Business data association graph display method, device, equipment, medium and product

Abstract

The application provides a business data association graph display method, device, equipment, medium and product, and relates to the field of big data. The method comprises the following steps: in response to a data query request of a user, determining a target node corresponding to to-be-queried business data based on an association graph corresponding to the business data, and determining an upstream associated node and a downstream associated node of the target node; determining a visualization parameter of the to-be-queried business data based on the target node, the upstream associated node and the downstream associated node; rendering a query subgraph corresponding to the to-be-queried business data according to the visualization parameter, and generating a visual display interface. The method solves the problem of chaotic index association relationship and missing hierarchical positioning in the prior art, enables the user to quickly identify the business attribution and hierarchical position of the index, not only realizes the ordered presentation of the association context of the target business data, but also improves the graph display clarity, query positioning efficiency and business analysis accuracy.

Description

Technical Field

[0001] This application relates to the field of big data, and in particular to a method, apparatus, device, medium, and product for displaying the correlation graph of business data. Background Technology

[0002] In the business data governance and business analysis of large enterprises in finance, telecommunications, e-commerce, etc., two-dimensional tables or node-edge graphs are usually used to display business data and its related information.

[0003] However, two-dimensional tables mainly present the attributes of business data and are difficult to reflect the hierarchical derivation and business relationships of business data. Staff often need to compare across tables and sort them manually. Although conventional graphs can show the relationships, they are prone to layout chaos, mixing of similar nodes and difficulty in identifying hierarchical positions when the scale of business data increases, which limits the efficiency and accuracy of analysis.

[0004] Therefore, how to improve the orderliness of the display of relationships, the clarity of hierarchical positioning, and the efficiency of analysis operations in large-scale business data scenarios has become an urgent technical problem to be solved. Summary of the Invention

[0005] This application provides a method, apparatus, device, medium, and product for displaying the relationship map of business data, in order to solve the technical problem of how to improve the orderliness of the relationship display, the clarity of the hierarchical positioning, and the efficiency of analysis operations in large-scale business data scenarios.

[0006] Firstly, this application provides a method for displaying the association graph of business data, including:

[0007] In response to a user's data query request, based on the association graph corresponding to the business data, the target node corresponding to the business data to be queried is determined, as well as the upstream and downstream related nodes of the target node are determined;

[0008] Based on the target node, upstream related nodes, and downstream related nodes, determine the visualization parameters of the business data to be queried;

[0009] According to the visualization parameters, the query subgraph corresponding to the business data to be queried is rendered and a visualization display interface is generated; the query subgraph is a local subgraph extracted from the association graph for the target node.

[0010] Secondly, this application provides a device for displaying the correlation graph of business data, comprising:

[0011] The processing module is used to respond to the user's data query request, determine the target node corresponding to the business data to be queried based on the association graph corresponding to the business data, and determine the upstream and downstream related nodes of the target node.

[0012] The processing module is also used to determine the visualization parameters of the business data to be queried based on the target node, upstream associated nodes, and downstream associated nodes.

[0013] The processing module is also used to render the query subgraph corresponding to the business data to be queried according to the visualization parameters, and generate a visualization display interface; the query subgraph is a local subgraph extracted from the association graph for the target node.

[0014] Thirdly, embodiments of this application provide an electronic device, including: a memory and a processor;

[0015] The memory stores the instructions that the computer executes;

[0016] The processor executes computer execution instructions stored in memory, causing the processor to perform the first aspect and / or various possible implementations of the first aspect as described above.

[0017] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the first aspect and / or various possible implementations of the first aspect.

[0018] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the first aspect and / or various possible implementations of the first aspect.

[0019] The business data association graph display method provided in this application, in response to a user's data query request, determines the target node corresponding to the business data to be queried, as well as the upstream and downstream associated nodes of the target node, based on the association graph corresponding to the business data; determines the node set of the business data to be queried based on the target node, upstream associated nodes, and downstream associated nodes; matches the node mapping relationship and multiple node association relationships according to the target node, upstream associated nodes, and downstream associated nodes to obtain the edge set of the business data to be queried; determines the target node mapping relationship and target node association relationship corresponding to the node set, and determines the layout constraints of the business data to be queried based on the target node mapping relationship and target node association relationship; performs layout calculation on the query subgraph according to the node set, edge set, and layout constraints to generate the coordinate position of each node in the node set and the connection path of the edge; renders the query subgraph according to the coordinate position of each node in the node set and the connection path of the edge, and generates a visual display interface. This method solves the problems of chaotic indicator relationships and missing hierarchical positioning in existing technologies, enabling users to quickly identify the business affiliation and hierarchical position of indicators, significantly improving the readability and understanding efficiency of indicator relationships, while reducing the display complexity of large-scale indicator data. It not only realizes the orderly presentation of the target business data relationship, but also improves the clarity of the graph display, query positioning efficiency, and business analysis accuracy. Attached Figure Description

[0020] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0021] Figure 1 This is a flowchart illustrating the method for displaying the association graph of business data provided in this application. Figure 1 .

[0022] Figure 2 This is a flowchart illustrating the method for displaying the association graph of business data provided in this application. Figure 2 .

[0023] Figure 3 This is a schematic diagram of the query subgraph provided in this application. Figure 1 .

[0024] Figure 4 This is a schematic diagram of the query subgraph provided in this application. Figure 2 .

[0025] Figure 5 This is a schematic diagram of the structure of the business data association map display device provided in this application.

[0026] Figure 6 This is a schematic diagram of the structure of the electronic device provided in this application.

[0027] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0028] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application.

[0029] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, storage, use, processing, transmission, provision, disclosure, and application of the relevant data all comply with the relevant laws, regulations, and standards of the relevant countries and regions, have taken necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation access points for users to choose to authorize or refuse.

[0030] Furthermore, the technical solution involved in this application, which involves big data analysis of user information (including but not limited to personal biometrics, identity data, consumption data, asset data, electronic terminal operation data, etc.) and the use of artificial intelligence technology for automated decision-making, and makes decisions that have a significant impact on personal rights based on the results of automated decision-making, provides users with corresponding operation entry points for users to choose to agree to or reject the results of automated decision-making; if the user chooses to reject, the process will proceed to the expert decision-making process.

[0031] It should be noted that the business data association graph display method, apparatus, equipment, medium and product provided in this application can be used in the field of big data, or in any field other than big data. The application field of the business data association graph display method, apparatus, equipment, medium and product in this application is not limited.

