Data processing methods, systems, and storage media for aviation operations

Abstract

This application provides a data processing method, system, and storage medium for aviation operations, relating to the field of data processing technology. The method includes: in response to a business operation, generating a target data table corresponding to an aviation business entity in a local database of a business system based on visual configuration information; generating at least one data transformation rule associated with the aviation business entity based on rule configuration operations for the target data table; receiving business data and transforming the business data based on the at least one data transformation rule to obtain transformed data; and storing the transformed data in the local database of the business system.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to a data processing method, system and storage medium for aviation operations. Background Technology

[0002] In the core business operations of air transport companies, such as aircraft asset lifecycle management, flight operation efficiency analysis, and financial middle-office accounting, the business rules and data entities (such as aircraft, routes, and revenue items) are complex and constantly evolving. To adapt to new regulatory policies (such as changes in aircraft leasing accounting standards) or meet new management analysis dimensions (such as newly added route operation indicators), business systems often face an urgent need to adjust the underlying data storage structure. This means creating new physical tables in the database or expanding fields in existing tables to accommodate and define new business entity attributes.

[0003] Currently, the mainstream technical solutions supporting this type of business logic treat the data model (i.e., the physical table structure of the database) as a predefined, static underlying infrastructure during the system design phase. When business requirements change and the data model needs to be adjusted, a lengthy and highly coupled technical process must be followed: developers must manually write database scripts, coordinate with database administrators for execution, then synchronously modify the relevant data access layer code, business logic, and rule definitions in the application, and finally go through a complete testing, packaging, and redeployment process. This static management model, deeply bound to the program code, results in a severe lag in the system's response to business changes, and each change is accompanied by high development and maintenance costs and the risk of system service interruption. Summary of the Invention

[0004] This application provides a data processing method, system, and storage medium for aviation operations, which enhances the overall agility and stability of the system in the face of change.

[0005] To achieve the above objectives, this application adopts the following technical solution: In a first aspect, a data processing method for aviation business is provided, the method comprising: in response to business operations, generating a target data table corresponding to an aviation business entity in a local database of a business system based on visual configuration information; generating at least one data transformation rule associated with the aviation business entity based on rule configuration operations for the target data table; receiving business data and performing transformation processing on the business data based on at least one data transformation rule to obtain transformed data; and storing the transformed data in the local database of the business system.

[0006] In one possible implementation, based on rule configuration operations for the target data table, at least one data transformation rule associated with the aviation business entity is generated, including: obtaining transformation rule information, which includes at least a source model table identifier, a target model table identifier, and an assignment rule configured for the target field in the physical table corresponding to the target model table identifier; wherein the formula type of the assignment rule includes at least one of source field, fixed value, file mapping, or enable condition; and generating the data transformation rule based on the transformation rule information.

[0007] In one possible implementation, the formula type of the assignment rule is file mapping; based on at least one data transformation rule, the business data is transformed to obtain the transformed data, including: determining the target information of the target field based on the preset mapping relationship, the file code associated with the file mapping, and the source field value in the business data; wherein, the preset mapping relationship is the mapping relationship between the file code, the source field value, and the target information.

[0008] In one possible implementation, business data is transformed based on at least one data transformation rule to obtain transformed data, including: obtaining the chained execution order corresponding to the data transformation rule; sequentially calling the execution interfaces corresponding to multiple data transformation rules according to the chained execution order to continuously transform the business data; wherein the output data of the previous data transformation rule is used as the input data of the next data transformation rule.

[0009] In one possible implementation, the visual configuration information includes form configuration information and field configuration information; based on the visual configuration information, a target data table corresponding to the aviation business entity is generated in the local database of the business system, including: validating the form configuration information and field configuration information, and generating a structured query language statement based on the validated configuration information after the validation passes; and generating the target data table in the local database based on the structured query language statement.

[0010] In one possible implementation, the method further includes: upon receiving a business operation request, verifying whether the current user has the permission to perform the business operation through an access control component; and after successful verification, receiving form configuration information and field configuration information input through the front-end interface.

[0011] In one possible implementation, the method further includes: receiving a model calculation request and calculation input data, wherein the model calculation request is associated with a corresponding rule group; performing conversion processing on the calculation input data according to the data conversion rules corresponding to the rule group to obtain calculation result data; and recording the calculation result data and the execution status of the model calculation request.

[0012] In one possible implementation, receiving model calculation requests and calculation input data includes: receiving calculation input data imported in batches via files based on a data import interface; and / or receiving calculation input data manually entered via a user interface based on a data input interface.

[0013] In one possible implementation, before responding to a business operation, the method further includes: deploying the compiled artifacts of the rule engine to the application server corresponding to the business system; and configuring the connection information and permission information between the rule engine component and the local database of the business system.

[0014] In one possible implementation, the method further includes: loading at least one data transformation rule and / or file mapping data into an in-memory database for caching; and synchronously updating the cached data in the in-memory database when at least one data transformation rule and / or file mapping data is updated.

[0015] Secondly, a data processing system is provided, comprising: a processing unit and a communication unit; the processing unit is configured to: generate a target data table corresponding to an aviation business entity in a local database of the business system based on visual configuration information in response to a business operation; the processing unit is further configured to: generate at least one data transformation rule associated with the aviation business entity based on rule configuration operations for the target data table; the communication unit is configured to: receive business data; the processing unit is further configured to: perform transformation processing on the business data based on at least one data transformation rule to obtain transformed data; the processing unit is further configured to: store the transformed data in the local database of the business system.

[0016] In one possible implementation, the processing unit is specifically configured to: acquire transformation rule information, which includes at least a source model table identifier, a target model table identifier, and an assignment rule configured for the target field in the physical table corresponding to the target model table identifier; wherein the formula type of the assignment rule includes at least one of source field, fixed value, file mapping, or enable condition; and generate data transformation rules based on the transformation rule information.

[0017] In one possible implementation, the formula type of the assignment rule is file mapping; the processing unit is specifically configured to: determine the target information of the target field based on the preset mapping relationship, the file code associated with the file mapping, and the source field value in the business data; wherein, the preset mapping relationship is the mapping relationship between the file code, the source field value, and the target information.

