Vehicle fault diagnosis and prediction system and method based on knowledge graph
Through a knowledge graph-based system, combined with acoustic vibration and image recognition technology, the rapid and accurate diagnosis and prediction of vehicle failures are achieved, and the time-consuming and labor-intensive and error problems caused by relying on manual experience in the prior art are solved.
Patent Information
- Application Number
- CN202410034981.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-01-10
- Publication Date
- 2025-07-11
AI Technical Summary
In the prior art, vehicle fault diagnosis and prediction mainly rely on people's work experience, which leads to time-consuming and labor-intensive and prone to errors, and cannot achieve fast and accurate diagnosis and prediction.
A knowledge graph-based system is adopted to diagnose vehicle failures through sound wave vibration and image recognition, and communicate information with knowledge graph terminals to achieve fast and accurate fault diagnosis and prediction.
It does not require relying on manual experience to quickly and accurately complete vehicle fault diagnosis and prediction, avoid diagnostic errors and improve efficiency and accuracy.
Smart Images
Figure CN120298718A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of vehicle fault diagnosis and prediction, and particularly to a system and method for vehicle fault diagnosis and prediction based on a knowledge graph. Background Art
[0002] A knowledge graph, also known as a scientific knowledge graph, is a concept in the field of library and information science. It is used to draw, analyze, and display the interrelationships between subjects or academic research entities, and is a visualization tool for revealing the development process and structural relationships of scientific knowledge. Specifically, a knowledge graph combines the theories and methods of disciplines such as applied mathematics, graphics, information visualization technology, and information science with methods such as bibliometric citation analysis and co-occurrence analysis, and uses a visualized graph to vividly display the core structure, development history, frontier fields, and overall knowledge architecture of a discipline to achieve the purpose of multi-disciplinary integration. It displays complex knowledge fields through data mining, information processing, knowledge measurement, and graph drawing, reveals the dynamic development laws of knowledge fields, and provides practical and valuable references for disciplinary research.
[0003] Currently, vehicle fault diagnosis and prediction mostly rely on human work experience to complete. In this way, it is not only time-consuming and laborious, but also prone to situations of incorrect fault diagnosis, which is not conducive to vehicle fault diagnosis and prediction. Therefore, there is an urgent need for a system and method for vehicle fault diagnosis and prediction based on a knowledge graph to solve the above technical problems. Summary of the Invention
[0004] The purpose of the present invention is to provide a system and method for vehicle fault diagnosis and prediction based on a knowledge graph to solve the problems raised in the above background art.
[0005] To achieve the above purpose, the present invention provides the following technical solutions: A system for vehicle fault diagnosis and prediction based on a knowledge graph realizes vehicle fault diagnosis and prediction through the knowledge graph, including: a vehicle fault diagnosis terminal, a vehicle fault prediction terminal, and a knowledge graph terminal; Vehicle fault diagnosis terminal: Communicates information with the knowledge graph terminal and realizes vehicle fault diagnosis based on the knowledge graph; Vehicle fault prediction terminal: Communicates information with the knowledge graph terminal and realizes vehicle fault prediction based on the knowledge graph; Knowledge graph terminal: Communicates information with the vehicle fault diagnosis terminal and the vehicle fault prediction terminal to ensure rapid and accurate diagnosis and prediction of vehicle faults.
[0006] As a further solution of the present invention: the vehicle fault diagnosis terminal includes: a vehicle fault diagnosis module, a diagnosis information collection module, a diagnosis information processing module, a diagnosis communication module, a diagnosis comparison module, and a vehicle fault diagnosis confirmation module.
[0007] As a further solution of the present invention: the vehicle fault diagnosis module includes: an acoustic wave vibration diagnosis unit and an image recognition diagnosis unit, which diagnose vehicle faults through acoustic wave vibration and image recognition, and collect relevant diagnosis information through the diagnosis information collection module. After collection, the relevant diagnosis information is processed by the diagnosis information processing module to obtain a preliminary diagnosis result of the vehicle fault.
[0008] As a further solution of the present invention: the diagnosis communication module is used for information communication between the vehicle fault diagnosis terminal and the knowledge graph terminal, and the preliminary diagnosis result of the vehicle fault is compared with the knowledge graph through the diagnosis comparison module, and then the vehicle fault diagnosis confirmation module makes a final judgment on the vehicle fault, so as to quickly and accurately realize the diagnosis of the vehicle fault.