[0032] In the business data governance and business analysis scenarios of large enterprises such as finance, telecommunications, and e-commerce, business data is usually collected by relying on databases, data warehouses, or data service interfaces, and the hierarchy, derivation, and correlation information between business data are displayed to users through a visual interface to support query analysis and business decision-making.

[0033] In existing technologies, business data is typically displayed using two-dimensional tables or node-side graphs. Two-dimensional tables primarily display the name, code, definition, and other attribute information of business data based on a field row and column structure. Their working principle is to statically list business data according to preset fields, which facilitates basic querying and storage management. Node-side graphs, on the other hand, represent various business data through nodes and the relationships between business data through connections, presenting some related structures in a graphical way.

[0034] However, in large-scale business data systems, two-dimensional tables are difficult to directly reflect the upstream and downstream relationships and local correlation ranges between business data. Users often need to switch between multiple tables and manually compare them before they can locate the correlation of a certain business data. Although conventional graphs can display node relationships, they are prone to problems such as layout congestion, mixed nodes at the same level, and unclear upstream and downstream identification of target nodes when the number of nodes increases, making it difficult for users to quickly determine the location of the business data to be queried and its related range.

[0035] Therefore, how to improve the orderliness of the display of business data relationships, the clarity of hierarchical positioning, and the efficiency of analysis operations in large-scale business data scenarios has become an urgent technical problem to be solved.

[0036] To address the aforementioned issues, this application provides a method for displaying the association graph of business data. This method uses the hierarchical derivation relationship of indicator nodes as the vertical organizational logic and business attribute classification as the horizontal aggregation rule, ensuring the continuity of hierarchical positioning through a dynamic path preservation mechanism. Upon receiving a user's data query request, it determines the target node and its upstream and downstream related nodes based on the association graph corresponding to the business data. Then, based on the target node, upstream and downstream related nodes, it determines the visualization parameters of the business data to be queried, and renders the query subgraph extracted for the target node according to these visualization parameters, generating a visual display interface. This method, tailored to business data query scenarios, identifies the association relationships of the queried business data in the association graph and generates appropriate display results based on its association structure characteristics. This allows the queried business data and its associated upstream and downstream business relationships to be presented in a clearer and more orderly manner, improving the hierarchy and ease of analysis of business data association relationships in large-scale business data scenarios.

[0037] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will be described below with reference to the accompanying drawings.

[0038] Figure 1This is a flowchart illustrating the method for displaying the association graph of business data provided in this application. Figure 1 The executing entity in this embodiment can be, for example, a business data management system. Figure 1 As shown, the method for displaying the association graph of business data provided in this embodiment includes:

[0039] S101: In response to the user's data query request, based on the association graph corresponding to the business data, determine the target node corresponding to the business data to be queried, and determine the upstream and downstream related nodes of the target node.

[0040] In this embodiment of the application, the business data management system can retrieve and locate relevant business data in the association map based on the data query request initiated by the user; the data query request may include a query identifier for identifying the business data to be queried.

[0041] Understandably, business data serves as the input data source for the graph display, carrying the business data to be queried and its related information.

[0042] The association graph is used to represent the relationships between business data. The nodes in the association graph represent each business data object, and the edges represent the relationships between the nodes. The nodes are named according to the abstract indicators of the corresponding business data.

[0043] The target node is the business data node that the user query request points to, and it is also the view center node for rendering the query subgraph in the future.

[0044] Upstream association nodes are used to express the source or input relationship of the target node, while downstream association nodes are used to express the destination or output relationship of the target node.

[0045] In practice, after receiving a data query request from a user, the business data management system can first process the query identifier carried in the data query request to determine the business data to be queried. Based on the business data to be queried, the system retrieves the corresponding node in the association graph to locate the target node.

[0046] After the target node is determined, the system traverses the association graph around the target node along the corresponding association relationship, and determines the upstream and downstream related nodes of the target node according to the relationship direction.

[0047] In this embodiment of the application, for candidate nodes connected to the target node, the system can perform necessary data validity checks to retain nodes that meet the conditions as upstream and downstream associated nodes, and synchronously record the relationship information between the target node and each associated node.

[0048] In one possible implementation, when there are multiple candidate target nodes, the system can also limit the target node determination based on the query context information carried by the data query request to ensure that the target node determination result is consistent with the current query request.

[0049] Understandably, by synchronously determining upstream and downstream related nodes with the target node as the center, a local related range around the business data to be queried can be directly formed, reducing interference from irrelevant information in the full graph and limiting the subsequent display objects to the structure related to the target node.

[0050] S102: Based on the target node, upstream associated nodes, and downstream associated nodes, determine the visualization parameters of the business data to be queried.

[0051] Understandably, visualization parameters are a set of parameters used to control the rendering effect of query subgraphs, and their objects can include nodes, edges, and the overall display method.

[0052] Once the target node, upstream associated nodes, and downstream associated nodes are determined, the business data management system calculates the data parameters for graphical representation based on the association relationship, relationship direction, and node attributes of each node, and writes the parameter results into the data structure to be rendered.

[0053] In its implementation, the system constructs a parameter calculation context centered on the target node, and combines upstream and downstream related nodes to determine the visualization parameters used to display the query subgraph, thus forming a display structure based on the target node.

[0054] In one possible implementation, visualization parameters can be used to define the relative positions, arrangement, and display methods of connection relationships of the target node, upstream associated nodes, and downstream associated nodes in the display interface.

[0055] In cases where there are multiple associated nodes, the system can also adjust relevant parameters based on the specific distribution of the nodes to improve the clarity of the display.

[0056] In this embodiment, the visual parameters of the target node can be distinguished from the visual parameters of the associated nodes (i.e., upstream and downstream associated nodes); in addition, the system can also set the display method of node style or connection relationship according to the user's display needs.

[0057] In one possible implementation, the visualization parameters may also include path display rules and visual differentiation rules corresponding to the target node; in another possible implementation, the visualization parameters may also be dynamically determined based on the business attributes of the target node, the number of upstream and downstream related nodes, and the node density of the query subgraph.

[0058] Understandably, visualization parameters are not simply display configurations, but rather transform the structural relationships in the local graph into computable and executable display constraints, enabling the subsequent rendering process to form a hierarchical display result around the target node.

[0059] S103: Render the query subgraph corresponding to the business data to be queried according to the visualization parameters, and generate a visualization display interface.

[0060] The query subgraph is a local subgraph extracted from the associated graph for the target node.

[0061] Understandably, a query subgraph is a local graph structure extracted from a relational graph for a target node. The query subgraph includes at least the target node and its upstream and downstream related nodes and corresponding edges.

