Aircraft product data storage and analysis method, system and equipment and storage medium

Abstract

The invention relates to an aircraft product data storage and analysis method, system and device and a storage medium. The data storage and analysis method comprises the following steps of S1, importing and processing original data; s2, establishing a relation model; s3, mapping, library falling and logic verification of the graph model; and S4, rendering and interacting. By developing a unified informatization tool, the data of the relational database is converted into the graph model, and the graph model is updated and synchronized into the graph database, so that synchronous updating from row and column data to hierarchical data and quick retrieval of relational nodes are realized; custom scene support and aging support are provided for data storage and analysis of aircraft products, and the working efficiency of business personnel and developers is remarkably improved.

Description

Technical Field

[0001] The present invention relates to the field of aircraft data processing, and in particular to a storage and analysis method, system, computer equipment and storage medium for aircraft product data. Background Art

[0002] In the digitalization and rapid modification analysis of the airframe structure design and physical model of an aircraft or flying vehicle, it is often necessary to generate manuals, analysis reports and other documents based on the aircraft product data management system and the update of the airframe structure data to support engineering personnel in business analysis.

[0003] At present, the query and analysis of hierarchical data mainly rely on manual operations. For example, in the configuration difference analysis, it is necessary to manually draw the configuration change tree diagram by querying all ECPs (Engineering Change Processes) to which the components belong, and then download and compare the ECPs of two configurations to evaluate the differences between them. ECP is a set of programs used to manage and control changes that occur in product design and manufacturing processes. It relies on manual evaluation of change requests and analysis of the impact of changes, which may involve multiple departments and has high labor costs. In the preparation of the structural change manual, it is necessary to determine whether the components have changed by establishing a statistical baseline for the structural identification module, download and analyze the electronic bill of materials (EBOM) of the changed components, and then update the statistical table of the structural identification module. Due to the complexity and large amount of aircraft structure data, these processing processes are not only cumbersome and inefficient, but also have performance bottlenecks and lag in the update of the diagram. Moreover, when the real-time requirements for data are high, the existing methods cannot meet the needs of some business scenarios.

[0004] There is currently no unified information technology tool to support the above-mentioned data analysis process and the retrieval of data involving data updates and hierarchical data. For the formation process of a complete hierarchical structure, business colleagues need to spend a lot of time and energy to sort it out, and the overall work is not efficient enough. In short, the current analysis and processing of hierarchical structures involves many scenarios, business data is scattered, and there are information islands in each scenario. In scenarios involving data path queries, traditional relational databases are not good at handling multi-layer associations, with low analysis efficiency and slow processing speed. In addition, the coverage of analysis scenarios is limited, and scenarios with high real-time requirements are currently unsupported. Summary of the invention

[0005] In view of the above-mentioned defects of the prior art, a first aspect of the present invention provides a method for storing and analyzing aircraft product data, which comprises the following steps:

[0006] Step S1: importing and processing raw data; importing the raw data of aircraft products from the business system and converting it into relational data; the relational data includes full data and incremental data;

[0007] Step S2: Establishing a relational model; using a two-dimensional table to organize the node relationships of the relational data and establish a relational model;

[0008] Step S3: Mapping, storage and logic verification of the graph model: mapping the relational model to a graph model, storing it in a graph database, and verifying the logical consistency between the graph model and the relational model;

[0009] Step S4: Rendering and interaction; customize logic expansion according to business needs, through the interaction between the front and back ends, call the graph model according to the different business needs of business personnel, and render it to the front end for business processing analysis; the business at least includes configuration difference analysis, structural maintenance manual analysis and physical documentary report analysis.

[0010] In the above-mentioned method for storing and analyzing aircraft product data, optionally, step S2 includes the following steps:

[0011] Step S2.1: define attributes according to business requirements; determine the attributes and data fields of each entity in the two-dimensional table according to the relational data based on different business requirements;

[0012] Step S2.2: Relationship identification: determining the relationship between the entities, including one-to-one, one-to-many, many-to-one, and many-to-many;

[0013] Step S2.3: Establish a full relational model; create nodes and organizational relationships of the relational model using offline tools according to the relationships between the entities, and form a full relational model with relational attributes;

[0014] Step S2.4: Synchronously update the incremental data; use the data migration tool to extract the incremental data to the cloud database, create a subscription instance through the data subscription component, connect the subscription information to the back-end application, determine different data consumption methods according to the type of the incremental data, and persist the incremental data to the graph database.

