Vehicle fault diagnosis method and system based on knowledge graph

Abstract

The invention provides a vehicle fault diagnosis method and system based on a knowledge graph. When a vehicle fault monitoring module of the vehicle terminal detects a vehicle fault, a fault snapshot image is generated. And the cloud server analyzes the vehicle information from the fault snapshot image, and performs progressive fault diagnosis in a fault knowledge graph in the cloud server based on the vehicle information to obtain a target maintenance suggestion node.

Description

Technical Field

[0001] The present invention relates to automated fault diagnosis of vehicles, and more particularly to a vehicle fault diagnosis method and system based on a knowledge graph. Background Art

[0002] The vehicle fault diagnosis method based on knowledge graph is a method that uses big data and artificial intelligence technology to build a knowledge graph by analyzing maintenance history data, and uses the knowledge graph to automatically identify and classify vehicle faults, provide maintenance suggestions, and greatly improve maintenance efficiency and accuracy.

[0003] The vehicle fault diagnosis method based on knowledge graph first extracts valuable information from massive maintenance data through entity extraction and relationship recognition technology, and then organizes this information into a knowledge graph. On this basis, the system diagnoses the vehicle faults, determines possible faulty parts, and provides maintenance guidance for the faulty parts through complex algorithm models (such as the XGBoost fault classification model). This not only improves the accuracy of fault diagnosis, but also greatly shortens the diagnosis time and improves maintenance efficiency. In addition, this method can continuously improve the accuracy of fault diagnosis through continuous learning and updating of the knowledge graph, providing strong data support for vehicle maintenance. Summary of the invention

[0004] The first aspect of the present disclosure provides a vehicle fault diagnosis method based on a knowledge graph. In some embodiments, the method may include: (a) when a vehicle fault monitoring module of a vehicle terminal detects a vehicle fault, a fault snapshot image is generated; (b) the vehicle terminal sends the fault snapshot image to a cloud server; (c) the cloud server parses the vehicle information from the fault snapshot image, and performs progressive fault diagnosis in the fault knowledge graph in the cloud server based on the vehicle information to obtain a target maintenance suggestion node; and (d) the cloud server sends the information of the target maintenance suggestion node as diagnostic information to the vehicle terminal. In some instances, the fault knowledge graph includes abnormal signal nodes, fault component nodes, and maintenance suggestion nodes. The abnormal signal node is associated with one or more fault component nodes associated with the generation of the vehicle fault. The fault component node is associated with one or more maintenance suggestion nodes that provide maintenance suggestions for the fault component. Multiple abnormal signal nodes that have a mutual influence or co-occurrence relationship on the generation of the vehicle fault can be associated with each other.

[0005] In some embodiments, the above step (c) may include: the cloud server extracts vehicle information associated with the vehicle fault from the fault snapshot image. The vehicle information may include the time of the fault, the vehicle driving mode, the vehicle status, the vehicle sensor data, the vehicle driving data, or any combination thereof. The maintenance suggestion node may include a description of the fault symptoms and treatment methods, an image of the faulty component, a maintenance video of the fault elimination, or any combination thereof.

[0006] In some embodiments, the above-mentioned progressive fault diagnosis may include: (i) identifying abnormal signals in vehicle information; (ii) locating abnormal signal nodes corresponding to abnormal signals in the knowledge graph; (iii) determining one or more faulty component nodes associated with the abnormal signal nodes as candidate faulty component nodes; (iv) obtaining one or more maintenance suggestion nodes associated with the candidate faulty component nodes; (v) performing vector matching on one or more feature vectors of one or more maintenance suggestion nodes and the fault feature vector extracted from the fault snapshot to obtain a target maintenance suggestion node; and (vi) sending the target maintenance suggestion node as diagnostic information to the vehicle terminal.

[0007] In some embodiments, the above step (iii) may include: determining whether there are one or more associated abnormal signal nodes associated with the abnormal signal node; if there are one or more associated abnormal signal nodes, determining whether the vehicle signal data corresponding to one or more associated abnormal signal nodes is abnormal; and if it is determined that the vehicle signal data is abnormal, determining one or more faulty component nodes associated with the corresponding one or more associated abnormal signal nodes as candidate faulty component nodes.