[0018] In one possible implementation, the processing unit is specifically configured to: obtain the chained execution order corresponding to the data transformation rules; sequentially call the execution interfaces corresponding to multiple data transformation rules according to the chained execution order to continuously transform the business data; wherein the output data of the previous data transformation rule is used as the input data of the next data transformation rule.

[0019] In one possible implementation, the visual configuration information includes form configuration information and field configuration information; the processing unit is specifically configured to: perform legality verification on the form configuration information and field configuration information, and after the verification is passed, generate a structured query language statement based on the verified configuration information; and generate a target data table in the local database based on the structured query language statement.

[0020] In one possible implementation, the processing unit is further configured to: upon receiving a business operation request, verify whether the current user has the permission to perform the business operation through the permission control component; and after successful verification, receive form configuration information and field configuration information input through the front-end interface.

[0021] In one possible implementation, the processing unit is further configured to: receive a model calculation request and calculation input data, wherein the model calculation request is associated with a corresponding rule group; perform conversion processing on the calculation input data according to the data conversion rules corresponding to the rule group to obtain calculation result data; and record the calculation result data and the execution status of the model calculation request.

[0022] In one possible implementation, the processing unit is specifically configured to: receive calculation input data imported in batches from files via a data import interface; and / or receive calculation input data manually entered via a user interface via a data input interface.

[0023] In one possible implementation, before responding to a business operation, the processing unit is further configured to: deploy the compiled artifacts of the rule engine to the application server corresponding to the business system; and configure the connection information and permission information between the rule engine component and the local database of the business system.

[0024] In one possible implementation, the processing unit is further configured to: load at least one data transformation rule and / or file mapping data into an in-memory database for caching; and synchronously update the cached data in the in-memory database when at least one data transformation rule and / or file mapping data is updated.

[0025] The technical effects of any implementation method in the second aspect can be found in the technical effects of any implementation method in the first aspect mentioned above, and will not be repeated here.

[0026] Thirdly, a computer device is provided, comprising: a processor and a memory, wherein the memory stores at least one computer program, and the at least one computer program is loaded and executed by the processor to implement the above-mentioned report generation method for civil aviation operations.

[0027] Fourthly, a computer-readable storage medium is provided, wherein at least one computer program is stored in the computer-readable storage medium, and the at least one computer program is loaded and executed by a processor to implement the above-mentioned report generation method for civil aviation operations.

[0028] Fifthly, a computer program product is provided, comprising a computer program or instructions, which, when executed by a processor, implement the above-mentioned report generation method for civil aviation operations.

[0029] The solutions provided in aspects two through five above are used to implement the method provided in aspect one above, and their specific implementations will not be described in detail here. The technical effects corresponding to any implementation method of the solutions provided in aspects two through five above can be found in the technical effects corresponding to any implementation method in aspect one above, and will not be described in detail here.

[0030] It should be noted that any of the possible implementations of any of the above aspects can be combined, provided that the solutions do not contradict each other.

[0031] The technical solution provided in this application offers at least the following benefits: First, by responding to business operations and generating target data tables based on visual configuration, business personnel can quickly define and extend data models without writing underlying database code. This liberates data model changes from the lengthy cycle of traditional code development, testing, and deployment, achieving agile data structure response. Second, by configuring rules for dynamically created target data tables, closely related data transformation rules are generated, ensuring that business logic can synchronously and accurately adapt to adjustments in the data model, avoiding a disconnect between rules and the model. Next, by receiving actual business data and applying the aforementioned rules for automated transformation processing, accurate and efficient calculations from raw data to the target format are achieved, reducing manual intervention and errors. Finally, the transformation results are directly stored in the business system's local database, ensuring closed-loop data processing and meeting private storage requirements. Overall, this method links the definition of the data model, the configuration of business rules, and the execution of data processing into a coherent automated process, significantly improving the system's efficiency, flexibility, and maintainability in responding to changes in business needs. Attached Figure Description

[0032] Figure 1 This is a schematic diagram of the structure of a data processing system provided in an embodiment of this application; Figure 2 A flowchart illustrating a data processing method for aviation operations provided in an embodiment of this application; Figure 3 A flowchart illustrating another data processing method for aviation operations provided in this application embodiment; Figure 4 A flowchart illustrating another data processing method for aviation operations provided in this application embodiment; Figure 5 This is a schematic diagram of another data processing system provided in an embodiment of this application; Figure 6 This is a schematic diagram of another data processing system provided in an embodiment of this application. Detailed Implementation

[0033] In the embodiments of this application, in order to clearly describe the technical solutions of the embodiments of this application, the terms "first" and "second" are used to distinguish identical or similar items with essentially the same function and effect. Those skilled in the art will understand that the terms "first" and "second" do not limit the quantity or execution order, and the terms "first" and "second" are not necessarily different. There is no sequential or hierarchical order among the technical features described by "first" and "second".

[0034] In the embodiments of this application, the terms "exemplary" or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design scheme described as exemplary or for example in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design schemes. Rather, the use of terms such as "exemplary" or "for example" is intended to present the relevant concepts in a specific manner to facilitate understanding.

[0035] In the embodiments of this application, at least one can also be described as one or more, and multiple can be two, three, four or more, and this application does not impose any restrictions.

[0036] Furthermore, the network architecture and scenarios described in the embodiments of this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided in the embodiments of this application. As those skilled in the art will know, with the evolution of network architecture and the emergence of new business scenarios, the technical solutions provided in the embodiments of this application are also applicable to similar technical problems.

[0037] In the digital operations of air transport companies, core business systems such as aircraft asset management, flight operation management, and financial settlement and reporting all involve a large number of complex and frequently changing business rules and data models. These rules and models are used to drive key business processes, such as asset valuation, operating revenue allocation, cost accounting, and compliance data reporting.

[0038] Currently, the mainstream technical implementation methods supporting this type of business logic in the industry have several inherent defects, which restrict the agility of business response, the economy of system maintenance, and the effectiveness of data governance.

[0039] To address these issues, relevant technical solutions typically employ a siloed system construction model. Each business unit or system develops independent and closed functionalities based on its specific rule calculation and data transformation needs. This leads to a high degree of functional duplication and the formation of numerous information silos. Not only are development and maintenance costs high, but due to inconsistent technology stacks and implementation logic, business rules across systems are difficult to reuse, and data definitions are inconsistent, severely hindering the integration and interconnection of enterprise-level data.

