A vehicle diagnostic topology map generation method and apparatus

Abstract

The present application relates to the technical field of vehicle diagnosis, and discloses a vehicle diagnosis topology graph generation method and device, wherein the electronic control unit and bus topology data of a target vehicle are called from a pre-stored database based on the identification information of the target vehicle obtained; the bus topology data is subjected to structured classification processing; based on the classified bus topology data, the electronic control unit and bus are automatically arranged through a gridding layout algorithm to generate layout information containing node positions and bus paths; a visual vehicle diagnosis topology graph is rendered and generated according to the layout information; and in response to user interaction operations, selection, view adjustment or diagnosis triggering operations are performed on the nodes or buses in the vehicle diagnosis topology graph. It can be seen that the present application can generate a vehicle diagnosis topology graph with clear levels, regular arrangement and high interactivity, so as to improve the vehicle diagnosis efficiency.

Description

Technical Field

[0001] This invention relates to the field of vehicle diagnostic technology, and in particular to a method and apparatus for generating vehicle diagnostic topology maps. Background Technology

[0002] In vehicle diagnostics and repair, accurately understanding the vehicle's electronic and electrical architecture is a prerequisite for troubleshooting. Existing vehicle diagnostic topology diagram generation solutions typically retrieve topology description information based on the vehicle model and output graphics according to a preset template.

[0003] Because the topology elements lack unified arrangement constraints and structural organization rules during generation, the output topology diagrams often exhibit a mixed arrangement of nodes and buses, unclear hierarchy of trunks and branches, and irregular and easily intersecting connection routes, making communication path tracing less intuitive. Furthermore, these topology diagrams are mostly displayed statically, lacking a linkage between the topology elements and the diagnostic operation process. This necessitates manual matching and location using node lists or address manuals during troubleshooting, resulting in lengthy diagnostic paths, cumbersome operations, and a high risk of errors.

[0004] Therefore, there is an urgent need for a method to generate vehicle diagnostic topology maps that are hierarchical, well-organized, and highly interactive, in order to improve vehicle diagnostic efficiency. Summary of the Invention

[0005] This invention provides a method and apparatus for generating vehicle diagnostic topology maps, which can generate vehicle diagnostic topology maps that are hierarchical, well-organized, and highly interactive, thereby improving vehicle diagnostic efficiency.

[0006] To address the aforementioned technical problems, the first aspect of this invention discloses a method for generating a vehicle diagnostic topology map, the method comprising: Obtain the identification information of the target vehicle, and retrieve the electronic control unit and bus topology data of the target vehicle from the pre-stored database based on the identification information; The bus topology data is processed by structured classification, including dividing the electronic control unit into node types and dividing the bus of the target vehicle into connection relationships; the electronic control unit is divided into entry nodes, hub nodes and functional nodes, and the bus is divided into backbone bus, ring bus and internal bus. Based on the classified bus topology data, the electronic control unit and the bus are automatically arranged using a grid layout algorithm to generate layout information that includes node positions and bus paths. A visual vehicle diagnostic topology diagram is generated based on the layout information; In response to user interaction, select, view adjust, or diagnostic trigger operations are performed on nodes or buses in the vehicle diagnostic topology diagram.

[0007] As an optional implementation, in the first aspect of the present invention, retrieving the electronic control unit and bus topology data of the target vehicle from a pre-stored database based on the identification information includes: Based on the identification information index pre-stored database, an initial topology dataset matching the target vehicle is extracted. The initial topology dataset contains the basic attributes of the electronic control unit of the target vehicle and the basic connection relationship of the bus of the target vehicle. The signal interaction logs in the initial topology dataset are parsed to identify the actual communication links between the electronic control units. Based on the communication links, the connection topology between the bus and the electronic control unit is reconstructed to generate composite topology data containing physical connection relationships and logical communication relationships. The composite topology data is traversed to detect whether there are isolated nodes with missing connection relationships. If there are isolated nodes, the potential connection path between the isolated node and the bus is inferred based on the physical installation location of the isolated node and the preset protocol compatibility rules, and the potential connection path is filled into the composite topology data. Extract the signal load rate and transmission delay parameters of each bus in the composite topology data, sort the buses by priority based on the signal load rate and transmission delay parameters, and generate bus topology data with priority attributes.

[0008] As an optional implementation, in the first aspect of the present invention, the automatic arrangement of the electronic control unit and the bus based on the classified bus topology data using a grid layout algorithm to generate layout information including node positions and bus paths includes: Based on the classified bus topology data, the node inclusion relationship of each bus is extracted, and it is identified whether the node set of any first bus is completely covered by the node set of the second bus. Multiple buses with complete coverage relationship are merged into the same bus cluster, and the number of nodes connected to each bus in the bus cluster is counted. The bus with the most connected nodes is determined as the reference bus. Using the mapping coordinates of the entry node in the preset grid as the layout origin, the reference bus is preferentially mapped to the central grid column where the layout origin is located, and the remaining buses belonging to the same bus cluster are folded and mapped to the grid column area occupied by the reference bus. The number of columns is not counted repeatedly. The grid is a virtual coordinate system used for node arrangement in the grid layout algorithm, which is a cell array composed of a preset number of rows and columns. Based on the total number of physical ports connected to the outside of the hub node, the radial extension path of the backbone bus is calculated, so that the backbone bus is arranged layer by layer from the central grid column to the outer grid column. At the same time, the ring bus is configured in the ring adjacent grid column preset by the hub node, and the internal bus is configured in the internal adjacent grid column preset by the hub node. Traverse the gap space between adjacent grid columns, count the number of parallel buses passing through the same gap space, and when the number of parallel buses exceeds the capacity threshold of a single gap, allocate buses with opposite signal flow directions to different vertical offset positions within the gap space according to the signal flow direction attributes carried on each bus, thereby generating non-overlapping bus paths. Based on the channel resources occupied by the bus path in the grid, the logical occupancy of the electronic control unit in the grid is deduced, and the node position is calculated based on the logical occupancy to generate layout information containing the node position and the bus path.

[0009] As an optional implementation, in the first aspect of the present invention, the step of rendering and generating a visualized vehicle diagnostic topology map based on the layout information includes: Read the node positions and bus paths contained in the layout information, and extract the merging level attribute of the bus cluster and the vertical offset position attribute of the bus path. Based on the merge hierarchy attribute, identify the remaining buses that are folded and mapped within the bus cluster, and assign different display hierarchy parameters to the base bus and the remaining buses respectively; Based on the vertical offset position attribute of the bus path, multiple buses with different vertical offset positions in the same gap space are identified, and buses with opposite signal flow directions are assigned different line type identifiers. Traverse all electronic control units in the layout information, identify functional nodes within the same bus cluster node set, and establish logical affiliation markers between the functional nodes and the reference bus of the bus cluster. In response to the drawing instructions on the display interface based on the layout information, the drawing order of the functional nodes and the front-to-back occlusion relationship of the bus path are determined during the rendering process according to the logical belonging mark and the display level parameters, and a visualized vehicle diagnostic topology diagram is generated.

[0010] As an optional implementation, in a first aspect of the invention, the user interaction operation includes a selection operation for the entry node or the hub node, a view adjustment operation for the bus cluster, and a diagnostic trigger operation for the topology; the step of performing the selection, view adjustment, or diagnostic trigger operation on a node or bus in the vehicle diagnostic topology diagram in response to the user interaction operation includes: In response to a selection operation for the entry node or the hub node, the logical communication relationship in the composite topology data is read, the associated electronic control unit that has a communication link with the entry node or the hub node is identified, and the associated electronic control unit is mapped as a candidate diagnostic object; Traverse the logical attribution markers in the layout information, detect the attribution correspondence between the candidate diagnostic object and the target bus cluster, and synchronously mark all functional nodes in the target bus cluster as active. In response to a view adjustment operation for the target bus cluster, the merge level attribute of the target bus cluster is extracted, the folding mapping relationship of the target bus cluster is deconstructed based on the merge level attribute, and the remaining buses of the folded mapping are expanded and displayed in the grid column area. In response to a diagnostic trigger operation for the expanded target bus cluster, the display layer parameters allocated to the reference bus and the other buses are extracted respectively. According to the front and back occlusion relationship determined by the display layer parameters, the buses and electronic control units in the target bus cluster are traversed layer by layer to perform diagnostic data acquisition. In response to the feedback of the diagnostic data acquisition results, a data transmission path is determined based on the signal flow direction attribute, and the data transmission path is mapped to the vertical offset position attribute of the bus path to update the layout information.

[0011] As an optional implementation, in the first aspect of the present invention, the method further includes: Monitor the operating status data of the target vehicle, and extract the fluctuation parameters that characterize the load changes of the electronic control unit and the throughput parameters that characterize the bus communication quality from the operating status data. Based on the fluctuation parameters and the throughput parameters, the real-time topology weights of the electronic control unit and the bus are calculated, and the real-time topology weights are mapped to the row and column coordinate calculation logic of the grid layout algorithm. The row and column coordinate calculation logic is an operation rule for determining the position of the node in the grid based on the grid layout algorithm. In response to the change in the value of the real-time topology weight, the logical occupancy of the electronic control unit in the grid and the vertical offset position of the bus path are dynamically adjusted to update the layout information. The logical occupancy is the occupancy information of the electronic control unit in the grid determined based on the channel resources occupied by the bus path in the grid. The updated layout information is traversed to identify the overlapping of grid column regions caused by the logical occupancy adjustment. The display layer parameters are redistributed according to the node density of the overlapping regions. The display layer parameters are layer differentiation parameters used to control the node drawing order and the front and back occlusion relationship of the bus, so as to generate a topology structure that adapts to the running state data.

[0012] As an optional implementation, in the first aspect of the present invention, the method further includes: Acquire multiple sets of historical topology data of the target vehicle within a preset historical time period, and extract the connection status changes of the electronic control unit and the signal transmission records of the bus in each set of historical topology data; The connection status changes and signal transmission records are analyzed to identify the activity frequency and correlation tightness of the electronic control unit and the bus in the historical operating cycle, and a topology correlation map characterizing the historical connection stability is generated. Based on the topology graph, the node type classification and connection relationship classification in the structured classification process are verified. When an implicit connection path not reflected in the bus topology data is detected in the topology graph, the implicit connection path is added to the bus topology data. The added bus topology data is a comprehensive topology dataset that integrates physical connection relationships and historical implicit connection relationships. Based on the supplementary results of the implicit connection paths, the node inclusion relationships in the gridded layout algorithm are updated to reconstruct the bus grouping hierarchy and the vertical offset position attributes of the bus paths based on the comprehensive topology dataset, thereby generating a topology structure containing historical operation characteristics.