[0008] In some embodiments, the above processing (v) may include: extracting one or more feature vectors from one or more maintenance suggestion nodes using a convolutional neural network model, and extracting a fault feature vector from a fault snapshot using a convolutional neural network model. In some embodiments, the above processing (v) may include: determining the maintenance suggestion node with the highest vector matching degree with the fault feature vector among the one or more maintenance suggestion nodes as the target maintenance suggestion node. In some embodiments, the above processing (v) may include: determining one or more maintenance suggestion nodes with a vector matching degree with the fault feature vector exceeding a preset value among the one or more maintenance suggestion nodes as the target maintenance suggestion node.

[0009] The second aspect of the present disclosure provides a vehicle fault diagnosis system based on a knowledge graph. In some embodiments, the vehicle fault diagnosis system may include: a vehicle terminal; and a cloud server. The vehicle terminal may include: a vehicle fault monitoring module, which is configured to generate a fault snapshot image when a vehicle fault is detected; and a vehicle fault sending module, which is configured to send the fault snapshot image to the cloud server. The cloud server may include: a fault knowledge graph; a fault snapshot receiving module; and a fault diagnosis module. The fault knowledge graph may include abnormal signal nodes, fault component nodes, and maintenance suggestion nodes. The abnormal signal node may be associated with one or more vehicle fault component nodes associated with the generation of the vehicle fault. The fault component node may be associated with one or more maintenance suggestion nodes that provide maintenance suggestions for the fault component. Multiple abnormal signal nodes that have a mutual influence or co-occurrence relationship on the generation of the vehicle fault may be associated with each other. The fault snapshot receiving module may be configured to receive a fault snapshot image sent from the vehicle terminal. The fault diagnosis module may be configured to parse the vehicle information from the snapshot image, perform progressive fault diagnosis in the fault knowledge graph in the cloud server based on the vehicle information to obtain a target maintenance suggestion node, and send the information of the target maintenance suggestion node as diagnostic information to the vehicle terminal.

[0010] In some embodiments, the fault diagnosis module may be further configured to extract vehicle information associated with the vehicle fault from the fault snapshot image. The vehicle information may include the time of the fault, the vehicle driving mode, the vehicle status, the vehicle sensor data, the vehicle driving data, or any combination thereof. In some embodiments, the maintenance suggestion node may include a description of the fault symptoms and treatment methods, an image of the faulty component, a maintenance video of the fault elimination, or any combination thereof.

[0011] In some embodiments, the fault diagnosis module can be further configured to: (i) identify abnormal signals in vehicle information; (ii) locate abnormal signal nodes corresponding to abnormal signal data in the knowledge graph; (iii) determine one or more faulty component nodes associated with the abnormal signal nodes as candidate faulty component nodes; (iv) obtain one or more maintenance suggestion nodes associated with the candidate faulty component nodes; (v) perform vector matching on one or more feature vectors of one or more maintenance suggestion nodes with the fault feature vector extracted from the fault snapshot to obtain a target maintenance suggestion node; and (vi) send the target maintenance suggestion node as diagnostic information to the vehicle terminal.

[0012] In some embodiments, the above processing (iii) may include: determining whether there are one or more associated abnormal signal nodes associated with the abnormal signal node; if there are one or more associated abnormal signal nodes, determining whether the vehicle signal data corresponding to one or more associated abnormal signal nodes is abnormal; and if it is determined that the vehicle signal data is abnormal, determining one or more faulty component nodes associated with the corresponding one or more associated abnormal signal nodes as candidate faulty component nodes.

[0013] In some embodiments, the above processing (v) may include: extracting one or more feature vectors from one or more maintenance suggestion nodes using a convolutional neural network model, and extracting a fault feature vector from a fault snapshot using a convolutional neural network model.

[0014] In some embodiments, the above process (v) may include: determining the maintenance suggestion node with the highest vector matching degree with the fault feature vector among the one or more maintenance suggestion nodes as the target maintenance suggestion node. In some embodiments, the above process (v) may include: determining the one or more maintenance suggestion nodes with the vector matching degree with the fault feature vector exceeding a preset value among the one or more maintenance suggestion nodes as the target maintenance suggestion node.

