Vehicle data management method and device based on graph database, computer equipment, medium and product

Abstract

The invention relates to a vehicle data management method and device based on a graph database, computer equipment, a medium and a product. The method comprises the steps of determining an entity association relationship in business data according to a data association relationship among the business data, constructing a graph database based on the entity association relationship, loading the graph database to a visual page, and screening the business data according to a screening request triggered by a user under the condition of receiving the screening request triggered by the user through the visual page. And executing a graph database query operation corresponding to the screening request, and displaying a query result to the user through a visual page. By adopting the method, the data query and analysis efficiency can be improved, and the associated data can be effectively processed.

Description

Technical Field

[0001] The present application relates to the field of vehicle data processing technology, and in particular to a vehicle data management method, device, computer equipment, medium and product based on a graph database. Background Art

[0002] In traditional relational databases, data is usually stored in the form of tables. This structure is more efficient when dealing with one-dimensional or two-dimensional relationships, but its limitations are fully exposed when facing multi-dimensional, cross-system complex data relationships. The chain connection between data is severed, resulting in serious data islands and a significant reduction in the relevance of data. As a result, the performance of relational databases is often limited when dealing with large-scale data sets and complex queries, making it difficult to achieve deep mining and analysis of data. Summary of the invention

[0003] Based on this, it is necessary to provide a vehicle data management method, device, computer equipment, medium and product based on a graph database that can effectively process relational data in order to address the above technical problems.

[0004] In a first aspect, the present application provides a vehicle data management method based on a graph database, comprising:

[0005] According to the data association relationship between business data, determine the entity association relationship in the business data; the entity is the business object in the business data;

[0006] Build a graph database based on entity association relationships and load the graph database into the visualization page;

[0007] When a filtering request triggered by a user is received through the visualization page, the graph database query operation corresponding to the filtering request is executed, and the query results are displayed to the user through the visualization page.

[0008] In one embodiment, the step of constructing a graph database based on entity association relationships includes:

[0009] A graph model is established based on entity association relationships; the nodes of the graph model represent entities, and the edges represent entity association relationships corresponding to the entities;

[0010] When the graph model is verified, corresponding node indexes and edge indexes are set for the nodes and edges of the graph model according to the business rules corresponding to the business data to obtain a graph database;

[0011] Divide the business data into multiple batches, and import the business data of corresponding batches into the graph database one by one.

[0012] In one embodiment, the filtering request includes a filtering keyword selected by a user through a visualization page; and the steps of executing a graph database query operation corresponding to the filtering request and displaying the query result to the user through the visualization page include:

[0013] Determine the query statement corresponding to the screening keyword, perform conditional search in the graph database through the query statement, obtain the corresponding search results, and display the search results to the user through a visualization page;

[0014] When a reset operation is received which is triggered by the user through the visualization page, the filtering keyword input by the user is retrieved, the step of determining the query statement corresponding to the filtering keyword is returned, and the execution is continued.

[0015] In one embodiment, the screening request includes a screening template file uploaded by a user; the steps of executing a graph database query operation corresponding to the screening request and displaying the query result to the user through a visualization page include:

[0016] Parse and filter the document content of template files;

[0017] Convert document content into query statements, perform conditional retrieval in the graph database through the query statements, and obtain corresponding retrieval results;

[0018] Generate a target document based on the retrieval results, and display the target document to the user through a visualization page to instruct the user to download and view the target document.

[0019] In one embodiment, the filtering request includes a bicycle jump operation triggered by a user through a visualization page; the steps of executing a graph database query operation corresponding to the filtering request and displaying the query result to the user through the visualization page include:

[0020] The jump page corresponding to the bicycle jump operation is displayed to the user through a visualization page, and the key component information of the corresponding vehicle is displayed on the jump page.

[0021] In one embodiment, the screening request includes a part keyword input by a user; the steps of performing a graph database query operation corresponding to the screening request and displaying the query result to the user through a visualization page include:

[0022] Query the historical change records and historical problem records of the corresponding parts in the graph database based on the part keywords;

[0023] Convert historical change records and historical problem records into chart form and present them to users through a visualization page.