[0015] In the storage and analysis method for aircraft product data as described above, optionally, in step S2.3, the offline tool defines a primary key for each of the two-dimensional tables to uniquely identify each record in the table, and then defines a foreign key to establish a relationship between the two-dimensional tables; in step S2.4, the data subscription component is a DTS (Data Transmission Service) component, and the types of incremental data include insert data, update data, and delete data.

[0016] In the storage and analysis method for aircraft product data as described above, optionally, in step S3, after the graph model is stored in the database, a logical search is performed on the graph model in the graph database, and the search results are compared with the business meaning of the relationship model to determine whether a parent-child relationship graph that matches the logic of the relationship model is established in the search results, thereby completing the logic verification.

[0017] In the aforementioned storage and analysis method for aircraft product data, optionally, the logical retrieval includes an upward tracing retrieval or a downward retrieval or a designated layer bidirectional retrieval.

[0018] In the above-mentioned method for storing and analyzing aircraft product data, optionally, in step S1, converting the original data into the relational data comprises the following steps:

[0019] Step S1.1: Determine whether the original data is heterogeneous data; if so, first parse the heterogeneous data into the relational data with relational attributes, and then execute step S2; otherwise, directly execute step S2.

[0020] In the storage and analysis method for aircraft product data as described above, optionally, Springboot (an application for configuring and managing data channels) is used to integrate the Mybatis framework, provide an API interface that meets different business requirements, and realize the interaction between the business system and the graph database.

[0021] In order to achieve the above-mentioned object, a second aspect of the present invention provides a data storage and analysis system for aircraft products, which includes:

[0022] The data processing and model mapping module includes a full data processing layer and an incremental data processing layer, which respectively maps the node relationship logic of the incremental data and the full data into a relational model according to the relational data, and converts the relational model into a graph model;

[0023] Cloud database; the cloud database includes a relational database and a graph database, which is used to store various model data and provide a retrieval and retrieval interface;

[0024] Business logic analysis module, which collects business scenarios from hierarchical data, customizes logic according to different business scenarios and expands them;

[0025] The rendering interaction module renders and displays the graph model according to the business scenario for business personnel to perform business operations.

[0026] In order to achieve the above-mentioned purpose, the third aspect of the present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor runs the program, the steps of the aircraft product data storage and analysis method as described in any one of the first aspects are implemented.

[0027] In order to achieve the above-mentioned purpose, the fourth aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions or computer programs, and when the computer-executable instructions or computer programs are processed and executed, the storage and analysis method of aircraft product data as described in any one of the first aspects above is implemented.

[0028] The present invention provides a storage and analysis method, system, device and storage medium for aircraft product data, which studies how to convert data in a relational database into a graph model and update and synchronize it into a graph database, thereby realizing synchronous update of row and column data to hierarchical data and rapid retrieval of relationship nodes, and has important practical application value and significance. The present invention develops a unified information tool to handle the update and retrieval of hierarchical data, provides custom scenario support and timeliness support for the storage and analysis of aircraft product data, can significantly improve work efficiency, reduce the time spent by business colleagues on data collation and analysis, and thus more efficiently promote the overall work.

[0029] The concept, specific structure and technical effects of the present invention will be further described below in conjunction with the accompanying drawings to fully understand the purpose, characteristics and effects of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 It is a flow chart of a method for storing and analyzing aircraft product data of the present invention;

[0031] Figure 2 yes Figure 1 A specific flow chart of a method for storing and analyzing aircraft product data;

[0032] Figure 3 yes Figure 1 Specific data processing flow chart of step S1;

[0033] Figure 4This is a flowchart for implementing incremental data processing from relational data to graph models based on the DTS data subscription component;

[0034] Figure 5 It is a schematic diagram of the link structure of a data storage and analysis system for an aircraft product of the present invention. DETAILED DESCRIPTION

[0035] In order to make the technical means, creative features, objectives and effects of the invention easier to understand, the invention is further described below with reference to specific diagrams. However, the invention is not limited to the following implementation cases.

[0036] It should be noted that the structures, proportions, sizes, etc. illustrated in the drawings in this specification are only used to match the contents disclosed in the specification so as to facilitate understanding and reading by persons familiar with this technology. They are not used to limit the conditions under which the present invention can be implemented, and therefore have no substantive technical significance. Any structural modification, change in proportion or adjustment of size, without affecting the effects and purposes that can be achieved by the present invention, should still fall within the scope of the technical contents disclosed by the present invention.

