on low-code or no-code platforms, data service interfaces can be quickly configured and generated through graphical interfaces or preset templates. The platform automatically handles data source connections, data transformation, and interface documentation generation. For example, through a low-code platform, the development team only needs to drag and drop the "employee information" data source and the "employee" physical model. The platform automatically generates a series of standardized APIs, including interfaces for querying, adding, modifying, and deleting employee information. Simultaneously, the platform automatically generates interface documentation for easy understanding and use by other teams.

[0106] It's worth noting that providing a unified and standardized data access method enables different systems and applications to obtain and manipulate master data through the same interface, reducing the workload of redundant development and maintenance. Furthermore, isolating data sources at the interface layer prevents security risks associated with direct access, while access control and data encryption protect master data from unauthorized access and manipulation.

[0107] Step S112: Obtain multiple data type fields of the target master data;

[0108] Step S114: Obtain multiple data validation rules for multiple data type fields, wherein the multiple data type fields and multiple data validation rules correspond one-to-one;

[0109] Step S116: Perform data validation on multiple data type fields according to multiple data validation rules to obtain a validation report.

[0110] In this embodiment, information about each field of the target master data is extracted from the master data management system, including field name, data type (such as number, text, date), and field length, to prepare for subsequent validation rule matching. For example, fields such as "Customer ID," "Customer Name," and "Creation Date" are obtained from the "Customer" master data; these fields will be used in the subsequent data validation process.

[0111] Furthermore, pre-defined validation rules are assigned to each data type field. These rules typically include uniqueness checks, format validation, and range restrictions to ensure the accuracy and validity of field values. For example, uniqueness validation rules are assigned to the "Customer ID" field, text length and format validation rules are assigned to the "Customer Name" field, and date format validation rules are assigned to the "Creation Date" field.

[0112] Furthermore, data validation tools or APIs are used to compare the value of each field in the target master data with the corresponding validation rules, recording all fields that do not conform to the rules and their specific issues. For example, a data quality inspection tool can be used to perform a uniqueness check on "Customer ID" to find duplicate ID values; a length check on "Customer Name" can be performed to find excessively long names; and a format check on "Creation Date" can be performed to record cases of incorrect date formats.

[0113] Finally, the data validation results are summarized to generate a detailed validation report. The report includes the validation status of each field, details of records that failed validation, and suggested corrective actions. For example, the generated "Customer" master data validation report lists the validation results for each field in detail. For fields that failed validation, the specific problems are recorded, such as duplicate "Customer ID", name length exceeding the limit, and incorrect creation date format, along with corresponding solutions.

[0114] Specifically, data type fields refer to fields defined in the master data that have a specific data type, such as integer, floating-point, date, and text.

[0115] Specifically, data validation rules refer to rules used to check whether the values ​​of data type fields meet the expected standards or formats, such as non-empty checks, uniqueness checks, numerical range checks, date format checks, etc.

[0116] As an optional implementation, after establishing the master data synchronization link and generating data services, data management personnel conduct quality assessments based on the six characteristics of data quality management (uniqueness, accuracy, timeliness, completeness, consistency, and effectiveness): First, configure data quality business rules, specifying the rule name, description, effective period, and status; second, based on the data quality assessment model, configure data quality technical rules, detailing the rule name, application system, target data source, associated business rules, rule classification, type, and weight, and setting rules for automatically generating problem identifiers; finally, configure data quality assessment tasks, defining the task name, associated technical rules, effective period, and execution frequency. After the system runs the quality tasks, it automatically generates detailed data quality reports for each master data set.

[0117] Optionally, for different types of fields in the master data, specific quality rules need to be set according to the data standards.

[0118] 1) Encoding class: Check uniqueness (primary key constraint), null values, format, and length.

[0119] 2) Time-related: Validate the validity of date and time formats.

[0120] 3) Enumerated value class: Check whether the value is within the preset value range.

[0121] 4) Numerical: Validate the range of maximum and minimum values.

[0122] 5) Business Logic Class: Set complex rules based on business logic, such as the employee's appointment date must not be later than the start date, and the employee's date of birth must be consistent with the information extracted from the ID card number, etc.

[0123] Furthermore, the generated data quality report must reach 100 points to be considered qualified. The system monitors the fluctuations in master data quality in real time, and once abnormal data is detected, an alarm is immediately triggered and timely correction is promoted.

[0124] It's worth noting that ensuring master data field values ​​conform to expected formats and standards improves data accuracy, consistency, and reliability. Furthermore, validation reports allow for quick identification of problematic fields and specific records, facilitating timely data correction and preventing data errors from impacting business operations.

