A data association method and system based on a graph database

Abstract

The present invention discloses a data association method and system based on a graph database. The method includes: automatically calculating the current requirements, analyzing the required entities and the connection relationships between the entities according to the current requirements, and constructing a physical model in the graph database; obtaining the data information in the system, performing standardized processing on the data information to obtain the result of standardized processing; then extracting the relevance of the data information to generate association information, and importing the result of standardized processing and the association information into the physical model to form a graph database with information association. By implementing the present invention, using the graph database technology, complex association relationships can be stored flexibly, solving the problem that when the association relationships change in a traditional relational database, it is too costly to change the table structure, etc., facilitating the maintenance, update, and addition of the association information of device data, and being able to support subsequent data association requirements.

Description

Technical Field

[0001] The present invention relates to the technical field of computer software development, and in particular, to a data association method and system based on a graph database. Background Art

[0002] There are numerous equipment in nuclear power plant units, and various equipment management systems are complex, resulting in the dispersion of various equipment information, making it impossible to effectively associate, analyze, and utilize equipment data. In order to achieve flexible association of equipment data and explore the inherent value of associated data, a data association technology is required to process and store the data. However, traditional relational database technologies have many problems such as insufficient complex relationship query capabilities, great impact of data scale on query efficiency, and excessive cost of model changes. Seeking a new data association technology has become the key to solving the problem. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a data association method and system based on a graph database for at least one defect existing in the related technologies mentioned in the above background art: how to improve the efficiency of equipment data analysis.

[0004] The technical solution adopted by the present invention to solve its technical problems is: to provide a data association method based on a graph database, including the following steps:

[0005] S10: Automatically calculate the current requirements, analyze the required entities and the connection relationships between the entities according to the current requirements, and construct a physical model in the graph database;

[0006] S20: Obtain the data information in the system, perform standardized processing on the data information to obtain a standardized processing result; then perform correlation extraction on the data information to generate correlation information, and import the standardized processing result and correlation information into the physical model to form a graph database with information association.

[0007] Preferably, in the data association method based on a graph database of the present invention, step S10 further includes:

[0008] S101: Construct a conceptual model according to the current requirements, list each entity in the requirements in the conceptual model, and arrange the relationships between the entities;

[0009] S102: Refine the entity information of the conceptual model to construct a logical model, and the logical model includes the attribute information and correlation information of the entity;

[0010] S103: Based on the attribute information and correlation information of the entity in the logical model, write and execute using the graph database language to generate a physical model for data storage.

[0011] Preferably, in the data association method based on a graph database according to the present invention, the physical model includes: an attribute name, an attribute type, and a unique identifier of the entity;

[0012] Wherein, the entity corresponds to the unique identifier of the entity;

[0013] The unique identifier of the entity corresponds to the attribute name and the attribute type.

[0014] Preferably, in the data association method based on a graph database according to the present invention, the standardization processing of the data information includes:

[0015] Perform standardization processing on the data information, perform encoding mapping on the unique identifier of the entity to form a unified standard unique identifier, and perform standardization processing on the attribute name and the attribute type.

[0016] Preferably, in the data association method based on a graph database according to the present invention, after step S20, it further includes:

[0017] S30: Read the data in the graph database associated with the information, generate a data access interface, and call the data access interface to obtain the associated data of the graph database associated with the information.

[0018] Preferably, in the data association method based on a graph database according to the present invention, the reading of the data in the graph database associated with the information includes:

[0019] Based on the development kit of the graph database, access and query the graph database associated with the information, and query the data in the graph database associated with the information by writing a query statement for graph data to generate a data set.

[0020] Preferably, in the data association method based on a graph database according to the present invention, step S20 includes:

[0021] The physical model includes an entity model and a relationship model. According to the standardization processing result of the standardization processing of the data information, entity model data is formed and imported into the entity model; according to the relationship between the entities extracted from the data information, relationship model data is formed and imported into the relationship model.