[0024] In a second aspect, the present application also provides a vehicle data management device based on a graph database, comprising:

[0025] The association determination module is used to determine the entity association relationship in the business data according to the data association relationship between the business data; the entity is the business object in the business data;

[0026] The database construction module is used to build a graph database based on entity association relationships and load the graph database into the visualization page;

[0027] The data query module is used to execute the graph database query operation corresponding to the filtering request when receiving the filtering request triggered by the user through the visualization page, and display the query results to the user through the visualization page.

[0028] In a third aspect, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the method steps of any one of the first aspects are implemented.

[0029] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, which implements any one of the method steps in the first aspect when the computer program is executed by a processor.

[0030] In a fifth aspect, the present application further provides a computer program product, comprising a computer program, which implements any one of the method steps in the first aspect when executed by a processor.

[0031] The vehicle data management method, apparatus, computer equipment, medium and product based on the graph database determine the entity association relationship in the business data according to the data association relationship between the business data, build a graph database based on the entity association relationship, and load the graph database to the visualization page. When a filtering request triggered by the user is received through the visualization page, the graph database query operation corresponding to the filtering request is executed, and the query results are displayed to the user through the visualization page, which can improve the efficiency of data query and analysis and effectively process the associated data. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0033] FIG1 is an application environment diagram of a vehicle data management method based on a graph database in one embodiment;

[0034] FIG2 is a schematic diagram of a process of a vehicle data management method based on a graph database in one embodiment;

[0035] FIG3 is a flow chart of a vehicle data management method based on a graph database in another embodiment;

[0036] FIG4 is a structural block diagram of a vehicle data management device based on a graph database in one embodiment;

[0037] FIG. 5 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0038] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0039] The vehicle data management method based on the graph database provided in the embodiment of the present application can be applied in the application environment shown in Figure 1. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be integrated on the server 104, or it can be placed on the cloud or other network servers. Among them, the terminal 102 and the server 104 can be used separately to execute the vehicle data management method based on the graph database of the present application, or they can be used together to execute the vehicle data management method based on the graph database of the present application. Taking the collaborative execution of the terminal 102 and the server 104 as an example, the server 104 is used to determine the entity association relationship in the business data based on the data association relationship between the business data, build a graph database based on the entity association relationship, and load the graph database to the visualization page of the terminal 102. When a filtering request triggered by the user is received through the visualization page, the graph database query operation corresponding to the filtering request is executed, and the query results are displayed to the user through the visualization page. The terminal 102 may be, but is not limited to, various personal computers, laptops, smart phones, tablet computers, IoT devices, and portable wearable devices. The IoT devices may be smart speakers, smart TVs, smart air conditioners, smart car devices, projection devices, etc. Portable wearable devices may be smart watches, smart bracelets, head-mounted devices, etc. Head-mounted devices may be virtual reality (VR) devices, augmented reality (AR) devices, smart glasses, etc. The server 104 may be an independent physical server, a server cluster or distributed system consisting of multiple physical servers, or a cloud server that provides cloud computing services.

[0040] In an exemplary embodiment, as shown in FIG2 , a vehicle data management method based on a graph database is provided, and the method is applied to the server 104 in FIG1 as an example for description, including the following steps 202 to 206 . Among them:

[0041] S202: Determine entity association relationships in the business data according to data association relationships among the business data; an entity is a business object in the business data.

[0042] Optionally, for business data, by sorting out the logical structure of business data, including the source of the data, storage method, update frequency, etc., and using visualization tools or methods (such as flow charts, ER diagrams, etc.) to describe the relationship between data, you can better understand the data structure and business logic.

[0043] Furthermore, an entity is the basic unit in business data, usually representing a specific business object, such as a vehicle, part, user, etc. The relationship between entities describes the connection and interaction between these objects, such as containment, belonging, correspondence, etc. After determining the entity relationship, the data is cleaned and preprocessed. When cleaning the data, it is necessary to remove invalid values ​​(such as null values, duplicate values, etc.), fill missing values ​​(interpolation, mode filling, etc. can be used), add flags (to identify the source and status of the data), and merge data columns (merge multiple related columns into one column to simplify data analysis).