[0125] Step S118: Obtain the data identification rules for the target master data;

[0126] Step S120: Determine the problem list of the target master data according to the data identification rules, wherein the problem list includes multiple master data problems;

[0127] Step S122: Obtain multiple solutions to multiple master data problems, wherein there is a one-to-one correspondence between multiple master data problems and multiple solutions;

[0128] Step S124: Process multiple master data issues according to multiple solutions.

[0129] In this embodiment, predefined data identification rules are obtained from the master data governance strategy. These rules can help identify inconsistencies, errors, or missing parts of the data. For example, for the "supplier" master data, a set of rules is obtained, including supplier ID uniqueness, supplier name length limits, and supplier address format requirements.

[0130] Furthermore, data scanning and analysis tools are used to compare the target master data with data identification rules, identifying data items that do not conform to the rules and creating a problem list. For example, using an ETL tool to perform a uniqueness check on the supplier IDs in the "supplier" master data, multiple duplicate IDs are found and recorded in the problem list.

[0131] Furthermore, based on the master data governance experience library, corresponding solutions are retrieved or designed for each master data issue in the issue list. For example, for the issue of duplicate supplier IDs in the "supplier" master data, solutions designed include: merging duplicate records, reassigning unique IDs, and updating the data entry process.

[0132] Finally, using data governance tools and database operations, issues in the target master data are repaired or adjusted according to the solution to ensure data consistency with the rules. This involves using database update commands to automatically adjust supplier IDs based on the solution, ensuring their uniqueness. Simultaneously, data entry forms are updated to prevent ID duplication issues from recurring in the future.

[0133] Specifically, data identification rules are a set of predefined standards used to check whether master data meets specific quality requirements, such as uniqueness, correct format, and completeness.

[0134] Specifically, the issue list is a list of all master data quality issues generated after data validation or scanning, providing guidance for subsequent data cleaning and correction activities.

[0135] As an optional implementation method, Data Management combines AI big data model capabilities with preset master data issue identification rules to automatically identify and generate master data issue work orders. After an issue is reported, the system automatically assigns it based on issue attribution principles, and the receiving party must confirm receipt of the assigned issue. If the receiving party fails to receive the issue within the specified time, the system automatically accepts the issue and generates a preliminary rectification plan based on preset issue acceptance and rectification plan rules. The receiving party (or the system automatically) must perform rectification operations on the accepted issue and submit evidence of rectification completion as planned. Finally, the issue proposer verifies and closes the processing results, forming a management closed loop.

[0136] It is worth noting that data identification rules make problem discovery more systematic and accurate, reducing the subjectivity and omissions of human judgment. Furthermore, the matching of problem lists with solutions accelerates the problem localization and resolution process, improving the efficiency of data governance. In addition, the correction of master data issues significantly improves data integrity, accuracy, and consistency, enhancing the reliability and stability of the system.

[0137] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or grid device, etc.) to execute the methods of the various embodiments of the present invention.

[0138] This embodiment also provides a data processing apparatus for implementing the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the apparatus described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0139] Figure 2 This is a structural block diagram of a data processing apparatus 200 according to one embodiment of the present invention, such as... Figure 2 As shown, the device includes: a first acquisition module 201, a first determination module 202, a first configuration module 203, and a second configuration module 204.

[0140] The first acquisition module 201 is used to acquire the data matrix of the master data, wherein the data matrix includes data attributes and operation types;

[0141] The first determining module 202 is used to determine the data source of the master data based on the data matrix;

[0142] The first configuration module 203 is used to configure the master data physical model according to the data source;

[0143] The second configuration module 204 is used to configure data services for the master data according to the data source and the master data physical model to obtain the target master data. The data service configuration includes data input parameter configuration, data output parameter configuration and filtering condition configuration.

[0144] Optionally, the first acquisition module 201 includes: an acquisition unit for acquiring master data processing requirements; a generation unit for generating master data processing tasks based on the master data maturity assessment model and the master data processing requirements; and a first determination unit for determining a data matrix based on the master data processing tasks.

[0145] Optionally, the first determining module 202 includes: a second determining unit, used to determine the output data items of the master data according to the data matrix; and a third determining unit, used to determine the data source according to the output data items and the data logic model.

[0146] Optionally, the first configuration module 203 includes: a fourth determining unit, used to determine the data attributes of the master data based on the data source and the master data identification model; and a configuration unit, used to configure the master data physical model based on the data attributes.

[0147] Optionally, the data processing apparatus 200 further includes a generation module for generating a data service interface for master data based on the data source and the master data physical model.

[0148] Optionally, the data processing device 200 further includes: a second acquisition module for acquiring multiple data type fields of the target master data; a third acquisition module for acquiring multiple data validation rules for the multiple data type fields, wherein the multiple data type fields and the multiple data validation rules correspond one-to-one; and a validation module for performing data validation on the multiple data type fields according to the multiple data validation rules to obtain a validation report.