[0013] A second aspect of the present invention discloses a vehicle diagnostic topology map generation apparatus, the apparatus comprising: The acquisition module is used to acquire the identification information of the target vehicle and retrieve the electronic control unit and bus topology data of the target vehicle from a pre-stored database based on the identification information. The classification module is used to perform structured classification processing on the bus topology data, including classifying the electronic control units into node types and classifying the bus connections of the target vehicle; the electronic control units are classified into entry nodes, hub nodes, and functional nodes, and the buses are classified into backbone buses, ring buses, and internal buses. The layout module is used to automatically arrange the electronic control unit and the bus based on the classified bus topology data using a grid layout algorithm, and generate layout information including node positions and bus paths. The generation module is used to render and generate a visual vehicle diagnostic topology map based on the layout information; The response module is used to perform selection, view adjustment, or diagnostic triggering operations on nodes or buses in the vehicle diagnostic topology diagram in response to user interaction operations.

[0014] As an optional implementation, in the second aspect of the present invention, the specific method by which the acquisition module retrieves the electronic control unit and bus topology data of the target vehicle from a pre-stored database based on the identification information includes: Based on the identification information index pre-stored database, an initial topology dataset matching the target vehicle is extracted. The initial topology dataset contains the basic attributes of the electronic control unit of the target vehicle and the basic connection relationship of the bus of the target vehicle. The signal interaction logs in the initial topology dataset are parsed to identify the actual communication links between the electronic control units. Based on the communication links, the connection topology between the bus and the electronic control unit is reconstructed to generate composite topology data containing physical connection relationships and logical communication relationships. The composite topology data is traversed to detect whether there are isolated nodes with missing connection relationships. If there are isolated nodes, the potential connection path between the isolated node and the bus is inferred based on the physical installation location of the isolated node and the preset protocol compatibility rules, and the potential connection path is filled into the composite topology data. Extract the signal load rate and transmission delay parameters of each bus in the composite topology data, sort the buses by priority based on the signal load rate and transmission delay parameters, and generate bus topology data with priority attributes.

[0015] As an optional implementation, in a second aspect of the present invention, the layout module automatically arranges the electronic control unit and the bus based on the classified bus topology data using a grid layout algorithm to generate layout information containing node positions and bus paths. Specific methods for this generation include: Based on the classified bus topology data, the node inclusion relationship of each bus is extracted, and it is identified whether the node set of any first bus is completely covered by the node set of the second bus. Multiple buses with complete coverage relationship are merged into the same bus cluster, and the number of nodes connected to each bus in the bus cluster is counted. The bus with the most connected nodes is determined as the reference bus. Using the mapping coordinates of the entry node in the preset grid as the layout origin, the reference bus is preferentially mapped to the central grid column where the layout origin is located, and the remaining buses belonging to the same bus cluster are folded and mapped to the grid column area occupied by the reference bus. The number of columns is not counted repeatedly. The grid is a virtual coordinate system used for node arrangement in the grid layout algorithm, which is a cell array composed of a preset number of rows and columns. Based on the total number of physical ports connected to the outside of the hub node, the radial extension path of the backbone bus is calculated, so that the backbone bus is arranged layer by layer from the central grid column to the outer grid column. At the same time, the ring bus is configured in the ring adjacent grid column preset by the hub node, and the internal bus is configured in the internal adjacent grid column preset by the hub node. Traverse the gap space between adjacent grid columns, count the number of parallel buses passing through the same gap space, and when the number of parallel buses exceeds the capacity threshold of a single gap, allocate buses with opposite signal flow directions to different vertical offset positions within the gap space according to the signal flow direction attributes carried on each bus, thereby generating non-overlapping bus paths. Based on the channel resources occupied by the bus path in the grid, the logical occupancy of the electronic control unit in the grid is deduced, and the node position is calculated based on the logical occupancy to generate layout information containing the node position and the bus path.

[0016] As an optional implementation, in a second aspect of the present invention, the specific method by which the generation module renders and generates a visualized vehicle diagnostic topology map based on the layout information includes: Read the node positions and bus paths contained in the layout information, and extract the merging level attribute of the bus cluster and the vertical offset position attribute of the bus path. Based on the merge hierarchy attribute, identify the remaining buses that are folded and mapped within the bus cluster, and assign different display hierarchy parameters to the base bus and the remaining buses respectively; Based on the vertical offset position attribute of the bus path, multiple buses with different vertical offset positions in the same gap space are identified, and buses with opposite signal flow directions are assigned different line type identifiers. Traverse all electronic control units in the layout information, identify functional nodes within the same bus cluster node set, and establish logical affiliation markers between the functional nodes and the reference bus of the bus cluster. In response to the drawing instructions on the display interface based on the layout information, the drawing order of the functional nodes and the front-to-back occlusion relationship of the bus path are determined during the rendering process according to the logical belonging mark and the display level parameters, and a visualized vehicle diagnostic topology diagram is generated.

[0017] As an optional implementation, in a second aspect of the invention, the user interaction operation includes a selection operation for the entry node or the hub node, a view adjustment operation for the bus cluster, and a diagnostic trigger operation for the topology; the specific manner in which the response module performs the selection, view adjustment, or diagnostic trigger operation on the nodes or buses in the vehicle diagnostic topology diagram in response to the user interaction operation includes: In response to a selection operation for the entry node or the hub node, the logical communication relationship in the composite topology data is read, the associated electronic control unit that has a communication link with the entry node or the hub node is identified, and the associated electronic control unit is mapped as a candidate diagnostic object; Traverse the logical attribution markers in the layout information, detect the attribution correspondence between the candidate diagnostic object and the target bus cluster, and synchronously mark all functional nodes in the target bus cluster as active. In response to a view adjustment operation for the target bus cluster, the merge level attribute of the target bus cluster is extracted, the folding mapping relationship of the target bus cluster is deconstructed based on the merge level attribute, and the remaining buses of the folded mapping are expanded and displayed in the grid column area. In response to a diagnostic trigger operation for the expanded target bus cluster, the display layer parameters allocated to the reference bus and the other buses are extracted respectively. According to the front and back occlusion relationship determined by the display layer parameters, the buses and electronic control units in the target bus cluster are traversed layer by layer to perform diagnostic data acquisition. In response to the feedback of the diagnostic data acquisition results, a data transmission path is determined based on the signal flow direction attribute, and the data transmission path is mapped to the vertical offset position attribute of the bus path to update the layout information.

[0018] As an optional implementation, in a second aspect of the invention, the apparatus further includes: The monitoring module is used to monitor the operating status data of the target vehicle, extract fluctuation parameters that characterize the load changes of the electronic control unit, and throughput parameters that characterize the quality of bus communication from the operating status data. The calculation module is used to calculate the real-time topology weights of the electronic control unit and the bus based on the fluctuation parameters and the throughput parameters, and to map the real-time topology weights to the row and column coordinate calculation logic of the grid layout algorithm. The row and column coordinate calculation logic is an operation rule for determining the position of the node in the grid based on the grid layout algorithm. The response module is also used to dynamically adjust the logical occupancy of the electronic control unit in the grid and the vertical offset position of the bus path in response to the numerical change of the real-time topology weight, so as to update the layout information. The logical occupancy is the occupancy information of the electronic control unit in the grid determined based on the channel resources occupied by the bus path in the grid. The generation module is also used to traverse the updated layout information, identify the overlapping of grid column regions caused by the logical occupancy adjustment, and reallocate the display layer parameters according to the node density of the overlapping region. The display layer parameters are layer differentiation parameters used to control the node drawing order and the front and rear occlusion relationship of the bus, so as to generate a topology structure that adapts to the running state data.

[0019] As an optional implementation, in the second aspect of the present invention, the acquisition module is further configured to acquire multiple sets of historical topology data of the target vehicle within a preset historical time period, and extract the connection status changes of the electronic control unit and the signal transmission records of the bus in each set of historical topology data; The device also includes: The analysis module is used to analyze the connection status changes and the signal transmission records, identify the activity frequency and correlation tightness of the electronic control unit and the bus in the historical operating cycle, and generate a topology correlation map characterizing the historical connection stability. The verification module is used to verify the node type division and connection relationship division in the structured classification process based on the topology association graph. When it is detected that there are implicit connection paths in the topology association graph that are not reflected in the bus topology data, the implicit connection paths are added to the bus topology data. The added bus topology data is a comprehensive topology dataset that integrates physical connection relationships and historical implicit connection relationships. The generation module is further configured to update the node inclusion relationship in the gridded layout algorithm based on the supplementary results of the implicit connection path, so as to reconstruct the bus grouping hierarchy and the vertical offset position attribute of the bus path based on the comprehensive topology dataset, and generate a topology structure containing historical running characteristics.

[0020] A third aspect of the present invention discloses another vehicle diagnostic topology map generation apparatus, the apparatus comprising: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the vehicle diagnostic topology map generation method disclosed in the first aspect of the present invention.

[0021] The fourth aspect of the present invention discloses a computer storage medium storing computer instructions, which, when invoked, are used to execute the vehicle diagnostic topology generation method disclosed in the first aspect of the present invention.

[0022] Compared with the prior art, the embodiments of the present invention have the following beneficial effects: In this embodiment of the invention, the identification information of the target vehicle is obtained, and the electronic control unit (ECU) and bus topology data of the target vehicle are retrieved from a pre-stored database based on the identification information. The bus topology data is then processed through structured classification, including node type classification of the ECU and connection relationship classification of the target vehicle's buses. The ECU is divided into entry nodes, hub nodes, and functional nodes, and the buses are divided into backbone buses, ring buses, and internal buses. Based on the classified bus topology data, the ECU and buses are automatically arranged using a grid layout algorithm to generate layout information containing node positions and bus paths. A visualized vehicle diagnostic topology diagram is generated based on the layout information. In response to user interaction, selection, view adjustment, or diagnostic triggering operations are performed on nodes or buses in the vehicle diagnostic topology diagram. Therefore, implementing this invention can improve the semantic organization and logic of the topology data by performing structured classification of the bus topology data, dividing the ECU into entry nodes, hub nodes, and functional nodes, and dividing the buses into backbone buses, ring buses, and internal buses. This provides a clear role-driven basis for subsequent layout, and facilitates the generation of a well-structured and hierarchical topology skeleton. The system can automatically arrange topology diagrams using a grid-based layout algorithm based on classification results, improving the automation and standardization of topology diagram generation. This reduces the arbitrariness and layout inconsistencies caused by manually preset coordinates, ensuring consistent readability and professionalism in the generated layout information. This, in turn, helps maintenance personnel quickly grasp the vehicle's network architecture and improves diagnostic preparation efficiency. Furthermore, the system can render and generate visual topology diagrams based on layout information, enhancing the intuitive presentation of diagnostic information and reducing the cognitive load of extracting effective information from complex data. This transforms abstract electrical connections into easily identifiable graphical language, reducing misreading and missed diagnoses and improving diagnostic accuracy. Finally, the system responds to rich user interactive operations, improving the operational flexibility and human-computer interaction efficiency of the topology diagram. This transforms the diagnostic process from passively viewing diagrams to actively exploring them, thereby shortening fault location time and achieving an efficient and convenient vehicle diagnostic experience. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a schematic flowchart of a vehicle diagnostic topology map generation method disclosed in an embodiment of the present invention; Figure 2This is a schematic flowchart of another vehicle diagnostic topology map generation method disclosed in an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of a vehicle diagnostic topology map generation device disclosed in an embodiment of the present invention; Figure 4 This is a schematic diagram of another vehicle diagnostic topology map generation device disclosed in an embodiment of the present invention; Figure 5 This is a schematic diagram of the structure of another vehicle diagnostic topology map generation device disclosed in an embodiment of the present invention. Detailed Implementation

[0025] To enable those skilled in the art to better understand the present invention, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0026] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, apparatus, product, or end that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or ends.

