CN116304207A - Data association method and system based on graph database - Google Patents
Data association method and system based on graph database Download PDFInfo
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/901—Indexing; Data structures therefor; Storage structures
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
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- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/901—Indexing; Data structures therefor; Storage structures
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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Abstract
The invention discloses a data association method and a system based on a graph database, wherein the method comprises the following steps: automatically calculating the current demand, analyzing the required entity and the connection relation between the entities according to the current demand, and constructing a physical model in a graph database; obtaining data information in a system, and carrying out standardized processing on the data information to obtain a standardized processing result; and performing relevance extraction on the data information to generate relevance information, and importing the standardized processing result and the relevance information into a physical model to form an information-relevant graph database. By implementing the invention, the complex association relation is flexibly stored by adopting the graph database technology, the problem of overlarge cost of changing the table structure and the like when the association relation is changed by the traditional relational database is solved, the association information of the equipment data is convenient to maintain, update and newly increase, and the subsequent data association requirement can be supported.
Description
Technical Field
The invention relates to the technical field of computer software development, in particular to a data association method and system based on a graph database.
Background
The nuclear power plant unit equipment is numerous, and various equipment management systems are complicated, so that various information of the equipment is distributed in a scattered manner, and the equipment data cannot be effectively associated, analyzed and utilized. In order to achieve flexible association of device data, and to mine the intrinsic value of the associated data, a data association technique is required to process and store the data. However, the conventional relational database technology has various problems of insufficient complex relational query capability, great influence on query efficiency due to data scale, overlarge model change cost and the like, and the key to solve the problems is to find a new data association technology.
Disclosure of Invention
The technical problem to be solved by the present invention is to address at least one of the drawbacks of the related art mentioned in the background art mentioned above: how to improve the efficiency of equipment data analysis, and a data association method and a system based on a graph database are provided.
The technical scheme adopted for solving the technical problems is as follows: the data association method based on the graph database comprises the following steps:
s10: automatically calculating the current demand, analyzing the required entity and the connection relation between the entities according to the current demand, and constructing a physical model in a graph database;
s20: acquiring data information in a system, and carrying out standardized processing on the data information to obtain a standardized processing result; and performing relevance extraction on the data information to generate relevance information, and importing the standardized processing result and the relevance information into the physical model to form an information-relevant graph database.
Preferably, in the graph database-based data association method according to the present invention, step S10 further includes:
s101: constructing a conceptual model according to the current demand, listing each entity in the demand one by one in the conceptual model, and arranging the relation among the entities;
s102: performing entity information refinement on the concept model, and constructing a logic model, wherein the logic model comprises attribute information and associated information of the entity;
s103: and based on the attribute information and the associated information of the entity of the logic model, writing the attribute information and the associated information by using a graph database language, and then executing the attribute information and the associated information to generate a physical model for data storage.
Preferably, in the graph database-based data association method according to the present invention, the physical model includes: attribute name, attribute type and unique identity of the entity;
wherein the entity corresponds to the unique identifier of the entity;
the unique identification of the entity corresponds to the attribute name and the attribute type.
Preferably, in the graph database-based data association method of the present invention, the normalizing the data information includes:
and carrying out standardization processing on the data information, carrying out coding mapping on the unique identifier of the entity to form a unified standard unique identifier, and carrying out standardization processing on the attribute name and the attribute type.
Preferably, in the graph database-based data association method according to the present invention, step S20 further includes:
s30: and reading data in the information-associated graph database, generating a data access interface, and calling the data access interface to obtain associated data of the information-associated graph database.
Preferably, in the method for associating data based on a graph database according to the present invention, the reading data in the graph database associated with the information includes:
and based on the development package of the graph database, performing access query on the graph database related to the information, and querying the data in the graph database related to the information through query sentences for writing the graph data to generate a data set.
Preferably, in the graph database-based data association method according to the present invention, step S20 includes:
the physical model comprises a physical model and a relation model, and according to the standardized processing result of the standardized processing of the data information, physical model data are formed and imported into the physical model; and according to the relation among the entities in the extracted data information, forming relation model data, and importing the relation model data into the relation model.
The invention also constructs a data association system based on the graph database, comprising:
the modeling module is used for automatically calculating the current demand, analyzing the required entity and the connection relation between the entities according to the current demand, and constructing a physical model in the graph database;
the data input module is used for acquiring data information in the system, carrying out standardized processing on the data information and obtaining a standardized processing result; and performing relevance extraction on the data information to generate relevance information, and importing the standardized processing result and the relevance information into the physical model to form an information-relevant graph database.
