Social relation data visualization optimization method and device based on attribute graph

Abstract

The invention relates to a social relation data visualization optimization method and device based on an attribute graph, and belongs to the technical field of graph data visualization. The method comprises the steps that a social relation visualization rule base is defined and updated in real time based on the attribute graph, wherein the social relation visualization rule base comprises a social relation rule, a relation screening rule, a relation rendering rule, an on-graph relation data statistical rule and an on-graph operation rule; determining relation graph data according to the unique identifier of the to-be-analyzed person, the social relation rule and the relation screening rule; analyzing the relational graph data according to a relational rendering rule, and determining a rendered relational graph; and according to the on-graph relational data statistical rule and the on-graph operation rule, checking the rendered relational graph and analyzing the social relation data of the personnel. According to the method and the system, the aim of timely, accurately, comprehensively and flexibly analyzing the social relation data of the personnel is fulfilled.

Description

Technical Field

[0001] The present invention relates to the technical field of graph data visualization, and in particular to a method and device for optimizing social relationship data visualization based on an attribute graph. Background Art

[0002] With the rapid progress of information technology, especially the maturity of big data, cloud computing and artificial intelligence technology, it has become possible to process and analyze massive social relationship data. The widespread use of the Internet and social media has changed the way people communicate, and a large amount of social data has been recorded and stored. Enterprises hope to improve team collaboration efficiency and employee satisfaction by analyzing employee social networks, while identifying potential network security risks. The government and society need to predict and respond to social events such as group behavior and social unrest by analyzing social relationship data. The analysis of personnel social relationship data is not only technically supported, but also shows important application value in many aspects. It is an important direction for the development of information technology in modern society.

[0003] The use of knowledge graph technology to analyze personnel social relationship data has significant advantages. It can intuitively display complex relationship networks in a graphical way, support efficient query and retrieval of complex relationships, and support dynamic update and expansion. However, it is found in actual construction applications that the visualization of personnel social relationship data also faces many difficulties, mainly including the following four aspects: ① Dynamic nature of relationships: Social relationships are not static, but constantly change and develop over time. To accurately capture and display this dynamic change, an efficient real-time data update and query analysis mechanism is required. ② Multi-dimensional and multi-level: Social relationships often have multiple dimensions and levels, such as family relationships, professional relationships, social relationships, etc. How to clearly present these different dimensions and levels of relationships in visualization and show their interactions is quite difficult. ③ Information overload: If a large amount of social relationship data is directly displayed in a picture, it may lead to information overload and then the content of the picture display interface will explode, making it difficult for observers to extract valuable information from it. ④ Uncertainty of relationships: The definition of some social relationships may be vague or uncertain. How to properly define this uncertainty in visualization is also a difficult problem.

[0004] In order to accurately capture and display the dynamic changes of relationships, to clearly present multi-dimensional and multi-level relationships, to avoid information overload that makes it difficult for observers to extract effective and valuable information, and to properly define the uncertainty of relationships, we also need a more timely, accurate, comprehensive, and flexible social relationship data visualization method to maximize the timeliness, accuracy, comprehensiveness, and convenience of users in analyzing people's social relationships. Summary of the invention

[0005] In view of this, it is necessary to provide a social relationship data visualization optimization method and device based on attribute graphs to solve the technical problems in the existing technology that it is difficult to capture in real time and display the dynamic changes of social relationships in multiple dimensions and levels, as well as the possible information overload and difficulty in properly defining and displaying the uncertainty of relationships when processing large amounts of data.

[0006] In order to solve the above problems, the present invention provides a social relationship data visualization optimization method based on an attribute graph, comprising: Based on the attribute graph, define and update the social relationship visualization rule base in real time, where the social relationship visualization rule base includes social relationship rules, relationship screening rules, relationship rendering rules, relationship data statistics rules on the graph, and operation rules on the graph; Determine the relationship graph data based on the unique identification, social relationship rules and relationship screening rules of the person to be analyzed; Parsing the relationship graph data according to the relationship rendering rule to obtain a rendered relationship graph; According to the relationship data statistics rules and operation rules on the graph, the rendered relationship graph is viewed and the social relationship data of personnel is analyzed.