[0044] Exemplarily, taking two core entities: VIN (Vehicle Identification Number) and claim part number as an example, the entity relationship obtained through analysis can be described as follows: a VIN may correspond to multiple claim part numbers (inclusion relationship), and a claim part number belongs to a specific VIN (belongs to relationship).

[0045] S204: Build a graph database based on the entity association relationship, and load the graph database into the visualization page.

[0046] Optionally, a graph database is a type of database specifically used to store and query complex related data. When a graph database is constructed by determining the entity relationship, it usually includes defining nodes and edges, where nodes represent entities and edges represent the relationship between entities. After the construction is completed, the graph database is loaded onto the visualization page, so that the user can directly query the specified related data through the visualization page.

[0047] S206: When a filtering request triggered by a user is received through the visualization page, a graph database query operation corresponding to the filtering request is executed, and the query result is displayed to the user through the visualization page.

[0048] Optionally, when a user initiates a filtering request through a visualization page, a corresponding graph database query operation is performed according to the request. Since the graph database is efficient in processing associated queries, the query results can be quickly returned and displayed to the user through the visualization page.

[0049] In the above-mentioned vehicle data management method based on the graph database, the entity association relationship in the business data is determined according to the data association relationship between the business data, a graph database is constructed based on the entity association relationship, and the graph database is loaded into the visualization page. When a filtering request triggered by the user is received through the visualization page, the graph database query operation corresponding to the filtering request is executed, and the query results are displayed to the user through the visualization page, which can improve the efficiency of data query and analysis and effectively process related data.

[0050] In an exemplary embodiment, the steps of constructing a graph database based on entity association relationships include: establishing a graph model based on the entity association relationships; the nodes of the graph model represent entities, and the edges represent entity association relationships corresponding to the entities; when the graph model is verified, corresponding node indexes and edge indexes are set for the nodes and edges of the graph model according to the business rules corresponding to the business data to obtain a graph database; dividing the business data into multiple batches, and importing the corresponding batches of business data into the graph database in sequence.

[0051] Optionally, first, build a suitable model based on the specific needs and logic of the business. This model is the basis for data integration. It defines the relationship and interaction between data. When building the model, you need to have a deep understanding of the business logic and determine which entities (such as users, products, orders, etc.) are important and how they are related to each other. After the model is built, it is necessary to conduct a detailed check on the rationality of the graph construction, including verifying whether the nodes (points) and edges in the graph accurately reflect the business logic and whether the relationship between the data is correctly expressed. If any unreasonable or wrong places are found, appropriate model adjustments need to be made to ensure the accuracy and availability of the graph database. This step is the key to ensure that the data model can correctly reflect the business logic.

[0052] Furthermore, we build the Schema according to the business rules, including creating nodes, edges, and corresponding indexes. The Schema is the data structure definition in the graph database, which determines what types of data can be stored in the graph database and the relationship between these data. When building the Schema, we need to carefully design the structure of nodes and edges, and establish appropriate indexes to improve data retrieval speed and processing efficiency. Finally, in order to ensure the smooth progress of data import, we need to reasonably set the partitions for data import and import the data into batches of appropriate size to avoid excessive pressure on the graph database.

[0053] In this embodiment, by setting indexes for the nodes and edges of the graph model, data query and retrieval can be accelerated and the performance of the graph database can be improved. Since the graph database can intuitively display the relationship between data, it is convenient for users to analyze and visualize data, thereby improving data import efficiency and providing flexibility and scalability.

[0054] In an exemplary embodiment, a filtering request includes a filtering keyword selected by a user through a visualization page; the step of executing a graph database query operation corresponding to the filtering request and displaying the query results to the user through the visualization page includes: determining a query statement corresponding to the filtering keyword, performing a conditional search in the graph database through the query statement, obtaining corresponding search results, and displaying the search results to the user through the visualization page; upon receiving a reset operation triggered by the user through the visualization page, re-acquiring the filtering keyword input by the user, returning to the step of determining the query statement corresponding to the filtering keyword, and continuing execution.