[0037] Terms such as “comprise” and “include” indicate that in addition to the components directly and explicitly stated in the specification and claims, the technical solution of the present invention does not exclude the situation where it has other components that are not directly or explicitly stated.

[0038] from Figure 1 and Figure 2 It can be seen that the storage and analysis method of the aircraft product data may include:

[0039] Step S1: import and process the original data;

[0040] Step S2: Establish a relationship model;

[0041] Step S3: Mapping, storage and logic verification of the graph model;

[0042] Step S4: Rendering and interaction.

[0043] Specifically, in step S1, the engineering staff imports the original data of the aircraft product from the business system end into the cloud platform, converts it into relational data, and stores it in a relational database on the cloud. In an optional embodiment, the relational database can be MySQL, PostgreSQL, Oracle, etc. of RDS (Relational Database Service).

[0044] In the data processing process of step S1, Figure 3As shown, first determine whether the imported raw data is heterogeneous data. If the raw data is heterogeneous data, then manually parse it into relational data with relational attributes, and then execute step S2; on the contrary, if the raw data is not heterogeneous data, directly execute step S2 to establish a relational model. It should be noted here that heterogeneous data refers to data that has differences in data structure, type or format. In short, the heterogeneous data in this embodiment is data from different business systems or personnel, which are different in storage, expression or organization. Since the relational database used by the system stores and manages data based on strict two-dimensional relational data and relational models, there may be some non-tabular data such as Json data of configuration files in the raw data, which need to be parsed and converted into relational data using scripts to provide a basis for the subsequent establishment of relational models and graph models. In an optional embodiment, a JSON extract function can be used, or a custom SDK function can be compiled according to specific business needs and data structures to clean, convert or format the data to achieve parsing, which is highly flexible.

[0045] Relational data refers to data in a specific format that can be stored in a relational database. This data is organized in the form of tables, and each table consists of rows (records) and columns (fields). In this embodiment, a connection relationship is established between tables through primary keys and foreign keys, allowing data to be associated between different tables. In addition, relational data is queryable, and data can be queried and operated using SQL (Structured Query Language), and SQL can provide rich query functions, such as select, insert, update, and delete operations. Relational data is suitable for application scenarios that require highly organized and structured data, and can be used in the present invention to process transactional data to ensure data integrity and consistency.

[0046] Relational data includes two types of data: full data and incremental data. Full data refers to the complete set of all data in the system, including historical data and current data. Incremental data refers to the data that has been added, modified, or deleted in the system since the last data backup or update. Incremental data is used to update data to reduce the burden of all data transmission and processing.

[0047] In step S2, a relational model is established based on the converted relational data and the nodes and relations between or within each relational data.

[0048] The relational model is a data model in the database field, which is used to organize and operate relational data. Relational data is stored in the form of tables, namely the relational data mentioned above. Each two-dimensional table is called a "relationship". Data is stored in a two-dimensional table, and each table consists of rows (records) and columns (attributes or fields). Each row represents a data item, and each column represents a data attribute. Attributes have specific data types and value ranges.

[0049] The relational model can extract and retain the required key information fields from the two-dimensional table of relational data according to business needs. For example, only two fields can be extracted from a table containing 100 rows of data and 10 fields, and the extracted fields can be reorganized into a new two-dimensional table.

[0050] Here, we should distinguish between relational data and relational models. Relational data emphasizes the organization and storage structure of data, while relational models are logical structures used to describe data and the relationships and operations between data. In short, relational data is the data actually stored in a relational database, while relational models are theoretical frameworks that guide how to store and operate these data. All data stored in a relational database is relational data, and relational models can be derived from relational databases. The relational model itself is an abstract concept used to guide the design and implementation of a database.

[0051] In an optional embodiment, SQL can be used to compile logic scripts according to business needs and establish a relational model. Specifically, SQL is a standard query and operation language for relational databases and is used to design database schemas, create tables, define relationships, insert data, etc.

[0052] Furthermore, step S2 may specifically include the following steps:

[0053] Step S2.1: Define attributes according to business requirements.

[0054] Step S2.2: Relationship identification.

[0055] Step S2.3: Establish a full relationship model.

[0056] Step S2.4: Synchronously update the incremental data.

