A gallery-based relationship depiction method and system thereof

Abstract

The application provides a gallery-based relationship description method and system, which comprises the following steps: acquiring and collecting multi-source heterogeneous data, analyzing the acquired multi-source heterogeneous data and performing entity abstraction, the entity abstraction comprises abstracting business data into a description of an entity or a description of a relationship between an entity and another entity, fusing entities in different records or different business systems to describe the connection between entities in different events, using a graph database to organize data into entities, attributes, edges representing the relationship between entities, and storing the data as a key-value underlying data structure, and with the efficient storage of entities and relationships by the graph database, a specific entity can be quickly located through relationship retrieval, and the related relationship can be efficiently expanded. The application simplifies the data pre-computation process, reduces the implementation cost, ensures the real-time performance of the data, greatly improves the relationship retrieval efficiency based on the accurate expansion query, and ensures the stability of the system.

Description

Technical Field

[0001] This invention belongs to the field of big data mining technology, specifically relating to a method and system for characterizing relationships based on graph libraries. Background Technology

[0002] In real life, individuals and their surroundings are intricately connected, and these relationships represent the patterns of development. Relationship mining is a key technology for discovering the value hidden behind these patterns, and relationship characterization is a crucial step in the field of relationship mining. With the continuous development of information technology, massive amounts of multi-source heterogeneous relational data are generated from different objects within the same domain, as well as from objects between different domains. The storage, fusion, and relationship characterization of multi-source heterogeneous data are currently hot topics and challenges in the field of big data mining.

[0003] Existing relationship characterization techniques either leverage the widespread use of relational databases and the simplicity and flexibility of SQL to depict relationships between entities, or employ big data analytics technologies like Hive and Spark to pre-compute these relationships. Both approaches have significant drawbacks. In recent years, with the increasing maturity of graph library research, more and more graph library products have become well-known, such as Neo4j, JanusGraph, TigerGraph, and NebulaGraph. Graph libraries possess unparalleled advantages in handling entity relationships.

[0004] Relationships in the real world are divided into direct relationships and indirect relationships. A direct relationship is one between two entities that occurs directly without any intermediary entity. For example, if entity A sends a package to entity B, then the delivery relationship between entity A and entity B is a direct relationship. An indirect relationship is one between two entities that occurs through a third entity. For example, if entity A takes flight A and entity B takes train A, then entity A and entity B have a "flight co-pilot" relationship through flight A. Direct relationships are relatively simple; this invention only addresses the more complex indirect relationships, using the representative "flight co-pilot" relationship as an example.

[0005] The existing solution uses the most commonly used relational database MySQL to store the two-dimensional data table and uses SQL statements to query the relationships. This computation is very time-consuming; when the data volume is even slightly large, it will time out or even exhaust computing resources, resulting in low computational efficiency.

[0006] To improve the response efficiency of real-time requests and reduce the pressure on the MySQL database, the existing Solution 2 introduces pre-computation. This involves storing the source data in a Hive table and using Spark to pre-compute the source data, calculating direct relationships from indirect relationships, and then writing the calculated relationship results into MySQL. This solution introduces a "Hive + Spark" pre-computation process, shifting the burden of complex table join calculations to this pre-computation step. This batch pre-computation step severely impacts data real-time performance, results in a large amount of redundant storage of entity information, significantly increases MySQL storage requirements, and substantially raises the required hardware storage costs.

[0007] In view of this, it is very meaningful to propose a graph-based relationship characterization method and system. Summary of the Invention

[0008] To address the existing problems, this invention provides a graph library-based relationship characterization method and system to resolve the aforementioned technical deficiencies.

[0009] In a first aspect, the present invention proposes a relationship characterization method based on a graph library, which includes the following steps:

[0010] Acquire and collect multi-source heterogeneous data, analyze the acquired multi-source heterogeneous data and perform entity abstraction, the entity abstraction includes abstracting business data into a description of a certain entity or a description of the relationship between a certain entity and another entity;

[0011] Entity fusion is performed on entities from different records or different business systems to characterize the relationships between entities in different events;

[0012] Graph databases are used to organize data into entities, attributes, and edges representing relationships between entities, storing it as a key-value data structure. The key stores the core primary key information of an entity, and the value stores the edges and attributes associated with that entity.

[0013] By leveraging the efficient storage of entities and relationships in the library, specific entities can be quickly located through relationship retrieval, and related relationships can be efficiently expanded.