[0062] Before the rendering process begins, the system generates a data object for the query subgraph based on the target node, upstream associated node, and downstream associated node determined in step S101; then, based on the visualization parameters determined in step S102, it performs layout calculations, primitive drawing, and interface output for the query subgraph.

[0063] In this embodiment of the application, the visualization display interface is the graph display result formed after the query subgraph is rendered. Its output content includes at least the target node position, the upstream associated node position, the downstream associated node position, and the connection path between each node.

[0064] In its implementation, the system first extracts a local subgraph centered on the target node from the associated graph dataset and creates a rendering data object. Then, the layout calculation module reads the visualization parameters and performs coordinate calculations on the query subgraph. After the coordinate calculations are complete, the system generates edge paths based on the node coordinates. When rendering nodes, the target node can apply a display style distinct from its associated nodes. When rendering edges between different nodes, the system can display them according to the relationship direction and type.

[0065] When generating the visualization interface, the system outputs the rendering results to the front-end display component. After receiving the rendering data, the front-end draws according to nodes, edges and text, and presents the target node and its upstream and downstream relationships in the visualization interface. After the interface is generated, users can directly view the location of the target node and its upstream and downstream range without having to search again in the full graph.

[0066] In one possible implementation, rendering can lay out the nodes in the query subgraph according to visualization parameters, and generate corresponding display effects based on the connection relationship between upstream and downstream related nodes to form a clear visual display interface.

[0067] In another possible implementation, rendering can also combine user data query requests to highlight target nodes and weaken the display of non-core display elements.

[0068] Understandably, by parametrically rendering the local subgraphs extracted for the target node, the system directly converts the local relationships in the association graph into a visual display interface, allowing users to identify the central location, source direction, and expansion direction of the business data to be queried in a single query result.

[0069] The business data association graph display method provided in this embodiment, in response to a user's data query request, determines the target node corresponding to the business data to be queried based on the association graph corresponding to the business data, and determines the upstream and downstream related nodes of the target node; based on the target node, upstream and downstream related nodes, it determines the visualization parameters of the business data to be queried; according to the visualization parameters, it renders the query subgraph corresponding to the business data to be queried and generates a visualization display interface. This method solves the problems of chaotic indicator association relationships and missing hierarchical positioning in the prior art, enabling users to quickly identify the business affiliation and hierarchical position of indicators. It not only achieves an orderly presentation of the association network of target business data, but also improves the clarity of graph display, query positioning efficiency, and business analysis accuracy.

[0070] Figure 2 This is a flowchart illustrating the method for displaying the association graph of business data provided in this application. Figure 2 .like Figure 2 As shown, in this embodiment... Figure 1 Based on the embodiments, the method for displaying the association graph of business data is described in detail. The method for displaying the association graph of business data shown in this embodiment includes:

[0071] S201: In response to the user's data query request, based on the association graph corresponding to the business data, determine the target node corresponding to the business data to be queried, and determine the upstream and downstream related nodes of the target node.

[0072] Step S201 is similar to step S101 above, and will not be repeated here.

[0073] In one possible implementation, before determining the target node corresponding to the business data to be queried based on the association graph corresponding to the business data in response to the user's data query request, the construction of the association graph includes: acquiring full-domain business data and extracting indicators from the full-domain business data to obtain multiple basic business indicators; clustering the multiple basic business indicators according to business semantics to obtain multiple business domain indicators; determining the business aggregation indicators corresponding to the full-domain business data based on the multiple business domain indicators; and constructing an association graph according to hierarchical relationships based on the multiple basic business indicators, the multiple business domain indicators, and the business aggregation indicators.

[0074] Among them, business aggregation metrics are the root nodes in the association graph; business domain metrics are the first-level child nodes of the root node; and basic business metrics are the second-level child nodes of the corresponding business domain metrics.

[0075] Understandably, full-domain business data is the source of all business data acquired and processed by the system. It can come from databases, data warehouses, or business service interfaces, and serves as the raw input for subsequent graph construction.

[0076] Indicator extraction for full-domain business data is a data processing process that identifies and extracts information with statistical or business significance from full-domain business data. This data processing process typically includes parsing the indicator name, code, definition, and scope to form basic business indicators that can be used for correlation modeling.

[0077] Business semantics refers to the business meaning or semantic features used to distinguish and merge indicators. The business data management system matches indicators based on their names, description fields, subject domain identifiers, or rule dictionaries to complete clustering and merging.

[0078] Business aggregation metrics are high-level metrics formed by summarizing multiple business domain metrics. They serve as the highest-level nodes in the relationship graph, carrying the merged results of the entire graph; for example, Figure 3 This is a schematic diagram of the query subgraph provided in this application. Figure 1 ,like Figure 3 As shown, the aggregated metrics for this business are Figure 3 The root node in.

[0079] For example, after receiving business data from the entire domain, the business data management system first standardizes the original fields, then filters out measurable, comparable indicators that can reflect the business status, and encapsulates each indicator into a basic business indicator object.

[0080] The system then performs semantic merging on multiple basic business indicators based on a pre-set semantic dictionary, topic domain labels, or vector similarity models, so that basic business indicators under the same business topic are aggregated into a single business domain indicator. Then, the system performs summary calculations or logical merging on each business domain indicator to obtain a business aggregate indicator that can summarize multiple business domains, and sets it as the root node of the association graph.

[0081] The system establishes parent-child connections based on the hierarchical relationship between the business aggregation indicators, business domain indicators, and basic business indicators acquired in the current transaction. Specifically, a first-level edge relationship is formed between business aggregation indicators and business domain indicators, and a second-level edge relationship is formed between business domain indicators and their corresponding basic business indicators. Ultimately, a relational graph that can be used for querying and location is generated.

[0082] Understandably, in this relational structure, the root node represents the global business aggregation layer, the first-level child nodes represent the business domain layer divided according to business semantics, and the second-level child nodes represent the specific basic business indicator layer. The relational graph constructed by the system in this way can provide a structured foundation for locating target nodes, extracting local subgraphs, and generating visual display interfaces based on data query requests.

[0083] Because the association graph has a clear hierarchical organization, target nodes can be directly mapped to the corresponding business domain and basic business indicator range during retrieval, thus making the location results of the query object clearer.

[0084] Understandably, during the graph construction phase, the business data management system first completes the indexing, semantic clustering, and hierarchical merging of all business data across the domain. This ensures that the graph structure upon which the subsequent target nodes are determined has a clear relationship between the root node, first-level child nodes, and second-level child nodes, thereby improving the accuracy of business data association and positioning and the orderliness of the display structure.