[0055] Optionally, the user can select a quick filter keyword through the visualization page, such as vehicle model, offline date, engine type, VIN, etc. After the user selects the filter keyword, he can choose to reset / start the search. The server determines the corresponding query statement API based on the filter keyword selected by the user to perform a conditional search in the graph database, obtains the search results that meet the filter conditions, and returns the search results to the visualization page to display to the user. If the user triggers the reset operation through the visualization page, it is necessary to re-acquire the filter keyword entered by the user (the user may have modified the filter condition), and re-determine the query statement corresponding to the new filter keyword, use the new query statement to continue to execute the graph database query operation, and display the new query results.

[0056] In this embodiment, through the visualization page, the user can intuitively select filtering keywords and view the query results. Since the query statement can be quickly generated based on the filtering keywords selected by the user, and efficient retrieval operations can be performed in the graph database, the efficiency of data processing is improved.

[0057] In an exemplary embodiment, a filtering request includes a filtering template file uploaded by a user; the steps of executing a graph database query operation corresponding to the filtering request and displaying the query results to the user through a visualization page include: parsing the document content of the filtering template file; converting the document content into a query statement, performing a conditional search in the graph database through the query statement, and obtaining corresponding search results; generating a target document based on the search results, and displaying the target document to the user through a visualization page to instruct the user to download the target document and view it.

[0058] Optionally, when the user selects the advanced filtering mode, the server will automatically provide a download link for the data filtering template through the visualization page. The user can download the template and fill in the filtering conditions in the specified format. After that, the user uploads the filled template file. The server reads and parses the filtering template file uploaded by the user, extracts the document content therein, and converts it into a query statement that the graph database can understand based on the parsed document content. The converted query statement is used to perform conditional search in the graph database to obtain search results that meet the filtering conditions. Based on the search results, the server generates a target document, which may be presented in a table, report, chart or other form so that the user can intuitively understand the query results. The target document is displayed to the user through the visualization page, and a download link or button is provided to instruct the user to download the target document and view it.

[0059] In this embodiment, since the screening template file allows the user to customize the screening conditions, a flexible data screening method is provided. By automatically parsing the template file and generating query statements, the efficiency of data processing can be improved.

[0060] In an exemplary embodiment, the filtering request includes a single-vehicle jump operation triggered by a user through a visualization page; the steps of executing a graph database query operation corresponding to the filtering request and displaying the query results to the user through a visualization page include: displaying a jump page corresponding to the single-vehicle jump operation to the user through a visualization page, and displaying key component information of the corresponding vehicle on the jump page.

[0061] Optionally, when the user needs to retrieve key parts, the user triggers a bicycle jump operation through the visualization page. The server responds to the user's jump operation and retrieves key component information related to the bicycle in the graph database based on the bicycle selected by the user (possibly through the bicycle ID, frame number or other unique identifier), and displays a bicycle-related jump page to the user through the visualization page. The jump page can be specially designed to display bicycle information and has a specific layout and style. On the jump page, the key component information of the corresponding vehicle is displayed, and the user can see the detailed component information of the selected bicycle on the page and perform further operations or analysis as needed.

[0062] In this embodiment, through the visualization page and the bicycle jump operation, the user can intuitively select a bicycle and view its key component information, thereby improving the efficiency of data query.

[0063] In an exemplary embodiment, a filtering request includes a part keyword input by a user; the steps of executing a graph database query operation corresponding to the filtering request and displaying the query results to the user through a visualization page include: querying the historical change records and historical problem records of the corresponding parts in the graph database according to the part keywords; converting the historical change records and historical problem records into a chart form, and displaying them to the user through a visualization page.

[0064] Optionally, users can enter multi-dimensional search conditions such as part number, part name, time, etc. through the visualization page. The server will query the corresponding part change records in the graph database based on these conditions, and return the query results to the visualization page in the form of tables and various statistical charts, so that users can intuitively view the change history of the parts. In addition, the server can also query the corresponding parts historical market problem statistics based on these conditions, and return them to the visualization page in the form of tables and various statistical charts to help users understand the past market performance and problem trends of the parts.

[0065] In this embodiment, by using the part keywords input by the user, the query operation can be quickly executed in the graph database and the results can be returned, so that the user can understand the historical change records and market problem trends of the parts based on the query results, which can improve the convenience of product quality control, market strategy formulation, etc.