Preferably, in the graph database-based data association system according to the present invention, the modeling module further includes:
the concept model unit is used for constructing a concept model according to the current demand, listing all entities in the demand one by one in the concept model, and arranging the relation among the entities;
the logic model unit is used for carrying out entity information refinement on the concept model and constructing a logic model, wherein the logic model comprises attribute information and associated information of the entity;
and the physical model unit is used for generating a physical model for data storage based on attribute information and associated information of the entity of the logic model, and executing after writing by using a graph database language.
Preferably, in the graph database-based data association system according to the present invention, the system further includes:
and the query module is used for reading the data in the information-associated graph database, generating a data access interface, and calling the data access interface to obtain the associated data of the information-associated graph database.
By implementing the invention, the following beneficial effects are achieved:
the invention discloses a data association method and a system based on a graph database, which are characterized in that the relationship between entities required by modeling of the graph database is obtained by analyzing the requirement required by modeling, a physical model is built by sorting, related data is collected, the data is subjected to standardized processing, the association relationship between the data is extracted, and the data association of the graph database is completed by updating the data into the physical model after sorting. By implementing the invention, the complex association relation is flexibly stored by adopting the graph database technology, the problem of overlarge cost of changing the table structure and the like when the association relation is changed by the traditional relational database is solved, the association information of the equipment data is convenient to maintain, update and newly increase, and the subsequent data association requirement can be supported.
Drawings
The invention will be further described with reference to the accompanying drawings and examples, in which:
FIG. 1 is a flow chart of a graph database-based data correlation method of the present invention;
FIG. 2 is a schematic flow chart of the invention for constructing a physical model;
FIG. 3 is a flow chart of the data write update of the present invention;
FIG. 4 is a flow chart of the data query of the present invention;
FIG. 5 is a block diagram of a data correlation system based on a graph database of the present invention.
Detailed Description
For a clearer understanding of technical features, objects and effects of the present invention, a detailed description of embodiments of the present invention will be made with reference to the accompanying drawings.
It should be noted that the flow diagrams depicted in the figures are merely exemplary and do not necessarily include all of the elements and operations/steps, nor are they necessarily performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the order of actual execution may be changed according to actual situations.
The block diagrams depicted in the figures are merely functional entities and do not necessarily correspond to physically separate entities. That is, the functional entities may be implemented in software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor devices and/or microcontroller devices.
The graph database is a data management system which takes points and edges as basic storage units and takes efficient storage and query graph data as design principles.
The graph concept is critical to understanding the graph database. A graph is a collection of points and edges, where a "point" represents an entity and a "edge" represents a relationship between entities. In the graph database, the relationship between data and the data itself are equally important, and they are stored as part of the data. Such an architecture enables the graph database to quickly respond to complex associative queries because relationships between entities have been previously stored in the database. The graph database can intuitively visualize the relationship, and is the optimal method for storing, inquiring and analyzing the highly interconnected data.
The graph database belongs to a non-relational database (NoSQL). The graph database is quite different from the relational database in terms of data storage, query, and data structure. The graph data structure directly stores the dependency relationships between nodes, while relational databases and other types of non-relational databases represent relationships between data in an indirect manner. The graph database stores the association between data as part of the data, labels, directions and attributes can be added to the association, and queries of other databases aiming at the relationship must be subjected to materialization operation at the runtime, which is also the reason that the graph database has great performance advantages in relation queries compared with other types of databases.
In this embodiment, as shown in fig. 1, the present invention provides a data association method based on a graph database, which includes the following steps:
s10: automatically calculating the current demand, analyzing the required entity and the connection relation between the entities according to the current demand, and constructing a physical model in a graph database;
s20: obtaining data information in a system, and carrying out standardized processing on the data information to obtain a standardized processing result; and performing relevance extraction on the data information to generate relevance information, and importing the standardized processing result and the relevance information into a physical model to form an information-relevant graph database.
Specifically:
in addition, in the present embodiment, as shown in fig. 2, step S10 further includes:
s101: constructing a conceptual model according to the current requirements, listing all entities in the requirements one by one in the conceptual model, and arranging relations among the entities;
s102: carrying out entity information refinement on the concept model, and constructing a logic model, wherein the logic model comprises attribute information and associated information of an entity;
s103: based on the attribute information and the associated information of the entities of the logic model, the physical model for data storage is generated by executing the physical model after writing the physical model by using a graph database language.