[0085] In one possible implementation, the construction of the association graph specifically includes: determining the corresponding graph node set based on multiple basic business indicators, multiple business domain indicators, and business aggregation indicators; constructing node mapping relationships between different nodes in the graph node set according to hierarchical relationships; for any basic business indicator, extracting other basic business indicators that are business-related to the basic business indicator and generating corresponding node association relationships; generating a corresponding graph edge set based on the node mapping relationships and multiple node association relationships; and generating an association graph corresponding to the entire domain business data based on the graph node set and the graph edge set.

[0086] Among them, the node association relationship includes at least one of the following: composition relationship, similarity relationship, derivation relationship, and derivative relationship; the node mapping relationship is used to record all first-level child nodes corresponding to the root node and the second-level child nodes corresponding to the first-level child nodes of each business domain indicator; each edge of the graph edge set corresponds to a set of parent nodes and child nodes that have a mapping relationship or association relationship.

[0087] Understandably, the graph node set includes root node, first-level child nodes, second-level child nodes, and other node information. The business data management system can uniformly encode basic business indicators, business domain indicators, and business aggregation indicators and write them into the node table, and associate each node with a hierarchical type, business domain identifier, and unique identifier.

[0088] The node mapping relationship is used to record the hierarchical affiliation of parent and child nodes. The system establishes parent-child mapping records based on the affiliation relationship between business aggregation indicators and business domain indicators, as well as the affiliation relationship between business domain indicators and basic business indicators.

[0089] Node association is used to record the business relationships between basic business indicators. The system can generate corresponding association type tags based on indicator definition, calculation source, semantic similarity, or derivation rules.

[0090] The graph edge set is used to convert mapping and association relationships into renderable edges. The edge record can simultaneously store the parent node identifier, child node identifier, and edge type for subsequent display differentiation.

[0091] In actual implementation, the system first performs indicator extraction and semantic clustering on the full-domain business data to obtain multiple basic business indicators, multiple business domain indicators, and business aggregation indicators. Then, it determines the graph node set based on hierarchical affiliation rules. Next, it generates node mapping relationships according to the relationship between business aggregation indicators and multiple business domain indicators, and between business domain indicators and multiple basic business indicators. For any basic business indicator, it retrieves other basic business indicators with the same caliber, source, or computable value to form node association relationships.

[0092] The system transforms the node mapping and node association relationships obtained in the current calculation into a graph edge set, and then generates the corresponding association graph of the whole domain business data by combining the node set obtained in the current calculation. Each edge corresponds to a set of parent and child nodes that have mapping or association relationships.

[0093] In this embodiment of the application, the node association relationship can be set as any one or more of the following: composition relationship, similarity relationship, derivative relationship, or derivation relationship, and different connection attributes are marked in the association graph accordingly. For example, composition relationship, similarity relationship, derivative relationship, and derivation relationship can be represented by lines of the same line type and thickness, and distinguished by different colors. Specifically, composition relationship can be represented by green, similarity relationship can be represented by purple, derivative relationship can be represented by yellow, and derivation relationship can be represented by light blue.

[0094] Understandably, by incorporating hierarchical mapping and business associations into the same graph structure, a unified organization of business metrics across the entire domain is achieved. Since node sets, node mapping relationships, and node association relationships each carry structural information at different levels, the generated association graph can simultaneously express the hierarchical affiliation of metrics and horizontal business connections. This allows the association graph to directly locate target nodes and their associated ranges during subsequent queries and rendering, while maintaining consistency in structural representation.

[0095] Meanwhile, since the edge set is generated uniformly according to the mapping relationship and node association relationship, the hierarchical links and business connections in the graph can be presented synchronously, thereby improving the manageability and visualization clarity of the entire domain business data.

[0096] S202: Based on the target node, upstream associated nodes, and downstream associated nodes, determine the set of nodes for the business data to be queried.

[0097] S203: Match the node mapping relationship and multiple node association relationships according to the target node, upstream associated nodes and downstream associated nodes to obtain the edge set of the business data to be queried.

[0098] S204: Determine the target node mapping relationship and target node association relationship corresponding to the node set, and determine the layout constraints of the business data to be queried based on the target node mapping relationship and target node association relationship.

[0099] The visualization parameters include node set, edge set, and layout constraints; the layout constraints include hierarchical arrangement constraints, grouping constraints of nodes in the same business domain, and rendering constraints that differentiate edges of different association types.

[0100] Understandably, the node set includes the display objects of the business data to be queried in the local graph (that is, the nodes that are rendered and displayed at this time). It usually includes the target node and the related nodes that have a direct upstream and downstream relationship with the target node. The related nodes can correspond to nodes at the same level, nodes at the previous level, or nodes at the next level.

[0101] The edge set is used to describe the connection relationship between nodes. Its generation is based on both node mapping relationship and multiple node association relationship, so that the edges of the association graph can reflect hierarchical mapping and business association at the same time.

[0102] Layout constraints are used to control the spatial arrangement and rendering of nodes and edges. Among them, the hierarchical arrangement constraint is used to separate the target node from its upstream and downstream related nodes according to the hierarchical direction. The same business domain node grouping constraint is used to group nodes belonging to the same business domain together. The differentiating rendering constraint for edges of different association types is used to display compositional relationships, similar relationships, derived relationships, or derived relationships using different line types, colors, or transparency.

[0103] In practice, after receiving a data query request for a target node, the business data management system first reads the node identifier, the business domain identifier, and the set information of its upstream and downstream related nodes from the association graph, and forms a set of nodes for the business data to be queried based on this information.

[0104] Then, based on the mapping relationship between the target node and its upstream and downstream related nodes, the corresponding parent and child node records are matched. At the same time, combined with the pre-stored relationship records between nodes, the connections that need to be displayed are selected to form an edge set.

[0105] The process of determining layout constraints can be based on the joint calculation of the hierarchical attributes in the node set, the business domain attributes, and the relation type attributes in the edge set, and output the hierarchical coordinates, group coordinates, and style parameters of the edges. Among them, the style parameters include at least line type, color, and thickness.

[0106] In one possible implementation, if a graph database or relational database is used to store the associated graph data, the node mapping table and relation table can be extracted through a query engine, and then the rendering configuration can be generated by the layout calculation module. This application does not impose any special limitations on the query engine and the layout calculation module.