[0007] In a possible implementation, the social relationship rules include at least surfing the Internet together, staying in the same hotel, riding the same train, riding the same flight, and being colleagues; The relationship screening rules at least include economic relationship, cultural relationship, family relationship, professional relationship, social relationship, legal relationship, relationship data source, relationship person age, relationship person label and relationship generation time; The relationship rendering rules at least include whether to enable the creation of relationship type aggregation node rules and aggregation node custom type screening and filtering; The statistical rules of the relationship data on the graph include statistics on the number of associations, statistics on the number of association types, statistics on the earliest association time, and statistics on the latest association time; The operation rules on the graph include that the graph supports drag events and zoom events, the nodes on the graph support drag displacement events, double-click events, single-click events, suspension events, and right-click events, and the edges on the graph support suspension events and right-click events.

[0008] In a possible implementation, determining the relationship graph data according to the unique identifier of the person to be analyzed, the social relationship rule, and the relationship screening rule includes: Build query statement templates based on the query language of graph databases; According to the unique identifier of the person to be analyzed, the placeholder in the query statement template is dynamically replaced to generate a graph relationship query statement; According to the graph query statement, the relationship graph data is obtained.

[0009] In a possible implementation, parsing the relationship graph data according to the relationship rendering rule and determining to render the relationship graph includes: Parsing relationship graph data; Assemble the parsed relationship graph data into data in a front-end visualization format, and return the data in the front-end visualization format to the front-end; Determine the rendered relationship graph according to the relationship rendering rules and the data in the visualization format, create a relationship type aggregation node, and cache and map the relationship data.

[0010] In a possible implementation, assembling the parsed relationship graph data into data in a front-end visualization format includes: Assemble the parsed relationship graph data into data that supports d3.js force-directed graph rendering.

[0011] In a possible implementation, dynamically replacing the placeholder in the query statement template according to the unique identifier of the person to be analyzed to generate a graph relationship query statement includes: Determine whether there are user-defined social relationship rules and relationship screening rules; If so, the custom social relationship rules and relationship screening rule related parameters are used to generate a graph relationship query statement; If not, the cache default rule parameters are used to generate the graph relationship query statement.

[0012] In a possible implementation, the real-time updating of the social relationship visualization rule base includes: Based on the social relationship visualization rule library, a real-time synchronization tool is used to update the extracted entity nodes and relationship edges and write them into the graph database in real time.

[0013] In a second aspect, the present invention further provides a device for optimizing visualization of social relationship data based on an attribute graph, comprising: A rule definition module is used to define and update the social relationship visualization rule base in real time based on the attribute graph, where the social relationship visualization rule base includes social relationship rules, relationship screening rules, relationship rendering rules, relationship data statistics rules on the graph, and operation rules on the graph; A relationship graph data determination module is used to determine the relationship graph data according to the unique identification of the person to be analyzed, the social relationship rules and the relationship screening rules; A rendering relationship graph generation module, used to parse the relationship graph data according to the relationship rendering rules and determine the rendering relationship graph; The visualization graph generation module is used to view the rendered relationship graph and analyze the social relationship data of people according to the relationship data statistical rules and operation rules on the graph.

[0014] In a third aspect, the present invention further provides an electronic device, comprising: a processor and a memory; The memory stores a computer-readable program executable by the processor; When the processor executes the computer-readable program, the steps in the social relationship data visualization optimization method based on the attribute graph are implemented as described above.

[0015] In a fourth aspect, the present invention also provides a computer-readable storage medium, which stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps in the social relationship data visualization optimization method based on attribute graph as described above.