[0022] The present invention also constructs a data association system based on a graph database, including:

[0023] A modeling module, configured to automatically calculate the current requirements, analyze the required entities and the connection relationships between the entities according to the current requirements, and construct a physical model in the graph database;

[0024] A data input module, which is used to obtain data information in the system, perform standardization processing on the data information to obtain a standardized processing result; then perform relevance extraction on the data information to generate association information, and import the standardized processing result and the association information into the physical model to form a graph database with information association.

[0025] Preferably, in the data association system based on a graph database according to the present invention, the modeling module further includes:

[0026] A concept model unit, which is used to construct a concept model according to the current requirements, list each entity in the requirements in the concept model, and arrange the relationships between the entities;

[0027] A logical model unit, which is used to refine the entity information of the concept model to construct a logical model, and the logical model includes the attribute information and association information of the entity;

[0028] A physical model unit, which is used to write and execute based on the attribute information and association information of the entity in the logical model using a graph database language to generate a physical model for data storage.

[0029] Preferably, in the data association system based on a graph database according to the present invention, the system further includes:

[0030] A query module, which is used to read the data in the graph database with information association, generate a data access interface, and call the data access interface to obtain the associated data of the graph database with information association.

[0031] By implementing the present invention, the following beneficial effects are achieved:

[0032] The present invention discloses a data association method and system based on a graph database. By analyzing the requirements for modeling, the entities required for graph database modeling and the relationships between the entities are obtained, sorted out to create a physical model, relevant data is collected, the data is standardized and the association relationships between the data are extracted, and after sorting out, it is updated into the physical model to complete the data association of the graph database. By implementing the present invention, using graph database technology, complex association relationships can be stored flexibly, solving the problem that when the association relationship changes in a traditional relational database, the cost of changing the table structure is too high, facilitating the maintenance, update, and addition of the association information of device data, and being able to support subsequent data association requirements. Description of the Drawings

[0033] The present invention will be further described below in conjunction with the drawings and embodiments. In the drawings:

[0034] Figure 1It is a schematic flowchart of the data association method based on the graph database of the present invention;

[0035] Figure 2 It is a schematic flowchart of constructing the physical model of the present invention;

[0036] Figure 3 It is a schematic flowchart of writing and updating graph data of the present invention;

[0037] Figure 4 It is a schematic flowchart of querying graph data of the present invention;

[0038] Figure 5 It is a block diagram of the data association system based on the graph database of the present invention. Detailed implementation manners

[0039] For a clearer understanding of the technical features, objectives, and effects of the present invention, the specific implementation manners of the present invention will now be described in detail with reference to the accompanying drawings.

[0040] It should be noted that the flowcharts shown in the accompanying drawings are only illustrative, and do not necessarily include all the contents and operations / steps, nor are they necessarily executed in the described order. For example, some operations / steps can be decomposed, while some operations / steps can be combined or partially combined. Therefore, the actual execution order may be changed according to the actual situation.

[0041] The block diagrams shown in the accompanying drawings are only functional entities, and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor devices and / or microcontroller devices.

[0042] A graph database is a data management system that uses points and edges as basic storage units and is designed to efficiently store and query graph data.

[0043] The graph concept is crucial for understanding the graph database. A graph is a set of points and edges. "Points" represent entities, and "edges" represent the relationships between entities. In a graph database, the relationships between data are as important as the data itself, and they are stored as part of the data. Such an architecture enables the graph database to quickly respond to complex association queries because the relationships between entities have been pre-stored in the database. The graph database can visually visualize relationships and is the optimal way to store, query, and analyze highly interconnected data.

[0044] Graph databases belong to non-relational databases (NoSQL). Graph databases are very different from relational databases in terms of data storage, query, and data structure. The graph data structure directly stores the dependencies between nodes, while relational databases and other types of non-relational databases represent the relationships between data in an indirect way. Graph databases store the associations between data as part of the data, and tags, directions, and attributes can be added to the associations. For other databases, the queries for relationships must be concretized at runtime, which is why graph databases have a huge performance advantage over other types of databases in relationship queries.