[0015] A third aspect of the present disclosure provides a system including one or more computer processors and a computer readable memory. The computer readable memory may include machine executable code. The machine executable code implements the vehicle fault diagnosis method based on the knowledge graph as disclosed in the present disclosure when executed by the one or more computer processors.

[0016] It should be understood that the present disclosure is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The novel features of the invention are set forth with particularity in the appended claims.A better understanding of the features and advantages of the invention will be obtained by referring to the following detailed description and accompanying drawings which set forth illustrative embodiments utilizing the principles of the invention.

[0018] Figure 1 The structure of an exemplary vehicle fault knowledge graph of the present disclosure is shown.

[0019] Figure 2 It is a flow chart of a vehicle fault diagnosis method according to an embodiment of the present disclosure.

[0020] Figure 3 It is a structural block diagram of a vehicle fault analysis device according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0021] Traditional vehicle fault diagnosis methods mainly rely on standard fault codes generated by the vehicle computer (ECU) after a fault occurs. By analyzing these fault codes and related log information, technicians can determine the cause of the fault and repair it. However, this diagnostic method that relies on fault codes has limitations in application due to the limited types of faults it covers.

[0022] Current fault diagnosis methods face several challenges: first, these methods rely too much on the personal experience of maintenance personnel, and these experience levels vary; second, analyzing the cause of the fault is often time-consuming and costly; finally, for frequently occurring faults, the experience of maintenance experts has not been effectively integrated and accumulated, resulting in the inability to fully utilize existing maintenance knowledge and solutions.

[0023] In view of the above-mentioned problems in the prior art, the present disclosure proposes a new fault diagnosis method, which comprehensively considers more comprehensive information such as the vehicle's driving mode, operating status, signals and driving data to achieve a more accurate diagnosis of vehicle faults.

[0024] like Figure 1 As shown, the present disclosure provides a vehicle fault knowledge graph. The knowledge graph constructs a semantic association network. In some instances, the knowledge graph of the present disclosure is a data structure based on a graph, comprising nodes (Point) and edges (Edge) connecting the nodes. In some instances, the knowledge graph structure of the present disclosure can integrate multiple types of nodes, such as abnormal signal nodes, faulty component nodes, and maintenance suggestion nodes, thereby providing a comprehensive information framework for vehicle fault diagnosis and maintenance. In some instances, the abnormal signal node represents an abnormal signal indicating a problem or fault that occurs in the system (for example, a signal indicating a low battery charge), the faulty component node represents one or more vehicle components (for example, a battery) that may cause the abnormal signal node, and the maintenance suggestion node provides one or more maintenance suggestions for the faulty component (for example, checking the connection or charge of the battery).

[0025] In some instances, an abnormal signal node may be associated with one or more faulty component nodes that may generate the abnormal signal. This association relationship may be represented by an edge between the abnormal signal node and the faulty component node. A faulty component node may be associated with one or more maintenance suggestion nodes that provide recommended maintenance measures for the faulty component. This association relationship may be represented by an edge between a faulty component node and a maintenance suggestion node. In some instances, the value of the edge between the faulty component node and the maintenance suggestion node may be 1. In some instances, the value of the edge between the faulty component node and the maintenance suggestion node may be an association probability value obtained based on statistics.

[0026] In a non-limiting example, for the fault of "unable to start the vehicle", the knowledge graph of the present disclosure may include (1) abnormal signal nodes, including "low battery abnormal signal node", "fuel system abnormal signal node" and "ignition system abnormal signal node"; (2) fault component nodes, including "battery node", "fuel pump node", "injector node", "fuel filter node", "spark plug node" and "ignition coil node"; and (3) maintenance suggestion nodes, including "battery repair node", "fuel pump repair node", "injector repair node", "fuel filter repair node", "spark plug repair node" and "ignition coil repair node". These maintenance suggestion nodes respectively include inspection / repair suggestions for the battery, fuel pump, injector, fuel filter, spark plug and ignition coil, such as text descriptions, audio and / or video tutorials. An abnormal signal node can be associated with one or more fault component nodes. For example, "low battery abnormal signal node" can be associated with "battery node". For example, a "fuel system abnormal signal node" can be associated with three fault component nodes, namely, a "fuel pump node", a "fuel injector node" and a "fuel filter node".