[0107] Understandably, during the process of determining visualization parameters, the target node is placed at the center of the graph or at a specified level. Upstream and downstream related nodes are distributed above and below it according to hierarchical arrangement constraints. Nodes in the same business domain are displayed centrally according to grouping constraints, and edge sets are rendered separately according to different association types. This allows the node range, hierarchical direction, and relationship type in the query subgraph to be presented synchronously, ensuring that the node organization and edge display of the local graph are consistent with the upstream and downstream structure of the target node. This enables the display results of the business data to be queried to have clearer hierarchical and relationship boundaries, thereby improving the accuracy of local association identification and the orderliness of the display.

[0108] S205: Based on the node set, edge set, and layout constraints, perform layout calculations on the query subgraph to generate the coordinate positions of each node in the node set and the connection paths of the edges.

[0109] S206: Render the query subgraph according to the coordinates of each node in the node set and the connection path of the edges, and generate a visual display interface.

[0110] In this embodiment of the application, the layout calculation of the visualization display interface can be completed by the graphics layout calculation module in the business data management system. After receiving the node set, edge set and layout constraints, the engine first determines the origin of the coordinate system, the interlayer spacing, the node spacing and the connection turning rules according to the layout constraints, and then calculates the two-dimensional coordinate position of each node according to the level of the node and the business domain.

[0111] For nodes with parent-child mapping relationships, the layout calculation module arranges them in layers along the vertical or horizontal direction, and makes nodes within the same business domain relatively concentrated in the same grouping area; for edges with different association types, the layout calculation module further assigns them different connection path styles so that the edges can be visually distinguished from mapping edges and node association edges.

[0112] The coordinates and connection paths obtained in this calculation can be represented as graphics rendering data, which can be directly called by the rendering module in the business data management system.

[0113] For example, after obtaining the node coordinates and edge connection paths, the rendering module in the business data management system draws the query subgraph according to the graphical rendering data, displaying the nodes as graphic symbols, text labels, or composite controls, and drawing straight lines, polylines, or curves based on the edge connection paths. Simultaneously, it matches the node colors, edge types, arrow directions, and line widths with the layout constraints. After rendering, a visual display interface is generated. This interface can clearly present the target node and its related local structures within a limited display area, maintaining consistency in identifying upstream and downstream relationships, domain relationships, and different association types. This application does not impose special restrictions on the rendering module.

[0114] Understandably, because layout constraints are incorporated into the coordinate calculation and connection path generation process, the query subgraph can maintain hierarchical order and clear relationships when displayed, which makes it easier for users to locate the business data to be queried and its related range. This allows the query subgraph to maintain good readability even in large-scale node scenarios, reduces node occlusion and line overlap, and makes the relationships of different association types clearer, thereby improving the accuracy of business data association display and interface recognition efficiency.

[0115] For example, such as Figure 3 As shown, the currently determined target node is the root node, and Figure 3In the "root node (1728)", 1728 represents the number of abstract indicators corresponding to all business data under the root node; for target nodes, the node identifier includes "a thick solid line on the outer circle and a diagonal line pointing to the upper right inside"; for business domain indicators, Figure 3 The node identifier corresponding to the first-level sub-node business domain 1 includes "a thin dashed line on the outer ring and a vertical line inside". The thin dashed line indicates that there are drill-down nodes for the current business domain 1, and there is a relationship between the drill-down stage and business domain 1.

[0116] Figure 4 This is a schematic diagram of the query subgraph provided in this application. Figure 2 ;like Figure 4 As shown, the currently identified target node is the node corresponding to the basic business metric, and Figure 4 In “Sample Target Node 1 (22)”, 22 represents the number of abstract indicators corresponding to all business data under the root node; at this time, the visualization display interface displays the upstream and downstream related nodes of the node corresponding to the basic business indicator. The upstream related nodes are “Root Node (1728)” and “Business Domain 4 (150)”; the downstream related nodes are “Sample Target Node 2, Sample Target Node 3, Sample Target Node 4, Sample Target Node 5, Sample Target Node 6, Sample Target Node 7”.

[0117] In one possible implementation, before rendering the query subgraph according to the coordinate positions of each node in the node set and the connection paths of the edges, the edge rendering identifier of the connection paths of each node is determined based on the rendering constraints that differentiate the edges of different association types in the layout constraints; the corresponding node rendering identifier is determined according to the node attributes of each node; the rendering of the query subgraph includes: rendering the query subgraph according to the node rendering identifier, coordinate positions, connection paths of the edges, and edge rendering identifiers of the connection paths of each node.

[0118] The rendering identifiers include at least one of line color, line type, and line thickness; the node attributes include node level type, whether the node supports drill-down operation, and whether the node is a target node; the node rendering identifiers include at least one of node fill color, node outline style, and node text size.

[0119] Understandably, node hierarchy type is used to characterize the hierarchical position of a node in the association graph, whether a node supports drill-down operations is used to characterize whether the node can continue to expand its downstream associated nodes, and whether a node is a target node is used to characterize whether the node corresponds to the central object of this query.

[0120] Edge rendering identifiers for edge connection paths are used to visually distinguish edges of different association types, while node rendering identifiers are used to differentiate the interface presentation of nodes with different attributes.

[0121] In this application example, the rendering identifier can be obtained by the rendering module based on layout constraints and node attribute mapping, and written into the style configuration of the corresponding node or edge.

[0122] In the specific implementation, after the business data management system completes the node set, edge set and layout calculation of the query subgraph, it first renders the constraints based on the different types of edges in the layout constraints, and maps the composition relationship, similar relationship, derivative relationship or derived relationship to the corresponding line color, line type or thickness parameters, and then writes the parameters into the style field of the edge object.

[0123] Then, based on the hierarchy type, drill-down marker, and target marker carried by the node object, determine the corresponding fill color, outline style, and text size, and use them as the node rendering style.

[0124] After reading the node coordinates and the connection path of the edges, the rendering module completes the drawing according to the style field, so that the target node, drill-down nodes and nodes at different levels have recognizable visual differences in the interface.

[0125] Understandably, by first styling the edges and nodes before rendering, and then drawing them uniformly by combining the coordinate positions and the connection paths of the edges, the query subgraph can maintain structural coherence while clearly distinguishing edges of different association types and nodes of different attributes. This makes the target nodes and their association range easier to identify in the interface, and the display results of the query subgraph have better hierarchical readability and relationship identification, which is convenient for subsequent drill-down viewing and association analysis.

[0126] In one possible implementation, downstream associated nodes are grouped according to node mapping relationships to obtain multiple node clusters; the visualization interface constraints corresponding to the multiple node clusters are determined; and then, based on the node set, edge set, layout constraints, and visualization interface constraints, the layout of the query subgraph is calculated to generate the coordinate positions of each node in the node set and the connection paths of the edges.