[0045] In this embodiment, as Figure 1 shown, the present invention provides a data association method based on a graph database, including the following steps:

[0046] S10: Automatically calculate the current requirements, analyze the required entities and the connection relationships between the entities according to the current requirements, and construct a physical model in the graph database;

[0047] S20: Obtain the data information in the system, perform standardization processing on the data information to obtain the result of standardization processing; then perform correlation extraction on the data information to generate correlation information, and import the result of standardization processing and correlation information into the physical model to form a graph database with information association.

[0048] Specifically:

[0049] And, in this embodiment, as Figure 2 shown, step S10 further includes:

[0050] S101: According to the current requirements, construct a conceptual model, list each entity in the requirements in the conceptual model, and arrange the relationships between the entities;

[0051] S102: Refine the entity information of the conceptual model to construct a logical model, and the logical model includes the attribute information and correlation information of the entities;

[0052] S103: Based on the attribute information and correlation information of the entities in the logical model, write and execute using the graph database language to generate a physical model for data storage.

[0053] The conceptual model is a model oriented to the real world, mainly used to describe the conceptual structure of the world, enabling designers to analyze and summarize the relationships between data and data at the initial stage of design, shielding the influence of database technology. The conceptual model mainly includes each entity in the requirements and the associations between them. The conceptual model lists each entity in the requirements one by one and analyzes the relationships between the entities on the basis of analyzing the business requirements.

[0054] The logical model is a model oriented to data storage, which is a comprehensive and accurate description of an enterprise's data assets. It uses a unified logical language to describe business and organizes and integrates various business data from diverse sources. The logical model includes the attribute information and association information of each entity object. Based on the conceptual model, the logical model further refines the attribute information of entities and lists the entity attributes to be applied according to business requirements.

[0055] The physical model is a model oriented to the physical representation of a computer, which describes the organizational structure of data on storage media. The creation form of the physical model is related to a specific database management system (DBMS), and specifically includes the model name, model type, attribute name, attribute type, and the unique identifier of each entity. Based on the logical model, the physical model is written using database modeling statements, and then the modeling statements are executed to form a data storage model in the database.

[0056] In addition, in this embodiment, the physical model includes: attribute name, attribute type, and the unique identifier of the entity;

[0057] Among them, the entity corresponds to the unique identifier of the entity;

[0058] The unique identifier of the entity corresponds to the attribute name and attribute type.

[0059] After the physical model of the graph database is created, the next step is to organize and collect data and write it into the physical model to achieve data writing, updating, and storage, providing a data basis for data association queries.

[0060] Among them, the standardization process of data information includes:

[0061] The standardization process of data information encodes and maps the unique identifier of the entity to form a unified standard unique identifier, and standardizes the attribute name and attribute type.

[0062] In this embodiment, step S20 includes:

[0063] The physical model includes an entity model and a relationship model. According to the standardization result of the standardization process of data information, entity model data is formed and imported into the entity model; according to the relationships between entities extracted from the data information, relationship model data is formed and imported into the relationship model.

[0064] The data of the graph database comes from multiple systems, and the basic steps of data update are as Figure 3As shown in the figure, first, data is obtained. Through a unified interface program, data is obtained from various interfaces, then the data is standardized, and then the preset entity data and associated relationship data are sorted out, and finally updated to the graph database.

[0065] The graph data model includes an entity model and a relationship model, and the data therein needs to be sorted out and merged from various systems.

[0066] First, data is obtained from various systems. Through the JDBC (Java Database Connectivity) interface of the database or the provided data interface, the ETL (Extract, Transform, Load) tool is used to query and obtain data from various source systems to form a data stream.