[0040] Secondly, at the architecture deployment level, common general-purpose rule engine products or middleware solutions mostly adopt a centralized, platform-based service model. These engines are typically deployed as independent services, physically separated from specific business systems. All data to be processed must be transmitted over the network between the business system and the rule engine service. This model cannot meet the aviation industry's stringent compliance and security requirements, which mandate that core business data (such as asset, financial, and operational data) be stored locally and subject to privatized control. Furthermore, cross-network data exchange introduces additional latency and points of failure, making it difficult to guarantee performance and reliability in high-frequency, high-volume data processing scenarios.

[0041] Furthermore, from a functional adaptability perspective, general-purpose open-source rule engine frameworks are designed for broad application scenarios and are not deeply customized for the specific complexities of aviation business. They generally lack the ability to visually define and manage dynamic data models. When business entity fields are added, removed, or their types change, it is necessary to modify the underlying data model code and redeploy the application, which is a cumbersome process and cannot quickly respond to dynamic adjustments in business.

[0042] Furthermore, existing frameworks typically use a single rule or a simple set of rules as the execution unit, lacking native support for visual orchestration and unified scheduling of multi-step, cross-data-table chain rule processes. This is precisely what is urgently needed in complex data transformation scenarios commonly encountered in aviation operations (such as multi-stage processing from raw operational data to financial accounting data).

[0043] Finally, existing solutions also have shortcomings in terms of control and scalability. Rule definitions, maintenance of mapping relationships (such as encoding conversion tables), and complex custom calculation logic are often tightly coupled with program code or scattered across different configuration files, lacking an integrated management view. Simultaneously, different business systems independently build their own permission management systems, resulting in inconsistent granularity of control over rule configuration, execution, and data access. This makes it impossible to achieve standardized, granular interface-level and operational-level permission control at the enterprise level, increasing internal security risks and management complexity.

[0044] Based on this, this application provides a data processing method for aviation operations. First, it responds to business operations and generates target data tables based on visual configuration, enabling business personnel to quickly define and extend data models without writing underlying database code. This liberates data model changes from the lengthy cycle of traditional code development, testing, and deployment, achieving agile data structure response. Second, it configures rules for the dynamically created target data tables, generating closely related data transformation rules. This ensures that business logic can synchronously and accurately adapt to adjustments in the data model, avoiding a disconnect between rules and the model. Next, it receives actual business data and applies the aforementioned rules for automated transformation processing, achieving accurate and efficient calculations from raw data to the target format, reducing manual intervention and errors. Finally, it directly stores the transformation results in the business system's local database, ensuring closed-loop data processing and meeting private storage requirements. Overall, this method links the definition of the data model, the configuration of business rules, and the execution of data processing into a coherent automated process, significantly improving the system's efficiency, flexibility, and maintainability in responding to changes in business needs.

[0045] The solution provided in this application can be applied to Figure 1 In the data processing system shown, such as Figure 1 This is a schematic diagram of the structure of a data processing system provided in an embodiment of this application.

[0046] For example, Figure 1 The data processing system shown includes an application server, a database server, a rules engine service component, and one or more user terminals.

[0047] In some embodiments, the application server serves as the runtime environment for the core business systems of an aviation enterprise (such as aircraft asset management systems and flight operation management systems). Its core function is to run the main applications of the business systems and provide an integration and runtime environment for the rule engine service components. In this embodiment, the application server adopts a Java-based Spring Cloud or Spring Boot microservice architecture. The business systems deployed on it integrate the rule engine service components as embedded, independently runnable JAR (Java Archive) packages into their own services through dependency injection.

[0048] This transforms the rules engine from an independent external platform into a technical component of the business system, achieving technical componentization. The application server manages the component's lifecycle (startup and shutdown) and seamlessly integrates the rules engine's front-end configuration page into the unified portal of the business system through internal network interfaces (such as RESTful APIs). Users can directly access all rule configuration functions from the business system entry point, achieving deep integration between private deployment and the business system.

[0049] For example, in an airline's aircraft asset management system, the application server cluster deploys the main service of the asset management system. This service uses Maven or Gradle build tools to import a rules engine component (e.g., air-rule-engine-core-1.0.0.jar) as its core dependency. When the asset management system starts, the rules engine service also starts and acquires a specific internal port (e.g., port 8081). The front end of the business system loads the configuration interface provided by the rules engine component through an internal gateway or direct call. When users are handling asset depreciation or valuation business, they can directly jump to the rules configuration module within the same system, providing a completely integrated and seamless experience.

[0050] In some embodiments, the database server provides a private database dedicated to the business system, serving as a critical infrastructure for achieving full data persistence and localized storage. This database server is deployed within the same security domain as the rules engine service component, typically an Oracle, MySQL, or PostgreSQL database on the enterprise intranet. All physical tables dynamically created by the rules engine, all configured rule metadata, and all business data generated by rule execution are persistently stored in this database, coexisting with the original data of the business system. This ensures that core aviation business data (such as aircraft assets and financial data) does not leak out of the enterprise intranet, fully meeting data security and compliance requirements. The database server connects to the rules engine service component via the standard Java Database Connectivity (JDBC) protocol.

[0051] For example, the aircraft asset management system uses an Oracle database cluster. During initialization, the rules engine component connects to the specified user schema of this Oracle database via a JDBC driver. When business users create a depreciation table through front-end configuration, the CREATE TABLE SQL statement generated by the engine is executed directly in this Oracle database, creating the physical table. Subsequently, the calculation results generated by the asset depreciation rules are also directly inserted into this physical table located in the business system's local Oracle database, completing closed-loop data processing and private storage.