[0027] In another non-limiting example, for the fault of "engine temperature is too high", the knowledge graph of the present disclosure may include: (1) abnormal signal nodes related to the fault of "engine temperature is too high", including "temperature sensor abnormal signal node" and "cooling system abnormal signal node"; (2) fault component nodes associated with "temperature sensor abnormal signal node" and "cooling system abnormal signal node", including "temperature sensor node", "water pump node", "radiator node" and "coolant tank node"; and (3) maintenance suggestion nodes associated with the fault component nodes, including "temperature sensor repair node", "water pump repair node", "radiator repair node" and "coolant tank repair node". These maintenance suggestion nodes respectively include inspection / repair suggestions for the temperature sensor, water pump, radiator and coolant tank, such as text descriptions, audio and / or video tutorials.

[0028] In a car, for a fault, there may be multiple abnormal signal nodes, which may appear at the same time or interact with each other. In some examples, in the knowledge graph of the present disclosure, these multiple abnormal signal nodes associated with the same vehicle fault can be associated with each other. This association relationship can be represented by the edges between multiple abnormal signal nodes. In a non-limiting example, for the fault of "brake system abnormality", there may be multiple abnormal signal nodes, such as "brake pad wear abnormal signal node", "brake fluid volume abnormal signal node", "brake fluid pipeline blockage abnormal signal node" and "brake fluid cleanliness abnormal signal node", and these abnormal signal nodes are usually associated with each other, affect each other or appear at the same time. For example, brake pad wear will reduce the braking force, and brake fluid contamination may further affect the braking effect; brake fluid leakage will cause the brake fluid to be unable to be normally transmitted to the brake device, and brake pipeline blockage will prevent the brake fluid from flowing. In another non-limiting example, for the fault of "unable to start the vehicle", there may be multiple abnormal signal nodes that are associated with each other, affect each other or appear at the same time, such as "battery power shortage abnormal signal node", "fuel system abnormal signal node" and "ignition system abnormal signal node".

[0029] The lines (edges) between abnormal signal nodes can be assigned values. In some instances, the lines (edges) between abnormal signal nodes can be assigned values ​​based on the probability of two abnormal signals occurring simultaneously. For example, based on prior statistical information, the probability of the fuel system abnormality and the ignition system abnormality occurring simultaneously is 0.4, so the line (edge) between the "fuel system abnormal signal node" and the "ignition system abnormal signal node" can be assigned a value of 0.4.

[0030] Figure 2 It is a flow chart of a vehicle fault diagnosis method according to an embodiment of the present disclosure. In some embodiments, the method may include steps S201-S204. In step S201, when the vehicle fault monitoring module of the vehicle terminal detects the occurrence of a fault, a fault snapshot image is generated. Detecting the occurrence of a fault may include the vehicle terminal detecting a fault signal (for example, such as a fault code) or an indicator light signal (for example, the low battery indicator light is on). Detecting the occurrence of a fault may also be a fault prompt such as a report of "unable to start the car" by the car's intelligent system. In some instances, the vehicle terminal can collect information through the CAN network, and the fault monitoring module collects and summarizes the collected information to generate a fault snapshot image. In step S202, the vehicle terminal sends the fault snapshot image to the cloud server.

[0031] In step S203, the cloud server parses the vehicle information from the fault snapshot image, and performs progressive fault diagnosis in the fault knowledge graph in the cloud server based on the vehicle information to obtain the target maintenance suggestion node. In some instances, parsing the vehicle information may include: the cloud server extracts the vehicle information associated with the fault from the fault snapshot image. The vehicle information may include the time of fault occurrence, vehicle driving mode, vehicle status, vehicle sensor data, vehicle driving data, or any combination thereof. The vehicle driving mode may include an automatic driving mode, an assisted driving mode, and a manual driving mode. The vehicle status may include vehicle start-up, vehicle acceleration, vehicle lane change, vehicle deceleration, vehicle braking, and vehicle stationary. The vehicle sensor data may include data collected by various components or sensors of the vehicle, including but not limited to battery temperature, battery power, coolant temperature, cabin temperature, etc. The vehicle driving data may include vehicle instantaneous speed, vehicle average speed, vehicle acceleration, etc. In step S204, the cloud server returns the information of the target maintenance suggestion node to the vehicle terminal as diagnostic information.