[0057] In step S2.1, the attributes and data fields of each entity in the two-dimensional table are determined according to the relational data based on different business demand objectives. The entity can be an order, a product, a component, etc. In step S2.2, the relationship between each of the entities is determined, including one-to-one, one-to-many, many-to-one, and many-to-many. In step S2.3, the nodes and organizational relationships of the relational model are created using an offline tool according to the relationship between each entity to form a full relational model with relational attributes. The offline tool is the above-mentioned logic script. In this embodiment, for the processing of the full amount of data, a logic script is used to convert the relational model of the full amount of data into a CSV (Comma-Separated Values) file and then store it in a relational database and export the graph model to the graph database. CSV is a file format that stores tabular data in plain text, using delimiters (such as commas) to separate the values ​​in each row. In this embodiment, the graph database is a Neo4j graph database, and the Cypher query language can be used to operate the nodes, relationships, and attributes in the graph database.

[0058] In an optional embodiment, these scripts can convert the full relational model into a graph model recognizable by the graph database through the Cypher (graph database programming language) language. Specifically, the graph model is a data structure used to represent entities and the relationships between them, which consists of nodes (representing entities) and edges (representing relationships). The Cypher language uses the syntax of graph patterns to describe and operate nodes and relationships, and maps the node relationships of the full relational model into nodes and edges to form a graph model. Different from directly converting relational data into a graph model, the graph model mapping method of the present invention records the mapping information from the two-dimensional table of the relational model to the business logic through the Map (Logic, File) method, ensuring the logical consistency from the relational model to the graph model. The user can complete the conversion of the graph model without understanding the business logic, which improves the overall conversion efficiency of the system and realizes the decoupling of data model conversion. When the business logic changes, only the mapping information needs to be updated without modifying the entire conversion logic.

[0059] In other optional embodiments, engineers can also use other programming languages ​​such as Java or ETL (Extract, Transform, Load) tools to automate the mapping of the graph model. This step is mainly automated through scripts, reducing the need for manual intervention.

[0060] like Figure 3As shown, after mapping the relational model to the graph model, first determine whether the data in the relational model has any logical changes, that is, whether the data has been updated. If so, re-execute step S1 to convert the updated original data into relational data, and then map the graph model. If the data has not been updated, import the data node relationship in the relational model into the graph database, execute step S3, and verify whether the logic of the graph model and the relational model are consistent, which will be described in detail later.

[0061] In step S2.4, the incremental data is extracted to the cloud database using a data migration tool, a subscription instance is created through a data subscription component, and the subscription information is connected to the backend application, which allows the system to monitor and receive data updates in real time, and then determine different data consumption methods according to the type of the incremental data, and persist the incremental data to the graph database. Persistence here means storing the newly added, modified or deleted incremental data in the graph database to ensure the long-term storage and reliability of these data, capturing the incremental data since the last synchronization through the data subscription tool, if the original format of the incremental data is not suitable for direct storage in the graph database, convert it to a format supported by the graph database (converted to a graph model), store the converted incremental data in the graph database, and synchronize the graph model according to the incremental data updated subsequently to the graph database to ensure that the data in the graph database always keeps the latest state and the logical consistency with the relational data. In this embodiment, the data migration tool is used to extract the incremental data from the Oracle server to the cloud database. This step uses the efficiency and scalability of the cloud database to reduce the pressure of the local self-built database.

[0062] like Figure 2 and Figure 4 As shown, in this embodiment, the processing of incremental data can specifically use the DTS data subscription component to receive data change messages from different business systems by subscribing to change events in the database, including insert, update, and delete data operations, parse the received incremental data and extract key change information such as table name, field, change type (Type), etc., set different modeling methods according to insert data, update data, and delete data, and convert the incremental data into a graph model according to timeliness requirements. Figure 4 The binlog log file shown in is automatically generated by MySQL. It records all operations that change the database (including insert, update, delete, etc.). When a database failure occurs, the binlog log file can also be used to restore the data to the state before the failure.