[0027] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0028] This invention discloses a method and apparatus for generating vehicle diagnostic topology diagrams. It can perform structured classification processing on bus topology data, dividing electronic control units into entry nodes, hub nodes, and functional nodes, and buses into backbone buses, ring buses, and internal buses. This improves the semantic organization and logical consistency of the topology data, providing clear role-driven basis for subsequent layout, and thus facilitating the generation of a well-structured and hierarchical topology skeleton. It can automatically arrange the topology diagram using a grid-based layout algorithm based on the classification results, improving the automation and standardization of topology diagram generation, reducing the arbitrariness and layout differences caused by manually preset coordinates, and ensuring consistent readability and professionalism of the generated layout information. This helps maintenance personnel quickly grasp the vehicle network architecture and improves diagnostic preparation efficiency. It can render and generate a visual topology diagram based on the layout information, improving the intuitive presentation of diagnostic information, reducing the cognitive load of extracting effective information from complex data, and transforming abstract electrical connections into easily identifiable graphical language, thereby reducing misreading and missed diagnosis and improving diagnostic accuracy. By responding to a wide range of user interactive operations, the system improves the operational flexibility and human-computer interaction efficiency of the topology diagram, transforming the diagnostic process from passive diagram viewing to active exploration. This, in turn, helps shorten fault location time and achieves an efficient and convenient vehicle diagnostic experience. These will be explained in detail below.

[0029] Example 1 Please see Figure 1 , Figure 1 This is a schematic flowchart of a vehicle diagnostic topology map generation method disclosed in an embodiment of the present invention. Figure 1 The described vehicle diagnostic topology generation method can be applied to vehicle diagnostic equipment, and also to intelligent devices associated with the vehicle diagnostic equipment. These intelligent devices include, but are not limited to, one or more of the following: switching equipment, cloud equipment, edge computing equipment, relay equipment, base station equipment, urban management equipment, and intelligent connected equipment. This invention does not limit the scope of these applications. Figure 1 As shown, the vehicle diagnostic topology map generation method may include the following operations: 101. Obtain the identification information of the target vehicle, and retrieve the electronic control unit and bus topology data of the target vehicle from the pre-stored database based on the identification information; In this embodiment of the invention, as an optional implementation, the above-mentioned retrieval of the target vehicle's electronic control unit and bus topology data from a pre-stored database based on identification information includes: Based on the identification information index of the pre-stored database, the initial topology dataset matching the target vehicle is extracted. The initial topology dataset contains the basic attributes of the electronic control unit of the target vehicle and the basic connection relationship of the bus of the target vehicle. The signal interaction logs in the initial topology dataset are parsed to identify the actual communication links between electronic control units. The connection topology between the bus and the electronic control unit is reconstructed based on the communication links, generating composite topology data that includes physical connection relationships and logical communication relationships. Traverse the composite topology data to detect whether there are isolated nodes with missing connections. If there are isolated nodes, infer the potential connection path between the isolated node and the bus based on the physical installation location of the isolated node and the preset protocol compatibility rules, and fill the potential connection path into the composite topology data. Extract the signal load rate and transmission delay parameters of each bus from the composite topology data, prioritize the buses according to the signal load rate and transmission delay parameters, and generate bus topology data with priority attributes.

[0030] In this embodiment of the invention, optionally, during the data retrieval process, the system first extracts an initial topology dataset based on a pre-stored database indexed by identification information. This dataset typically only contains the physical connection relationships configured at the factory. To reconstruct the actual communication state, the system further parses the signal interaction logs recorded in this dataset, which record the actual sending and receiving of messages between electronic control units. The system identifies these actual communication links and reconstructs the connection topology between the bus and the controller accordingly, thereby supplementing the original physical connection relationships with logical communication relationships and generating composite topology data. Next, the system traverses this composite data to detect whether there are isolated nodes with missing connection relationships, i.e., controllers that are not defined in the physical connection relationships but are active in the logical communication relationships. For such isolated nodes, the system infers which bus they may have established communication with based on the regional attributes of their physical installation location (such as engine compartment, cockpit) and preset protocol compatibility rules (such as the supported CANID range), and completes the potential connection path into the data. Finally, the system extracts the signal load rate and transmission delay parameters of each bus. These two parameters reflect the bus's busyness and response speed. Based on these two parameters, the system sorts the buses from high to low priority, so that important and high-speed buses are given priority in subsequent processing, thereby generating topology data with priority attributes.

[0031] Optionally, when parsing signal interaction logs, the system can employ a rule-based parser or a machine learning model. For CAN bus logs, the rule parser maps the original frame ID to the specific sender ECU based on the DBC database file. For more complex Ethernet (SOME / IP, DoIP) logs or encrypted communications, the system can invoke a pre-trained message classification model to identify and associate the source and destination of the communication by analyzing message length, interaction patterns, and time-series characteristics.

[0032] Optionally, when inferring potential connection paths, the system can employ a graph theory-based algorithm. Known buses are treated as nodes, and the physical location region and protocol compatibility of the ECU are quantified as edge weights. The system then searches for the known bus with the highest connection weight within k hops on the "physical location-protocol" graph for isolated nodes. Furthermore, the accuracy of inference can be improved by combining the context of the isolated node's appearance in the logs (e.g., always coordinating with a specific ECU on a certain bus to respond to an event) through association rule mining.

[0033] As can be seen, implementing this optional embodiment can identify actual communication links and reconstruct the connection topology by parsing the signal interaction logs in the initial topology dataset, improving the authenticity and accuracy of the acquired topology data. This ensures that the generated composite topology data not only includes physical connections but also reflects the actual logical communication relationships, thus facilitating the generation of a diagnostic view that more closely reflects the actual operating state of the vehicle. By detecting and intelligently inferring potential connection paths of isolated nodes, the integrity and robustness of the topology data are improved, reducing the risk of diagnostic blind spots due to data loss. This allows the system to construct a relatively complete topology structure even when database information is incomplete or the vehicle has been modified, thereby improving the applicability and reliability of the diagnostic system. By extracting bus signal load rate and transmission delay parameters and prioritizing them, the system improves the differentiation of the importance of different buses in subsequent processing. This allows the layout algorithm to prioritize the visual performance and layout space of critical buses, thus highlighting the core communication paths that affect diagnostic efficiency in complex topology diagrams and improving the targeting of diagnostic analysis.

[0034] 102. Perform structured classification processing on the bus topology data, including dividing the electronic control unit into node types and dividing the bus into connection relationships of the target vehicle; the electronic control unit is divided into entry nodes, hub nodes and functional nodes, and the bus is divided into backbone bus, ring bus and internal bus. 103. Based on the classified bus topology data, the electronic control unit and bus are automatically arranged using a grid layout algorithm to generate layout information including node positions and bus paths; In this embodiment of the invention, as another optional implementation, the above-mentioned electronic control unit and bus are automatically arranged using a grid layout algorithm based on the classified bus topology data to generate layout information including node positions and bus paths, including: Based on the classified bus topology data, the node inclusion relationship of each bus is extracted, and it is identified whether the node set of any first bus is completely covered by the node set of the second bus. Multiple buses with complete coverage relationship are merged into the same bus cluster, and the number of nodes connected to each bus in the bus cluster is counted. The bus with the most connected nodes is determined as the reference bus. Using the mapping coordinates of the entry node in the preset grid as the layout origin, the reference bus is preferentially mapped to the central grid column where the layout origin is located, and the remaining buses belonging to the same bus cluster are folded and mapped to the grid column area occupied by the reference bus. The number of columns is not counted repeatedly. The grid is a virtual coordinate system used for node arrangement in the grid layout algorithm, which is a cell array composed of a preset number of rows and columns. Based on the total number of physical ports connected to the hub node, the radial extension path of the backbone bus is calculated, so that the backbone bus is arranged layer by layer from the central grid column to the outer grid column. At the same time, the ring bus is configured in the ring adjacent grid column preset by the hub node, and the internal bus is configured in the internal adjacent grid column preset by the hub node. Traverse the gap space between adjacent grid columns, count the number of parallel buses passing through the same gap space, and when the number of parallel buses exceeds the capacity threshold of a single gap, allocate buses with opposite signal flow directions to different vertical offset positions in the gap space according to the signal flow direction attributes carried on each bus, thereby generating non-overlapping bus paths. Based on the channel resources occupied by the bus path in the grid, the logical occupancy of the electronic control unit in the grid is deduced, and the node position is calculated based on the logical occupancy to generate layout information containing the node position and bus path.

[0035] In this embodiment of the invention, optionally, when executing the grid layout algorithm, the system first extracts the node inclusion relationship of each bus, that is, determines whether all nodes on one bus are also nodes on another bus. If the node set of the first bus is completely covered by the node set of the second bus, then these two buses are merged into the same bus cluster. Within the cluster, the number of nodes connected to each bus is counted, and the bus with the most connected nodes is determined as the base bus. During layout, the origin is taken as the mapping coordinates of the entry node in the preset grid, and the base bus is preferentially mapped to the central column where the origin is located. Other buses belonging to the same cluster no longer occupy new columns separately, but are folded and mapped to the column area where the base bus is located, and the column number is not counted repeatedly, thereby compressing the horizontal space. The system calculates the extension path of the backbone bus based on the total number of physical ports connected to the hub node, so that it is arranged radially from the central column to the outside; at the same time, the ring bus is configured in a preset specific column close to the hub node, and the internal bus is configured in another preset adjacent column. When traversing the gap space between adjacent columns, the system counts the number of parallel buses passing through the same gap. If the number exceeds the maximum allowed number of parallel buses for that gap, buses with opposite flow directions are assigned to different vertical heights (vertical offset positions) within the gap based on the signal flow attributes (such as transmit or receive) carried on each bus, to avoid line overlap. Finally, based on the channel resources occupied by the bus paths, the logical occupancy of the electronic control unit in the grid is deduced and converted into specific pixel coordinates.

[0036] Optionally, to achieve "non-overlapping," the system calculates a unique longitudinal coordinate (i.e., vertical offset position) for each bus passing through the same gap. During calculation, the system first groups buses with the same signal flow direction. Next, within the vertical space of the gap, an initial track interval is assigned to each group of buses. Then, within each track interval, the system calculates a specific longitudinal coordinate for each bus based on bus priority or the number of node connections, ensuring that the difference between the coordinates of any two buses is greater than a preset minimum readable spacing. This layout strategy, based on flow direction grouping and further subdivision within groups, can efficiently and reliably avoid visual overlap of traces within a limited space.