[0052] In some embodiments, the rule engine service component includes backend services for multiple functional modules. These modules include at least: 1) a model management module, responsible for parsing visual configurations and dynamically generating and executing table creation SQL; 2) a rule configuration and management module, responsible for parsing and compiling the rule logic (such as conditional judgments, file mappings, and formula calculations) configured on the front end into executable rule knowledge packages (such as decision sets based on a modified URule framework), and persistently storing them; 3) a rule execution engine module, responsible for loading the rule knowledge package and, in conjunction with input business data, driving the rule calculation and data transformation process; 4) a process orchestration module (such as a modified LiteFlow framework), responsible for managing a chained process in which multiple rules are executed in a specific order; 5) a cache management module, utilizing in-memory databases such as Redis to cache hot rule configurations and file mapping data to improve execution performance; and 6) a permission adaptation module, integrating unified permission control components such as Zhuque to achieve fine-grained access control at the rule operation interface level and button level.

[0053] For example, when the rule engine service component receives a request to create a model, its model management module performs field validity checks and assembles a complete Data Definition Language (DDL) statement. When its rule execution engine module executes depreciation rules involving file mapping, it first calls the cache management module to query the pre-warmed depreciation method and asset type mapping table from Redis. If no match is found, it queries the database and populates the cache, thus achieving millisecond-level data mapping queries and greatly improving rule execution efficiency.

[0054] In some embodiments, a user terminal refers to a computer device with a web browser installed, used by business personnel or system administrators. Users access the business system through this terminal to view the integrated rule engine front-end page. The front-end of the rule engine provides a complete visual configuration interface, which interacts with the back-end by calling various Representational State Transfer (RESTful APIs) provided by the rule engine service components. The front-end interface mainly includes: a model designer (for drag-and-drop or form-based configuration of physical table structures), a rule designer (for graphical configuration of transformation logic using Excel formulas or conditional branches), a file mapping manager (for maintaining encoding transformation relationship tables), a rule group orchestrator (for visually dragging and dropping rule nodes and setting the execution order), and a task monitoring panel (for viewing rule execution history and results).

[0055] For example, the asset administrator logs into the aircraft asset management system in the Chrome browser on their work computer. They find and click the rules engine configuration entry in the system menu, and the browser loads the front-end interface developed using Vue.js or React frameworks provided by the rules engine component. On the model management page of this interface, they fill out a form to specify the new depreciation table fields. On the rules configuration page, they use a graphical formula editor to configure the calculation formula for the monthly depreciation amount field. All these operations are performed by the front-end using asynchronous JavaScript and XML (AJAX) technology to call the API published by the back-end rules engine service component, thus saving and applying the configuration data.

[0056] Figure 2 This is a flowchart illustrating a data processing method for aviation operations provided in an embodiment of this application. The method is applied to... Figure 1 The data processing system in China.

[0057] like Figure 2 As shown in the embodiments of this application, the data processing method for aviation operations may include: S201: In response to business operations, based on visual configuration information, generate a target data table corresponding to the aviation business entity in the local database of the business system.

[0058] The visual configuration information includes form configuration information and field configuration information.

[0059] In some embodiments, the form configuration information and field configuration information are validated for legality. After the validation is passed, a structured query language statement is generated based on the validated configuration information. Then, the target data table is generated in the local database based on the structured query language statement.

[0060] For example, taking aircraft maintenance record management as an example, in response to the business operation of creating a data table in the aircraft asset management system to record detailed information on major maintenance, the following process is executed based on the visual configuration information received through the front-end interface: The visual configuration information includes form configuration information and field configuration information. In the form configuration area, the system receives the form code AC_REPAIR_MAJOR, the form name "Aircraft Major Maintenance Record Table," and the selection of the form type's main table. In the field configuration area, the system receives attribute configurations for multiple fields, including: configuring the REPAIR_ID field with the meaning of the maintenance order number, VARCHAR type, and length 32, specifying it as a primary key field and setting it to a NOT NULL constraint; configuring the AIRCRAFT_REG field with the meaning of the aircraft registration number, VARCHAR type, and length 20; configuring the REPAIR_DATE field with the meaning of the maintenance date and DATE type; configuring the REPAIR_TYPE field with the meaning of the maintenance type, VARCHAR type, and length 50; and configuring the REPAIR_COST field with the meaning of the maintenance cost, DECIMAL type, and precision (15,2). After configuration, a commit operation is triggered.

[0061] In some other embodiments, before generating the target data table and after receiving a business operation request, the permission control component verifies whether the current user has the permission to perform the business operation, and after the verification is successful, receives the form configuration information and field configuration information input through the front-end interface.

[0062] For example, consider creating a maintenance record table. Before generating the target data table, when a business user triggers a new model operation through the front-end interface, the front-end sends a business operation request to the rule engine service. This request includes an operation identifier such as / api / model / create. The access control component intercepts this request, extracts the current user's identity from the request's session information, and queries the access control configuration library to verify whether the user's role is authorized to execute the MODEL_MANAGE:CREATE operation.

[0063] If the permission verification is successful, the business operation request is allowed to be processed, and then the form configuration information and field configuration information entered through the front-end interface can be received.

[0064] If the permission verification fails, for example, if the user's role is only a data viewer, the permission control component will intercept the request and return an unauthorized access response to the front end, thereby preventing the subsequent data table configuration and generation process.

[0065] In addition, before responding to business operations, the compiled output of the rule engine is deployed to the application server corresponding to the business system, and the connection information and permission information between the rule engine component and the local database of the business system are configured.

[0066] For example, when deploying or integrating a rules engine into an aircraft asset management system, operations personnel first upload the integrated, compiled rules engine component (e.g., including all dependent executable JAR packages, i.e., the compiled artifacts) to the application server corresponding to the business system. Then, by modifying the application configuration file, they configure the connection information between the rules engine component and the business system's local database (such as an Oracle database), including the database driver class name, Uniform Resource Locator (URL), username, and password. Simultaneously, they configure the interface information with the access control component to complete the integration of access control information. After configuration, the application server is restarted, and the rules engine service starts and becomes ready locally on the business system.

[0067] S202: Based on the rule configuration operation for the target data table, generate at least one data transformation rule associated with the aviation business entity.

[0068] In some embodiments, transformation rule information is obtained, and data transformation rules are generated based on the transformation rule information.

[0069] The transformation rule information includes, but is not limited to, the source model table identifier, the target model table identifier, and the assignment rules configured for the target field in the physical table corresponding to the target model table identifier.