[0016] The beneficial effects of the present invention are as follows: first, based on the attribute graph, a social relationship visualization rule base is defined and updated in real time, wherein the social relationship visualization rule base includes social relationship rules, relationship screening rules, relationship rendering rules, relationship data statistics rules on the graph, and operation rules on the graph; by supporting user-defined rules, the uncertainty problem of the relationship is solved, thereby improving the accuracy of social relationship visualization. Subsequently, the relationship graph data is determined according to the unique identification of the person to be analyzed, the social relationship rules, and the relationship screening rules; the real-time storage and update of the attribute graph model is used to solve the dynamic problem of the relationship, and then the relationship graph data is parsed according to the relationship rendering rules, and the rendering relationship graph is determined, and the relationship type aggregation node is created to reduce the rendering workload and solve the information overload problem; finally, according to the relationship data statistics rules on the graph and the operation rules on the graph, the rendered relationship graph is viewed and the social relationship data of the personnel is analyzed, and through a variety of interactive operations, it is convenient for users to view and analyze the social relationship data. Thus, the present invention achieves the goal of timely, accurate, comprehensive, and flexible analysis of the social relationship data of personnel. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A method flow chart of an embodiment of a method for optimizing social relationship data visualization based on an attribute graph provided by the present invention; Figure 2 for Figure 1 A method flow chart of an embodiment of step S102; Figure 3 A schematic diagram of the social relationship effect without applying the post-query rendering rule in the social relationship data visualization optimization method based on the attribute graph provided by the present invention; Figure 4 A schematic diagram of the social relationship effect of applying the post-query rendering rule in the social relationship data visualization optimization method based on the attribute graph provided by the present invention; Figure 5 It is a schematic diagram of an embodiment of a social relationship data visualization optimization device based on an attribute graph provided by the present invention; Figure 6 It is a schematic diagram of the operating environment of an electronic device provided by the present invention. DETAILED DESCRIPTION

[0018] The preferred embodiments of the present invention are described in detail below in conjunction with the accompanying drawings, wherein the accompanying drawings constitute a part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not used to limit the scope of the present invention.

[0019] A specific embodiment of the present invention discloses a method for optimizing visualization of social relationship data based on attribute graphs. Figure 1 ,include: S101. Based on the attribute graph, define and update the social relationship visualization rule base in real time, wherein the social relationship visualization rule base includes social relationship rules, relationship screening rules, relationship rendering rules, relationship data statistics rules on the graph, and operation rules on the graph; S102, determining relationship graph data according to the unique identifier of the person to be analyzed, the social relationship rule, and the relationship screening rule; S103, parsing the relationship graph data according to the relationship rendering rule, and determining a rendering relationship graph; S104: View the rendered relationship graph and analyze the social relationship data of personnel according to the relationship data statistics rules and the operation rules on the graph.

[0020] In this embodiment, firstly, based on the attribute graph, a social relationship visualization rule base is defined and updated in real time, wherein the social relationship visualization rule base includes social relationship rules, relationship screening rules, relationship rendering rules, relationship data statistics rules on the graph, and operation rules on the graph; by supporting user-defined rules, the uncertainty problem of the relationship is solved, thereby improving the accuracy of social relationship visualization. Then, the relationship graph data is determined according to the unique identification of the person to be analyzed, the social relationship rules, and the relationship screening rules; the real-time storage and update of the attribute graph model is used to solve the dynamic problem of the relationship, and the social relationship data that needs to be queried and rendered is reduced by pre-query relationship screening, which can solve the information overload problem to a certain extent. Then, the relationship graph data is parsed according to the relationship rendering rules, and the rendering relationship graph is determined, and the relationship type aggregation node is created to reduce the rendering workload and solve the information overload problem; finally, according to the relationship data statistics rules on the graph and the operation rules on the graph, the rendered relationship graph is viewed and the social relationship data of the personnel is analyzed, and through a variety of interactive operations, it is convenient for users to view and analyze the social relationship data. Thus, the present invention achieves the goal of timely, accurate, comprehensive, and flexible analysis of the social relationship data of personnel.