[0127] The visualization parameters also include visualization interface constraints; the visualization interface constraints include at least one of the following: dynamically adjusting the spacing between nodes based on user operation behavior; redetermining the distribution density of each node cluster based on changes in the size of the visualization display interface; and adjusting the horizontal indentation ratio of each level of nodes based on changes in the number of nodes.

[0128] Node mapping relationships are used to characterize the hierarchical correspondence between a target node and its downstream associated nodes. Multiple node clusters are used to aggregate downstream associated nodes in the same level or the same business domain into a local set so that they can be displayed in a clustered manner in the visualization interface.

[0129] Visual interface constraints are used to describe the layout restrictions, scaling adaptation relationships, and interactive response rules of node clusters within the display area. Their content can be updated in real time by the rendering module based on the current window size, node density, and interactive status.

[0130] For example, after obtaining the downstream related nodes corresponding to the target node, the business data management system first determines the level, business domain or related branch to which each downstream related node belongs based on the node mapping relationship, and then groups the nodes that satisfy the same mapping relationship into the same node cluster. Then, combined with the width and height of the current visualization display interface, the scaling ratio and the user's drag, zoom or expand operation on the map, it determines the minimum spacing between each node cluster, the average spacing of nodes within the cluster and the occupancy boundary between clusters, and writes them into the layout parameter set as visualization interface constraints.

[0131] Specifically, when the size of the visualization interface changes, the system recalculates the distribution density of each node cluster to maintain a relatively stable reading order of the nodes in the new visible area; when the number of nodes increases or decreases, the horizontal indentation ratio is recalculated according to the layer depth to ensure that nodes on the same layer still maintain alignment.

[0132] After obtaining the node set, edge set, layout constraints, and visualization interface constraints, the layout calculation module will perform coordinate solving on the query subgraph. Combining the spacing constraints, hierarchy depth constraints, and interface boundary constraints of the node clusters, it will generate the two-dimensional coordinates of each node and the connection path of each edge.

[0133] Specifically, for nodes within the same node cluster, the system assigns local coordinates according to the cluster sorting rules; for connections between different node clusters, it generates polyline or curved paths based on inter-cluster spacing and edge routing rules, so that the connections remain clearly discernible within the limited interface space.

[0134] For example, such as Figure 4 As shown, the downstream associated nodes of the current target node (sample target node 1) are grouped to obtain the corresponding node clusters A, B, and C.

[0135] Understandably, by introducing visual interface constraints and linking them with node mapping relationships, the query subgraph can synchronously adjust the inter-cluster distance, distribution density, and horizontal indentation ratio according to user operations and interface changes during rendering. This allows the local graph to maintain a relatively stable hierarchical structure and readability under different display conditions, while ensuring that the calculation results of node coordinates and edge connection paths can be directly used by the rendering module.

[0136] In one possible implementation, the association path of the target node is determined based on the target node, upstream associated nodes, and downstream associated nodes, and the nodes in the association path are highlighted.

[0137] The highlighting includes at least one of the following: changing the node color; adding a node border; enlarging the node font.

[0138] Understandably, the associated path of a target node refers to the set of local paths formed by tracing upstream associated nodes and downstream associated nodes from the target node as the center according to the edge relationships in the associated graph. This path may include, for example, the target node itself, parent nodes, child nodes and related nodes at the same level that have direct or indirect connections with the target node.

[0139] Upstream and downstream related nodes can be extracted from the established set of nodes and edges. Specifically, the path range can be limited based on the node hierarchy type, mapping relationship, and node association relationship, so that the related path only covers the local subgraph that is directly related to the current query business data.

[0140] After determining the associated path, the system can highlight each node in the path according to whether the target node is the focus of the query, the hierarchical relationship between upstream and downstream associated nodes, and the relative position of the node in the graph.

[0141] For target nodes, a different highlighting style can be used than that of associated nodes to make them more visually recognizable in the local graph; for upstream and downstream associated nodes, consistent or differentiated highlighting styles can be used according to their association strength and hierarchical distance with the target node to maintain the continuity and readability of the path.

[0142] For example, the node color can be changed by using a different fill color or stroke color than that of a normal node; the node border can be increased by using a bold outer frame, a double border, or a dashed border; and the node font can be enlarged by increasing the font size, bolding the font, or simultaneously adjusting the label spacing.

[0143] Understandably, the highlighting style can be automatically generated by the rendering module based on the node attribute parameters, and synchronously superimposed on the node rendering data after the query subgraph layout is completed, so that the associated paths form a continuous and clear visual link in the visualization display interface; by locally enhancing the nodes in the associated paths with color, border or font size, the target node and its upstream and downstream relationships can be quickly distinguished from the complex graph, making it convenient for users to drill down to view, investigate associations and confirm the scope around the target business data.

[0144] The business data association graph display method provided in this embodiment, in response to a user's data query request, determines the target node corresponding to the business data to be queried, as well as the upstream and downstream associated nodes of the target node, based on the association graph corresponding to the business data; determines the node set of the business data to be queried based on the target node, upstream associated nodes, and downstream associated nodes; matches the node mapping relationship and multiple node association relationships according to the target node, upstream associated nodes, and downstream associated nodes to obtain the edge set of the business data to be queried; determines the target node mapping relationship and target node association relationship corresponding to the node set, and determines the layout constraints of the business data to be queried based on the target node mapping relationship and target node association relationship; performs layout calculation on the query subgraph according to the node set, edge set, and layout constraints to generate the coordinate position of each node in the node set and the connection path of the edge; renders the query subgraph according to the coordinate position of each node in the node set and the connection path of the edge, and generates a visual display interface. This method solves the problems of chaotic indicator relationships and missing hierarchical positioning in existing technologies, enabling users to quickly identify the business affiliation and hierarchical position of indicators, significantly improving the readability and understanding efficiency of indicator relationships, while reducing the display complexity of large-scale indicator data. It not only realizes the orderly presentation of the target business data relationship, but also improves the clarity of the graph display, query positioning efficiency, and business analysis accuracy.

[0145] Figure 5 This is a schematic diagram of the structure of the business data association map display device provided in this application. For example... Figure 5 As shown, this application provides a business data association graph display device 500, which includes:

[0146] The processing module 501 is used to respond to the user's data query request, determine the target node corresponding to the business data to be queried based on the association graph corresponding to the business data, and determine the upstream and downstream associated nodes of the target node.

[0147] The processing module 501 is also used to determine the visualization parameters of the business data to be queried based on the target node, upstream associated nodes and downstream associated nodes.