[0067] Then, the data is standardized. The unique identifier of the entity in the data is encoded and mapped to form a unified standard unique identifier, which is convenient for data integration and association. At the same time, the attribute data of the object entity is standardized to form entity model data.

[0068] Then, the association relationships between entities are extracted from the data to form relationship model data.

[0069] Finally, the completed entity model data and relationship model data are imported into the graph database through database tools to complete the writing and updating of the data.

[0070] In this embodiment, after step S20, it further includes:

[0071] S30: Read the data in the graph database associated with the information, generate a data access interface, and call the data access interface to obtain the associated data of the graph database associated with the information.

[0072] Further, reading the data in the graph database associated with the information includes:

[0073] Based on the development package of the graph database, access and query the graph database associated with the information, and query the data in the graph database associated with the information by writing a query statement for graph data to generate a data set.

[0074] The graph data query is through a Java-developed REST interface, as Figure 4 shown, read data from the graph database to form a data interface, and then read the data returned by the interface on the front-end page and render it on the page to generate a graph association display.

[0075] First, based on the Java development kit provided by the graph database, develop a Java access program to access and query the graph database. Query the data in the graph database by writing graph data query statements Gsql and organize them into a data set required by the business.

[0076] Obtain the returned data set through Java development, organize the data into the JSON structure of the data interface, and develop a data access interface for the Java program. External programs can directly obtain the associated data of the graph database by calling the data access interface.

[0077] On the front-end page, obtain the associated data returned by the interface through JavaScript and render the data into a knowledge graph display graph.

[0078] In this embodiment, as Figure 5 shown, the present invention also constructs a data association system based on a graph database, including:

[0079] A modeling module for automatically calculating the current requirements, analyzing the required entities and the connection relationships between the entities according to the current requirements, and constructing a physical model in the graph database;

[0080] A data input module for obtaining data information in the system, performing standardized processing on the data information to obtain a standardized processing result; then performing correlation extraction on the data information to generate correlation information, and importing the standardized processing result and the correlation information into the physical model to form an information-associated graph database.

[0081] Specifically:

[0082] And, in this embodiment, as Figure 2 shown, the modeling module further includes:

[0083] A concept model unit for constructing a concept model according to the current requirements, listing each entity in the requirements in the concept model, and arranging the relationships between the entities;

[0084] A logical model unit for refining the entity information of the concept model to construct a logical model, and the logical model includes the attribute information and correlation information of the entity;

[0085] A physical model unit for writing and executing based on the attribute information and correlation information of the entities in the logical model using the graph database language to generate a physical model for data storage.

[0086] The conceptual model is a model oriented to the real world, mainly used to describe the conceptual structure of the world, enabling designers to shield the influence of database technology during the initial design stage and analyze and summarize the relationships between data and data. The conceptual model mainly includes various entities in the requirements and the associated relationships between them. The conceptual model lists each entity in the requirements one by one based on the analysis of business requirements and analyzes the relationships between the entities.

[0087] The logical model is a model oriented to data storage, which is a comprehensive and accurate description of an enterprise's data assets. It uses a unified logical language to describe the business and organizes and integrates various business data from diverse sources. The logical model includes the attribute information and association information of each entity object. The logical model further refines the attribute information of the entity based on the conceptual model and lists the entity attributes to be applied based on business requirements.

[0088] The physical model is a model oriented to the physical representation of a computer, which describes the organizational structure of data on storage media. The creation form of the physical model is related to the specific database management system (DBMS). Specifically, it includes the model name, model type, attribute name, attribute type, and the unique identifier of each entity. The physical model is written using database modeling statements based on the logical model, and then the modeling statements are executed to form a data storage model in the database.

[0089] In addition, in this embodiment, the physical model includes: attribute name, attribute type, and the unique identifier of the entity;

[0090] Among them, the entity corresponds to the unique identifier of the entity;

[0091] The unique identifier of the entity corresponds to the attribute name and attribute type.