[0014] More preferably, the entities are composed of their own unique attributes, and the relationships are composed of the details of the connections between the entities.

[0015] More preferably, the business data includes flight bookings, which are abstracted into a travel relationship between a person entity and a flight entity. The person entity consists of name and ID number fields, the flight entity consists of flight number and departure time, and the travel relationship between the person entity and the flight entity consists of departure station, departure time, arrival station, and arrival time fields.

[0016] Further optimization utilizes the core primary key field to merge the abstracted entities, where the key attribute of the person entity is the ID number, and the flight is uniquely identified by the flight number and departure time.

[0017] A further preferred method includes quickly locating an entity through its primary key index, then obtaining the corresponding value information from the key, and thus obtaining the relationship information connected to that entity.

[0018] Secondly, embodiments of the present invention also provide a relationship characterization system based on a graph library, comprising:

[0019] The data acquisition module is used to acquire and collect multi-source heterogeneous data;

[0020] The entity abstraction module is used to analyze the acquired multi-source heterogeneous data and perform entity abstraction. The entity abstraction includes abstracting business data into a description of a certain entity or a description of the relationship between a certain entity and another entity.

[0021] The entity fusion module is used to merge entities from different records or different business systems in order to characterize the relationships between entities in different events.

[0022] The graph database module is used to organize data into entities, attributes, and edges representing relationships between entities using a graph database, and store it as a key-value underlying data structure, where the key is used to store the core primary key information of the entity, and the value is used to store the edges and attributes associated with the entity.

[0023] The relationship retrieval module is used to efficiently store entities and relationships in the library, quickly locate specific entities through relationship retrieval, and efficiently expand to related relationships.

[0024] Thirdly, embodiments of the present invention provide an electronic device, including: one or more processors; and a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any implementation of the first aspect.

[0025] Fourthly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method as described in any of the implementations of the first aspect.

[0026] Compared with the prior art, the beneficial results of the present invention are as follows:

[0027] (1) By abstracting and fusing entities from multi-source heterogeneous data, storing them in a graph library, and providing efficient relationship retrieval; entity abstraction enables the organization and granular refinement of business data, entity fusion enables the aggregation of redundant information, graph library storage enables efficient storage of entities and relationships, and graph library-based relationship retrieval enables accurate positioning of entities and relationships and rapid response; this invention simplifies the data pre-calculation process, reduces implementation costs, ensures the real-time nature of data, and greatly improves the efficiency of relationship retrieval based on accurate extended line queries, which also ensures the stability of the system.

[0028] (2) With the rapid development of data collection, storage and governance technologies, the relationship between various entities, including humans, and similar or different entities around them is attracting more and more attention from the public; it is becoming increasingly urgent to characterize the relationship from multi-source heterogeneous data and to explore the value behind it; the relationship characterization method based on graph library proposed in this invention greatly restores human cognitive habits and ways of thinking about things, and provides guiding ideas for the production and construction of relationship characterization. Attached Figure Description

[0029] The accompanying drawings are included to provide a further understanding of the embodiments and are incorporated in and constitute a part of this specification. The drawings illustrate embodiments and, together with the description, serve to explain the principles of the invention. Other embodiments and many anticipated advantages of the embodiments will be readily recognized as they become better understood through reference to the following detailed description. Elements in the drawings are not necessarily to scale. The same reference numerals refer to corresponding similar parts.

[0030] Figure 1 This is an exemplary device architecture diagram in which an embodiment of the present invention can be applied;

[0031] Figure 2 This is a simplified flowchart of the data storage process in the existing Scheme 1;

[0032] Figure 3 Here is a simplified flowchart of the existing Scheme 2;

[0033] Figure 4 This is a flowchart illustrating the relationship characterization method based on a graph library according to an embodiment of the present invention;

[0034] Figure 5 This is a schematic diagram of the data format after entity fusion in the graph-based relationship characterization method according to an embodiment of the present invention;

[0035] Figure 6 This is a flowchart illustrating the relationship characterization system based on a graph library, as described in an embodiment of the present invention.