[0070] For example, after completing the micro-definition of the assignment rules, business personnel also need to perform macro-level sequential management of the overall data processing logic through the rule flow orchestration interface. The system provides a visual rule flow management module, allowing business personnel to build complex business logic chains by dragging and dropping nodes. For example, in the asset appraisal process for the A319 aircraft, business personnel add and arrange five core processing nodes sequentially in the rule flow canvas, corresponding to: asset access verification, parameter standardization, economic calculation, residual value assessment, and result output. After orchestration, the system transforms this ordered execution sequence into the underlying process control logic, ensuring that each transformation rule can be executed in the preset order when the rule engine runs, thus guaranteeing the logical rigor of data processing. See Table 1 below for details.

[0071] Table 1

[0072] Furthermore, in the specific configuration process of the conversion rule information, business personnel use the rule configuration area of ​​the front-end interface to set precise assignment logic for each field of the aforementioned target data table, thereby specifically implementing the assignment rules. Taking the assignment rules for fields related to the estimated cost of left engine overhaul as an example, the specific configuration includes: configuring a condition-based calculation rule for the left engine overhaul cost field, with the logical expression IF([leftEngineOverhaulCount]>0,[leftEngineOverhaulCount]*14000×1.2, 0), which dynamically calculates the cost based on the number of left engine overhaul cycles; configuring a fixed value assignment rule for the engineType (engine model) field, setting its value to CFM56-5B; and configuring a source field mapping rule for the number field from the last overhaul cycle, allowing it to directly obtain the corresponding cycle count data from the source table MAINTENANCE_WORK_ORDER. Through these fine-grained configurations, the specific algorithm and conditions for converting source data into target data are established.

[0073] See Table 2 below for details.

[0074] Table 2

[0075] For example, taking the creation of a target data table for major aircraft maintenance records as an example, during the model field configuration phase, business personnel define the specific physical table structure through the form and field configuration area of ​​the front-end interface. The specific configuration operation is as follows: Enter the detailed attributes of each field in the field list, including: field name, data type, constraints, related tables, related fields, etc., as shown in Table 3 below.

[0076] Table 3

[0077] In addition, the total flight hours field can be defined as an integer, and the evaluation baseline date field can be defined as a date. Each field entry must specify its field name, Chinese meaning, data type, and constraint status. The system backend will automatically assemble and generate the corresponding database Data Definition Language (DDL) statements based on this configuration information, thereby completing the physical layer modeling work.

[0078] Another example is that, in actual business operations, the data processing system supports batch triggering and automated processing of configured rules. When executing a batch of asset appraisal tasks (e.g., batch number 2025122500...), the system displays the execution status throughout the entire lifecycle on the batch execution monitoring interface. After execution, business personnel can use the result verification interface to compare the target data automatically calculated by the system (e.g., rightEnginePurchase is $2,717,038, flightHoursTotal is 60,008 hours, etc., shown on the right) with the original business documents (source data shown on the left). If the verification is correct, business personnel can click the confirmation button to make the data effective and enter the database; if there are discrepancies, they can perform anomaly investigation and rule backtracking correction based on the monitoring status.

[0079] For example, the formula type of the assignment rule includes at least one of source field, fixed value, file mapping, or enable condition.

[0080] For example, 1. Source field type: The assignment rule is configured as [MAINTENANCE_WORK_ORDER.WORK_ORDER_NO], which means that the value of the target field comes directly from the WORK_ORDER_NO field of the MAINTENANCE_WORK_ORDER table.

[0081] 2. Fixed value type: Suppose that the data source needs to be marked for all new records, the DATA_SOURCE field in the target table can be configured with a fixed string System_A as the assignment rule. No matter what the source data is, the value of this target field will always be System_A.

[0082] 3. File Mapping Type: The assignment rule is configured as ARCHIVE_REPAIR_TYPE(MAINTENANCE_WORK_ORDER.WORK_TYPE), where ARCHIVE_REPAIR_TYPE is a predefined file code. During execution, the system uses the actual value of the WORK_TYPE field (e.g., Engine_Overhaul) as the key to query the file and returns the mapped target value (e.g., engine overhaul).

[0083] 4. Enable Conditional Type: The assignment rule is configured as IF([AIRCRAFT_BASIC_INFO.AGE]>15, aging aircraft, non-aging aircraft). This rule is a conditional function, where IF is the conditional keyword. The parentheses contain three parts: the conditional expression [AIRCRAFT_BASIC_INFO.AGE]>15, the return value "aging aircraft" when the condition is true, and the return value "non-aging aircraft" when the condition is false. During system execution, the conditional expression is first evaluated. If true, the target field is assigned the value "aging aircraft"; otherwise, it is assigned the value "non-aging aircraft".

[0084] In some other embodiments, at least one data transformation rule and / or file mapping data is loaded into an in-memory database for caching, and when at least one data transformation rule and / or file mapping data is updated, the cached data in the in-memory database is updated synchronously.

[0085] For example, after the rule configuration is completed, the backend service, while generating the rule knowledge package, will load the rule's hot configuration information (such as condition expressions and mapping keys) into the Redis in-memory database to form a cache. Archive mapping data will also be fully or incrementally loaded into the Redis cache when an archive is created or updated, with a reasonable expiration time set to accelerate subsequent queries.

[0086] Furthermore, the system uses the archive code ARCHIVE_REPAIR_TYPE and the source field value "ENG_OH" to form a query key. The system first searches the Redis in-memory database cache; if a match is found, the result is returned directly; otherwise, the system queries the business system database and writes the result back to the Redis cache for subsequent requests. When the background performs add, delete, or modify operations on the ARCHIVE_REPAIR_TYPE archive details, the system synchronously triggers the clearing of the corresponding cached data in Redis and automatically loads the latest data on the next query, thus ensuring consistency between the cached data and the source data.

[0087] S203: Receive business data and, based on at least one data transformation rule, transform the business data to obtain the transformed data.

[0088] In some embodiments, if the formula type of the assignment rule includes file mapping, the target information of the target field is determined based on the preset mapping relationship, the file code associated with the file mapping, and the source field value in the business data.

[0089] The preset mapping relationship is the mapping relationship between file code, source field value and target information.