[0021] It should be noted that social relations refer to the sum of mutual relationships formed by people in the process of common material and spiritual activities. It covers various connections formed between people in all areas of social life, including economic relations (sharing the same house), cultural relations (classmates), family relations (same household registration), professional relations (colleagues), social relations (surfing the Internet together, staying in the same hotel, taking the same train, taking the same flight), legal relations (violating traffic rules on the same car, being involved in the same case), etc.

[0022] It should be noted that the Property Graph is a graph database model that allows nodes (Vertices) and edges (Edges) to have multiple properties. These properties can be any type of data, such as strings, numbers, Boolean values, etc. The Property Graph provides a high degree of flexibility and expressiveness for modeling complex data. In the analysis of personnel social relations, the nodes and edges of the Property Graph can carry a variety of attribute features, which are used to define and describe specific social relations.

[0023] In step S101, the social relationship rules at least include surfing the Internet together, staying in the same hotel, taking the same train, taking the same flight and colleagues; the relationship screening rules at least include economic relationships, cultural relationships, family relationships, professional relationships, social relationships, legal relationships, relationship data sources, relationship personnel ages, relationship personnel labels and relationship generation time; the relationship rendering rules at least include whether to enable the creation of relationship type aggregation node rules and aggregation node custom type screening and filtering; the relationship data statistics rules on the graph include association count statistics, association relationship type count statistics, earliest association time statistics and latest association time statistics; the graph operation rules include the graph supporting drag events and zoom events, the nodes on the graph supporting drag displacement events, double-click events, single-click events, suspension events, right-click events, and the edges on the graph supporting suspension events and right-click events.

[0024] Furthermore, for example, the default rules for hotel roommates include ① check-in time interval within 10 minutes, ② check-out time interval within 10 minutes, and ③ room number interval within 4. The rules for train rides include ① two people in the same carriage, ② two people from the same departure point, ③ two people to the same destination, ④ two people's ticket purchase time interval within 5 minutes, and ⑤ two people's seat number interval within 4.

[0025] Furthermore, the nodes and edges of the attribute graph can carry multiple attribute features, such as: Hotel Relationship: Attribute characteristics: check-in time, check-out time, room number, etc.

[0026] Rule example: Check-in time interval is within 10 minutes, check-out time interval is within 10 minutes, and room number interval is within 4.

[0027] Train ride relationship: Attribute characteristics: carriage number, origin, destination, ticket purchase time, seat number, etc.

[0028] Example of rules: Two people in the same carriage, same origin, same destination, time interval between ticket purchases within 5 minutes, and seat number interval within 4.

[0029] Therefore, through the system based on the attribute graph, users can customize the relationship definition parameters according to their specific needs. For example, users can adjust parameters such as time intervals and room number intervals to more accurately define social relationships, so that the system can adapt to different application scenarios and user needs. Especially when dealing with those relationships that are vaguely defined or uncertain, users can adjust parameters according to actual conditions to improve the accuracy of relationship definition. And users can dynamically adjust the relationship definition rules based on actual data and analysis results to further verify and optimize the accuracy of relationship definition.

[0030] In some embodiments, the relationship graph data is determined based on the unique identification of the person to be analyzed, the social relationship rules and the relationship screening rules. Figure 2 ,include: S201. Construct a query statement template based on the query language of the graph database; S202, dynamically replacing the placeholder in the query statement template according to the unique identifier of the person to be analyzed, and generating a graph relationship query statement; S203. Acquire relationship graph data according to the graph query statement.

[0031] In this embodiment, the system determines whether the user input contains a customized social relationship definition rule and a customized pre-query relationship screening rule. If so, the user-defined rule parameters are used, and if not, the default rule parameters cached by the system are used. For example, the user modified the system's preset judgment rules for the relationship of taking the same train, and removed the rule of the relationship of taking the same train ⑤ The number of seat numbers between two people is within 4; at the same time, the user chose to query only the four types of relationship data of the person to be analyzed within one year, including surfing the Internet together, taking the same train, taking the same flight, and staying in the same hotel. The above rule parameter modification is only valid for the current user this time, and does not affect the system preset rule parameters. Only the administrator can modify the system preset rule parameters.