[0066] In an exemplary embodiment, as shown in FIG3 , a vehicle data management method based on a graph database is provided, the method comprising the following steps:

[0067] According to the data association relationship between business data, the entity association relationship in the business data is determined; the entity is the business object in the business data.

[0068] A graph model is established based on entity association relationships; the nodes of the graph model represent entities, and the edges represent entity association relationships corresponding to the entities; when the graph model is verified, corresponding node indexes and edge indexes are set for the nodes and edges of the graph model according to the business rules corresponding to the business data to obtain a graph database; the business data is divided into multiple batches, and the corresponding batches of business data are imported into the graph database in sequence.

[0069] Load the graph database into the visualization page.

[0070] In the case of receiving a filtering request triggered by a user through a visualization page, including a filtering keyword selected by the user through the visualization page, determining a query statement corresponding to the filtering keyword, performing a conditional search in the graph database through the query statement, obtaining a corresponding search result, and displaying the search result to the user through the visualization page; in the case of receiving a reset operation triggered by the user through the visualization page, re-acquiring the filtering keyword input by the user, returning to the step of determining the query statement corresponding to the filtering keyword, and continuing execution.

[0071] When a user-triggered filtering request including a user-uploaded filtering template file is received through a visualization page, the document content of the filtering template file is parsed; the document content is converted into a query statement, and a conditional search is performed in the graph database through the query statement to obtain the corresponding search result; a target document is generated according to the search result, and the target document is displayed to the user through a visualization page to instruct the user to download and view the target document.

[0072] When a filtering request triggered by a user is received through a visualization page, including a single-vehicle jump operation triggered by the user through the visualization page, a jump page corresponding to the single-vehicle jump operation is displayed to the user through the visualization page, and key component information of the corresponding vehicle is displayed on the jump page.

[0073] When a user-triggered filtering request including a part keyword input by the user is received through the visualization page, the historical change records and historical problem records of the corresponding part in the graph database are queried according to the part keyword; the historical change records and historical problem records are converted into a chart form and displayed to the user through the visualization page.

[0074] In this embodiment, the entity association relationship in the business data is determined according to the data association relationship between the business data, a graph database is constructed based on the entity association relationship, and the graph database is loaded into the visualization page. When a filtering request triggered by the user is received through the visualization page, the graph database query operation corresponding to the filtering request is executed, and the query results are displayed to the user through the visualization page, which can improve the efficiency of data query and analysis and effectively process associative data.

[0075] It should be understood that, although the steps in the flowcharts involved in the above embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above embodiments may include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0076] Based on the same inventive concept, the embodiment of the present application also provides a vehicle data management device based on a graph database for implementing the vehicle data management method based on a graph database involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more embodiments of the vehicle data management device based on a graph database provided below can refer to the limitations of the vehicle data management method based on a graph database above, and will not be repeated here.

[0077] In an exemplary embodiment, as shown in FIG4 , a vehicle data management device based on a graph database is provided, comprising: an association determination module 10, a database construction module 20 and a data query module 30, wherein:

[0078] The association determination module 10 is used to determine the entity association relationship in the business data according to the data association relationship between the business data; the entity is the business object in the business data.

[0079] The database construction module 20 is used to construct a graph database based on entity association relationships and load the graph database into a visualization page.

[0080] The data query module 30 is used to execute the graph database query operation corresponding to the filtering request when receiving the filtering request triggered by the user through the visualization page, and display the query result to the user through the visualization page.

[0081] In an exemplary embodiment, the database construction module 20 is also used to establish a graph model based on entity association relationships; the nodes of the graph model represent entities, and the edges represent entity association relationships corresponding to the entities; when the graph model is verified, corresponding node indexes and edge indexes are set for the nodes and edges of the graph model according to the business rules corresponding to the business data to obtain a graph database; the business data is divided into multiple batches, and the corresponding batches of business data are imported into the graph database in sequence.