[0037] Optionally, the threshold for accommodating a single gap is not a fixed value. The system dynamically calculates this threshold based on the current zoom level of the display interface, the preset rendering width of the bus path, and the minimum discernible spacing set by the user. For example, in a zoomed-in view, the threshold can be increased to accommodate more details; in a global overview, the threshold is decreased to ensure overall clarity.

[0038] Optionally, during the reverse logic occupancy process, the system calculates the "center of gravity" for all bus paths connected to each ECU node. This center is a weighted average of the coordinates of each connection point in the grid, with the weights determined by the priority or signal flow of the corresponding bus. Nodes are placed in the unoccupied grid cell closest to their "center of gravity," and in case of conflicts, nodes connected to higher-priority buses are prioritized.

[0039] Optionally, the system stores and manages the merge hierarchy attributes of bus clusters using a tree structure or nested dictionary. The base bus serves as the root node or primary key, and its contained subordinate buses serve as child nodes or nested values. The hierarchy depth records the number of nested containment relationships. During collapse mapping, the system assigns each subordinate bus an offset index relative to the base bus's position, which determines the vertical order of the subordinate bus during subsequent expansion or highlighting. This data structure makes lookup, expansion, and collapse operations on any bus within a cluster highly efficient.

[0040] Optionally, the preset number of rows and columns is determined dynamically. The system initializes the grid using an iterative algorithm based on the total number of bus clusters, the total number of functional nodes, and the estimated wiring complexity, combined with the display area size. For example, the initial number of columns can be set to (number of bus clusters * 2) + 1 to ensure a centered layout, while the number of rows dynamically expands based on the total number of nodes and the estimated hierarchy depth. During the layout process, if nodes cannot be properly arranged within the initial grid, the system can dynamically insert new rows or columns and trigger a rapid local rearrangement, rather than recalculating the global layout, thus ensuring the efficiency and adaptability of the layout algorithm.

[0041] Optionally, in addition to priority rules, the system provides several conflict resolution strategies. For example: 1) Secondary layout method: Temporarily ignore conflicting nodes, complete the layout of other nodes first, and then anchor the conflicting node as a "floating label" in the nearest available position and connect it with a leader. 2) Fine-tuning method: Slightly adjust the positions of already laid-out nodes around the conflicting node to free up space. 3) Aggregated display method: If multiple low-priority nodes conflict and belong to the same logical cluster, they can be aggregated and displayed as a single "parent node," which can be expanded upon clicking. Users or the system can choose the most suitable strategy based on the complexity of the current topology and screen space.

[0042] As can be seen, implementing this optional embodiment can improve the abstraction and simplicity of the topology by identifying the node inclusion relationship between buses and grouping buses with overlapping relationships into bus clusters. This reduces the redundancy and complexity of visual elements, allowing the layout to be efficiently organized in "clusters," which is beneficial for clearly displaying complex multi-level bus systems within a limited display space. The layout strategy of placing the reference bus in the central column with the entry node as the origin and folding and mapping buses in the same cluster improves the hierarchy and central focus of the topology diagram, making the diagnostic entry point and core network path immediately apparent. This helps maintenance personnel quickly locate the diagnostic starting point and understand the network backbone, improving navigation efficiency. By assigning different vertical offset positions to buses with opposite flow directions within the gap space according to signal flow attributes, the probability of visual intersection and overlap of bus paths is reduced, improving the readability and regularity of the topology diagram connections. This facilitates accurate tracking of signal transmission paths, avoids misreading, and thus improves the accuracy and efficiency of fault diagnosis. By inferring the logical occupancy of electronic control units from the channel resources occupied by the bus path, the logical consistency between node location and connection relationship is improved, thereby ensuring the close visual association between the node and the bus it is connected to. This helps users intuitively understand the specific location and connection method of each control unit in the bus network.

[0043] 104. Render and generate a visualized vehicle diagnostic topology diagram based on the layout information; In this optional embodiment, as an optional implementation, the above-described method of rendering and generating a visualized vehicle diagnostic topology map based on layout information includes: Read the node positions and bus paths contained in the layout information, and extract the merging level attribute of the bus cluster and the vertical offset position attribute of the bus path. Based on the merge hierarchy attribute, identify the remaining buses that are folded and mapped within the bus cluster, and assign different display hierarchy parameters to the base bus and the remaining buses respectively. Based on the vertical offset position attribute of the bus path, multiple buses with different vertical offset positions in the same gap space are identified, and buses with opposite signal flow directions are assigned different line type identifiers. Traverse all electronic control units in the layout information, identify functional nodes within the same bus cluster node set, and establish logical affiliation markers between the functional nodes and the reference bus of the bus cluster. In response to the drawing instructions on the display interface based on the layout information, the drawing order of functional nodes and the front and rear occlusion relationship of bus paths are determined during the rendering process according to the logical ownership mark and display hierarchy parameters, and a visual vehicle diagnostic topology diagram is generated.

[0044] In this embodiment of the invention, optionally, during the rendering stage, the system reads layout information and extracts the merging hierarchy attribute of the bus cluster (i.e., which buses are merged together) and the vertical offset position attribute of the bus path. Based on the merging hierarchy attribute, the system identifies the remaining buses that are folded and maps, and assigns different display hierarchy parameters to the base bus and the remaining buses. This parameter determines the order of drawing. Based on the vertical offset position attribute, the system identifies buses with different heights within the same gap and assigns different line type identifiers to buses with opposite signal flows, such as solid lines representing transmission and dashed lines representing reception. The system traverses all electronic control units, identifies functional nodes within the same bus cluster node set, and establishes logical attribution tags for these nodes and the base bus. When responding to drawing commands, the system determines whether to draw nodes or buses first, and how to handle occlusion relationships, based on the logical attribution tags and display hierarchy parameters, ultimately generating a topology map with a clear sense of hierarchy.

[0045] Optionally, the assigned display hierarchy parameters will be passed to the graphics rendering engine (such as those based on HTML5Canvas, WebGL, or native graphics interfaces) as the basis for the Z-order or rendering queue depth. Larger values ​​result in later drawing of the corresponding element, thus placing it visually closer to the top. Line style identifiers (such as solid lines and dashed lines) are mapped to specific brush styles in the rendering engine. During rendering, the engine sorts all graphic elements (node ​​icons, bus lines) according to the display hierarchy parameters and draws them sequentially from low to high, thereby achieving correct automatic occlusion.

[0046] Optionally, in addition to line type, the system can employ multiple visual encodings to enhance information density. For example, the color saturation of a bus path can map its signal load rate (the busier the path, the darker the color), the thickness of the path can map its transmission rate (e.g., CAN FD is thicker than CAN), or small indicators can be overlaid on node icons to reflect their real-time status (e.g., green for normal, red for fault, and flashing for configuration). For historical implicit connection paths, semi-transparent animated dashed lines with timing arrows can be rendered to distinguish them from current physical connections.

[0047] Optionally, the logical affiliation marker is represented within the system as a hash map, where the key is the unique ID of the functional node and the value is the ID of its respective bus cluster. When a functional node is marked as "active," the rendering engine not only changes its icon appearance (e.g., highlighting), but also synchronously queries this map to visually emphasize all nodes and bus paths within its bus cluster in a coordinated manner (e.g., using the same color scheme), achieving an intelligent linkage effect of "selecting one point and displaying the entire cluster."

[0048] As can be seen, implementing this optional embodiment improves the precise control over the order of graphic elements during rendering by assigning different display layer parameters to the base bus and the other buses that are folded and mapped. This allows for automatic handling of visual occlusion relationships within the bus cluster, resulting in clear visual layering and highlighting the core bus, thus resolving the problem of mutual interference between elements within complex clusters. Assigning different line type identifiers to buses with opposite signal flows enhances the visualization dimension of the information carried by the bus path, making signal transmission directions intuitively distinguishable. This helps maintenance personnel understand the initiation and response relationships of communication, aiding in fault tracing. Establishing logical attribution markers between functional nodes and the base bus of the bus cluster improves the rendering engine's ability to perceive the logical relationships between topology elements. This enables linked highlighting of related elements during interaction, enhancing the logic and intelligence of topology graph interaction and improving the user experience. By determining the drawing order and occlusion relationship based on logical attribution markers and display hierarchy parameters, the visual hierarchy and professional expressiveness of the final generated topology map are improved. This allows complex topological structures to be presented in a clear and organized manner, thereby reducing the user's cognitive load and improving information acquisition efficiency.

[0049] 105. In response to user interaction, perform selection, view adjustment, or diagnostic trigger operations on nodes or buses in the vehicle diagnostic topology diagram.

[0050] In this optional embodiment, as another optional implementation, the aforementioned user interaction operations include selection operations for entry nodes or hub nodes, view adjustment operations for bus clusters, and diagnostic trigger operations for topology structures; in response to the user interaction operations, performing selection, view adjustment, or diagnostic trigger operations on nodes or buses in the vehicle diagnostic topology diagram includes: In response to the selection operation for the entry node or hub node, the logical communication relationship in the composite topology data is read, the associated electronic control unit that has a communication link with the entry node or hub node is identified, and the associated electronic control unit is mapped as a candidate diagnostic object; Traverse the logical attribution markers in the layout information, detect the attribution correspondence between candidate diagnostic objects and target bus clusters, and synchronously mark all functional nodes in the target bus cluster as active. In response to a view adjustment operation on the target bus cluster, the merge level attribute of the target bus cluster is extracted, the folding mapping relationship of the target bus cluster is deconstructed based on the merge level attribute, and the remaining buses of the folded mapping are expanded and displayed in the grid column area. In response to a diagnostic trigger operation for the target bus cluster to be displayed, the display layer parameters allocated to the reference bus and other buses are extracted. According to the front and back occlusion relationship determined by the display layer parameters, the buses and electronic control units in the target bus cluster are traversed layer by layer to perform diagnostic data acquisition. In response to the feedback from the diagnostic data acquisition results, the data transmission path is determined based on the signal flow direction attribute, and the data transmission path is mapped to the vertical offset position attribute of the bus path to update the layout information.

[0051] In this embodiment of the invention, optional user interaction operations include selecting an entry or hub node, adjusting the view of a bus cluster, and triggering a diagnostic operation for the topology. When responding to an entry or hub node selection operation, the system reads the logical communication relationships in the composite topology data, identifies all associated controllers with direct communication links, and maps them as candidate diagnostic objects. It then traverses the logical affiliation markers, detects which target bus cluster these candidate objects belong to, and marks all functional nodes within that cluster as active. When responding to a view adjustment operation, the system extracts the merging hierarchy attribute of the target bus cluster, deconstructs the previous folding mapping relationship based on this attribute, and expands the remaining folded buses within the grid column area for the user to view details. When responding to a diagnostic trigger operation, the system extracts the display hierarchy parameters of the base bus and the remaining buses, and traverses the devices within the cluster layer by layer from top to bottom or from bottom to top according to the occlusion relationship determined by the parameters to collect data. After receiving data feedback, the system determines the data transmission path based on the signal flow attribute and updates the vertical offset position attribute in the layout information.