[0032] In some embodiments, the progressive fault diagnosis performed by the cloud server may include: (i) identifying abnormal signals in vehicle information; (ii) locating abnormal signal nodes corresponding to the abnormal signals in the knowledge graph; (iii) determining one or more faulty component nodes associated with the abnormal signal nodes as candidate faulty component nodes; (iv) obtaining one or more maintenance suggestion nodes associated with the candidate faulty component nodes; (v) matching one or more feature vectors of one or more maintenance suggestion nodes with the fault feature vector extracted from the fault snapshot, and taking the maintenance suggestion node with the highest vector matching degree as the target maintenance suggestion node, or one or more maintenance suggestion nodes whose matching degree with the fault feature vector exceeds a preset value may be taken as the target maintenance suggestion node; and (vi) sending the target maintenance suggestion node as diagnostic information to the vehicle terminal.

[0033] In some instances, identifying an abnormal signal in vehicle information may include: the cloud server extracts vehicle information associated with the fault that occurred from the fault snapshot image. In some instances, the vehicle information may include the time of the fault, the vehicle driving mode, the vehicle status, the vehicle sensor data, the vehicle driving data, or any combination thereof. In some instances, after parsing the vehicle information such as the time of the fault, the vehicle driving mode, the vehicle status, the vehicle signal, the vehicle driving data, etc. from the fault snapshot image, the vehicle information may be compared with a normal value or value range. When one or several vehicle information exceeds the normal value range, the vehicle information that exceeds the normal data is identified as an abnormal signal. In some instances, vehicle information whose rate of change exceeds a preset threshold may be identified as an abnormal signal.

[0034] In a non-limiting example, if the "engine temperature value" indicated by the "engine temperature signal" parsed from the fault snapshot image is higher than a preset threshold, the "engine temperature signal" is identified as an abnormal signal. Subsequently, the abnormal signal node "engine temperature signal node" corresponding to the abnormal signal "engine temperature signal" is located in the knowledge graph, and the faulty component nodes associated with the abnormal signal node "engine temperature signal node", including but not limited to the temperature sensor node, water pump node, radiator node and coolant tank node, are determined as candidate faulty component nodes. Then, the maintenance suggestion nodes associated with the temperature sensor node, water pump node, radiator node and coolant tank node are obtained, including but not limited to "check temperature sensor node", "check water pump node", "check radiator node" and "check coolant node".

[0035] In order to ensure the accuracy of fault diagnosis, in some instances, a machine learning model (e.g., a convolutional neural network model (CNN)) can be used to extract the fault feature vector in the fault snapshot and the feature vector of each maintenance suggestion node and perform vector matching, so as to obtain the most accurate target maintenance suggestion node. In some instances, the maintenance suggestion node with the highest vector matching degree with the fault feature vector in one or more maintenance suggestion nodes can be determined as the target maintenance suggestion node. In some instances, one or more maintenance suggestion nodes with a vector matching degree with the fault feature vector exceeding a preset value in one or more maintenance suggestion nodes can be determined as the target maintenance suggestion node. In some instances, vector matching can be implemented using a similarity matching algorithm. This matching process can be based on a variety of criteria, such as Euclidean distance or cosine similarity, to find the maintenance suggestion node with the highest vector matching degree with the fault feature vector in one or more maintenance suggestion nodes. In this way, when a new fault is detected by the vehicle terminal, the machine learning model can quickly provide accurate maintenance suggestions, greatly reducing the diagnosis time, improving maintenance efficiency and equipment reliability. In addition, as time goes by, this matching process can be continuously optimized, and by continuously learning new fault cases, the maintenance suggestions are made more and more accurate, thereby realizing intelligent maintenance recommendations.

[0036] In some embodiments, determining one or more faulty component nodes associated with an abnormal signal node as candidate faulty component nodes may include: determining whether there are one or more associated abnormal signal nodes associated with the abnormal signal node; if there are one or more associated abnormal signal nodes, determining whether there is an abnormality in vehicle signal data corresponding to the one or more associated abnormal signal nodes; if it is determined that the vehicle signal data corresponding to the one or more associated abnormal signal nodes is abnormal, then also determining one or more faulty component nodes associated with the corresponding one or more associated abnormal signal nodes as candidate faulty component nodes.