[0149] Optionally, the data processing device 200 further includes: a fourth acquisition module for acquiring data identification rules for the target master data; a second determination module for determining a problem list for the target master data according to the data identification rules, wherein the problem list includes multiple master data problems; a fifth acquisition module for acquiring multiple solutions to the multiple master data problems, wherein the multiple master data problems and the multiple solutions correspond one-to-one; and a processing module for processing the multiple master data problems according to the multiple solutions.

[0150] Embodiments of the present invention also provide an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to perform the above-described data processing method.

[0151] Optionally, in this embodiment, the electronic device may be configured to store a computer program for performing the following steps:

[0152] Step S102: Obtain the data matrix of the master data, wherein the data matrix includes data attributes and operation types;

[0153] Step S104: Determine the data source of the master data based on the data matrix;

[0154] Step S106: Configure the master data physical model according to the data source;

[0155] Step S108: Configure data services for the master data based on the data source and the master data physical model to obtain the target master data. The data service configuration includes data input parameter configuration, data output parameter configuration, and filtering condition configuration.

[0156] Optionally, the processor may also perform the following steps when executing the program: obtaining master data processing requirements; generating master data processing tasks based on the master data maturity assessment model and master data processing requirements; and determining the data matrix based on the master data processing tasks.

[0157] Optionally, the processor may also perform the following steps when executing the program: determining the output data items of the master data based on the data matrix; and determining the data source based on the output data items and the data logic model.

[0158] Optionally, the processor may also perform the following steps when executing the program: determining the data attributes of the master data based on the data source and the master data identification model; and configuring the master data physical model based on the data attributes.

[0159] Optionally, the processor also performs the following steps when executing the program: generating a data service interface for the master data based on the data source and the master data physical model.

[0160] Optionally, when the processor executes the program, it also performs the following steps: obtaining multiple data type fields of the target master data; obtaining multiple data validation rules for the multiple data type fields, wherein the multiple data type fields and the multiple data validation rules correspond one-to-one; performing data validation on the multiple data type fields according to the multiple data validation rules, and obtaining a validation report.

[0161] Optionally, when the processor executes the program, it also performs the following steps: obtaining the data identification rules of the target master data; determining the problem list of the target master data according to the data identification rules, wherein the problem list includes multiple master data problems; obtaining multiple solutions to the multiple master data problems, wherein the multiple master data problems and the multiple solutions correspond one-to-one; and processing the multiple master data problems according to the multiple solutions.

[0162] Optionally, specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementations, and will not be repeated here.

[0163] Embodiments of the present invention also provide a computer-readable storage medium storing a computer program configured to perform the above-described data processing method when run on a computer or processor.

[0164] Optionally, in this embodiment, the computer-readable storage medium may be configured to store a computer program for performing the following steps:

[0165] Step S102: Obtain the data matrix of the master data, wherein the data matrix includes data attributes and operation types;

[0166] Step S104: Determine the data source of the master data based on the data matrix;

[0167] Step S106: Configure the master data physical model according to the data source;

[0168] Step S108: Configure data services for the master data based on the data source and the master data physical model to obtain the target master data. The data service configuration includes data input parameter configuration, data output parameter configuration, and filtering condition configuration.

[0169] Optionally, the storage medium is configured to store program code for performing the following steps: obtaining master data processing requirements; generating master data processing tasks based on the master data maturity assessment model and the master data processing requirements; and determining a data matrix based on the master data processing tasks.

[0170] Optionally, the storage medium is configured to store program code for performing the following steps: determining the output data items of the master data based on the data matrix; and determining the data source based on the output data items and the data logical model.

[0171] Optionally, the storage medium is configured to store program code for performing the following steps: determining the data attributes of the master data based on the data source and the master data identification model; configuring the master data physical model based on the data attributes.

[0172] Optionally, the storage medium is configured to store program code for performing the following steps: generating a data service interface for master data based on the data source and the master data physical model.

[0173] Optionally, the storage medium is configured to store program code for performing the following steps: obtaining multiple data type fields of the target master data; obtaining multiple data validation rules for the multiple data type fields, wherein the multiple data type fields and the multiple data validation rules correspond one-to-one; performing data validation on the multiple data type fields according to the multiple data validation rules, and obtaining a validation report.

[0174] Optionally, the storage medium is configured to store program code for performing the following steps: obtaining data identification rules for the target master data; determining a problem list for the target master data based on the data identification rules, wherein the problem list includes multiple master data problems; obtaining multiple solutions for the multiple master data problems, wherein the multiple master data problems and the multiple solutions correspond one-to-one; and processing the multiple master data problems according to the multiple solutions.

[0175] Optionally, specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementations, and will not be repeated here.

[0176] Embodiments of the present invention also provide a computer program product, including a computer program, wherein the computer program, when executed by a processor, implements the steps of the above-described data processing method.