[0052] Optionally, signal flow attributes are obtained through quantitative analysis of signal interaction logs. The system parses the source and destination addresses of network packets (such as CAN frames) in the logs. For any two electronic control units (ECUs) communicating with each other, the system calculates the ratio of the number of packets sent from unit A to unit B to the number of reverse packets within the analysis time window. If this ratio exceeds a preset threshold (e.g., 10:1), a dominant signal flow from A to B is identified. For diagnostic communication, the path from the request message sent by the diagnostic instrument to the response from the ECU is directly defined as the determined signal flow. The signal flow attribute of each bus is determined by comprehensively considering the dominant flows of all communication pairs it carries.

[0053] Optionally, when a bus cluster expands, the system triggers a local re-layout to prevent layout chaos. Only the grid column region containing the expanded bus cluster and its adjacent limited columns (such as the first 1-2 columns before and after it) are recalculated, adjusting node positions and paths to ensure the expanded content itself is clear and does not affect the overall macroscopic structure of the topology. The re-layout process is accompanied by a smooth animation transition.

[0054] Optionally, after updating the layout information, the system can drive richer diagnostic visualizations. For example, when an ECU fault is diagnosed, the system can dynamically highlight potential impact paths along the opposite direction of the signal flow attribute to simulate fault propagation; or when the bus error rate increases sharply, the bus path can be rendered in an alarm color (such as red) and a pulsating effect can be added. These intelligent feedbacks upgrade the topology diagram from a static architecture diagram to a dynamic, interactive diagnostic narrative dashboard, greatly improving fault analysis efficiency.

[0055] As can be seen, implementing this optional embodiment can improve the intelligence and relevance of diagnostic target selection by automatically mapping associated candidate diagnostic objects according to logical communication relationships and simultaneously activating all functional nodes within their respective bus clusters when selecting an entry or hub node. This allows users to cover all relevant devices within a logical functional unit with a single click, greatly simplifying the operation steps during multi-device collaborative diagnosis and improving diagnostic efficiency. Furthermore, by deconstructing the folding mapping relationship based on the merging hierarchy attribute during view adjustment operations, the flexibility and controllability of users viewing topology details are improved, allowing users to smoothly switch between global overviews and local details, thus facilitating the free exploration of network information at different levels according to diagnostic needs. Finally, by traversing devices layer by layer according to the front-to-back occlusion relationship determined by the display hierarchy parameters when a diagnosis is triggered, the orderliness and systematic nature of diagnostic data collection are improved. This ensures that the diagnostic process follows the logical order from the bottom physical connection to the upper application layer, thereby helping to avoid conflicts or omissions in diagnostic requests and improving the completeness and reliability of the diagnosis. By updating the layout information by mapping the data transmission path of diagnostic feedback, the dynamic feedback capability of the topology diagram is improved, so that the results of diagnostic activities can be reflected on the topology diagram in real time and intuitively. This transforms the static network architecture diagram into a dynamic diagnostic process visualization tool, enhancing the visualization and tracking capability of the diagnostic process.

[0056] As can be seen, implementing the embodiments of the present invention can improve the semantic organization and logical consistency of the topology data by performing structured classification processing on the bus topology data, dividing the electronic control unit into entry nodes, hub nodes, and functional nodes, and dividing the bus into backbone buses, ring buses, and internal buses. This provides a clear role-driven basis for subsequent layout, which is conducive to generating a well-structured and hierarchical topology skeleton. The automatic arrangement using a grid layout algorithm based on the classification results improves the automation and standardization of topology map generation, reduces the arbitrariness and layout differences caused by manually preset coordinates, and ensures that the generated layout information has consistent readability and professionalism. This helps maintenance personnel quickly grasp the vehicle network architecture and improves diagnostic preparation efficiency. The ability to render and generate a visual topology map based on the layout information improves the intuitive presentation of diagnostic information, reduces the cognitive load of extracting effective information from complex data, and transforms abstract electrical connection relationships into easily identifiable graphical language, thereby reducing misreading and missed diagnosis and improving diagnostic accuracy. By responding to a wide range of user interaction operations, the topology diagram is made more flexible and the human-computer interaction efficiency is improved. This transforms the diagnostic process from passively viewing diagrams to actively exploring, thereby shortening the fault location time and achieving an efficient and convenient vehicle diagnostic experience.

[0057] Example 2 Please see Figure 2 , Figure 2 This is a schematic flowchart of another vehicle diagnostic topology map generation method disclosed in an embodiment of the present invention. Figure 2 The described vehicle diagnostic topology generation method can be applied to vehicle diagnostic equipment, and also to intelligent devices associated with the vehicle diagnostic equipment. These intelligent devices include, but are not limited to, one or more of the following: switching equipment, cloud equipment, edge computing equipment, relay equipment, base station equipment, urban management equipment, and intelligent connected equipment. This invention does not limit the scope of these applications. Figure 2 As shown, the vehicle diagnostic topology map generation method may include the following operations: 201. Obtain the identification information of the target vehicle, and retrieve the electronic control unit and bus topology data of the target vehicle from the pre-stored database based on the identification information; 202. Perform structured classification processing on the bus topology data, including dividing the electronic control unit into node types and dividing the bus connection relationships of the target vehicle; the electronic control unit is divided into entry nodes, hub nodes and functional nodes, and the bus is divided into backbone bus, ring bus and internal bus. 203. Based on the classified bus topology data, the electronic control unit and bus are automatically arranged using a grid layout algorithm to generate layout information including node positions and bus paths; 204. Render and generate a visual vehicle diagnostic topology diagram based on the layout information; 205. In response to user interaction operations, perform selection, view adjustment, or diagnostic trigger operations on nodes or buses in the vehicle diagnostic topology diagram; In this embodiment of the invention, optionally, the method first obtains the identification information of the target vehicle, which may be a vehicle identification code or a combination of vehicle model and year, and is used to index the basic attributes and bus connection relationships of the corresponding electronic control unit in a pre-stored database. The obtained bus topology data includes physical pin definitions and logical communication matrices. Subsequently, the data is processed by structured classification, dividing the electronic control unit into entry nodes, hub nodes, and functional nodes according to its functional role. The entry node represents a diagnostic access point, the hub node represents a gateway or routing device, and the functional node represents a controller that performs a specific function. At the same time, the bus is divided into backbone bus, ring bus, and internal bus according to the connection form. Based on the classification results, a grid layout algorithm is used for automatic arrangement. This algorithm presets a virtual coordinate system composed of rows and columns, and generates layout data containing specific location information by calculating the coordinates of the nodes in the grid and the routing path of the bus. Based on the layout information, node icons and connection lines are drawn on the display interface to form a visual topology map. When a user clicks, drags, or selects a box through an input device, the system performs operations such as selecting, zooming, panning, or starting a diagnostic process on the corresponding element in the topology map.

[0058] For further explanations regarding steps 201-205, please refer to the supplementary explanations for steps 101-105 in Embodiment 1. These will not be repeated in this embodiment of the invention.

[0059] 206. Monitor the operating status data of the target vehicle, and extract the fluctuation parameters that characterize the load changes of the electronic control unit and the throughput parameters that characterize the quality of bus communication from the operating status data. 207. Based on fluctuation parameters and throughput parameters, calculate the real-time topology weights of the electronic control unit and bus, and map the real-time topology weights to the row and column coordinate calculation logic of the grid layout algorithm. The row and column coordinate calculation logic is the operation rule for determining the position of the node in the grid based on the grid layout algorithm. 208. In response to the real-time changes in topology weights, dynamically adjust the logical occupancy of the electronic control unit in the grid and the vertical offset position of the bus path to update the layout information. The logical occupancy is the occupancy information of the electronic control unit in the grid determined based on the channel resources occupied by the bus path in the grid. 209. Traverse the updated layout information, identify the overlapping of grid column areas caused by logical occupancy adjustments, and reallocate the display hierarchy parameters according to the node density of the overlapping areas. The display hierarchy parameters are used to control the hierarchy distinction parameters for the drawing order of nodes and the front and rear occlusion relationship of the bus, so as to generate a topology structure that adapts to the running status data.

[0060] In this embodiment of the invention, optionally, the method also monitors the real-time operating status data of the vehicle, extracting fluctuation parameters characterizing controller load fluctuations and throughput parameters characterizing bus communication quality. Based on these two parameters, a real-time topology weight is obtained through weighted calculation, which reflects the current activity level of nodes and the bus. This weight is mapped to the row and column coordinate calculation of the grid layout algorithm, so that nodes with higher weights occupy more important positions. When the weight values ​​change, the logical occupancy of nodes in the grid and the vertical offset position of the bus are dynamically adjusted to update the layout. The updated layout is traversed, and if it is found that the adjustment has caused regional overlap, the display hierarchy parameters are redistributed according to the node density of the overlapping area to generate a dynamic topology structure that adapts to the real-time operating status.

[0061] Optionally, fluctuation parameters can be specifically represented as the variance of the electronic control unit's CPU utilization or the jitter value of the task scheduling cycle. Throughput parameters can be specifically represented as the effective data frame rate successfully transmitted by the bus per unit time, or the real-time change rate of bandwidth utilization. The calculation of real-time topology weights involves normalizing these specific parameter values ​​and then combining them using a preset weighting formula.

[0062] Optionally, the real-time topology weight can be calculated using the following comprehensive evaluation function: Weight = α * Load normalized value + β * (1 / Latency normalized value) + γ * Error rate weighting factor. Where α, β, and γ are adjustable coefficients, and the error rate weighting factor increases significantly when persistent communication errors are detected. The system allows users or allows different coefficient schemes to be preset according to the diagnostic scenario (such as "performance priority" or "stability priority" modes).

[0063] Optionally, the dynamic adjustment process is a constrained optimization process. The system converts real-time topology weights into "attraction" or "repulsion" parameters in the layout algorithm. Nodes with higher weights exert a stronger attraction on the layout origin (or hub node), causing it to tend to move towards the center; simultaneously, abnormal nodes with surging weights exert temporary repulsion on their surrounding nodes to prevent local congestion. Through several iterations, the system quickly calculates new node positions that satisfy these new attraction / repulsion relationships and do not violate basic layout rules (such as non-overlap), achieving smooth and adaptive evolution of the layout.

[0064] As can be seen, implementing the embodiments of the present invention can improve the dynamic correlation between the topology diagram and the current operating status of the vehicle by monitoring operational status data and calculating real-time topology weights. This transforms the topology diagram from a static architectural display into a dynamic system status indicator, which helps maintenance personnel intuitively identify "hot spots" or buses with excessive load or abnormal latency, enabling proactive early warning. By mapping real-time topology weights to the layout algorithm and dynamically adjusting node occupancy and bus paths, the adaptability and expressiveness of the topology diagram layout are improved. This allows important, active, or abnormal network parts to be automatically highlighted visually, thereby achieving proactive visual guidance for potential fault areas and improving the efficiency of fault prediction and location. By identifying and handling layout overlaps after dynamic adjustment and reallocating display hierarchy parameters, the stability and readability of the topology diagram during continuous changes are improved. This ensures that the adaptive layout does not introduce new visual clutter, thus maintaining the consistent clarity of the topology diagram in reflecting real-time status and guaranteeing a consistent user experience.