[0036] Figure 7This is a schematic diagram of the structure of a computer device suitable for implementing electronic devices according to embodiments of the present invention. Detailed Implementation

[0037] In the following detailed description, reference is made to the accompanying drawings, which form part of the detailed description and are illustrated by specific illustrative embodiments in which the invention may be practiced. In this regard, directional terms such as “top,” “bottom,” “left,” “right,” “up,” “down,” etc., are used with reference to the orientation of the described figures. Because components of the embodiments can be positioned in several different orientations, directional terms are used for illustrative purposes and are by no means limiting. It should be understood that other embodiments may be utilized or logical changes may be made without departing from the scope of the invention. Therefore, the following detailed description should not be taken in a limiting sense, and the scope of the invention is defined by the appended claims.

[0038] It should be understood that Figure 1 The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included.

[0039] Figure 1 An exemplary system architecture 100 for processing information, or for processing information, to which embodiments of the present invention can be applied, is shown.

[0040] like Figure 1 As shown, system architecture 100 may include terminal devices 101, 102, and 103, a network 104, and a server 105. Network 104 serves as the medium for providing communication links between terminal devices 101, 102, and 103 and server 105. Network 104 may include various connection types, such as wired or wireless communication links, or fiber optic cables, etc.

[0041] Users can use terminal devices 101, 102, and 103 to interact with server 105 via network 104 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 101, 102, and 103, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social media platform software, etc.

[0042] Terminal devices 101, 102, and 103 can be various electronic devices with communication functions, including but not limited to smartphones, tablets, laptops, and desktop computers.

[0043] Server 105 can be a server that provides various services, such as a background information processing server that processes verification request information sent by terminal devices 101, 102, and 103. The background information processing server can analyze and process the received verification request information and obtain processing results (such as verification success information used to indicate that the verification request is a valid request).

[0044] It should be noted that the information processing method provided in the embodiments of the present invention is generally executed by server 105, and correspondingly, the device for processing information is generally disposed in server 105. Furthermore, the information sending method provided in the embodiments of the present invention is generally executed by terminal devices 101, 102, and 103, and correspondingly, the device for sending information is generally disposed in terminal devices 101, 102, and 103.

[0045] It should be noted that a server can be either hardware or software. When the server is hardware, it can be implemented as a distributed server cluster consisting of multiple servers, or as a single server. When the server is software, it can be implemented as multiple software programs or software modules (for example, used to provide distributed services), or as a single software program or multiple software modules; no specific limitations are made here.

[0046] In the real world, relationships are divided into direct and indirect relationships. A direct relationship is one between two entities that occurs directly without any intermediary. For example, if entity A sends a package to entity B, then the delivery relationship between entity A and entity B is a direct relationship. An indirect relationship is one between two entities that occurs through a third entity. For example, if entity A takes flight A and entity B takes train A, then entity A and entity B have a "flight co-pilot" relationship through flight A. Direct relationships are relatively simple; this patent only deals with the more complex indirect relationships, using the representative "flight co-pilot" relationship as an example.

[0047] Existing Option 1:

[0048] Option 1 uses the most commonly used relational database MySQL to store the two-dimensional data table and uses SQL statements to query the relationships. The data storage process is briefly as follows: Figure 2 As shown.

[0049] The underlying data table "t_flight_data" for same-flight relationships in the MySQL database is organized as follows:

[0050] As shown in Table 1:

[0051]

[0052] When describing the relationships between Zhang San and other passengers on the same flight, the SQL statement is: "select b.*from t_flight_data ajoin t_flight_data b where a.idCard='44xxx001'and a.flightNo=b.flightNoand a.startTime=b.startTime and b.idCard!='44xxx001'", which is a self-join query within the same table, with a complexity of O(n^2). 2 The calculation is very time-consuming, and when the amount of data is slightly large, it will time out or even exhaust computing resources.

[0053] Not only is the computational efficiency low, but as can be seen from the table above, the information of the same flight entity will be stored repeatedly. When N people have taken the flight, the information of the flight entity will be stored N times.

[0054] Another approach to this solution is to store the person's information and flight information separately. While this method avoids storing the intermediate entity (flight information) repeatedly, it requires an additional table join when retrieving relationship details, which further reduces computational efficiency.

[0055] Existing Option 2

[0056] To improve the response efficiency of real-time requests and reduce the pressure on the MySQL database, Solution 2 introduces pre-computation. The source data is stored in a Hive table, and Spark is used to pre-compute the source data, calculating direct relationships from indirect relationships. The calculated relationship results are then written to MySQL. A simplified process is as follows: Figure 3 As shown.