[0063] In such Figure 4In the embodiment shown, the incremental data is extracted from the business system end (such as Oracle database), and the cloud product data migration tool DTS is used to transfer the data to the RDS (Relational Database Service) cloud database, and the subscription instance is created using the DTS data subscription component, and the subscription information is connected to the back-end application, and the relational data is persisted in the graph database. In this embodiment, the use of cloud databases can improve availability and scalability. DTS can automatically detect node failures and switch to spare nodes without manually compiling fault switching and recovery mechanisms to ensure the stable operation of the business system. In addition, DTS has built-in monitoring strategies to monitor the accumulation state of the data consumption queue. When the threshold of the number of accumulation items is reached, the system will automatically trigger the full data synchronization mechanism to eliminate delays and ensure the real-time update of the graph model. The threshold can be set according to business needs. Different business needs have different sensitivities to the frequency of data updates. For example, some businesses can accept data delays of one day, while some businesses need to be updated every five minutes. For business needs with high real-time requirements, the threshold of the number of accumulated items can be set lower. Conversely, for business needs with low real-time requirements, the threshold of the number of accumulated items can be set higher (such as tens of thousands).

[0064] In an optional embodiment, a logic script is written according to different business requirements to determine the data consumption mode so as to adopt different modeling methods to keep the graph model and the relational model logically consistent. The data consumption mode is the mode of processing different types of data, which specifically includes the following steps:

[0065] For insert operations, new data is added to the graph database to form new nodes or relationships. For example, when insert data is received, the DTS component processes the inserted record. If a mapping record from E to F is inserted, an F node and a relationship between E and F will be established in the graph model.

[0066] For update operations, existing nodes or relationships in the graph database are updated. For example, when update data is received, the DTS component will update the corresponding nodes or relationships in the graph model. If an attribute of a record is changed, the DTS component will update the attribute of the corresponding node in the graph model. Of course, if it is an attribute of a relationship on a node and the business does not need this relationship attribute, there is no need to change it.

[0067] For the delete operation, a node or relationship is deleted from the graph database. For example, when receiving delete data, the DTS component deletes the corresponding node or relationship from the graph model. If a record is deleted, the DTS component deletes the corresponding node and its related relationship in the graph model.

[0068] In an optional embodiment, the incremental data is processed after the complete amount of data is processed, so that the integrity of the entire data can be guaranteed.

[0069] In step S3, after the graph model is stored in the database, a logical search is performed on the graph model in the graph database, and the search result is compared with the relationship model to see whether their business meanings are logically consistent. It should be noted here that storing the graph model in the database refers to the process of storing the graph model data and its relationships in the graph database.

[0070] Specifically, in this step, the logical search verification of the graph model is mainly performed by judging whether a parent-child relationship graph that logically matches the relationship model is established in the search results. Logical search in the graph database can mainly include the following steps:

[0071] Query the data types and relationships to be retrieved, determine the starting point or end point or path of the query, and execute the query statement in the graph database. For example, in Cypher, you can use the MATCH statement to match nodes and relationships. Logical retrieval can include upward tracing retrieval, downward retrieval, or bidirectional retrieval with a specified number of layers. Specifically, taking upward tracing retrieval as an example, upward tracing retrieval refers to finding all the upper-level components or source components of a component in the graph database, starting from the node of the target component, tracing upward along the edge until the top-level source component or the node that meets specific conditions is found, which is convenient for business personnel to understand the source and composition structure of the component or trace its historical change information. Similarly, downward retrieval refers to finding all the lower-level nodes or sub-components of a node of the graph model in the graph database, which is convenient for business personnel to understand the detailed composition structure of a component or system, or to find all the direct / indirect child nodes of a node, so as to quickly obtain the required information. Bidirectional retrieval with a specified number of layers is to start from a certain node, and simultaneously trace upward and downward retrieval, so that business personnel can understand all the related nodes of a certain node within a specific level range.

[0072] It should be noted here that since relational databases themselves are not good at handling more than ten or twenty levels of association, the present invention uses a graph database and system integration to solve the performance problem of inefficient multi-layer associations in relational databases. Since graph databases naturally have the characteristics of processing graph models that include complex hierarchical relationships, retrieval can be achieved in seconds in the graph database after the graph model is hierarchically associated. For example, in the business of configuration difference analysis, business personnel can enter the starting component for retrieval and obtain the summarized change information; in the business of structural maintenance manuals, business personnel can enter the component number for retrieval, obtain the update information of the lower-level part number, and mark the addition, update or deletion; in the business of physical documentary report analysis, business personnel can search according to a set of component numbers to obtain summary information of parts with a specified number of levels.

[0073] like Figure 5 As shown, in step S4, rendering the graph model refers to the process of converting the graph model into a visual image or animation. The Mybatis framework is integrated with Springboot to provide an API interface that meets different business needs, so that business personnel can interact with the graph database on the UI page and analyze the business. In this embodiment, the business includes but is not limited to the following scenarios: configuration difference analysis, structural maintenance manual analysis, and physical documentary report analysis. This design allows the system to use the graph model as the smallest processing unit, encapsulate specific business logic, and provide an intuitive and easy-to-use Web version of the hierarchical data analysis platform, which can perform comprehensive operations on relational data and node data through web pages.