[0065] In an optional implementation of this invention, the method further includes: Acquire multiple sets of historical topology data of the target vehicle within a preset historical time period, and extract the connection status changes of the electronic control unit and the signal transmission records of the bus in each set of historical topology data. Analyze connection status changes and signal transmission records to identify the activity frequency and correlation tightness of electronic control units and buses in historical operating cycles, and generate a topology correlation map characterizing the stability of historical connections; Based on the topology graph, the node type classification and connection relationship classification in the structured classification process are verified. When a hidden connection path not reflected in the bus topology data is detected in the topology graph, the hidden connection path is added to the bus topology data. The added bus topology data is a comprehensive topology dataset that integrates physical connection relationships and historical hidden connection relationships. Based on the supplementary results of implicit connection paths, the node containment relationships in the gridded layout algorithm are updated to reconstruct the bus grouping hierarchy and the vertical offset position attributes of the bus paths based on the comprehensive topology dataset, generating a topology structure that includes historical operation characteristics.

[0066] Optionally, in this embodiment of the invention, the method can also utilize historical data to optimize topology generation. Multiple sets of historical topology data for the target vehicle within a historical time period are acquired, and the connection status changes of the controllers and the signal transmission records of the bus are extracted from each set of data. These changes are analyzed to identify the activity frequency and correlation tightness of the controllers and buses within the historical period, generating a topology correlation map. Based on this map, the above-mentioned node types and connection relationships are verified. If the map shows high-frequency historical communication (implicit connection paths) between certain controllers, and these paths are not reflected in the current physical topology data, these implicit paths are added to the bus topology data to form a comprehensive topology dataset. Based on the addition results, the node inclusion relationships in the mesh layout algorithm are updated, the merging hierarchy of bus clusters and the vertical offset attributes of bus paths are reconstructed, thereby generating a more comprehensive topology structure that includes historical operating characteristics.

[0067] Optionally, when constructing the topological association graph, the system can divide the historical operating cycle into multiple time slices, generating an instantaneous topological snapshot within each time slice. By comparing consecutive snapshots, the co-occurrence probability and conditional probability of the connection relationship between any two ECUs are calculated to form a weighted directed graph. Subsequently, a community detection algorithm (such as the Louvain algorithm) is used to identify historically stable functional modules (communities). This community information can be used to verify and optimize the initial classification results.

[0068] Optionally, the system calculates a confidence score for each inferred historical implicit connection path, based on its historical co-occurrence probability, duration, and association strength. In subsequent layout and rendering, the system can selectively display only high-quality implicit paths with confidence scores above a threshold, based on user settings or default rules. During rendering, the path's visual salience (such as the transparency of dashed lines and flicker frequency) can be correlated with the confidence score, thus visually conveying the historical reliability of the connection to the user.

[0069] Optionally, the confidence score (CS) can be calculated using a formulaic model, for example: CS = w1 * P(co-occurrence) + w2 * Avg(Duration) + w3 * MI(A, B). Here, P(co-occurrence) is the historical co-occurrence probability, Avg(Duration) is the average duration of the connection, MI(A, B) is the mutual information value between the communicating parties, and w1, w2, and w3 are weighting coefficients. This quantitative model provides an objective and interpretable basis for the screening and visualization of hidden paths.

[0070] As can be seen, implementing this optional embodiment can generate a topology association map by analyzing historical topology data, improving the system's ability to understand the long-term operating patterns and implicit laws of the vehicle network. This allows for the discovery of frequently occurring logical connections beyond the original physical design, providing deeper, data-driven insights for diagnosis and solving the problem of reproducing and locating intermittent faults. By using the historical association map to verify and supplement the current topology data, the comprehensiveness and accuracy of the generated integrated topology dataset are improved. This allows the topology map to integrate physical connections and historical logical connections, presenting a holographic view of "physical + historical," which is beneficial for diagnosing complex faults that are intermittent and rely on historical context for triggering. By reconstructing bus groups and layouts based on the supplemented integrated data, the topology map's ability to represent the vehicle's real and complex communication patterns is improved. This allows maintenance personnel to see potential communication relationships inferred from historical data, providing valuable visual clues for analyzing fault phenomena that appear unrelated but actually have hidden couplings between certain ECUs.

[0071] Example 3 Please see Figure 3 , Figure 3 This is a schematic diagram of a vehicle diagnostic topology map generation device disclosed in an embodiment of the present invention. The vehicle diagnostic topology map generation device can be applied to vehicle diagnostic equipment, and also to intelligent devices associated with the vehicle diagnostic equipment. These intelligent devices include, but are not limited to, one or more of the following: switching equipment, cloud equipment, edge computing equipment, relay equipment, base station equipment, urban management equipment, and intelligent connected devices. The present invention does not limit the application of these devices. Figure 3 As shown, the vehicle diagnostic topology map generation device may include: The acquisition module 301 is used to acquire the identification information of the target vehicle and retrieve the electronic control unit and bus topology data of the target vehicle from the pre-stored database based on the identification information. The classification module 302 is used to perform structured classification processing on the bus topology data, including dividing the electronic control unit into node types and dividing the bus into connection relationships of the target vehicle; the electronic control unit is divided into entry nodes, hub nodes and functional nodes, and the bus is divided into backbone bus, ring bus and internal bus. The layout module 303 is used to automatically arrange the electronic control unit and bus based on the classified bus topology data using a grid layout algorithm, and generate layout information including node positions and bus paths. The generation module 304 is used to render and generate a visual vehicle diagnostic topology diagram based on the layout information. The response module 305 is used to perform selection, view adjustment, or diagnostic triggering operations on nodes or buses in the vehicle diagnostic topology diagram in response to user interaction operations.

[0072] As can be seen, implementing the embodiments of the present invention can improve the semantic organization and logical consistency of the topology data by performing structured classification processing on the bus topology data, dividing the electronic control unit into entry nodes, hub nodes, and functional nodes, and dividing the bus into backbone buses, ring buses, and internal buses. This provides a clear role-driven basis for subsequent layout, which is conducive to generating a well-structured and hierarchical topology skeleton. The automatic arrangement using a grid layout algorithm based on the classification results improves the automation and standardization of topology map generation, reduces the arbitrariness and layout differences caused by manually preset coordinates, and ensures that the generated layout information has consistent readability and professionalism. This helps maintenance personnel quickly grasp the vehicle network architecture and improves diagnostic preparation efficiency. The ability to render and generate a visual topology map based on the layout information improves the intuitive presentation of diagnostic information, reduces the cognitive load of extracting effective information from complex data, and transforms abstract electrical connection relationships into easily identifiable graphical language, thereby reducing misreading and missed diagnosis and improving diagnostic accuracy. By responding to a wide range of user interaction operations, the topology diagram is made more flexible and efficient in human-computer interaction. This transforms the diagnostic process from passively viewing diagrams to actively exploring them, thereby shortening fault location time and achieving an efficient and convenient vehicle diagnostic experience.

[0073] In this embodiment of the invention, as an optional implementation, the specific method by which the acquisition module 301 retrieves the electronic control unit and bus topology data of the target vehicle from the pre-stored database based on the identification information includes: Based on the identification information index of the pre-stored database, the initial topology dataset matching the target vehicle is extracted. The initial topology dataset contains the basic attributes of the electronic control unit of the target vehicle and the basic connection relationship of the bus of the target vehicle. The signal interaction logs in the initial topology dataset are parsed to identify the actual communication links between electronic control units. Based on the communication links, the connection topology between the bus and the electronic control units is reconstructed to generate composite topology data containing physical connection relationships and logical communication relationships. Traverse the composite topology data to detect whether there are isolated nodes with missing connections. If there are isolated nodes, infer the potential connection path between the isolated node and the bus based on the physical installation location of the isolated node and the preset protocol compatibility rules, and fill the potential connection path into the composite topology data. Extract the signal load rate and transmission delay parameters of each bus from the composite topology data, prioritize the buses according to the signal load rate and transmission delay parameters, and generate bus topology data with priority attributes.

[0074] As can be seen, implementing this optional embodiment can identify actual communication links and reconstruct the connection topology by parsing the signal interaction logs in the initial topology dataset, improving the authenticity and accuracy of the acquired topology data. This ensures that the generated composite topology data not only includes physical connections but also reflects the actual logical communication relationships, thus facilitating the generation of a diagnostic view that more closely reflects the actual operating state of the vehicle. By detecting and intelligently inferring potential connection paths of isolated nodes, the integrity and robustness of the topology data are improved, reducing the risk of diagnostic blind spots due to data loss. This allows the system to construct a relatively complete topology structure even when database information is incomplete or the vehicle has been modified, thereby improving the applicability and reliability of the diagnostic system. By extracting bus signal load rate and transmission delay parameters and prioritizing them, the system improves the differentiation of the importance of different buses in subsequent processing. This allows the layout algorithm to prioritize the visual performance and layout space of critical buses, thus highlighting the core communication paths that affect diagnostic efficiency in complex topology diagrams and improving the targeting of diagnostic analysis.

[0075] In this embodiment of the invention, as another optional implementation, the layout module 303, based on the classified bus topology data, automatically arranges the electronic control unit and bus using a grid layout algorithm to generate layout information containing node positions and bus paths. The specific methods for this include: Based on the classified bus topology data, the node inclusion relationship of each bus is extracted, and it is identified whether the node set of any first bus is completely covered by the node set of the second bus. Multiple buses with complete coverage relationship are merged into the same bus cluster, and the number of nodes connected to each bus in the bus cluster is counted. The bus with the most connected nodes is determined as the reference bus. Using the mapping coordinates of the entry node in the preset grid as the layout origin, the reference bus is preferentially mapped to the central grid column where the layout origin is located, and the remaining buses belonging to the same bus cluster are folded and mapped to the grid column area occupied by the reference bus. The number of columns is not counted repeatedly. The grid is a virtual coordinate system used for node arrangement in the grid layout algorithm, which is a cell array composed of a preset number of rows and columns. Based on the total number of physical ports connected to the hub node, the radial extension path of the backbone bus is calculated, so that the backbone bus is arranged layer by layer from the central grid column to the outer grid column. At the same time, the ring bus is configured in the ring adjacent grid column preset by the hub node, and the internal bus is configured in the internal adjacent grid column preset by the hub node. Traverse the gap space between adjacent grid columns, count the number of parallel buses passing through the same gap space, and when the number of parallel buses exceeds the capacity threshold of a single gap, allocate buses with opposite signal flow directions to different vertical offset positions in the gap space according to the signal flow direction attributes carried on each bus, thereby generating non-overlapping bus paths. Based on the channel resources occupied by the bus path in the grid, the logical occupancy of the electronic control unit in the grid is deduced, and the node position is calculated based on the logical occupancy to generate layout information containing the node position and bus path.