[0057] The MySQL database's table "t_flight_data" storing the results of same-flight relationships is organized as shown in Table 2 below:

[0058]

[0059] When describing the relationship of Zhang San on the same flight, the SQL statement is simplified to: "select * from t_fight_data where nameJ='Zhang San'".

[0060] This solution introduces a pre-computation process using "Hive + Spark," shifting the computational burden of complex table joins to this pre-computation stage. The initial computation of existing data requires a full join operation on the Hive tables using Spark, maintaining a time complexity of O(n log n). 2Furthermore, incremental data still needs to be correlated with the corresponding historical partition tables for calculation. Moreover, since correlating each new record with existing data in Hive is extremely time-consuming, a batch approach is often used in production, where the correlation calculation is performed periodically and then synchronized to MySQL.

[0061] This scheme introduces a batch pre-calculation step, which seriously affects the real-time performance of the data. At the same time, since the result table is the result table after Cartesian product, a large amount of entity information will be redundantly stored, which will greatly expand the storage on the MySQL side and greatly increase the required hardware storage cost.

[0062] This invention discloses a graph database-based relationship characterization method, which includes entity abstraction, entity fusion, graph database storage, and relationship retrieval techniques. It abstracts entities from source business data, fuses entities using primary key attributes, and leverages the inherent high-efficiency storage characteristics of graph databases for multi-source heterogeneous data. This ensures data real-time performance while reducing computational and storage costs, and provides flexible and efficient relationship retrieval services.

[0063] Figure 4 An embodiment of the present invention discloses a graph library-based relationship characterization method, such as... Figure 4 As shown, the method includes the following steps:

[0064] S1. Acquire and collect multi-source heterogeneous data, analyze the acquired multi-source heterogeneous data and perform entity abstraction, wherein the entity abstraction includes abstracting business data into a description of a certain entity or a description of the relationship between a certain entity and another entity.

[0065] Specifically, entity abstraction includes: each piece of business data can be abstracted into a description of a certain entity or a description of the relationship between a certain entity and another entity. Entities are composed of their own unique attributes, and relationships are composed of the details of the connections between entities.

[0066] In this embodiment, taking flight data as an example, each record can be abstracted into a relationship between a person entity and a flight entity. The person entity consists of fields such as name and ID number (there are many attributes in reality, but only the core attributes are taken in this embodiment), and the flight entity consists of flight number and departure time. The relationship between the person entity and the flight entity consists of fields such as departure station, departure time, arrival station, and arrival time.

[0067] Assuming the type identifier for the Person entity is Person, the type identifier for the Flight entity is Flight, and the type identifier for the boarding relationship is R_001, then after entity abstraction, the Person entity, Flight entity, and boarding relationship in the flight information table are represented as shown in Table 3 below:

[0068]

[0069]

[0070] S2. Merge entities from different records or different business systems to characterize the relationships between entities in different events;

[0071] Specifically, entity fusion includes: different records within the same business system often contain the same entity, such as multiple people taking the same flight, in which case the source data will repeatedly describe the flight information; different business systems also often point to the same entity, just as one person can take a flight and also check into a hotel. Merging entities from different records or different business systems is crucial for characterizing the relationships between entities in different events.

[0072] The abstracted entities are merged using the core primary key field. The key attribute for the "person" entity is the identification number, while "flight" is uniquely identified by the flight number and departure time. The merged data format is as follows: Figure 5 As shown.

[0073] Depend on Figure 5 As can be seen, after entity fusion, the same entity and its relationships in different data records are brought together, thus making it easy to obtain the same flight relationship between Zhang San and Li Si, and Zhang San and Wang Wu. Furthermore, entity fusion avoids redundant storage of any entity's information, greatly reducing storage pressure.

[0074] S3. Utilize a graph database to organize data into entities, attributes, and edges representing relationships between entities, storing them as a key-value underlying data structure. The key stores the core primary key information of the entity, and the value stores the edges and attributes associated with the entity.

[0075] Specifically, graph databases organize data into entities, attributes, and edges (i.e., relationships between entities), storing it as a key-value data structure. The key stores the core primary key information of the entity, and the value stores the edges and attributes associated with the entity. An entity can be quickly located using its primary key index, and the corresponding value information can be obtained from the key, thus revealing the relationships connected to that entity.

[0076] Compared to relational databases, the attribute graph storage model, which stores entities, attributes, and edges, refines the granularity of information description and enhances the relevance of information organization.