[0074] Finally, the logic scripts involved in the present invention are summarized. Compiling these scripts is mainly based on the different business requirements, and then converting them into code languages. Facing each business requirement, compiling once can be reused infinitely. The whole process can have the following stages:

[0075] (1) Requirements analysis phase: First, we need to sort out and clarify specific business requirements. This is the basis of the entire data processing process and ensures that all subsequent work is carried out around these requirements.

[0076] (2) Data extraction phase: Secondly, write logic scripts from existing relational databases (such as MySQL) to handle the above business needs. The required full amount of data is obtained through automated execution of logic scripts.

[0077] (3) Data processing stage: Then, use Shell script to process the acquired data and convert it into an operational mapping format. This step is the key link in model transformation and ensures the availability and accuracy of the data.

[0078] (4) Script writing phase: During the data conversion process, multiple scripts are used to implement logical mapping. The multiple scripts may specifically include the above-mentioned logic script, import script, and data conversion script. The import script is used to import data into the processing environment. The data conversion script can be implemented using the query language of the graph database (such as Cypher) to ensure that the data can be mapped and converted in the expected manner.

[0079] (5) Logical mapping implementation: Finally, through the collaborative work of the above three scripts, the data logical mapping in business requirements is realized. This step is an important link to ensure that data transformation meets business goals.

[0080] It should be noted here that the above logic scripts only need to be compiled once by the staff for each business demand when it is first executed, because these script codes are compiled once, and their functional modules are built. After that, each time this business demand is encountered, it can be directly converted into the required format for calling. In an optional embodiment, the logic script can adopt the design pattern of domain-driven design (DDD) to modularly write the existing analysis scenarios into different service services. This modular design allows each business demand to be independently called and maintained to achieve the accumulation and modularization and componentization of the knowledge base. Whenever the same business demand is encountered, the existing module can be directly called without recompiling the script, reducing the workload of repeated development. With the continuous accumulation of business needs, there are more and more service modules and components in the knowledge base, which can cover more business scenarios.

[0081] In summary, the system design of the present invention adopts a modular and component-based design concept, which reduces the workload of developers.

[0082] In order to achieve the above-mentioned object, the present invention further provides a data storage and analysis system for aircraft products, which may specifically include:

[0083] The data processing and model mapping module includes a full data processing layer and an incremental data processing layer, which respectively maps the node relationship logic of the incremental data and the full data into a relational model according to the relational data, and converts the relational model into a graph model;

[0084] Cloud database; the cloud database includes a relational database and a graph database, which is used to store various model data and provide a retrieval and retrieval interface;

[0085] Business logic analysis module, which collects business scenarios from hierarchical data, customizes logic according to different business scenarios and expands them;

[0086] The rendering interaction module renders and displays the graph model according to the business scenario for business personnel to perform business operations.

[0087] Figure 5 The complete technical chain of the data storage and analysis system for the entire aircraft product is shown in Figure 1. The original data source of the system is the Oracle database on the business system side. After the relational data is converted into a graph model by the above data processing and model mapping module, it is stored in the graph database, and the relational data is imported into the relational database (RDS-MySQL) at the same time. The Mybatis framework is integrated through the back-end service Springboot to provide an API interface that meets different business logics, so that the front-end page can interact with the back-end API interface call to complete the front-end page rendering and business processing analysis.

[0088] In order to achieve the above-mentioned purpose, the present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor runs the program, the steps of the method for storing and analyzing aircraft product data as described in any of the aforementioned embodiments can be implemented.

[0089] The processor and the memory may be provided separately or integrated together, for example, integrated into a system on chip (SOC) of a terminal device.

[0090] In order to achieve the above-mentioned purpose, the present invention also provides a computer-readable storage medium, which stores computer-executable instructions or computer programs. When the computer-executable instructions or computer programs are processed and executed, the storage and analysis method of aircraft product data as described above is implemented.

[0091] The computer-readable storage medium is, for example, a memory. The memory may be a volatile memory or a nonvolatile memory, or the memory may include both a volatile memory and a nonvolatile memory. Among them, the nonvolatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct rambus RAM (DR RAM).