[0090] For example, configuring an archive mapping rule for the REPAIR_TYPE field will trigger a corresponding mapping query process when the rule engine performs data transformation containing this mapping rule. In the received business data, the value of the WORK_TYPE field in the source table MAINTENANCE_WORK_ORDER (e.g., ENG_OH) is used as the source field value. The system determines the associated archive code ARCHIVE_REPAIR_TYPE according to the rule configuration and combines this code with the source field value to form the query conditions.

[0091] The system executes queries based on query conditions. If a match is found in the in-memory database, it returns the result directly; otherwise, it queries the business system database and can populate the in-memory database with the result. The target information obtained from the query (e.g., "engine overhaul") is then assigned to the REPAIR_TYPE field of the target table AIRCRAFT_MAJOR_REPAIR, thus completing the automated conversion from internal coding to standardized description.

[0092] In some other embodiments, the chained execution order corresponding to the data transformation rules is obtained, and the execution interfaces corresponding to multiple data transformation rules are called sequentially according to the chained execution order to continuously transform the business data.

[0093] The output data of the previous data transformation rule is used as the input data of the next data transformation rule.

[0094] For example, taking a business scenario involving cost accounting and financial voucher generation as an example, this scenario determines the ordered execution sequence of three data transformation rules through rule group configuration.

[0095] Rule A (RULE_COST_COLLECT) is used to calculate the total maintenance cost from the source table and output it to the first intermediate table TMP_REPAIR_COST.

[0096] Rule B (RULE_COST_ALLOCATE) is used to allocate the total cost to different cost centers and output it to the second intermediate table TMP_COST_ALLOCATION.

[0097] Rule C (RULE_VOUCHER_GEN) is used to generate a draft financial voucher based on the allocation results and output it to the final target table FIN_VOUCHER_DRAFT. When a business system initiates a processing request for specific business data (such as work order WO-2023-001), the rule engine executes it sequentially according to a chained execution order. First, rule A is executed to generate a cost summary result based on the original business data. Then, using the output of rule A as input, rule B is executed to complete the cost allocation. Finally, using the output of rule B as input, rule C is executed to generate the financial voucher. Through this chained execution process, a multi-step, cross-data model automated continuous transformation from original business data to final financial data is achieved.

[0098] S204: Store the converted data in the local database of the business system.

[0099] For example, after the rule engine completes the transformation of business data, the generated transformed data needs to be persistently stored in a specified target physical table. Taking the implementation of maintenance record transformation as an example, after the rule engine successfully transforms and calculates the source work order data, it generates one or more transformed data records that conform to the AIRCRAFT_MAJOR_REPAIR table structure.

[0100] The system then establishes a connection with the business system's local MySQL database via a Java Database Connectivity (JDBC) driver. Storage operations are typically performed in batch insert mode to improve write efficiency. The system will assemble the transformed data records containing field values ​​such as REPAIR_ID, AIRCRAFT_REG, REPAIR_DATE, REPAIR_TYPE, and REPAIR_COST into parameterized SQL INSERT statements for storage.

[0101] After successful data storage, the rules engine updates the status of the relevant execution logs and feeds back the storage results (such as the number of successful records and the reasons for failure) to the business system that initiated the call, thus completing a full closed loop from data transformation to final storage. This process ensures that all data processed by the rules engine is ultimately stored in the business system's own private database, meeting the core requirements of the aviation industry for data ownership, security, and compliance.

[0102] In summary, the data processing method for aviation operations provided in this application responds to business operations and generates target data tables based on visual configuration. This allows business personnel to quickly define and extend data models without writing underlying database code, freeing data model changes from the lengthy cycle of traditional code development, testing, and deployment, thus achieving agile data structure response. Secondly, rule configuration is performed on the dynamically created target data table, generating closely related data transformation rules. This ensures that business logic can synchronously and accurately adapt to adjustments in the data model, avoiding a disconnect between rules and the model. Next, actual business data is received and automatically transformed using the aforementioned rules, achieving accurate and efficient calculations from raw data to the target format, reducing manual intervention and errors. Finally, the transformation results are directly stored in the local database of the business system, ensuring closed-loop data processing and meeting private storage requirements. Overall, this method connects the definition of the data model, the configuration of business rules, and the execution of data processing into a coherent automated process, significantly improving the system's efficiency, flexibility, and maintainability in responding to changes in business needs.

[0103] For example, such as Figure 3 The diagram shown is a flowchart illustrating another data processing method for aviation operations provided in an embodiment of this application. This method is applied to... Figure 1 The data processing system in China.

[0104] like Figure 3 As shown in the embodiments of this application, the data processing method for aviation operations may include: S301: Receive model calculation requests and calculation input data. The model calculation request is associated with the corresponding rule group.

[0105] In some embodiments, the measurement input data is received in batches via a data import interface.

[0106] For example, in the application scenario of batch assessment of aircraft fleet asset residual value, to perform unified calculations for multiple aircraft, input data can be provided through file import. The operator selects the batch calculation function on the front-end interface and obtains a pre-set spreadsheet template file that matches the asset residual value assessment field structure of the calculation model. This template file includes, but is not limited to, column headers for fields such as aircraft registration number, manufacturing date, total flight hours, and market sentiment index. After organizing the file containing information for multiple aircraft according to the template, the operator uploads it through the file import function provided on the front-end interface. The corresponding data import interface on the back-end receives the file and performs automatic parsing and verification.

[0107] The validation process includes, but is not limited to: verifying the file format, checking the completeness of required fields, validating the format of numeric fields, and confirming the existence of key identifier fields within the system. For example, for the TOTAL_FLIGHT_HOURS field, it verifies whether its value is positive. After successful validation, the system parses each line of data in the file into an independent calculation data object and temporarily stores it, while also providing feedback on the number of successfully parsed records. This method is suitable for scenarios in aviation operations where a unified model calculation is required for a large number of business entities.

[0108] In some other embodiments, the measurement input data is received manually via the interface, based on the data input interface.