[0082] In an exemplary embodiment, the filtering request includes the filtering keywords selected by the user through the visualization page; the data query module 30 is also used to determine the query statement corresponding to the filtering keyword, perform conditional search in the graph database through the query statement, obtain the corresponding search results, and display the search results to the user through the visualization page; when a reset operation triggered by the user through the visualization page is received, the filtering keyword input by the user is retrieved, the step of determining the query statement corresponding to the filtering keyword is returned, and execution continues.

[0083] In an exemplary embodiment, the screening request includes a screening template file uploaded by a user; the data query module 30 is also used to parse the document content of the screening template file; convert the document content into a query statement, perform conditional search in the graph database through the query statement, and obtain corresponding search results; generate a target document based on the search results, and display the target document to the user through a visualization page to instruct the user to download and view the target document.

[0084] In an exemplary embodiment, the filtering request includes a single-vehicle jump operation triggered by a user through a visualization page; the data query module 30 is also used to display the jump page corresponding to the single-vehicle jump operation to the user through the visualization page, and display the key component information of the corresponding vehicle on the jump page.

[0085] In an exemplary embodiment, the filtering request includes a part keyword input by a user; the data query module 30 is also used to query the historical change records and historical problem records of the corresponding parts in the graph database based on the part keywords; the historical change records and historical problem records are converted into a chart form, and displayed to the user through a visualization page.

[0086] Each module in the vehicle data management device based on the graph database can be implemented in whole or in part by software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute the operations corresponding to each module.

[0087] In an exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be shown in FIG5. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O), and a communication interface. The processor, the memory, and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store business data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a vehicle data management method based on a graph database is implemented.

[0088] Those skilled in the art will understand that the structure shown in FIG. 5 is merely a block diagram of a partial structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different arrangement of components.

[0089] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the following steps when executing the computer program: determining entity association relationships in business data based on data association relationships between business data; an entity is a business object in the business data; building a graph database based on the entity association relationships, and loading the graph database into a visualization page; when a filtering request triggered by a user is received through the visualization page, executing a graph database query operation corresponding to the filtering request, and displaying the query results to the user through the visualization page.

[0090] In one embodiment, the construction of a graph database based on entity association relationships involved in the execution of a computer program by a processor includes: establishing a graph model based on entity association relationships; the nodes of the graph model represent entities, and the edges represent entity association relationships corresponding to the entities; when the graph model is verified, corresponding node indexes and edge indexes are set for the nodes and edges of the graph model according to the business rules corresponding to the business data to obtain a graph database; dividing the business data into multiple batches, and importing the corresponding batches of business data into the graph database in sequence.

[0091] In one embodiment, the filtering request includes the filtering keywords selected by the user through the visualization page; the processor executes the computer program involved in executing the graph database query operation corresponding to the filtering request, and displays the query results to the user through the visualization page, including: determining the query statement corresponding to the filtering keyword, performing conditional search in the graph database through the query statement, obtaining the corresponding search results, and displaying the search results to the user through the visualization page; when receiving a reset operation triggered by the user through the visualization page, re-acquiring the filtering keyword input by the user, returning to the step of determining the query statement corresponding to the filtering keyword, and continuing execution.

[0092] In one embodiment, a screening request includes a screening template file uploaded by a user; a graph database query operation corresponding to the execution of the screening request is involved when the processor executes a computer program, and the query result is displayed to the user through a visualization page, including: parsing the document content of the screening template file; converting the document content into a query statement, performing a conditional search in the graph database through the query statement, and obtaining a corresponding search result; generating a target document according to the search result, and displaying the target document to the user through a visualization page to instruct the user to download the target document and view it.

[0093] In one embodiment, the filtering request includes a single-vehicle jump operation triggered by a user through a visualization page; the processor executes a computer program involving a graph database query operation corresponding to the execution of the filtering request, and displays the query results to the user through a visualization page, including: displaying a jump page corresponding to the single-vehicle jump operation to the user through a visualization page, and displaying key component information of the corresponding vehicle on the jump page.

[0094] In one embodiment, a filtering request includes a part keyword input by a user; the processor executes a graph database query operation corresponding to the filtering request when executing a computer program, and displays the query results to the user through a visualization page, including: querying the historical change records and historical problem records of the corresponding parts in the graph database according to the part keyword; converting the historical change records and historical problem records into a chart form, and displaying them to the user through a visualization page.