[0092] After completing the creation of the physical model of the graph database, the next step is to organize and collect data and write it into the physical model to achieve data writing, updating, and storage, providing a data basis for data association queries.

[0093] Among them, the standardization process of data information includes:

[0094] The standardization process of data information encodes and maps the unique identifier of the entity to form a unified standard unique identifier, and standardizes the attribute name and attribute type.

[0095] In this embodiment, the data input module includes:

[0096] The physical model includes an entity model and a relationship model. According to the standardization processing results of the data information standardization, entity model data is formed and imported into the entity model; according to the relationships between entities extracted from the data information, relationship model data is formed and imported into the relationship model.

[0097] The data of the graph database comes from multiple systems. The basic steps for data update are as Figure 3 shown. First, data is obtained. Through a unified interface program, the data is obtained from each interface, then the data is standardized, then the preset entity data and associated relationship data are sorted out, and finally updated to the graph database.

[0098] The graph data model includes an entity model and a relationship model, and the data therein needs to be sorted out and merged from each system.

[0099] First, data is obtained from each system. Through the JDBC (Java Database Connectivity) interface of the database or the provided data interface, the ETL (Extract, Transform, Load) tool is used to query and obtain the data from each source system to form a data stream.

[0100] Then the data is standardized. The unique identifier of the entity in the data is encoded and mapped to form a unified standard unique identifier, which is convenient for data integration and association. At the same time, the attribute data of the object entity is standardized to form entity model data.

[0101] Then the association relationships between entities are extracted from the data to form relationship model data.

[0102] Finally, the sorted entity model data and relationship model data are imported into the graph database through database tools to complete the data writing and update.

[0103] In this embodiment, the system further includes:

[0104] A query module for reading the data in the graph database associated with the information, generating a data access interface, and calling the data access interface to obtain the associated data of the graph database associated with the information.

[0105] Further, reading the data in the graph database associated with the information includes:

[0106] Based on the development package of the graph database, the graph database associated with the information is accessed and queried, and the data in the graph database associated with the information is queried by writing a query statement for graph data to generate a data set.

[0107] The graph data query is through a java-developed rest interface, such asFigure 4 As shown, data is read from the graph database to form a data interface, and then the data returned by the interface is read in the front-end page and rendered on the page to generate a graph association display.

[0108] First, based on the Java development kit provided by the graph database, a Java access program is developed to access and query the graph database. The data in the graph database is queried by writing a graph data query statement Gsql and organized into a data set required by the business.

[0109] The returned data set is obtained through Java development, and the data is organized into the JSON structure of the data interface. A data access interface for the Java program is developed, and external programs can directly obtain the associated data of the graph database by calling the data access interface.

[0110] On the front-end page, the associated data returned by the interface is obtained through JavaScript and rendered into a knowledge graph display graph.

[0111] By implementing the present invention, the following beneficial effects are achieved:

[0112] The present invention discloses a data association method and system based on a graph database. By analyzing the requirements for modeling, the entities required for graph database modeling and the relationships between the entities are obtained, and a physical model is created after sorting. Relevant data is collected, the data is standardized and the association relationships between the data are extracted, and after sorting, the physical model is updated to complete the data association of the graph database. By implementing the present invention, the graph database technology is adopted to flexibly store complex association relationships, solving the problem that when the association relationship changes in a traditional relational database, it is too costly to change the table structure, etc., facilitating the maintenance, update, and addition of the association information of device data, and being able to support subsequent data association requirements.

[0113] It can be understood that the above embodiments only represent the preferred implementation modes of the present invention, and the description is relatively specific and detailed, but it cannot be construed as a limitation on the scope of the patent of the present invention. It should be pointed out that for those of ordinary skill in the art, without departing from the concept of the present invention, the above technical features can be freely combined, and several deformations and improvements can also be made, which all belong to the protection scope of the present invention. Therefore, all equivalent transformations and modifications made to the scope of the claims of the present invention shall fall within the scope covered by the claims of the present invention.