[0032] In some embodiments, parsing the relationship graph data according to the relationship rendering rule and determining to render the relationship graph includes: Parsing relationship graph data; Assemble the parsed relationship graph data into data in a front-end visualization format, and return the data in the front-end visualization format to the front-end; Determine the rendered relationship graph according to the relationship rendering rules and the data in the visualization format, create a relationship type aggregation node, and cache and map the relationship data.

[0033] In this embodiment, the rendering rules of the relationship graph after query include ① whether to enable the creation of relationship type aggregation nodes ② the aggregation nodes support custom type filtering. When there are many original social relationship data nodes and relationship edges, the rendered graph effect can be as follows: Figure 3 As shown in the figure, it is a dense explosion. The user opens the rule ① to create a relationship type aggregation node. The rendered graph effect is as follows Figure 4 The diagram is very concise, and this rule can further solve the problem of information overload. By using rule ② to filter the rendered aggregation nodes on the diagram by custom type, only the currently concerned type of relationship data can be displayed, which is convenient for relationship analysis on the diagram. Combining whether to create relationship type aggregation nodes and custom type filtering of aggregation nodes can clearly present multi-dimensional and multi-level relationships.

[0034] Furthermore, the front-end JavaScript script will support the rendering data preprocessing of the d3.js force-directed graph according to the system's preset post-query relationship rendering rules, classify the data according to the social relationship type, cache the graph data of each relationship type separately and do not participate in the initial graph rendering. At the same time, new unique aggregation nodes and aggregation relationship edges of the corresponding relationship type are added to participate in the graph rendering. The aggregation node is connected to the central person (person to be analyzed) node. At the same time, the front-end page must also retain the mapping relationship between the relationship type aggregation node and the graph data of the corresponding relationship type for subsequent graph operations.

[0035] For example, if the system automatically creates relationship type aggregation nodes by default and the user does not turn it off, the front end will pre-process the data before rendering the graph relationship based on the d3.js force-directed graph. Only the central person (person to be analyzed) and the relationship type aggregation nodes and aggregation edges will be rendered. The graph rendering is not only fast, but also concise and clear.

[0036] Furthermore, the dragging event on the graph supports customized displacement of the entire graph, the zooming event on the graph supports the zooming display of the graph, the node dragging event supports customized displacement of the current node and associated edges, the node double-clicking event supports expanding or collapsing the graph relationship data of the current node (if the current node is an aggregate node, all relationship data graphs of the corresponding relationship type are expanded or collected [according to the mapping graph relationship data retained in S106, the graph relationship data of this type of node is additionally rendered on the graph]; if the current node is a non-central personnel node, the social relationship data of the current person is automatically queried and expanded before expansion or collapse is supported. The specific expansion or collection action depends on the collapse status of the current node), the node single-click event supports viewing the simple file of the current node, the node suspension event supports viewing the detailed properties of the current node, and the node right-click event supports popping up more operation menus (the menu also includes expanding all relationships of the current node, expanding a certain relationship of the current node, adding the current node to the interest group filtering list to facilitate the display of only the relationship data of people in the interest group, hiding aggregate nodes and aggregate edges, etc.). The edge hover event supports viewing the detailed properties of the current relationship edge, and the edge right-click event supports viewing the detailed list data of the current relationship edge (including the property data of the starting point and the destination point).