[0148] The processing module 501 is also used to render the query subgraph corresponding to the business data to be queried according to the visualization parameters, and generate a visualization display interface; the query subgraph is a local subgraph extracted from the association graph for the target node.

[0149] In one possible implementation, the processing module 501 is further configured to acquire full-domain business data and extract indicators from the full-domain business data to obtain multiple basic business indicators.

[0150] The processing module 501 is also used to cluster multiple basic business indicators according to business semantics to obtain multiple business domain indicators.

[0151] The processing module 501 is also used to determine the business aggregation indicators corresponding to the full-domain business data based on multiple business domain indicators.

[0152] Processing module 501 is also used to construct a relationship graph based on multiple basic business indicators, multiple business domain indicators and business aggregation indicators according to hierarchical relationships;

[0153] Among them, business aggregation metrics are the root nodes in the association graph; business domain metrics are the first-level child nodes of the root node; and basic business metrics are the second-level child nodes of the corresponding business domain metrics.

[0154] In one possible implementation, the processing module 501 is further configured to determine the corresponding set of graph nodes based on multiple basic business indicators, multiple business domain indicators and business aggregation indicators.

[0155] The processing module 501 is also used to construct the node mapping relationship of different nodes in the graph node set according to the hierarchical relationship; the node mapping relationship is used to record all first-level child nodes corresponding to the root node and the second-level child nodes corresponding to the first-level child nodes of each business domain indicator.

[0156] The processing module 501 is also used to extract other basic business indicators that are related to the basic business indicator for any basic business indicator, and generate the corresponding node association relationship.

[0157] Among them, the node association relationship includes at least one of the following: composition relationship, similarity relationship, derivation relationship, and derivative relationship.

[0158] The processing module 501 is also used to generate a corresponding graph edge set based on the node mapping relationship and the association relationship of multiple nodes; each edge in the graph edge set corresponds to a set of parent nodes and child nodes that have a mapping relationship or association relationship.

[0159] The processing module 501 is also used to generate a relational graph corresponding to the global business data based on the graph node set and the graph edge set.

[0160] In one possible implementation, the processing module 501 is further configured to determine the set of nodes for the business data to be queried based on the target node, the upstream associated node, and the downstream associated node.

[0161] The processing module 501 is also used to match the node mapping relationship and multiple node association relationships according to the target node, upstream associated node and downstream associated node to obtain the edge set of the business data to be queried.

[0162] The processing module 501 is also used to determine the target node mapping relationship and target node association relationship corresponding to the node set, and to determine the layout constraints of the business data to be queried based on the target node mapping relationship and target node association relationship;

[0163] The layout constraints include hierarchical arrangement constraints, grouping constraints of nodes in the same business domain, and distinguishing rendering constraints of edges of different association types.

[0164] In one possible implementation, the processing module 501 is further configured to perform layout calculations on the query subgraph based on the node set, edge set, and layout constraints, and generate the coordinate positions of each node in the node set and the connection paths of the edges.

[0165] The processing module 501 is also used to render the query subgraph according to the coordinate position of each node in the node set and the connection path of the edge, and generate a visual display interface.

[0166] In one possible implementation, the processing module 501 is further configured to determine the edge rendering identifier of the connection path of each node's edge based on the distinguishing rendering constraints of different association types of edges in the layout constraints.

[0167] The processing module 501 is also used to determine the corresponding node rendering identifier based on the node attributes of each node.

[0168] The rendering identifiers include at least one of line color, line type, and line thickness; the node attributes include node level type, whether the node supports drill-down operation, and whether the node is a target node; the node rendering identifiers include at least one of node fill color, node outline style, and node text size.

[0169] The processing module 501 is also used to render the query subgraph according to the node rendering identifier, coordinate position, edge connection path and edge rendering identifier of each node.

[0170] In one possible implementation, the processing module 501 is further configured to group downstream associated nodes according to node mapping relationships to obtain multiple node clusters.

[0171] The processing module 501 is also used to determine the visual interface constraints corresponding to multiple node clusters;

[0172] The visual interface constraints include at least one of the following:

[0173] The spacing between nodes is dynamically adjusted based on user actions.

[0174] Based on the changes in the size of the visualization interface, the distribution density of each node cluster is redefined;

[0175] Adjust the horizontal indentation ratio of nodes at each level according to the changes in the number of nodes.

[0176] The processing module 501 is also used to perform layout calculations on the query subgraph based on the node set, edge set, layout constraints, and visualization interface constraints, and to generate the coordinate positions of each node in the node set and the connection paths of the edges.

[0177] In one possible implementation, the processing module 501 is further configured to determine the association path of the target node based on the target node, the upstream associated node, and the downstream associated node, and to highlight the nodes in the association path.

[0178] The highlighted content includes at least one of the following:

[0179] Change node color; add node border; enlarge node font.

[0180] The control device for the building HVAC system provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0181] Figure 6 A schematic diagram of the structure of the electronic device provided in this application. Figure 6 As shown, the electronic device 600 provided in this embodiment includes at least one processor 601 and a memory 602. Optionally, the device 600 further includes a communication component 603. The processor 601, memory 602, and communication component 603 are connected via a bus 604.

[0182] In a specific implementation, at least one processor 601 executes computer execution instructions stored in memory 602, causing at least one processor 601 to perform the above-described method.

[0183] The specific implementation process of processor 601 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0184] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0185] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0186] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0187] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0188] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.

[0189] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to this application.

[0190] It should be further noted that although the steps in the flowchart are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowchart may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0191] It should be understood that the above-described device embodiments are merely illustrative, and the device of this application can also be implemented in other ways. For example, the division of units / modules in the above embodiments is only a logical functional division, and there may be other division methods in actual implementation. For example, multiple units, modules, or components may be combined, or integrated into another system, or some features may be ignored or not executed.

[0192] Furthermore, unless otherwise specified, the functional units / modules in the various embodiments of this application can be integrated into one unit / module, or each unit / module can exist physically separately, or two or more units / modules can be integrated together. The integrated units / modules described above can be implemented in hardware or as software program modules.

[0193] When integrated units / modules are implemented in hardware, the hardware can be digital circuits, analog circuits, etc. The physical implementation of the hardware structure includes, but is not limited to, transistors, memristors, etc. Unless otherwise specified, the processor can be any suitable hardware processor, such as a CPU, GPU, FPGA, DSP, and ASIC, etc. Unless otherwise specified, the storage unit can be any suitable magnetic or magneto-optical storage medium, such as Resistive Random Access Memory (RRAM), Dynamic Random Access Memory (DRAM), Static Random Access Memory (SRAM), Enhanced Dynamic Random Access Memory (EDRAM), High-Bandwidth Memory (HBM), Hybrid Memory Cube (HMC), etc.