[0095] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented: determining entity association relationships in business data based on data association relationships between business data; an entity is a business object in the business data; building a graph database based on the entity association relationships, and loading the graph database into a visualization page; when a filtering request triggered by a user is received through the visualization page, executing a graph database query operation corresponding to the filtering request, and displaying the query results to the user through the visualization page.

[0096] In one embodiment, a computer program executed by a processor involves constructing a graph database based on entity association relationships, including: establishing a graph model based on entity association relationships; the nodes of the graph model represent entities, and the edges represent entity association relationships corresponding to the entities; when the graph model is verified, corresponding node indexes and edge indexes are set for the nodes and edges of the graph model according to business rules corresponding to the business data to obtain a graph database; dividing the business data into multiple batches, and importing the corresponding batches of business data into the graph database in sequence.

[0097] In one embodiment, the filtering request includes the filtering keywords selected by the user through the visualization page; the computer program executed by the processor involves executing a graph database query operation corresponding to the filtering request, and displaying the query results to the user through the visualization page, including: determining the query statement corresponding to the filtering keyword, performing a conditional search in the graph database through the query statement, obtaining the corresponding search results, and displaying the search results to the user through the visualization page; upon receiving a reset operation triggered by the user through the visualization page, re-acquiring the filtering keyword input by the user, returning to the step of determining the query statement corresponding to the filtering keyword, and continuing execution.

[0098] In one embodiment, a screening request includes a screening template file uploaded by a user; when a computer program is executed by a processor, a graph database query operation corresponding to the screening request is performed, and the query result is displayed to the user through a visualization page, including: parsing the document content of the screening template file; converting the document content into a query statement, performing a conditional search in the graph database through the query statement, and obtaining a corresponding search result; generating a target document according to the search result, and displaying the target document to the user through a visualization page to instruct the user to download the target document and view it.

[0099] In one embodiment, the filtering request includes a single-vehicle jump operation triggered by a user through a visualization page; when the computer program is executed by the processor, a graph database query operation corresponding to the execution of the filtering request is involved, and the query results are displayed to the user through a visualization page, including: displaying a jump page corresponding to the single-vehicle jump operation to the user through a visualization page, and displaying key component information of the corresponding vehicle on the jump page.

[0100] In one embodiment, a filtering request includes a part keyword input by a user; when a computer program is executed by a processor, a graph database query operation corresponding to the filtering request is performed, and the query results are displayed to the user through a visualization page, including: querying the historical change records and historical problem records of the corresponding parts in the graph database according to the part keyword; converting the historical change records and historical problem records into a chart form, and displaying them to the user through a visualization page.

[0101] In one embodiment, a computer program product is provided, including a computer program, which implements the following steps when executed by a processor: determining entity association relationships in business data based on data association relationships between business data; an entity is a business object in the business data; building a graph database based on the entity association relationships, and loading the graph database into a visualization page; when a filtering request triggered by a user is received through the visualization page, executing a graph database query operation corresponding to the filtering request, and displaying the query results to the user through the visualization page.

[0102] In one embodiment, a computer program executed by a processor involves constructing a graph database based on entity association relationships, including: establishing a graph model based on entity association relationships; the nodes of the graph model represent entities, and the edges represent entity association relationships corresponding to the entities; when the graph model is verified, corresponding node indexes and edge indexes are set for the nodes and edges of the graph model according to business rules corresponding to the business data to obtain a graph database; dividing the business data into multiple batches, and importing the corresponding batches of business data into the graph database in sequence.

[0103] In one embodiment, the filtering request includes the filtering keywords selected by the user through the visualization page; the computer program executed by the processor involves executing a graph database query operation corresponding to the filtering request, and displaying the query results to the user through the visualization page, including: determining the query statement corresponding to the filtering keyword, performing a conditional search in the graph database through the query statement, obtaining the corresponding search results, and displaying the search results to the user through the visualization page; upon receiving a reset operation triggered by the user through the visualization page, re-acquiring the filtering keyword input by the user, returning to the step of determining the query statement corresponding to the filtering keyword, and continuing execution.