[0037] In a non-limiting example, if the vehicle fault "brake system abnormality" is detected and the abnormal signal "brake fluid shortage" is parsed in the fault snapshot image, the abnormal signal node "brake fluid leakage abnormal signal node" corresponding to the abnormal signal "brake fluid shortage" is located in the knowledge graph. Subsequently, the fault component node "brake fluid tank" associated with the abnormal signal node "brake fluid leakage abnormal signal node" is determined as a candidate fault component node. In addition, it is also determined whether there are one or more associated abnormal signal nodes associated with the abnormal signal node "brake fluid leakage abnormal signal node". In this example, there are other normal signal nodes "brake pad wear abnormal signal node", "brake fluid pipeline blockage abnormal signal node" and "brake fluid contamination abnormal signal node" associated with the abnormal signal node "brake fluid leakage abnormal signal node". Next, it is determined whether the vehicle signal data corresponding to the above-mentioned associated abnormal signal nodes, such as brake pad thickness signal and brake fluid cleanliness, are abnormal. If the brake pad thickness signal is abnormal, the "brake pad node" associated with the "brake pad wear abnormal signal node" corresponding to the brake pad thickness signal is also determined as a candidate fault component node.

[0038] Figure 3 1 is a structural block diagram of a vehicle fault analysis device according to an embodiment of the present disclosure. In some embodiments, the vehicle fault diagnosis system based on the knowledge graph of the present disclosure may include: a vehicle terminal 310 and a cloud server 320. The vehicle terminal 310 may include a vehicle fault monitoring module and a vehicle fault sending module. The vehicle fault monitoring module may be configured to generate a fault snapshot image when a vehicle fault is detected. The vehicle fault sending module may be configured to send the fault snapshot image to the cloud server 320.

[0039] The cloud server 320 of the present disclosure may include a fault knowledge graph (not shown), a fault snapshot receiving module 321 and a fault diagnosis module 322. In some embodiments, the fault knowledge graph may include an abnormal signal node, a fault component node and a maintenance suggestion node. The abnormal signal node may be associated with one or more fault component nodes associated with the generation of a vehicle fault. The fault component node is associated with one or more maintenance suggestion nodes that provide maintenance suggestions for the fault component. In some instances, multiple abnormal signal nodes that have a mutual influence or co-occurrence relationship on the generation of a vehicle fault may be associated with each other.

[0040] In some embodiments, the fault snapshot receiving module 321 can be configured to receive a fault snapshot image sent from a vehicle terminal. The fault diagnosis module 322 can be configured to parse vehicle information from the snapshot image, perform progressive fault diagnosis in the fault knowledge graph in the cloud server based on the vehicle information to obtain a target maintenance suggestion node, and send the information of the target maintenance suggestion node as diagnostic information to the vehicle terminal. In some embodiments, the fault diagnosis module 322 can be further configured to extract vehicle information associated with the vehicle fault from the fault snapshot image. The vehicle information may include the time of occurrence of the fault, the vehicle driving mode, the vehicle status, the vehicle sensor data, the vehicle driving data, or any combination thereof.

[0041] In some embodiments, the fault diagnosis module 322 can be further configured to: (i) identify abnormal signals in vehicle information; (ii) locate abnormal signal nodes corresponding to abnormal signal data in the knowledge graph; (iii) determine one or more faulty component nodes associated with the abnormal signal nodes as candidate faulty component nodes; (iv) obtain one or more maintenance suggestion nodes associated with the candidate faulty component nodes; (v) perform vector matching on one or more feature vectors of one or more maintenance suggestion nodes with the fault feature vector extracted from the fault snapshot to obtain a target maintenance suggestion node; and (vi) send the target maintenance suggestion node as diagnostic information to the vehicle terminal.

[0042] In some embodiments, the above (iii) performed by the fault diagnosis module 322 to determine one or more faulty component nodes associated with the abnormal signal node as candidate faulty component nodes may include: determining whether there are one or more associated abnormal signal nodes associated with the abnormal signal node; if there are one or more associated abnormal signal nodes, determining whether the vehicle signal data corresponding to one or more associated abnormal signal nodes is abnormal; and if it is determined that the vehicle signal data is abnormal, determining one or more faulty component nodes associated with the corresponding one or more associated abnormal signal nodes as candidate faulty component nodes.