The conceptual model is a model oriented to the real world, and is mainly used for describing a conceptual structure of the world, so that a designer can shield the influence of database technology in the initial stage of design, and analyze and generalize the relation between data. The conceptual model mainly comprises various entities in the demand and association relations among the entities. The concept model is based on analyzing the business requirements, listing each entity in the requirements one by one, and analyzing the relationship among the entities.
The logic model is a model facing data storage, is a comprehensive and accurate description of enterprise data assets, uses a unified logic language to describe business, and organizes and integrates various business data with various sources. The logical model includes attribute information and association information of each entity object. The logic model is based on a conceptual model, further refines attribute information of the entity, and lists entity attributes required to be applied based on service requirements.
The physical model is a model oriented to a computer physical representation, describing an organization structure of data on a storage medium, and is created in a form related to a specific database management system (DatabaseManagement System, DBMS), specifically including a model name, a model type, an attribute name, an attribute type, and a unique identification of each entity. The physical model is written by using database modeling sentences on the basis of a logic model, and then the modeling sentences are executed to form a data storage model in the database.
In addition, in the present embodiment, the physical model includes: attribute name, attribute type, and unique identity of the entity;
wherein, the entity corresponds to the unique identifier of the entity;
the unique identity of the entity corresponds to the attribute name and attribute type.
After the physical model of the graph database is built, the next step is to organize and collect data and write the data into the physical model, so as to realize the writing, updating and storage of the data and provide a data base for the data association query.
Wherein, standardized processing is carried out to data information, includes:
and carrying out standardization processing on the data information, carrying out coding mapping on the unique identifier of the entity to form a unified standard unique identifier, and carrying out standardization processing on the attribute name and the attribute type.
In this embodiment, step S20 includes:
the physical model comprises a physical model and a relation model, and according to a standardized processing result of standardized processing of the data information, physical model data are formed and imported into the physical model; and forming relation model data according to the relation among the entities in the extracted data information, and importing the relation model data into a relation model.
The basic steps of data updating of the graph database are as shown in fig. 3, wherein the basic steps of data updating are that firstly, data is acquired from each interface through a unified interface program, then the data is subjected to standardized processing, and then the preset entity data and association relation data are arranged and finally updated into the graph database.
The graph data model comprises a solid model and a relation model, wherein data are required to be arranged and combined from each system.
Firstly, data is obtained from each system, and is queried and obtained from each source system by using ETL (data warehouse technology) tools through JDBC (Java database connection (Java Databaseconnect)) interfaces of databases or provided data interfaces to form data streams.
And then carrying out standardization processing on the data, carrying out coding mapping on entity unique identifiers in the data to form unified standard unique identifiers, facilitating data integration and association, and simultaneously carrying out standardization processing on attribute data of the object entity to form entity model data.
And then extracting the association relation between the entities from the data to form relation model data.
And finally, importing the tidied entity model data and the relational model data into a graph database through a database tool to finish writing and updating of the data.
In this embodiment, step S20 further includes:
s30: and reading data in the information-associated graph database, generating a data access interface, and calling the data access interface to obtain associated data of the information-associated graph database.
Further, reading data in the information-associated graph database, including:
based on the development package of the graph database, the graph database related to the information is accessed and inquired, and the data in the graph database related to the information is inquired through inquiry sentences for compiling the graph data, so that a data set is generated.
The graph data query is to develop a rest interface through java, read data from a graph database to form a data interface, read the data returned by the interface in a front page, and render and generate graph association display on the page as shown in fig. 4.
Firstly, based on a java development kit provided by a graph database, developing a java access program to access and inquire the graph database, inquiring data in the graph database by writing a graph data inquiry statement Gsql, and organizing the data into a data set required by a service.
The returned data set is obtained through java development, the data is organized into a json structure of a data interface, a java program is developed into a data access interface, and an external program can directly obtain the associated data of the graph database by calling the data access interface.
And on the front-end page, acquiring the associated data returned by the interface through javascript, and rendering the data into a knowledge graph display graph.
In this embodiment, as shown in fig. 5, the present invention further constructs a data association system based on a graph database, including:
the modeling module is used for automatically calculating the current demand, analyzing the required entity and the connection relation between the entities according to the current demand, and constructing a physical model in the graph database;
the data input module is used for acquiring data information in the system, and carrying out standardized processing on the data information to acquire a standardized processing result; and performing relevance extraction on the data information to generate relevance information, and importing the standardized processing result and the relevance information into a physical model to form an information-relevant graph database.