[0109] For example, when conducting a single assessment of a specific aircraft asset, calculation data can be provided manually via an interface. The system front-end presents a dynamic input form bound to the selected calculation model. The operator uses this form to enter or select the required data for the calculation in the corresponding field input controls. For example, selecting a specific registration number from the aircraft registration number drop-down list, selecting a date from the assessment reference date control, and entering corresponding values ​​in the input boxes for total flight hours, engine remaining cycles, etc. The front-end interface can perform real-time format validation of the input content. After data entry is complete, the operator triggers the calculation command, and the front-end submits the form data to the back-end in a structured data format through the corresponding data input interface. After receiving the data, the back-end interface performs server-side validation to ensure data integrity and logical correctness. This method provides support for flexible and rapid targeted calculations for individual business entities.

[0110] S302: Based on the data transformation rules corresponding to the rule group, the measurement input data is transformed to obtain the measurement result data.

[0111] For example, the rule group associated with the model calculation request has a corresponding rule group identifier. This rule group is pre-configured to include a chained execution order of multiple data transformation rules. The rule engine obtains its ordered rule list based on the rule group identifier and executes the chained transformation process sequentially. Taking a rule group that includes rule technical status assessment, economic parameter calculation, and final value estimation as an example, the engine first takes the original calculation input data as input and executes the first rule RULE_TECH_STATUS_ASSESS. The formula built into this rule calculates intermediate results such as technical status coefficients based on parameters such as flight hours and cycle count.

[0112] Subsequently, the engine uses the output of the previous rule as input to execute the next rule, RULE_ECON_PARAM_CALC, which calculates the "economic adjustment factor" based on market parameters. Finally, the engine executes the rule RULE_FINAL_VALUE_EST, which comprehensively applies all the aforementioned intermediate parameters and calculates the final "measured residual value" as the measurement result using a preset calculation formula (e.g., residual value valuation = benchmark purchase price × technical condition coefficient × economic adjustment factor × age decay factor). Through the chained calls of rule groups, automated and configurable calculations for complex professional assessment models are achieved.

[0113] S303: Record the calculation result data and the execution status of the model calculation request.

[0114] For example, the system fully records the execution process and results of each model calculation task. For successful calculations, the system persistently stores the generated calculation results data in a dedicated result table in the local database of the business system.

[0115] Simultaneously, a master record is generated in the task record table, recording the core metadata of this task, including: the task batch number automatically generated by the system, the associated rule group identifier, the calculation model identifier, the total amount of input data processed, the amount of data successfully processed, the amount of data that failed, the executor, the task start and end times, and the overall execution status. If some data fails to execute during the calculation process, the system will record the failure details of that part of the data in the error log table and update the success and failure counts and the overall status in the task master record accordingly. All records can be queried and traced by task batch number to meet the requirements of aviation business for the rigor of the calculation process and the auditability of the results.

[0116] For example, such as Figure 4 The diagram shown is a flowchart illustrating another data processing method for aviation operations provided in an embodiment of this application. This method is applied to... Figure 1 The data processing system in China.

[0117] like Figure 4 As shown in the embodiments of this application, the data processing method for aviation operations may include: S401: Start.

[0118] S402: Configure conversion rules.

[0119] That is, configure the conversion rules between the source model and the target model.

[0120] S403: Configure field rule values.

[0121] That is, configure the field rule values ​​and select the corresponding formula type.

[0122] S404: Generate rule formulas and store them in the rule table.

[0123] That is, generate rule formulas based on the configuration and store them in the rule table.

[0124] S405: Convert to a rule engine-compatible format.

[0125] That is, converting the generated rules into a format compatible with the rule engine.

[0126] S406: The business system initiates an execution request.

[0127] That is, the business system sends an execution request to the rules engine.

[0128] S407: Physical table existence check.

[0129] If the test fails, proceed to step S408; If the verification passes, then execute S409.

[0130] S408: Error returned.

[0131] S409: Field consistency check.

[0132] If the test fails, proceed to step S408; If the verification passes, proceed with S410.

[0133] S410: Perform rule transformation.

[0134] S411: Determine whether the execution was successful.

[0135] If not, then execute S412; If so, then execute S413.

[0136] S412: Log errors.

[0137] S413: Target physical table for data import.

[0138] S414: Return the result.

[0139] S415: End.

[0140] By way of example, this application also provides another data processing system, which is used to implement the above method embodiments.

[0141] like Figure 5 This is a schematic diagram of another data processing system provided in an embodiment of this application. The data processing system includes a processing module 501 and a communication module 502. The processing module 501 is used to execute... Figure 2The operations S201, S202, S203, and S204 in the illustrated method are used to execute... Figure 3 The illustrated method includes operations S302 and S303; the communication module 502 is used to execute... Figure 3 The operation of S301 in the illustrated method.

[0142] In some embodiments, the data processing system described above includes hardware structures and / or software modules corresponding to the execution of each function in order to achieve the above-described functions. Those skilled in the art will readily recognize that, based on the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0143] This application embodiment can divide the data processing system into functional modules according to the above method embodiment. For example, each function can be divided into its own functional module, or two or more functions can be integrated into one processing module. The integrated module can be implemented in hardware or as a software functional module. It should be noted that the module division in this application embodiment is illustrative and only represents one logical functional division. In actual implementation, there may be other division methods.

[0144] like Figure 6 As shown, another data processing system provided in this application embodiment may include a processor 1101, a bus 1102, a communication interface 1103, and a memory 1104. The processor 1101, the memory 1104, and the communication interface 1103 communicate with each other via the bus 1102. It should be understood that this application does not limit the number of processors and memories in the network device.

[0145] Bus 1102 can be a PCI bus, an Extended Industry Standard Architecture (EISA) bus, or a UB bus, etc. Buses can be divided into address buses, data buses, control buses, etc. For ease of representation, Figure 6 The bus 1102 may be represented by a single line, but this does not mean that there is only one bus or a bus of one data mode. The bus 1102 may include a path for transmitting information between various components of the network device (e.g., memory 1104, processor 1101, communication interface 1103).

[0146] Processor 1101 may include any one or more processors such as CPU, graphics processing unit (GPU), microprocessor (MP), or digital signal processor (DSP).

[0147] The memory 1104 may include volatile memory, such as random access memory (RAM). The processor 1101 may also include non-volatile memory, such as read-only memory (ROM), flash memory, hard disk drive (HDD), or solid state drive (SSD).