[0043] In some embodiments, the vector matching operation performed by the fault diagnosis module 322 may include: extracting one or more feature vectors from one or more maintenance suggestion nodes using a convolutional neural network model, and extracting a fault feature vector from a fault snapshot using a convolutional neural network model. In one example, the maintenance suggestion node with the highest vector matching degree with the fault feature vector in one or more maintenance suggestion nodes may be used as a target maintenance suggestion node. In one example, one or more maintenance suggestion nodes whose vector matching degree with the fault feature vector exceeds a preset value in one or more maintenance suggestion nodes may be determined as a target maintenance suggestion node.

[0044] The present disclosure also provides a system including one or more computer processors and a computer readable memory. The computer readable memory may include machine executable code, which, when executed by the one or more computer processors, implements the vehicle fault diagnosis method based on the knowledge graph of the present disclosure.

[0045] Although preferred embodiments of the present invention have been shown and described herein, it will be apparent to those skilled in the art that these embodiments are provided by way of example only. Other variations, changes and substitutions may be conceived by those skilled in the art without departing from the present invention.

Claims

1. A vehicle fault diagnosis method based on knowledge graph, the method comprising: (a) When the vehicle fault monitoring module of the vehicle terminal detects that a vehicle fault occurs, a fault snapshot image is generated; (b) the vehicle terminal sends the fault snapshot image to a cloud server; (c) the cloud server parses vehicle information from the fault snapshot image, and performs progressive fault diagnosis in a fault knowledge graph in the cloud server based on the vehicle information to obtain a target maintenance suggestion node; as well as (d) the cloud server sends the information of the target maintenance suggestion node as diagnostic information to the vehicle terminal, Among them, the fault knowledge graph includes abnormal signal nodes, fault component nodes and maintenance suggestion nodes, wherein the abnormal signal node is associated with one or more fault component nodes associated with the occurrence of the vehicle fault, the fault component node is associated with one or more maintenance suggestion nodes that provide maintenance suggestions for the fault component, and wherein multiple abnormal signal nodes that have a mutual influence or co-occurrence relationship on the occurrence of the vehicle fault are associated with each other.

2. The method of claim 1, wherein (c) comprises: The cloud server extracts the vehicle information associated with the vehicle fault from the fault snapshot image, wherein the vehicle information includes fault occurrence time, vehicle driving mode, vehicle status, vehicle sensor data, vehicle travel data, or any combination thereof.

3. The method according to claim 1, wherein the maintenance suggestion node includes a description of fault symptoms and treatment methods, an image of a faulty component, a maintenance video of troubleshooting, or any combination thereof.

4. The method according to any one of claims 1 to 3, wherein the progressive fault diagnosis comprises: (i) identifying an abnormal signal in the vehicle information; (ii) locating the abnormal signal node corresponding to the abnormal signal in the knowledge graph; (iii) determining one or more of the faulty component nodes associated with the abnormal signal node as candidate faulty component nodes; (iv) obtaining one or more maintenance suggestion nodes associated with the candidate faulty component node; (v) performing vector matching on one or more feature vectors of the one or more maintenance suggestion nodes and a fault feature vector extracted from the fault snapshot, thereby obtaining a target maintenance suggestion node; as well as (vi) sending the target maintenance suggestion node as the diagnosis information to the vehicle terminal.

5. The method according to claim 4, wherein step (iii) comprises: determining whether there are one or more associated abnormal signal nodes associated with the abnormal signal node; If the one or more associated abnormal signal nodes exist, determining whether the vehicle signal data corresponding to the one or more associated abnormal signal nodes is abnormal; as well as If it is determined that the vehicle signal data is abnormal, one or more of the faulty component nodes associated with the corresponding one of the plurality of associated abnormal signal nodes are determined as the candidate faulty component nodes.

6. The method of claim 4, wherein (v) comprises: The one or more feature vectors are extracted from one or more maintenance suggestion nodes using a convolutional neural network model, and the fault feature vector is extracted from the fault snapshot using the convolutional neural network model.

7. The method of claim 4, wherein (v) comprises: A maintenance suggestion node having the highest vector matching degree with the fault feature vector among the one or more maintenance suggestion nodes is determined as the target maintenance suggestion node.