[0037] With the operation function on the diagram, when the user wants to expand and view the detailed relationship diagram of a certain type of relationship, just double-click the aggregation node of the corresponding relationship type to expand it; when you want to view the common relationship persons of two types of relationships, just double-click the aggregation nodes of the corresponding two types of relationships to expand them, and you can also right-click to hide the aggregation nodes and relationships you are not interested in. If you want to check whether there are people with key or high-risk labels such as fugitives, people with major criminal records, and people who are not allowed to enter or leave the country in the social relations of the center personnel, you can directly filter by label. If you want to view the people with the most connections among the center personnel, you can directly view them in reverse order in the relationship data statistics list.

[0038] Based on the above-mentioned social relationship data visualization optimization method based on attribute graph, the embodiment of the present invention also provides a social relationship data visualization optimization device based on attribute graph, please refer to Figure 5 ,include: A rule definition module 510 is used to define and update in real time a social relationship visualization rule base based on the attribute graph, wherein the social relationship visualization rule base includes social relationship rules, relationship screening rules, relationship rendering rules, relationship data statistics rules on the graph, and operation rules on the graph; A relationship graph data determination module 520, for determining relationship graph data according to the unique identification of the person to be analyzed, the social relationship rules and the relationship screening rules; A rendering relationship graph generation module 530 is used to parse the relationship graph data according to the relationship rendering rule and determine a rendering relationship graph; The visualization graph generation module 540 is used to view the rendered relationship graph and analyze the social relationship data of personnel according to the relationship data statistical rules and operation rules on the graph.

[0039] like Figure 6 As shown, based on the above-mentioned social relationship data visualization optimization method based on attribute graph, the present invention also provides an electronic device, which can be a computing electronic device such as a mobile terminal, a desktop computer, a notebook, a palm computer, and a server. The electronic device includes a processor 610, a memory 620, and a display 630. Figure 6 Only some components of the electronic device are shown, but it should be understood that it is not required to implement all of the components shown, and more or fewer components may be implemented instead.

[0040] In some embodiments, the memory 620 may be an internal storage unit of the electronic device, such as a hard disk or memory of the electronic device. In other embodiments, the memory 620 may also be an external storage electronic device of the electronic device, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device. Further, the memory 620 may also include both an internal storage unit of the electronic device and an external storage electronic device. The memory 620 is used to store application software and various types of data installed in the electronic device, such as program codes for installing the electronic device. The memory 620 may also be used to temporarily store data that has been output or is to be output. In one embodiment, a social relationship data visualization optimization program 640 based on an attribute graph is stored on the memory 620, and the social relationship data visualization optimization program 640 based on an attribute graph can be executed by the processor 610, thereby realizing the social relationship data visualization optimization method based on an attribute graph in each embodiment of the present application.

[0041] In some embodiments, the processor 610 may be a central processing unit (CPU), a microprocessor or other data processing chip, used to run program codes or process data stored in the memory 620, such as executing a social relationship data visualization optimization method based on an attribute graph.

[0042] In some embodiments, the display 630 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, etc. The display 630 is used to display information of the electronic device for optimizing social relationship data visualization based on the attribute graph and to display a visualized user interface. The components 610-630 of the electronic device communicate with each other via a system bus.

[0043] Those skilled in the art will appreciate that all or part of the processes of the above-mentioned embodiments can be implemented by instructing related hardware through a computer program, and the program can be stored in a computer-readable storage medium, wherein the computer-readable storage medium is a disk, an optical disk, a read-only storage memory, or a random access memory, etc.

[0044] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by any technician familiar with the technical field within the technical scope disclosed by the present invention should be covered within the protection scope of the present invention.

Claims

1. A social relationship data visualization optimization method based on attribute graph, characterized in that: include: Based on the attribute graph, define and update the social relationship visualization rule base in real time, where the social relationship visualization rule base includes social relationship rules, relationship screening rules, relationship rendering rules, relationship data statistics rules on the graph, and operation rules on the graph; Determine the relationship graph data based on the unique identification, social relationship rules and relationship screening rules of the person to be analyzed; Parsing the relationship graph data according to the relationship rendering rule to obtain a rendered relationship graph; According to the relationship data statistics rules and operation rules on the graph, the rendered relationship graph is viewed and the social relationship data of personnel is analyzed.