[0148] The communication interface 1103 uses transceiver modules such as, but not limited to, network interface cards and transceivers to enable communication between network devices and other devices or communication networks.

[0149] The memory 1104 stores executable program code, and the processor 1101 executes the executable program code to implement the functions of the aforementioned method embodiments. That is, the memory 1104 stores instructions for executing the aforementioned report generation method for civil aviation operations.

[0150] On the one hand, a computer-readable storage medium is provided, in which at least one computer program is stored, the at least one computer program being loaded and executed by a processor to implement the report generation method for civil aviation operations as provided in the above-described method embodiments.

[0151] In another aspect, a computer program product is provided, which includes a computer program or instructions that, when executed by a processor, implement the report generation method for civil aviation operations provided in the above-described method embodiments.

[0152] Through the above description of the implementation methods, those skilled in the art will clearly understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the module can be divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, modules, and units described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0153] Since the indicator generation module, computer-readable storage medium, and computer program product in the embodiments of the present invention can be applied to the above method, the technical effects obtained can also be referred to the above method embodiments, and the embodiments of the present invention will not be repeated here.

[0154] The method steps in this embodiment can be implemented in hardware or by a processor executing software instructions. The software instructions can consist of corresponding software modules, which can be stored in random access memory (RAM), flash memory, read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), registers, hard disks, portable hard disks, CD-ROMs, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor, enabling the processor to read information from and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and storage medium can reside in an ASIC. Alternatively, the ASIC can reside in a network device. Of course, the processor and storage medium can also exist as discrete components in the network device.

[0155] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer programs or instructions. When a computer program or instruction is loaded and executed on a computer, the processes or functions of the embodiments of this application are performed, in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, a network device, a user equipment, or other programmable module. The computer program or instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another. For example, a computer program or instructions can be transferred from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium, such as a floppy disk, hard disk, or magnetic tape; it can also be an optical medium, such as a digital video disc (DVD); or it can be a semiconductor medium, such as a solid-state drive (SSD). The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A data processing method for aviation operations, characterized in that, The method includes: In response to business operations, based on visual configuration information, a target data table corresponding to the aviation business entity is generated in the local database of the business system; Based on the rule configuration operation for the target data table, at least one data transformation rule associated with the aviation business entity is generated; Receive business data, and perform transformation processing on the business data based on the at least one data transformation rule to obtain transformed data; The converted data is stored in the local database of the business system.

2. The method according to claim 1, characterized in that, The step of generating at least one data transformation rule associated with the aviation business entity based on the rule configuration operation for the target data table includes: Obtain conversion rule information, which includes at least a source model table identifier, a target model table identifier, and an assignment rule configured for the target field in the physical table corresponding to the target model table identifier; wherein, the formula type of the assignment rule includes at least one of source field, fixed value, file mapping, or enable condition; The data transformation rules are generated based on the transformation rule information.

3. The method according to claim 2, characterized in that, The formula type of the assignment rule is file mapping; The process of transforming the business data based on the at least one data transformation rule to obtain transformed data includes: Based on the preset mapping relationship, the file code associated with the file mapping, and the source field values ​​in the business data, the target information of the target field is determined; The preset mapping relationship is the mapping relationship between the file code, the source field value, and the target information.

4. The method according to claim 1, characterized in that, The process of transforming the business data based on the at least one data transformation rule to obtain transformed data includes: Obtain the chained execution order corresponding to the data transformation rules; Following the chained execution order, the execution interfaces corresponding to multiple data transformation rules are called sequentially to continuously transform the business data; wherein, the output data of the previous data transformation rule is used as the input data of the next data transformation rule.

5. The method according to claim 1, characterized in that, The visual configuration information includes form configuration information and field configuration information; The process of generating target data tables corresponding to aviation business entities in the local database of the business system based on visual configuration information includes: The form configuration information and the field configuration information are validated for legality. After the validation is passed, a structured query language statement is generated based on the validated configuration information. The target data table is generated in the local database based on the structured query language statement.

6. The method according to claim 5, characterized in that, The method further includes: Upon receiving a business operation request, the access control component verifies whether the current user has the necessary permissions to perform the business operation. After successful verification, the form configuration information and field configuration information input through the front-end interface are received.

7. The method according to claim 1, characterized in that, The method further includes: Receive model calculation requests and calculation input data, wherein the model calculation requests are associated with the corresponding rule groups; The calculation input data is transformed according to the data transformation rules corresponding to the rule group to obtain the calculation result data; Record the calculation results data and the execution status of the model calculation request.

8. The method according to claim 7, characterized in that, The receiving model calculation request and calculation input data include: Based on the data import interface, the calculation input data is received in batches via files; and / or, Based on the data input interface, the calculation input data is received manually entered through the interface.

9. The method according to claim 1, characterized in that, Prior to responding to a business operation, the method further includes: Deploy the compiled artifacts of the rules engine to the application server corresponding to the business system; Configure the connection information and permission information between the components of the rule engine and the local database of the business system.

10. The method according to claim 1, characterized in that, The method further includes: Load the at least one data transformation rule and / or file mapping data into an in-memory database for caching; When at least one data transformation rule and / or file mapping data is updated, the cached data in the memory database is updated synchronously.

11. A data processing system, characterized in that, The system includes: a processing unit and a communication unit; The processing unit is configured to: in response to a business operation, generate a target data table corresponding to the aviation business entity in the local database of the business system based on visual configuration information; The processing unit is further configured to: generate at least one data transformation rule associated with the aviation business entity based on the rule configuration operation for the target data table; The communication unit is configured to receive service data; The processing unit is further configured to: perform transformation processing on the business data based on the at least one data transformation rule to obtain transformed data; The processing unit is further configured to store the converted data in the local database of the business system.

12. A computer device, characterized in that, The computer device includes a processor and a memory, the memory storing at least one computer program, the at least one computer program being loaded and executed by the processor to implement the method as claimed in any one of claims 1 to 10.

13. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores at least one computer program, which is loaded and executed by a processor to implement the method as described in any one of claims 1 to 10.

14. A computer program product, characterized in that, The computer program product includes a computer program or instructions that, when executed by a processor, implement the method as described in any one of claims 1 to 10.

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