[0194] If the integrated unit / module is implemented as a software program module and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard drive, magnetic disk, or optical disk.

[0195] In the above embodiments, the descriptions of each embodiment have their own emphasis. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments. The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification.

[0196] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein.

[0197] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope.

Claims

1. A method for displaying the association graph of business data, characterized in that, include: In response to a user's data query request, based on the association graph corresponding to the business data, the target node corresponding to the business data to be queried is determined, and the upstream and downstream associated nodes of the target node are determined; Based on the target node, the upstream associated node, and the downstream associated node, determine the visualization parameters of the business data to be queried; According to the visualization parameters, the query subgraph corresponding to the business data to be queried is rendered, and a visualization display interface is generated. The query subgraph is a local subgraph extracted from the association graph for the target node.

2. The method according to claim 1, characterized in that, Before determining the target node corresponding to the business data to be queried based on the association graph corresponding to the business data in response to the user's data query request, the method further includes: Acquire full-domain business data and extract metrics from the full-domain business data to obtain multiple basic business metrics; The multiple basic business indicators are clustered according to business semantics to obtain multiple business domain indicators; Based on the multiple business domain metrics, determine the business aggregation metrics corresponding to the overall business data; Based on the multiple basic business indicators, the multiple business domain indicators, and the business aggregation indicators, the association graph is constructed according to the hierarchical relationship. Wherein, the business aggregation metric is the root node in the association graph; the business domain metric is the first-level child node of the root node; and the basic business metric is the second-level child node of the corresponding business domain metric.

3. The method according to claim 2, characterized in that, The construction of the association graph based on the multiple basic business indicators, the multiple business domain indicators, and the business aggregation indicators, according to hierarchical relationships, includes: Based on the multiple basic business indicators, the multiple business domain indicators, and the business aggregation indicators, determine the corresponding set of graph nodes; The node mapping relationship of different nodes in the graph node set is constructed according to the hierarchical relationship; the node mapping relationship is used to record all first-level child nodes corresponding to the root node and the second-level child nodes corresponding to the first-level child nodes of each business domain indicator. For any basic business indicator, extract other basic business indicators that are related to the basic business indicator and generate the corresponding node association relationship; Based on the node mapping relationship and multiple node association relationships, a corresponding graph edge set is generated; each edge in the graph edge set corresponds to a set of parent and child nodes that have a mapping relationship or association relationship. Based on the graph node set and the graph edge set, generate the associated graph corresponding to the global business data; The node association relationship includes at least one of the following: composition relationship, similarity relationship, derivation relationship, and derivative relationship.

4. The method according to claim 3, characterized in that, The visualization parameters include a node set, an edge set, and layout constraints; determining the visualization parameters of the business data to be queried based on the target node, the upstream associated nodes, and the downstream associated nodes includes: Based on the target node, the upstream associated node, and the downstream associated node, determine the node set of the business data to be queried; According to the target node, the upstream associated node, and the downstream associated node, the node mapping relationship and multiple node association relationships are matched to obtain the edge set of the business data to be queried; Determine the target node mapping relationship and target node association relationship corresponding to the node set, and determine the layout constraints of the business data to be queried based on the target node mapping relationship and the target node association relationship; The layout constraints include hierarchical arrangement constraints, grouping constraints of nodes in the same business domain, and distinguishing rendering constraints of edges of different association types.

5. The method according to claim 4, characterized in that, The step of rendering the query subgraph corresponding to the business data to be queried according to the visualization parameters and generating a visualization display interface includes: Based on the node set, the edge set, and the layout constraints, the layout calculation is performed on the query subgraph to generate the coordinate positions of each node in the node set and the connection paths of the edges; The query subgraph is rendered based on the coordinates of each node in the node set and the connection paths of the edges, and a visual display interface is generated.

6. The method according to claim 5, characterized in that, Before rendering the query subgraph according to the coordinate positions of each node in the node set and the connection paths of the edges, the method further includes: Based on the differentiated rendering constraints of edges of different association types in the layout constraints, the edge rendering identifier of the connection path of each node is determined. Based on the node attributes of each node, determine the corresponding node rendering identifier; The rendering identifier includes at least one of line color, line type, and line thickness; the node attributes include node level type, whether the node supports drill-down operation, and whether the node is the target node; the node rendering identifier includes at least one of node fill color, node outline style, and node text size. The step of rendering the query subgraph according to the coordinate positions of each node in the node set and the connection paths of the edges includes: The query subgraph is rendered according to the node rendering identifier, coordinate position, edge connection path, and edge rendering identifier of each node.

7. The method according to claim 5, characterized in that, The visualization parameters also include visualization interface constraints; the method further includes: According to the node mapping relationship, the downstream associated nodes are grouped to obtain multiple node clusters; Determine the visual interface constraints corresponding to the plurality of node clusters; The visual interface constraints include at least one of the following: The spacing between nodes is dynamically adjusted based on user actions. Based on the size change of the visualization interface, the distribution density of each node cluster is re-determined; Adjust the horizontal indentation ratio of nodes at each level according to the change in the number of nodes; The step of performing layout calculations on the query subgraph based on the node set, the edge set, and the layout constraints to generate the coordinate positions of each node in the node set and the connection paths of the edges includes: Based on the node set, the edge set, the layout constraints, and the visualization interface constraints, the layout calculation is performed on the query subgraph to generate the coordinate positions of each node in the node set and the connection paths of the edges.

8. The method according to claim 1, characterized in that, The method further includes: Based on the target node, the upstream associated node, and the downstream associated node, the associated path of the target node is determined, and the nodes in the associated path are highlighted. The highlighted display includes at least one of the following: Change node color; add node border; enlarge node font.

9. A device for displaying the correlation graph of business data, comprising: The processing module is used to respond to the user's data query request, determine the target node corresponding to the business data to be queried based on the association graph corresponding to the business data, and determine the upstream and downstream association nodes of the target node; The processing module is also used to determine the visualization parameters of the business data to be queried based on the target node, the upstream associated node, and the downstream associated node; The processing module is further configured to render the query subgraph corresponding to the business data to be queried according to the visualization parameters, and generate a visualization display interface; the query subgraph is a local subgraph extracted from the association graph for the target node.

10. An electronic device, characterized in that, include: Memory, processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory, causing the processor to perform the method as described in any one of claims 1-8.

11. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1-8.

12. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method according to any one of claims 1-8.

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