[0076] As can be seen, implementing this optional embodiment can improve the abstraction and simplicity of the topology by identifying the node inclusion relationship between buses and grouping buses with overlapping relationships into bus clusters. This reduces the redundancy and complexity of visual elements, allowing the layout to be efficiently organized in "clusters," which is beneficial for clearly displaying complex multi-level bus systems within a limited display space. The layout strategy of placing the reference bus in the central column with the entry node as the origin and folding and mapping buses in the same cluster improves the hierarchy and central focus of the topology diagram, making the diagnostic entry point and core network path immediately apparent. This helps maintenance personnel quickly locate the diagnostic starting point and understand the network backbone, improving navigation efficiency. By assigning different vertical offset positions to buses with opposite flow directions within the gap space according to signal flow attributes, the probability of visual intersection and overlap of bus paths is reduced, improving the readability and regularity of the topology diagram connections. This facilitates accurate tracking of signal transmission paths, avoids misreading, and thus improves the accuracy and efficiency of fault diagnosis. By inferring the logical occupancy of electronic control units from the channel resources occupied by the bus path, the logical consistency between node location and connection relationship is improved, thereby ensuring the close visual association between the node and the bus it is connected to. This helps users intuitively understand the specific location and connection method of each control unit in the bus network.

[0077] In this optional embodiment, as an optional implementation method, the specific way in which the generation module 304 renders and generates a visualized vehicle diagnostic topology map based on the layout information includes: Read the node positions and bus paths contained in the layout information, and extract the merging level attribute of the bus cluster and the vertical offset position attribute of the bus path. Based on the merge hierarchy attribute, identify the remaining buses that are folded and mapped within the bus cluster, and assign different display hierarchy parameters to the base bus and the remaining buses respectively. Based on the vertical offset position attribute of the bus path, multiple buses with different vertical offset positions in the same gap space are identified, and buses with opposite signal flow directions are assigned different line type identifiers. Traverse all electronic control units in the layout information, identify functional nodes within the same bus cluster node set, and establish logical affiliation markers between the functional nodes and the reference bus of the bus cluster. In response to the drawing instructions on the display interface based on the layout information, the drawing order of functional nodes and the front and rear occlusion relationship of the bus path are determined during the rendering process according to the logical ownership mark and display level parameters, and a visual vehicle diagnostic topology diagram is generated.

[0078] As can be seen, implementing this optional embodiment improves the precise control over the order of graphic elements during rendering by assigning different display layer parameters to the base bus and the other buses that are folded and mapped. This allows for automatic handling of visual occlusion relationships within the bus cluster, resulting in clear visual layering and highlighting the core bus, thus resolving the problem of mutual interference between elements within complex clusters. Assigning different line type identifiers to buses with opposite signal flows enhances the visualization dimension of the information carried by the bus path, making signal transmission directions intuitively distinguishable. This helps maintenance personnel understand the initiation and response relationships of communication, aiding in fault tracing. Establishing logical attribution markers between functional nodes and the base bus of the bus cluster improves the rendering engine's ability to perceive the logical relationships between topology elements. This enables linked highlighting of related elements during interaction, enhancing the logic and intelligence of topology graph interaction and improving the user experience. By determining the drawing order and occlusion relationship based on logical attribution markers and display hierarchy parameters, the visual hierarchy and professional expressiveness of the final generated topology map are improved. This allows complex topological structures to be presented in a clear and organized manner, thereby reducing the user's cognitive load and improving information acquisition efficiency.

[0079] In this optional embodiment, as another optional implementation, the aforementioned user interaction operations include selection operations for entry nodes or hub nodes, view adjustment operations for bus clusters, and diagnostic trigger operations for topology structures; the specific ways in which the response module 305 performs selection, view adjustment, or diagnostic trigger operations on nodes or buses in the vehicle diagnostic topology diagram in response to the user interaction operations include: In response to the selection operation for the entry node or hub node, the logical communication relationship in the composite topology data is read, the associated electronic control unit that has a communication link with the entry node or hub node is identified, and the associated electronic control unit is mapped as a candidate diagnostic object; Traverse the logical attribution markers in the layout information, detect the attribution correspondence between candidate diagnostic objects and target bus clusters, and synchronously mark all functional nodes in the target bus cluster as active. In response to a view adjustment operation on the target bus cluster, the merge level attribute of the target bus cluster is extracted, the folding mapping relationship of the target bus cluster is deconstructed based on the merge level attribute, and the remaining buses of the folded mapping are expanded and displayed in the grid column area. In response to a diagnostic trigger operation for the target bus cluster to be displayed, the display layer parameters allocated to the reference bus and other buses are extracted. According to the front and back occlusion relationship determined by the display layer parameters, the buses and electronic control units in the target bus cluster are traversed layer by layer to perform diagnostic data acquisition. In response to the feedback from the diagnostic data acquisition results, the data transmission path is determined based on the signal flow direction attribute, and the data transmission path is mapped to the vertical offset position attribute of the bus path to update the layout information.

[0080] As can be seen, implementing this optional embodiment can improve the intelligence and relevance of diagnostic target selection by automatically mapping associated candidate diagnostic objects according to logical communication relationships and simultaneously activating all functional nodes within their respective bus clusters when selecting an entry or hub node. This allows users to cover all relevant devices within a logical functional unit with a single click, greatly simplifying the operation steps during multi-device collaborative diagnosis and improving diagnostic efficiency. Furthermore, by deconstructing the folding mapping relationship based on the merging hierarchy attribute during view adjustment operations, the flexibility and controllability of users viewing topology details are improved, allowing users to smoothly switch between global overviews and local details, thus facilitating the free exploration of network information at different levels according to diagnostic needs. Finally, by traversing devices layer by layer according to the front-to-back occlusion relationship determined by the display hierarchy parameters when a diagnosis is triggered, the orderliness and systematic nature of diagnostic data collection are improved. This ensures that the diagnostic process follows the logical order from the bottom physical connection to the upper application layer, thereby helping to avoid conflicts or omissions in diagnostic requests and improving the completeness and reliability of the diagnosis. By updating the layout information by mapping the data transmission path of diagnostic feedback, the dynamic feedback capability of the topology diagram is improved, so that the results of diagnostic activities can be reflected on the topology diagram in real time and intuitively. This transforms the static network architecture diagram into a dynamic diagnostic process visualization tool, enhancing the visualization and tracking capability of the diagnostic process.

[0081] In an optional embodiment, such as Figure 4 As shown, the device also includes: The monitoring module 306 is used to monitor the operating status data of the target vehicle, extract fluctuation parameters that characterize the load changes of the electronic control unit, and throughput parameters that characterize the quality of bus communication from the operating status data. The calculation module 307 is used to calculate the real-time topology weight of the electronic control unit and bus based on the fluctuation parameters and throughput parameters, and to map the real-time topology weight to the row and column coordinate calculation logic of the grid layout algorithm. The row and column coordinate calculation logic is the operation rule for determining the position of the node in the grid based on the grid layout algorithm. The response module 305 is also used to respond to the real-time topology weight numerical change, dynamically adjust the logical occupancy of the electronic control unit in the grid and the vertical offset position of the bus path, so as to update the layout information. The logical occupancy is the occupancy information of the electronic control unit in the grid determined based on the channel resources occupied by the bus path in the grid. The generation module 304 is also used to traverse the updated layout information, identify the overlap of grid column areas caused by logical occupancy adjustments, and reallocate the display layer parameters according to the node density of the overlapping areas. The display layer parameters are layer differentiation parameters used to control the node drawing order and the front and rear occlusion relationship of the bus, so as to generate a topology structure that adapts to the running status data.

[0082] As can be seen, implementing the embodiments of the present invention can improve the dynamic correlation between the topology diagram and the current operating status of the vehicle by monitoring operational status data and calculating real-time topology weights. This transforms the topology diagram from a static architectural display into a dynamic system status indicator, which helps maintenance personnel intuitively identify "hot spots" or buses with excessive load or abnormal latency, enabling proactive early warning. By mapping real-time topology weights to the layout algorithm and dynamically adjusting node occupancy and bus paths, the adaptability and expressiveness of the topology diagram layout are improved. This allows important, active, or abnormal network parts to be automatically highlighted visually, thereby achieving proactive visual guidance for potential fault areas and improving the efficiency of fault prediction and location. By identifying and handling layout overlaps after dynamic adjustment and reallocating display hierarchy parameters, the stability and readability of the topology diagram during continuous changes are improved. This ensures that the adaptive layout does not introduce new visual clutter, thus maintaining the consistent clarity of the topology diagram in reflecting real-time status and guaranteeing a consistent user experience.

[0083] In another optional embodiment, the acquisition module 301 described above is further used to acquire multiple sets of historical topology data of the target vehicle within a preset historical time period, and extract the connection status changes of the electronic control unit and the signal transmission records of the bus in each set of historical topology data. Optional, such as Figure 4 As shown, the device also includes: The analysis module 308 is used to analyze connection status changes and signal transmission records, identify the activity frequency and correlation tightness of electronic control units and buses in historical operating cycles, and generate a topology correlation map characterizing the stability of historical connections. The verification module 309 is used to verify the node type division and connection relationship division in the structured classification process based on the topology association graph. When it is detected that there are implicit connection paths in the topology association graph that are not reflected in the bus topology data, the implicit connection paths are added to the bus topology data. The added bus topology data is a comprehensive topology dataset that integrates physical connection relationships and historical implicit connection relationships. The generation module 304 is also used to update the node inclusion relationship in the gridded layout algorithm based on the supplementary results of the implicit connection path, so as to reconstruct the bus grouping hierarchy and the vertical offset position attribute of the bus path based on the comprehensive topology dataset, and generate a topology structure containing historical running characteristics.

[0084] As can be seen, implementing this optional embodiment can generate a topology association map by analyzing historical topology data, improving the system's ability to understand the long-term operating patterns and implicit laws of the vehicle network. This allows for the discovery of frequently occurring logical connections beyond the original physical design, providing deeper, data-driven insights for diagnosis and solving the problem of reproducing and locating intermittent faults. By using the historical association map to verify and supplement the current topology data, the comprehensiveness and accuracy of the generated integrated topology dataset are improved. This allows the topology map to integrate physical connections and historical logical connections, presenting a holographic view of "physical + historical," which is beneficial for diagnosing complex faults that are intermittent and rely on historical context for triggering. By reconstructing bus groups and layouts based on the supplemented integrated data, the topology map's ability to represent the vehicle's real and complex communication patterns is improved. This allows maintenance personnel to see potential communication relationships inferred from historical data, providing valuable visual clues for analyzing fault phenomena that appear unrelated but actually have hidden couplings between certain ECUs.

[0085] Example 4 Please see Figure 5 , Figure 5 This is a schematic diagram of another vehicle diagnostic topology map generation device disclosed in an embodiment of the present invention. This vehicle diagnostic topology map generation device can be applied to vehicle diagnostic equipment, and also to intelligent devices associated with the vehicle diagnostic equipment. These intelligent devices include, but are not limited to, one or more of the following: switching equipment, cloud equipment, edge computing equipment, relay equipment, base station equipment, urban management equipment, and intelligent connected equipment. The embodiments of the present invention do not impose any limitations. Figure 5 As shown, the vehicle diagnostic topology map generation device may include: Memory 401 that stores executable program code.