Specifically:
also, in the present embodiment, as shown in fig. 2, the modeling module further includes:
the concept model unit is used for constructing a concept model according to the current requirements, listing all entities in the requirements one by one in the concept model, and arranging the relations among the entities;
the logic model unit is used for carrying out entity information refinement on the concept model, constructing a logic model, and the logic model comprises attribute information and associated information of an entity;
and the physical model unit is used for generating a physical model for data storage based on attribute information and associated information of the entity of the logic model, and executing after writing by using a graph database language.
The conceptual model is a model oriented to the real world, and is mainly used for describing a conceptual structure of the world, so that a designer can shield the influence of database technology in the initial stage of design, and analyze and generalize the relation between data. The conceptual model mainly comprises various entities in the demand and association relations among the entities. The concept model is based on analyzing the business requirements, listing each entity in the requirements one by one, and analyzing the relationship among the entities.
The logic model is a model facing data storage, is a comprehensive and accurate description of enterprise data assets, uses a unified logic language to describe business, and organizes and integrates various business data with various sources. The logical model includes attribute information and association information of each entity object. The logic model is based on a conceptual model, further refines attribute information of the entity, and lists entity attributes required to be applied based on service requirements.
The physical model is a model oriented to a computer physical representation, describing an organization structure of data on a storage medium, and is created in a form related to a specific database management system (DatabaseManagement System, DBMS), specifically including a model name, a model type, an attribute name, an attribute type, and a unique identification of each entity. The physical model is written by using database modeling sentences on the basis of a logic model, and then the modeling sentences are executed to form a data storage model in the database.
In addition, in the present embodiment, the physical model includes: attribute name, attribute type, and unique identity of the entity;
wherein, the entity corresponds to the unique identifier of the entity;
the unique identity of the entity corresponds to the attribute name and attribute type.
After the physical model of the graph database is built, the next step is to organize and collect data and write the data into the physical model, so as to realize the writing, updating and storage of the data and provide a data base for the data association query.
Wherein, standardized processing is carried out to data information, includes:
and carrying out standardization processing on the data information, carrying out coding mapping on the unique identifier of the entity to form a unified standard unique identifier, and carrying out standardization processing on the attribute name and the attribute type.
In this embodiment, the data input module includes:
the physical model comprises a physical model and a relation model, and according to a standardized processing result of standardized processing of the data information, physical model data are formed and imported into the physical model; and forming relation model data according to the relation among the entities in the extracted data information, and importing the relation model data into a relation model.
The basic steps of data updating of the graph database are as shown in fig. 3, wherein the basic steps of data updating are that firstly, data is acquired from each interface through a unified interface program, then the data is subjected to standardized processing, and then the preset entity data and association relation data are arranged and finally updated into the graph database.
The graph data model comprises a solid model and a relation model, wherein data are required to be arranged and combined from each system.
Firstly, data is obtained from each system, and is queried and obtained from each source system by using ETL (data warehouse technology) tools through JDBC (Java database connection (Java Databaseconnect)) interfaces of databases or provided data interfaces to form data streams.
And then carrying out standardization processing on the data, carrying out coding mapping on entity unique identifiers in the data to form unified standard unique identifiers, facilitating data integration and association, and simultaneously carrying out standardization processing on attribute data of the object entity to form entity model data.
And then extracting the association relation between the entities from the data to form relation model data.
And finally, importing the tidied entity model data and the relational model data into a graph database through a database tool to finish writing and updating of the data.
In this embodiment, the system further includes:
and the query module is used for reading the data in the information-associated graph database, generating a data access interface, and calling the data access interface to obtain the associated data of the information-associated graph database.
Further, reading data in the information-associated graph database, including:
based on the development package of the graph database, the graph database related to the information is accessed and inquired, and the data in the graph database related to the information is inquired through inquiry sentences for compiling the graph data, so that a data set is generated.
The graph data query is to develop a rest interface through java, read data from a graph database to form a data interface, read the data returned by the interface in a front page, and render and generate graph association display on the page as shown in fig. 4.
Firstly, based on a java development kit provided by a graph database, developing a java access program to access and inquire the graph database, inquiring data in the graph database by writing a graph data inquiry statement Gsql, and organizing the data into a data set required by a service.
The returned data set is obtained through java development, the data is organized into a json structure of a data interface, a java program is developed into a data access interface, and an external program can directly obtain the associated data of the graph database by calling the data access interface.
And on the front-end page, acquiring the associated data returned by the interface through javascript, and rendering the data into a knowledge graph display graph.