2. The social relationship data visualization optimization method based on attribute graph according to claim 1 is characterized in that: The social relationship rules at least include surfing the Internet together, staying in the same hotel, taking the same train, taking the same flight, and being colleagues; The relationship screening rules at least include economic relationship, cultural relationship, family relationship, professional relationship, social relationship, legal relationship, relationship data source, relationship person age, relationship person label and relationship generation time; The relationship rendering rules at least include whether to enable the creation of relationship type aggregation node rules and aggregation node custom type screening and filtering; The statistical rules of the relationship data on the graph include statistics on the number of associations, statistics on the number of association types, statistics on the earliest association time, and statistics on the latest association time; The operation rules on the graph include that the graph supports drag events and zoom events, the nodes on the graph support drag displacement events, double-click events, single-click events, suspension events, and right-click events, and the edges on the graph support suspension events and right-click events.

3. The social relationship data visualization optimization method based on attribute graph according to claim 2 is characterized in that: Determining the relationship graph data according to the unique identification of the person to be analyzed, the social relationship rules and the relationship screening rules includes: Build query statement templates based on the query language of graph databases; According to the unique identifier of the person to be analyzed, the placeholder in the query statement template is dynamically replaced to generate a graph relationship query statement; According to the graph query statement, the relationship graph data is obtained.

4. The social relationship data visualization optimization method based on attribute graph according to claim 2 is characterized in that: The step of parsing the relationship graph data according to the relationship rendering rule and determining to render the relationship graph includes: Parsing relationship graph data; Assemble the parsed relationship graph data into data in a front-end visualization format, and return the data in the front-end visualization format to the front-end; Determine the rendered relationship graph according to the relationship rendering rules and the data in the visualization format, create a relationship type aggregation node, and cache and map the relationship data.

5. The social relationship data visualization optimization method based on attribute graph according to claim 4 is characterized in that: The step of assembling the parsed relationship graph data into data in a front-end visualization format includes: Assemble the parsed relationship graph data into data that supports d3.js force-directed graph rendering.

6. The social relationship data visualization optimization method based on attribute graph according to claim 3 is characterized in that: The method of dynamically replacing the placeholder in the query statement template according to the unique identifier of the person to be analyzed to generate a graph relationship query statement includes: Determine whether there are user-defined social relationship rules and relationship screening rules; If so, the custom social relationship rules and relationship screening rule related parameters are used to generate a graph relationship query statement; If not, the cache default rule parameters are used to generate the graph relationship query statement.

7. The social relationship data visualization optimization method based on attribute graph according to claim 1 is characterized in that: The real-time updating of the social relationship visualization rule base includes: Based on the social relationship visualization rule library, a real-time synchronization tool is used to update the extracted entity nodes and relationship edges and write them into the graph database in real time.

8. A social relationship data visualization optimization device based on attribute graph, characterized in that: include: A rule definition module is used to define and update the social relationship visualization rule base in real time based on the attribute graph, where the social relationship visualization rule base includes social relationship rules, relationship screening rules, relationship rendering rules, relationship data statistics rules on the graph, and operation rules on the graph; A relationship graph data determination module is used to determine the relationship graph data according to the unique identification of the person to be analyzed, the social relationship rules and the relationship screening rules; A rendering relationship graph generation module, used to parse the relationship graph data according to the relationship rendering rules and determine the rendering relationship graph; The visualization graph generation module is used to view the rendered relationship graph and analyze the social relationship data of people according to the relationship data statistical rules and operation rules on the graph.

9. An electronic device, characterized in that: include: Processor and memory; The memory stores a computer-readable program executable by the processor; When the processor executes the computer-readable program, the steps in the social relationship data visualization optimization method based on the attribute graph are implemented as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps in the social relationship data visualization optimization method based on the attribute graph as described in any one of claims 1-7.

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