[0077] S4. By leveraging the efficient storage of entities and relationships in the library, specific entities can be quickly located through relationship retrieval, and related relationships can be efficiently expanded.

[0078] Specifically, leveraging the efficient storage of entities and relationships within the graph library, relationship retrieval can quickly locate specific entities and efficiently expand to related relationships. After entity abstraction, entity fusion, and graph library storage, the Gremlin query "gV().has(“idCard”,”44xxx001”).outE(“R_001”).inV().inE(“R_001”).outV().Path()" to find passengers on the same flight as Zhang San can quickly retrieve information about passengers on the same flight as Zhang San and details of their relationships.

[0079] Precise relationship retrieval based on the image library is highly efficient because its efficiency is only affected by the size of the hit range and has little to do with the size of the full dataset.

[0080] This invention provides a graph-based relationship characterization method that performs entity abstraction and fusion on multi-source heterogeneous data, stores it in the graph library, and provides efficient relationship retrieval. Entity abstraction enables the organization and granular refinement of business data, entity fusion enables the aggregation of redundant information, graph library storage enables efficient storage of entities and relationships, and graph-based relationship retrieval enables precise location and rapid response of entities and relationships. This invention simplifies the data pre-calculation process, reduces implementation costs, ensures data real-time performance, and significantly improves the efficiency of relationship retrieval based on precise extended line queries, thus guaranteeing system stability.

[0081] With the rapid development of data collection, storage, and governance technologies, the relationships between various entities, including humans, and their surrounding similar or dissimilar entities are attracting increasing public attention. Characterizing relationships from multi-source heterogeneous data and extracting the underlying value has become increasingly urgent. This invention proposes a graph-based relationship characterization method that largely replicates human cognitive habits and thought processes, providing guiding principles for the development of relationship characterization methods.

[0082] Secondly, embodiments of the present invention also disclose a relationship characterization system based on a graph library, such as... Figure 6 As shown, it includes: a data acquisition module 61, an entity abstraction module 62, an entity fusion module 63, a graph database module 64, and a relationship retrieval module 65.

[0083] In one specific embodiment, the data acquisition module 61 is used to acquire and collect multi-source heterogeneous data; the entity abstraction module 62 is used to analyze the acquired multi-source heterogeneous data and perform entity abstraction, wherein the entity abstraction includes abstracting business data into a description of a certain entity or a description of the relationship between a certain entity and another entity; the entity fusion module 63 is used to fuse entities from different records or different business systems to characterize the relationship between entities in different events; the graph database module 64 is used to organize data into entities, attributes, and edges representing relationships between entities using a graph database, and store it as a key-value underlying data structure, wherein the key is used to store the core primary key information of the entity, and the value is used to store the edges and attributes associated with the entity; the relationship retrieval module 65 is used to leverage the efficient storage of entities and relationships in the graph database, quickly locate a specific entity through relationship retrieval, and efficiently expand to related relationships.

[0084] The following is for reference. Figure 7 It illustrates an electronic device suitable for implementing embodiments of the present invention (e.g., Figure 1 The diagram shows the structure of a computer device 700 (a server or terminal device). Figure 7 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0085] like Figure 7 As shown, the computer device 700 includes a central processing unit (CPU) 701 and a graphics processing unit (GPU) 702, which can perform various appropriate actions and processes according to programs stored in read-only memory (ROM) 703 or programs loaded from storage section 709 into random access memory (RAM) 706. The RAM 704 also stores various programs and data required for the operation of the device 700. The CPU 701, GPU 702, ROM 703, and RAM 704 are interconnected via a bus 705. An input / output (I / O) interface 706 is also connected to the bus 705.

[0086] The following components are connected to I / O interface 706: an input section 707 including a keyboard, mouse, etc.; an output section 708 including an LCD, speakers, etc.; a storage section 709 including a hard disk, etc.; and a communication section 710 including a network interface card, such as a LAN card or modem. The communication section 710 performs communication processing via a network such as the Internet. A drive 711 may also be connected to I / O interface 706 as needed. A removable medium 712, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on drive 711 as needed so that computer programs read from it can be installed into storage section 709 as needed.

[0087] In particular, according to the embodiments disclosed in this invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 710, and / or installed from removable medium 712. When the computer program is executed by central processing unit (CPU) 701 and graphics processing unit (GPU) 702, the functions defined in the methods of this invention are performed.