[0092] If the integrated units in the above embodiments are implemented in the form of software functional units and sold or used as independent products, they can be stored in the above computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, 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, which is stored in a storage medium and includes several instructions for enabling one or more computer devices (which can be personal computers, servers or network devices, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention.

[0093] The preferred specific embodiments of the present invention are described in detail above. It should be understood that ordinary technicians in the field can make many modifications and changes based on the concept of the present invention without creative work. Therefore, all technical solutions that can be obtained by technicians in the technical field based on the concept of the present invention through logical analysis, reasoning or limited experiments on the basis of the prior art should be within the scope of protection determined by the claims.

Claims

1. A method for storing and analyzing aircraft product data, characterized in that: The steps include: Step S1: importing and processing raw data; importing the raw data of aircraft products from the business system and converting it into relational data; the relational data includes full data and incremental data; Step S2: Establishing a relational model; using a two-dimensional table to organize the node relationships of the relational data and establish a relational model; Step S3: Mapping, storage and logic verification of the graph model: mapping the relational model to a graph model, storing it in a graph database, and verifying the logical consistency between the graph model and the relational model; Step S4: Rendering and interaction; customize logic expansion according to business needs, through the interaction between the front and back ends, call the graph model according to the different business needs of business personnel, and render it to the front end for business processing analysis; the business at least includes configuration difference analysis, structural maintenance manual analysis and physical documentary report analysis.

2. The method for storing and analyzing aircraft product data according to claim 1, characterized in that: The step S2 comprises the following steps: Step S2.1: define attributes according to business requirements; determine the attributes and data fields of each entity in the two-dimensional table according to the relational data based on different business requirements; Step S2.2: Relationship identification: determining the relationship between the entities, including one-to-one, one-to-many, many-to-one, and many-to-many; Step S2.3: Establish a full relational model; create nodes and organizational relationships of the relational model using offline tools according to the relationships between the entities, and form a full relational model with relational attributes; Step S2.4: Synchronously update the incremental data; use the data migration tool to extract the incremental data to the cloud database, create a subscription instance through the data subscription component, connect the subscription information to the back-end application, determine different data consumption methods according to the type of the incremental data, and persist the incremental data to the graph database.

3. The method for storing and analyzing aircraft product data according to claim 2, characterized in that: In step S2.3, the offline tool defines a primary key for each of the two-dimensional tables to uniquely identify each record in the table, and then defines a foreign key to establish a relationship between the two-dimensional tables; In step S2.4, the data subscription component is a DTS component, and the types of the incremental data include insert data, update data, and delete data.

4. The method for storing and analyzing aircraft product data according to claim 1, characterized in that: In step S3, after the graph model is stored in the database, a logical search is performed on the graph model in the graph database, and the search results are compared with the business meaning of the relationship model to determine whether a parent-child relationship graph that matches the logic of the relationship model is established in the search results, thereby completing the logic verification.

5. The method for storing and analyzing aircraft product data according to claim 4, characterized in that: The logical search includes an upward tracing search, a downward search, or a bidirectional search at a specified number of levels.

6. The method for storing and analyzing aircraft product data according to claim 1, characterized in that: In the step S1, converting the original data into the relational data comprises the following steps: Step S1.1: Determine whether the original data is heterogeneous data; if so, first parse the heterogeneous data into the relational data with relational attributes, and then execute step S2; otherwise, directly execute step S2.

7. The method for storing and analyzing aircraft product data according to claim 1, characterized in that: In step S4, Springboot is used to integrate the Mybatis framework, providing an API interface that meets different business requirements and realizing the interaction between the business system and the graph database.

8. A data storage and analysis system for aircraft products, characterized in that: include: The data processing and model mapping module includes a full data processing layer and an incremental data processing layer, which respectively maps the node relationship logic of the incremental data and the full data into a relational model according to the relational data, and converts the relational model into a graph model; Cloud database; The cloud database includes a relational database and a graph database, which are used to store various model data and provide a retrieval and retrieval interface; Business logic analysis module, which collects business scenarios from hierarchical data, customizes logic according to different business scenarios and expands them; The rendering interaction module renders and displays the graph model according to the business scenario for business personnel to perform business operations.

9. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor runs the program, the steps of the method for storing and analyzing aircraft product data according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions or a computer program, and when the computer-executable instructions or the computer program are processed and executed, the method for storing and analyzing aircraft product data according to any one of claims 1 to 7 is implemented.

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