[0177] Optionally, in this embodiment, the computer program product described above may be configured to store a computer program for performing the following steps:

[0178] Step S102: Obtain the data matrix of the master data, wherein the data matrix includes data attributes and operation types;

[0179] Step S104: Determine the data source of the master data based on the data matrix;

[0180] Step S106: Configure the master data physical model according to the data source;

[0181] Step S108: Configure data services for the master data based on the data source and the master data physical model to obtain the target master data. The data service configuration includes data input parameter configuration, data output parameter configuration, and filtering condition configuration.

[0182] Optionally, when the computer program executes the program, it also performs the following steps: obtaining master data processing requirements; generating master data processing tasks based on the master data maturity assessment model and master data processing requirements; and determining the data matrix based on the master data processing tasks.

[0183] Optionally, when the computer program executes the program, it also performs the following steps: determining the output data items of the master data based on the data matrix; and determining the data source based on the output data items and the data logic model.

[0184] Optionally, when the computer program executes the program, it also performs the following steps: determining the data attributes of the master data based on the data source and the master data identification model; configuring the master data physical model based on the data attributes.

[0185] Optionally, when the computer program executes the program, it also performs the following steps: generating a data service interface for the master data based on the data source and the master data physical model.

[0186] Optionally, when the computer program executes the program, it also performs the following steps: obtaining multiple data type fields of the target master data; obtaining multiple data validation rules for the multiple data type fields, wherein the multiple data type fields and the multiple data validation rules correspond one-to-one; performing data validation on the multiple data type fields according to the multiple data validation rules, and obtaining a validation report.

[0187] Optionally, when the computer program executes the program, it also performs the following steps: obtaining the data identification rules of the target master data; determining the problem list of the target master data according to the data identification rules, wherein the problem list includes multiple master data problems; obtaining multiple solutions to the multiple master data problems, wherein the multiple master data problems and the multiple solutions correspond one-to-one; and processing the multiple master data problems according to the multiple solutions.

[0188] Optionally, specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementations, and will not be repeated here.

[0189] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0190] In the embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some interfaces; indirect couplings or communication connections between units or modules may be electrical or other forms.

[0191] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0192] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0193] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or grid device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0194] The above are merely preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A data processing method, characterized in that, include: Obtain the data matrix of the master data, wherein the data matrix includes data attributes and operation types; The data source for the master data is determined based on the data matrix. Configure the master data physical model according to the data source; Based on the data source and the master data physical model, data service configuration is performed on the master data to obtain the target master data. The data service configuration includes data input parameter configuration, data output parameter configuration, and filtering condition configuration.

2. The data processing method according to claim 1, characterized in that, The data matrix used to obtain the master data includes: Obtain master data processing requirements; Generate master data processing tasks based on the master data maturity assessment model and the master data processing requirements; The data matrix is ​​determined based on the master data processing task.

3. The data processing method according to claim 1, characterized in that, The data sources for determining the master data based on the data matrix include: The output data items of the master data are determined based on the data matrix; The data source is determined based on the output data items and the data logic model.

4. The data processing method according to claim 1, characterized in that, Configuring the master data physical model based on the data source includes: The data attributes of the master data are determined based on the data source and the master data identification model; Configure the master data physical model according to the data attributes.

5. The data processing method according to claim 1, characterized in that, The method further includes: The data service interface for the master data is generated based on the data source and the master data physical model.

6. The data processing method according to claim 1, characterized in that, The method further includes: Obtain multiple data type fields of the target master data; Obtain multiple data validation rules for the multiple data type fields, wherein the multiple data type fields and the multiple data validation rules correspond one-to-one; Data validation is performed on the multiple data type fields according to the multiple data validation rules to obtain a validation report.

7. The data processing method according to claim 1, characterized in that, The method further includes: Data identification rules for obtaining the target master data; A problem list for the target master data is determined based on the data identification rules, wherein the problem list includes multiple master data problems; Obtain multiple solutions to the multiple master data problems, wherein the multiple master data problems and the multiple solutions correspond one-to-one; The multiple master data issues are addressed using the multiple solutions described above.

8. A data processing apparatus, characterized in that, include: The first acquisition module is used to acquire a data matrix of master data, wherein the data matrix includes data attributes and operation types; The first determining module is used to determine the data source of the master data based on the data matrix; The first configuration module is used to configure the master data physical model according to the data source; The second configuration module is used to configure data services for the master data according to the data source and the master data physical model to obtain the target master data. The data service configuration includes data input parameter configuration, data output parameter configuration and filtering condition configuration.

9. An electronic device comprising a memory and a processor, characterized in that, The memory stores a computer program, and the processor is configured to run the computer program to perform the data processing method described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, wherein the computer program is configured to perform the data processing method described in any one of claims 1 to 7 when executed on a computer or processor.

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