[0088] It should be noted that the computer-readable medium described in this invention can be a computer-readable signal medium, a computer-readable medium, or any combination thereof. A computer-readable medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor device, apparatus, or any combination thereof. More specific examples of a computer-readable medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this invention, a computer-readable medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution apparatus, device, or device. In this invention, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than a computer-readable medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution device, apparatus, or apparatus. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0089] Computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as "C" or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0090] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using dedicated hardware-based devices that perform the specified functions or operations, or using a combination of dedicated hardware and computer instructions.

[0091] The modules described in the embodiments of the present invention can be implemented in software or hardware. The described modules can also be located in a processor.

[0092] In another aspect, the present invention also provides a computer-readable medium, which may be included in the electronic device described in the above embodiments; or it may exist independently and not assembled into the electronic device. The computer-readable medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to: acquire and collect multi-source heterogeneous data; analyze the acquired multi-source heterogeneous data and perform entity abstraction, wherein the entity abstraction includes abstracting business data into a description of a certain entity or a description of the relationship between one entity and another; merge entities from different records or different business systems to characterize the connections between entities in different events; organize data into entities, attributes, and edges representing relationships between entities using a graph database, and store it as a key-value underlying data structure, where the key is used to store the core primary key information of the entity, and the value is used to store the edges and attributes associated with the entity; and, with the efficient storage of entities and relationships using the graph database, quickly locate a specific entity through relationship retrieval and efficiently expand to related relationships.

[0093] The above description is merely a preferred embodiment of the present invention and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention is not limited to the specific combination of the above-described technical features, but also includes other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the inventive concept. For example, technical solutions formed by substituting the above-described features with (but not limited to) technical features with similar functions disclosed in this invention.

Claims

1. A method for characterizing relationships based on a graph library, characterized in that, The method includes the following steps: Acquire and collect multi-source heterogeneous data, analyze the acquired multi-source heterogeneous data and perform entity abstraction, the entity abstraction includes abstracting business data into a description of a certain entity or a description of the relationship between a certain entity and another entity; Entity fusion is performed on entities from different records or different business systems to depict the relationships between entities in different events. The entity fusion is achieved through the core primary key field, where the key attribute of the human entity is the ID number, and the flight entity is uniquely identified by the flight number and departure time. Graph databases are used to organize data into entities, attributes, and edges representing relationships between entities, and store them as a key-value underlying data structure, where the key is used to store the core primary key information of the entity, and the value is used to store the edges and attributes associated with the entity. as well as By leveraging the efficient storage of entities and relationships in the library, a specific entity can be quickly located through relationship retrieval, and related relationships can be efficiently expanded. The relationship retrieval quickly locates a specific entity through the primary key index of the entity, and then obtains the corresponding value information from the key to obtain the relationship information connected to that entity.

2. The graph-based relationship characterization method according to claim 1, characterized in that, The entities are composed of their own unique attributes, and the relationships are composed of the details of the connections between the entities.

3. The graph-based relationship characterization method according to claim 2, characterized in that, The business data includes flight bookings. Each record is abstracted into a travel relationship between a person entity and a flight entity. The person entity consists of fields for name and ID number, while the flight entity consists of flight number and departure time. The travel relationship between the person entity and the flight entity consists of fields for departure station, departure time, arrival station, and arrival time.

4. A relationship characterization system based on a graph library, characterized in that, Including the method as described in any one of claims 1-3, further comprising: The data acquisition module is used to acquire and collect multi-source heterogeneous data; The entity abstraction module is used to analyze the acquired multi-source heterogeneous data and perform entity abstraction. The entity abstraction includes abstracting business data into a description of a certain entity or a description of the relationship between a certain entity and another entity. The entity fusion module is used to merge entities from different records or different business systems in order to characterize the relationships between entities in different events. The graph database module is used to organize data into entities, attributes, and edges representing relationships between entities using a graph database, and store them as a key-value underlying data structure. The key is used to store the core primary key information of the entity, and the value is used to store the edges and attributes associated with the entity. The relationship retrieval module is used to efficiently store entities and relationships in the library, quickly locate specific entities through relationship retrieval, and efficiently expand to related relationships.

5. An electronic device, comprising: One or more processors; Storage device for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any one of claims 1 to 3.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1 to 3.

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