[0086] Processor 402 coupled to memory 401.

[0087] The processor 402 calls the executable program code stored in the memory 401 to execute the steps in the vehicle diagnostic topology map generation method described in Embodiment 1 or Embodiment 2 of the present invention.

[0088] Example 5 This invention discloses a computer storage medium storing computer instructions. When these computer instructions are invoked, they are used to execute the steps in the vehicle diagnostic topology map generation method described in Embodiment 1 or Embodiment 2 of this invention.

[0089] Example 6 This invention discloses a computer program product, which includes a non-transitory computer storage medium storing a computer program, and the computer program is operable to cause a computer to perform the steps in the vehicle diagnostic topology generation method described in Embodiment 1 or Embodiment 2.

[0090] The device embodiments described above are merely illustrative. The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; that is, they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0091] Through the detailed description of the above embodiments, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, including read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically-Erasable Programmable Read-Only Memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0092] Finally, it should be noted that the vehicle diagnostic topology map generation method and apparatus disclosed in the embodiments of the present invention are merely preferred embodiments of the present invention and are only used to illustrate the technical solutions of the present invention, not to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for generating a vehicle diagnostic topology map, characterized in that, The method includes: Obtain the identification information of the target vehicle, and retrieve the electronic control unit and bus topology data of the target vehicle from the pre-stored database based on the identification information; The bus topology data is processed by structured classification, including dividing the electronic control unit into node types and dividing the bus of the target vehicle into connection relationships; the electronic control unit is divided into entry nodes, hub nodes and functional nodes, and the bus is divided into backbone bus, ring bus and internal bus. Based on the classified bus topology data, the electronic control unit and the bus are automatically arranged using a grid layout algorithm to generate layout information that includes node positions and bus paths. A visual vehicle diagnostic topology diagram is generated based on the layout information; In response to user interaction, select, view adjust, or diagnostic trigger operations are performed on nodes or buses in the vehicle diagnostic topology diagram.

2. The vehicle diagnostic topology map generation method according to claim 1, characterized in that, The step of retrieving the electronic control unit and bus topology data of the target vehicle from the pre-stored database based on the identification information includes: Based on the identification information index pre-stored database, an initial topology dataset matching the target vehicle is extracted. The initial topology dataset contains the basic attributes of the electronic control unit of the target vehicle and the basic connection relationship of the bus of the target vehicle. The signal interaction logs in the initial topology dataset are parsed to identify the actual communication links between the electronic control units. Based on the communication links, the connection topology between the bus and the electronic control unit is reconstructed to generate composite topology data containing physical connection relationships and logical communication relationships. The composite topology data is traversed to detect whether there are isolated nodes with missing connection relationships. If there are isolated nodes, the potential connection path between the isolated node and the bus is inferred based on the physical installation location of the isolated node and the preset protocol compatibility rules, and the potential connection path is filled into the composite topology data. Extract the signal load rate and transmission delay parameters of each bus in the composite topology data, sort the buses by priority based on the signal load rate and transmission delay parameters, and generate bus topology data with priority attributes.

3. The vehicle diagnostic topology generation method according to claim 1, characterized in that, Based on the classified bus topology data, the electronic control unit and the bus are automatically arranged using a grid layout algorithm to generate layout information containing node positions and bus paths, including: Based on the classified bus topology data, the node inclusion relationship of each bus is extracted, and it is identified whether the node set of any first bus is completely covered by the node set of the second bus. Multiple buses with complete coverage relationship are merged into the same bus cluster, and the number of nodes connected to each bus in the bus cluster is counted. The bus with the most connected nodes is determined as the reference bus. Using the mapping coordinates of the entry node in the preset grid as the layout origin, the reference bus is preferentially mapped to the central grid column where the layout origin is located, and the remaining buses belonging to the same bus cluster are folded and mapped to the grid column area occupied by the reference bus. The number of columns is not counted repeatedly. The grid is a virtual coordinate system used for node arrangement in the grid layout algorithm, which is a cell array composed of a preset number of rows and columns. Based on the total number of physical ports connected to the outside of the hub node, the radial extension path of the backbone bus is calculated, so that the backbone bus is arranged layer by layer from the central grid column to the outer grid column. At the same time, the ring bus is configured in the ring adjacent grid column preset by the hub node, and the internal bus is configured in the internal adjacent grid column preset by the hub node. Traverse the gap space between adjacent grid columns, count the number of parallel buses passing through the same gap space, and when the number of parallel buses exceeds the capacity threshold of a single gap, allocate buses with opposite signal flow directions to different vertical offset positions within the gap space according to the signal flow direction attributes carried on each bus, thereby generating non-overlapping bus paths. Based on the channel resources occupied by the bus path in the grid, the logical occupancy of the electronic control unit in the grid is deduced, and the node position is calculated based on the logical occupancy to generate layout information containing the node position and the bus path.

4. The vehicle diagnostic topology map generation method according to claim 3, characterized in that, The step of rendering and generating a visualized vehicle diagnostic topology map based on the layout information includes: Read the node positions and bus paths contained in the layout information, and extract the merging level attribute of the bus cluster and the vertical offset position attribute of the bus path. Based on the merge hierarchy attribute, identify the remaining buses that are folded and mapped within the bus cluster, and assign different display hierarchy parameters to the base bus and the remaining buses respectively; Based on the vertical offset position attribute of the bus path, multiple buses with different vertical offset positions in the same gap space are identified, and buses with opposite signal flow directions are assigned different line type identifiers. Traverse all electronic control units in the layout information, identify functional nodes within the same bus cluster node set, and establish logical affiliation markers between the functional nodes and the reference bus of the bus cluster. In response to the drawing instructions on the display interface based on the layout information, the drawing order of the functional nodes and the front-to-back occlusion relationship of the bus path are determined during the rendering process according to the logical belonging mark and the display level parameters, and a visualized vehicle diagnostic topology diagram is generated.

5. The vehicle diagnostic topology generation method according to claim 4, characterized in that, The user interaction operations include selection operations for the entry node or the hub node, view adjustment operations for the bus cluster, and diagnostic trigger operations for the topology. The operation of selecting, adjusting the view, or triggering a diagnosis on a node or bus in the vehicle diagnostic topology diagram in response to a user interaction includes: In response to a selection operation for the entry node or the hub node, the logical communication relationship in the composite topology data is read, the associated electronic control unit that has a communication link with the entry node or the hub node is identified, and the associated electronic control unit is mapped as a candidate diagnostic object; Traverse the logical attribution markers in the layout information, detect the attribution correspondence between the candidate diagnostic object and the target bus cluster, and synchronously mark all functional nodes in the target bus cluster as active. In response to a view adjustment operation for the target bus cluster, the merge level attribute of the target bus cluster is extracted, the folding mapping relationship of the target bus cluster is deconstructed based on the merge level attribute, and the remaining buses of the folded mapping are expanded and displayed in the grid column area. In response to a diagnostic trigger operation for the expanded target bus cluster, the display layer parameters allocated to the reference bus and the other buses are extracted respectively. According to the front and back occlusion relationship determined by the display layer parameters, the buses and electronic control units in the target bus cluster are traversed layer by layer to perform diagnostic data acquisition. In response to the feedback of the diagnostic data acquisition results, a data transmission path is determined based on the signal flow direction attribute, and the data transmission path is mapped to the vertical offset position attribute of the bus path to update the layout information.

6. The vehicle diagnostic topology map generation method according to any one of claims 1-5, characterized in that, The method further includes: Monitor the operating status data of the target vehicle, and extract the fluctuation parameters that characterize the load changes of the electronic control unit and the throughput parameters that characterize the bus communication quality from the operating status data. Based on the fluctuation parameters and the throughput parameters, the real-time topology weights of the electronic control unit and the bus are calculated, and the real-time topology weights are mapped to the row and column coordinate calculation logic of the grid layout algorithm. The row and column coordinate calculation logic is an operation rule for determining the position of the node in the grid based on the grid layout algorithm. In response to the change in the value of the real-time topology weight, the logical occupancy of the electronic control unit in the grid and the vertical offset position of the bus path are dynamically adjusted to update the layout information. The logical occupancy is the occupancy information of the electronic control unit in the grid determined based on the channel resources occupied by the bus path in the grid. The updated layout information is traversed to identify the overlapping of grid column regions caused by the logical occupancy adjustment. The display layer parameters are redistributed according to the node density of the overlapping regions. The display layer parameters are layer differentiation parameters used to control the node drawing order and the front and back occlusion relationship of the bus, so as to generate a topology structure that adapts to the running state data.

7. The vehicle diagnostic topology map generation method according to any one of claims 1-5, characterized in that, The method further includes: Acquire multiple sets of historical topology data of the target vehicle within a preset historical time period, and extract the connection status changes of the electronic control unit and the signal transmission records of the bus in each set of historical topology data; The connection status changes and signal transmission records are analyzed to identify the activity frequency and correlation tightness of the electronic control unit and the bus in the historical operating cycle, and a topology correlation map characterizing the historical connection stability is generated. Based on the topology graph, the node type classification and connection relationship classification in the structured classification process are verified. When an implicit connection path not reflected in the bus topology data is detected in the topology graph, the implicit connection path is added to the bus topology data. The added bus topology data is a comprehensive topology dataset that integrates physical connection relationships and historical implicit connection relationships. Based on the supplementary results of the implicit connection paths, the node inclusion relationships in the gridded layout algorithm are updated to reconstruct the bus grouping hierarchy and the vertical offset position attributes of the bus paths based on the comprehensive topology dataset, thereby generating a topology structure containing historical operation characteristics.

8. A vehicle diagnostic topology map generation device, characterized in that, The device includes: The acquisition module is used to acquire the identification information of the target vehicle and retrieve the electronic control unit and bus topology data of the target vehicle from a pre-stored database based on the identification information. The classification module is used to perform structured classification processing on the bus topology data, including classifying the electronic control units into node types and classifying the bus connections of the target vehicle; the electronic control units are classified into entry nodes, hub nodes, and functional nodes, and the buses are classified into backbone buses, ring buses, and internal buses. The layout module is used to automatically arrange the electronic control unit and the bus based on the classified bus topology data using a grid layout algorithm, and generate layout information including node positions and bus paths. The generation module is used to render and generate a visual vehicle diagnostic topology map based on the layout information; The response module is used to perform selection, view adjustment, or diagnostic triggering operations on nodes or buses in the vehicle diagnostic topology diagram in response to user interaction operations.

9. A vehicle diagnostic topology map generation device, characterized in that, The device includes: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the vehicle diagnostic topology map generation method as described in any one of claims 1-7.

10. A computer storage medium, characterized in that, The computer storage medium stores computer instructions, which, when invoked, are used to execute the vehicle diagnostic topology map generation method as described in any one of claims 1-7.

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