Claims

1. A data association method based on a graph database, characterized in that, It includes the following steps: S10: Automatically calculate the current requirements, analyze the required entities and the connection relationships between the entities according to the current requirements, and construct a physical model in the graph database; S20: Obtain the data information in the system, perform standardized processing on the data information to obtain a standardized processing result; then perform relevance extraction on the data information to generate association information, and import the standardized processing result and the association information into the physical model to form an information-associated graph database; Step S10 further includes: S101: According to the current requirements, construct a conceptual model, list each entity in the requirements in the conceptual model, and arrange the relationships between the entities; S102: Refine the entity information of the conceptual model to construct a logical model, and the logical model includes the attribute information and association information of the entity; S103: Based on the attribute information and association information of the entity in the logical model, write and execute using the graph database language to generate a physical model for data storage; The physical model includes: attribute name, attribute type, and the unique identifier of the entity; Wherein, the entity corresponds to the unique identifier of the entity; The unique identifier of the entity corresponds to the attribute name and the attribute type; The performing standardized processing on the data information includes: Perform standardized processing on the data information, perform encoding mapping on the unique identifier of the entity to form a unified standard unique identifier, and perform standardized processing on the attribute name and the attribute type; After step S20, it further includes: Based on the java development package provided by the graph database, develop a java access program to access and query the graph database, query the data in the graph database by writing a graph data query statement Gsql, and organize it into a data set required by the business; Obtain the returned data set through java development, organize the data into a json structure of a data interface, and develop a data access interface for the java program. An external program can obtain the associated data of the graph database by calling the data access interface; On the front-end page, obtain the associated data returned by the interface through javascript and render the data into a knowledge graph display graph.

2. The data association method based on a graph database according to claim 1, wherein Step S20 includes: The physical model includes an entity model and a relationship model. According to the standardized processing result of performing standardized processing on the data information, form entity model data and import it into the entity model; according to the relationship between the entities extracted from the data information, form relationship model data and import it into the relationship model.

3. A data association system based on a graph database, characterized in that, It includes: A modeling module, which is used to automatically calculate the current requirements, analyze the required entities and the connection relationships between the entities according to the current requirements, and construct a physical model in the graph database; A data input module, which is used to obtain the data information in the system, perform standardized processing on the data information to obtain a standardized processing result; then perform relevance extraction on the data information to generate association information, and import the standardized processing result and the association information into the physical model to form an information-associated graph database; The modeling module further includes: A concept model unit, configured to construct a concept model according to the current requirements, list each entity in the requirements in the concept model, and arrange the relationships between the entities; A logical model unit, configured to refine the entity information of the concept model and construct a logical model, where the logical model includes the attribute information and association information of the entities; A physical model unit, configured to perform writing and execution using a graph database language based on the attribute information and association information of the entities in the logical model, and generate a physical model for data storage; The physical model includes: an attribute name, an attribute type, and a unique identifier of the entity; Wherein, the entity corresponds to the unique identifier of the entity; The unique identifier of the entity corresponds to the attribute name and the attribute type; The standardization processing of the data information includes: Performing standardization processing on the data information, performing encoding mapping on the unique identifier of the entity to form a unified standard unique identifier, and performing standardization processing on the attribute name and the attribute type; The system further includes a query module: Based on the Java development package provided by the graph database, developing a Java access program to access and query the graph database, querying the data in the graph database by writing a graph data query statement Gsql, and organizing it into a data set required by the business; Obtaining the returned data set through Java development, organizing the data into a JSON structure of a data interface, and developing a data access interface for the Java program. An external program can obtain the associated data of the graph database by calling the data access interface; On the front-end page, obtaining the associated data returned by the interface through JavaScript and rendering the data into a knowledge graph display graph.

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