By implementing the invention, the following beneficial effects are achieved:
the invention discloses a data association method and a system based on a graph database, which are characterized in that the relationship between entities required by modeling of the graph database is obtained by analyzing the requirement required by modeling, a physical model is built by sorting, related data is collected, the data is subjected to standardized processing, the association relationship between the data is extracted, and the data association of the graph database is completed by updating the data into the physical model after sorting. By implementing the invention, the complex association relation is flexibly stored by adopting the graph database technology, the problem of overlarge cost of changing the table structure and the like when the association relation is changed by the traditional relational database is solved, the association information of the equipment data is convenient to maintain, update and newly increase, and the subsequent data association requirement can be supported.
It is to be understood that the above examples only represent preferred embodiments of the present invention, which are described in more detail and are not to be construed as limiting the scope of the invention; it should be noted that, for a person skilled in the art, the above technical features can be freely combined, and several variations and modifications can be made without departing from the scope of the invention; therefore, all changes and modifications that come within the meaning and range of equivalency of the claims are to be embraced within their scope.
Claims (10)
1. A graph database-based data association method, comprising the steps of:
s10: automatically calculating the current demand, analyzing the required entity and the connection relation between the entities according to the current demand, and constructing a physical model in a graph database;
s20: acquiring data information in a system, and carrying out standardized processing on the data information to obtain a standardized processing result; and performing relevance extraction on the data information to generate relevance information, and importing the standardized processing result and the relevance information into the physical model to form an information-relevant graph database.
2. The graph database-based data correlation method according to claim 1, wherein step S10 further comprises:
s101: constructing a conceptual model according to the current demand, listing each entity in the demand one by one in the conceptual model, and arranging the relation among the entities;
s102: performing entity information refinement on the concept model, and constructing a logic model, wherein the logic model comprises attribute information and associated information of the entity;
s103: and based on the attribute information and the associated information of the entity of the logic model, writing the attribute information and the associated information by using a graph database language, and then executing the attribute information and the associated information to generate a physical model for data storage.
3. The graph database based data correlation method of claim 1, wherein the physical model comprises: attribute name, attribute type and unique identity of the entity;
wherein the entity corresponds to the unique identifier of the entity;
the unique identification of the entity corresponds to the attribute name and the attribute type.
4. A graph database based data correlation method as claimed in claim 3, wherein the normalizing the data information includes:
and carrying out standardization processing on the data information, carrying out coding mapping on the unique identifier of the entity to form a unified standard unique identifier, and carrying out standardization processing on the attribute name and the attribute type.
5. The graph database-based data correlation method according to claim 1, further comprising, after step S20:
s30: and reading data in the information-associated graph database, generating a data access interface, and calling the data access interface to obtain associated data of the information-associated graph database.
6. The graph database based data correlation method of claim 5, wherein the reading the data in the information-correlated graph database includes:
and based on the development package of the graph database, performing access query on the graph database related to the information, and querying the data in the graph database related to the information through query sentences for writing the graph data to generate a data set.
7. The graph database-based data correlation method according to claim 1, wherein step S20 includes:
the physical model comprises a physical model and a relation model, and according to the standardized processing result of the standardized processing of the data information, physical model data are formed and imported into the physical model; and according to the relation among the entities in the extracted data information, forming relation model data, and importing the relation model data into the relation model.
8. A graph database-based data correlation system, comprising:
the modeling module is used for automatically calculating the current demand, analyzing the required entity and the connection relation between the entities according to the current demand, and constructing a physical model in the graph database;
the data input module is used for acquiring data information in the system, carrying out standardized processing on the data information and obtaining a standardized processing result; and performing relevance extraction on the data information to generate relevance information, and importing the standardized processing result and the relevance information into the physical model to form an information-relevant graph database.
9. The graph database based data correlation system of claim 8, wherein the modeling module further comprises:
the concept model unit is used for constructing a concept model according to the current demand, listing all entities in the demand one by one in the concept model, and arranging the relation among the entities;
the logic model unit is used for carrying out entity information refinement on the concept model and constructing a logic model, wherein the logic model comprises attribute information and associated information of the entity;
and the physical model unit is used for generating a physical model for data storage based on attribute information and associated information of the entity of the logic model, and executing after writing by using a graph database language.
10. The graph database based data correlation system of claim 8, wherein the system further comprises:
and the query module is used for reading the data in the information-associated graph database, generating a data access interface, and calling the data access interface to obtain the associated data of the information-associated graph database.
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