[0104] In one embodiment, a screening request includes a screening template file uploaded by a user; when a computer program is executed by a processor, a graph database query operation corresponding to the screening request is performed, and the query result is displayed to the user through a visualization page, including: parsing the document content of the screening template file; converting the document content into a query statement, performing a conditional search in the graph database through the query statement, and obtaining a corresponding search result; generating a target document according to the search result, and displaying the target document to the user through a visualization page to instruct the user to download the target document and view it.

[0105] In one embodiment, the filtering request includes a single-vehicle jump operation triggered by a user through a visualization page; when the computer program is executed by the processor, a graph database query operation corresponding to the execution of the filtering request is involved, and the query results are displayed to the user through a visualization page, including: displaying a jump page corresponding to the single-vehicle jump operation to the user through a visualization page, and displaying key component information of the corresponding vehicle on the jump page.

[0106] In one embodiment, a filtering request includes a part keyword input by a user; when a computer program is executed by a processor, a graph database query operation corresponding to the filtering request is performed, and the query results are displayed to the user through a visualization page, including: querying the historical change records and historical problem records of the corresponding parts in the graph database according to the part keyword; converting the historical change records and historical problem records into a chart form, and displaying them to the user through a visualization page.

[0107] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0108] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0109] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A vehicle data management method based on a graph database, characterized in that: The method comprises: Determine entity association relationships in the business data according to data association relationships between the business data; an entity is a business object in the business data; Building a graph database based on the entity association relationship, and loading the graph database into a visualization page; When the filtering request triggered by the user is received through the visualization page, a graph database query operation corresponding to the filtering request is executed, and the query result is displayed to the user through the visualization page.

2. The method according to claim 1, characterized in that: The constructing a graph database based on the entity association relationship includes: Establishing a graph model according to the entity association relationship; the nodes of the graph model represent entities, and the edges represent entity association relationships corresponding to the entities; When the graph model is verified, corresponding node indexes and edge indexes are set for the nodes and edges of the graph model according to the business rules corresponding to the business data to obtain a graph database; The business data is divided into multiple batches, and the business data of corresponding batches are imported into the graph database in sequence.

3. The method according to claim 1, characterized in that The filtering request includes the filtering keyword selected by the user through the visualization page; the performing the graph database query operation corresponding to the filtering request and displaying the query result to the user through the visualization page includes: Determine a query statement corresponding to the screening keyword, perform conditional search in the graph database through the query statement, obtain corresponding search results, and display the search results to the user through the visualization page; In case of receiving a reset operation triggered by the user through the visualization page, the filtering keyword input by the user is re-acquired, and the step of determining the query statement corresponding to the filtering keyword is returned and continued.

4. The method according to claim 1, characterized in that The screening request includes a screening template file uploaded by the user; the performing of a graph database query operation corresponding to the screening request and displaying the query result to the user through the visualization page includes: Parsing the document content of the screening template file; Convert the document content into a query statement, perform conditional search in the graph database through the query statement, and obtain corresponding search results; A target document is generated according to the search result, and the target document is displayed to the user through the visualization page to instruct the user to download and view the target document.

5. The method according to claim 1, characterized in that The screening request includes a bicycle jump operation triggered by the user through the visualization page; The executing the graph database query operation corresponding to the screening request and displaying the query result to the user through the visualization page includes: The jump page corresponding to the bicycle jump operation is displayed to the user through the visualization page, and key component information of the corresponding vehicle is displayed on the jump page.

6. The method according to claim 1, characterized in that The screening request includes the part keyword input by the user; the performing of the graph database query operation corresponding to the screening request and displaying the query result to the user through the visualization page includes: According to the part keyword, query the historical change records and historical problem records of the corresponding part in the graph database; The historical change records and the historical problem records are converted into a chart form, and are displayed to the user through the visualization page.

7. A vehicle data management device based on a graph database, characterized in that: The device comprises: An association determination module, used to determine entity association relationships in the business data according to data association relationships between the business data; an entity is a business object in the business data; A database construction module, used to construct a graph database based on the entity association relationship, and load the graph database into a visualization page; The data query module is used to execute the graph database query operation corresponding to the filtering request triggered by the user through the visualization page, and display the query results to the user through the visualization page.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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