8. The method of claim 4, wherein (v) comprises: One or more maintenance suggestion nodes whose vector matching degree with the fault feature vector exceeds a preset value among the one or more maintenance suggestion nodes are determined as the target maintenance suggestion nodes.

9. A vehicle fault diagnosis system based on a knowledge graph, the vehicle fault diagnosis system comprising: Vehicle terminal; and Cloud Servers, The vehicle terminal includes: a vehicle fault monitoring module, which is configured to generate a fault snapshot image when a vehicle fault is detected; and a vehicle fault sending module, which is configured to send the fault snapshot image to a cloud server. The cloud server includes: a fault knowledge graph; a fault snapshot receiving module; and a fault diagnosis module. wherein the fault knowledge graph comprises an abnormal signal node, a fault component node and a maintenance suggestion node, wherein the abnormal signal node is associated with one or more vehicle fault component nodes associated with the generation of the vehicle fault, the fault component node is associated with one or more maintenance suggestion nodes providing maintenance suggestions for the fault component, and wherein a plurality of abnormal signal nodes having a mutual influence or co-occurrence relationship on the generation of the vehicle fault are associated with each other; wherein the fault snapshot receiving module is configured to receive the fault snapshot image sent from the vehicle terminal; The fault diagnosis module is configured to parse vehicle information from the snapshot image, perform progressive fault diagnosis in the fault knowledge graph in the cloud server based on the vehicle information to obtain a target repair suggestion node, and send the information of the target repair suggestion node as diagnostic information to the vehicle terminal.

10. The vehicle fault diagnosis system according to claim 9, wherein the fault diagnosis module is further configured to extract the vehicle information associated with the vehicle fault from the fault snapshot image, wherein the vehicle information includes fault occurrence time, vehicle driving mode, vehicle status, vehicle sensor data, vehicle driving data or any combination thereof. 11 . The vehicle fault diagnosis system according to claim 9 , wherein the maintenance suggestion node comprises a description of fault symptoms and treatment methods, an image of a faulty component, a maintenance video of troubleshooting, or any combination thereof.

12. The vehicle fault diagnosis system according to any one of claims 9 to 11, wherein the fault diagnosis module is further configured as follows: (i) identifying an abnormal signal in the vehicle information; (ii) locating the abnormal signal node corresponding to the abnormal signal data in the knowledge graph; (iii) determining one or more of the faulty component nodes associated with the abnormal signal node as candidate faulty component nodes; (iv) obtaining one or more maintenance suggestion nodes associated with the candidate faulty component node; (v) performing vector matching on one or more feature vectors of the one or more maintenance suggestion nodes and a fault feature vector extracted from the fault snapshot, thereby obtaining a target maintenance suggestion node; as well as (vi) sending the target maintenance suggestion node as the diagnosis information to the vehicle terminal.

13. The vehicle fault diagnosis system according to claim 12, wherein (iii) comprises: determining whether there are one or more associated abnormal signal nodes associated with the abnormal signal node; If the one or more associated abnormal signal nodes exist, determining whether the vehicle signal data corresponding to the one or more associated abnormal signal nodes is abnormal; and If it is determined that the vehicle signal data is abnormal, one or more of the faulty component nodes associated with the corresponding one of the plurality of associated abnormal signal nodes are determined as the candidate faulty component nodes.

14. The vehicle fault diagnosis system according to claim 12, wherein (v) comprises: The one or more feature vectors are extracted from one or more maintenance suggestion nodes using a convolutional neural network model, and the fault feature vector is extracted from the fault snapshot using the convolutional neural network model.

15. The vehicle fault diagnosis system according to claim 12, wherein (v) comprises: A maintenance suggestion node having the highest vector matching degree with the fault feature vector among the one or more maintenance suggestion nodes is determined as the target maintenance suggestion node.

16. The vehicle fault diagnosis system according to claim 12, wherein (v) comprises: One or more maintenance suggestion nodes whose vector matching degree with the fault feature vector exceeds a preset value among the one or more maintenance suggestion nodes are determined as the target maintenance suggestion nodes.

17. A system comprising one or more computer processors and a computer-readable memory, wherein the computer-readable memory comprises machine executable code, and when the machine executable code is executed by the one or more computer processors, the knowledge graph-based vehicle fault diagnosis method as described in any one of claims 1-8 is implemented.

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