[0009] As a further solution of the present invention: the vehicle fault prediction terminal includes: a vehicle fault information collection module, a vehicle fault information processing module, a prediction communication module, a prediction comparison module, and a vehicle fault prediction module.
[0010] As a further solution of the present invention: the vehicle fault information collection module is used for collecting relevant information of vehicle faults, and the information is processed by the vehicle fault information processing module. The prediction communication module is used to realize information communication between the vehicle fault prediction terminal and the knowledge graph terminal, and the processed vehicle fault information is compared with the knowledge graph through the prediction comparison module, and then the vehicle fault prediction module completes the quick and accurate prediction of the vehicle fault.
[0011] As a further solution of the present invention: the knowledge graph terminal includes: a knowledge graph storage module, a diagnosis intercommunication module, a prediction intercommunication module, a database module, and a control module. The control module is the core module of the entire vehicle fault diagnosis and prediction system, and is used to realize the core control of the vehicle fault diagnosis terminal, the vehicle fault prediction terminal, and the knowledge graph terminal to ensure the normal operation of the vehicle fault diagnosis terminal, the vehicle fault prediction terminal, and the knowledge graph terminal.
[0012] As a further solution of the present invention: the knowledge graph storage module is used for storing the graph of vehicle fault-related information and storing it through the database module.
[0013] As a further solution of the present invention: The diagnostic communication module is used to realize the information interaction between the vehicle fault diagnosis terminal and the knowledge graph terminal, facilitating the quick and accurate diagnosis of vehicle faults, and the prediction communication module is used to realize the information interaction between the vehicle fault prediction terminal and the knowledge graph terminal, facilitating the quick and accurate prediction of vehicle faults.
[0014] A method for vehicle fault diagnosis and prediction based on a knowledge graph, comprising the following steps: S1: Vehicle fault diagnosis: Diagnose vehicle faults through acoustic wave vibration and image recognition, collect the relevant diagnostic information through the diagnostic information collection module, and process the relevant diagnostic information by the diagnostic information processing module after collection, so as to obtain a preliminary diagnosis result of the vehicle fault, and compare the preliminary diagnosis result of the vehicle fault with the knowledge graph through the diagnostic comparison module, and then make a final judgment on the vehicle fault by the vehicle fault diagnosis confirmation module, so as to quickly and accurately realize the diagnosis of vehicle faults; S2: Vehicle fault prediction: Collect the relevant information of vehicle faults through the vehicle fault information collection module, process the information by the vehicle fault information processing module, compare the processed vehicle fault information with the knowledge graph through the prediction comparison module, and then complete the quick and accurate prediction of vehicle faults by the vehicle fault prediction module.
[0015] Compared with the prior art, the beneficial effects of the present invention are: The present invention realizes the diagnosis and prediction of vehicle faults through a knowledge graph, diagnoses vehicle faults through acoustic wave vibration and image recognition, collects the relevant diagnostic information through the diagnostic information collection module, processes the relevant diagnostic information by the diagnostic information processing module after collection, so as to obtain a preliminary diagnosis result of the vehicle fault, compares the preliminary diagnosis result of the vehicle fault with the knowledge graph through the diagnostic comparison module, and then makes a final judgment on the vehicle fault by the vehicle fault diagnosis confirmation module, so as to quickly and accurately realize the diagnosis of vehicle faults, and collects the relevant information of vehicle faults through the vehicle fault information collection module, processes the information by the vehicle fault information processing module, compares the processed vehicle fault information with the knowledge graph through the prediction comparison module, and then completes the quick and accurate prediction of vehicle faults by the vehicle fault prediction module. The diagnosis and prediction of vehicle faults of the present invention do not need to rely on human work experience to complete, which saves time and effort, effectively avoids the situation of wrong fault diagnosis, and is beneficial to vehicle fault diagnosis and prediction. Description of the Drawings
[0016] Figure 1 It is a structural block diagram of a system for vehicle fault diagnosis and prediction based on a knowledge graph.
[0017] Figure 2 It is a block diagram of a vehicle fault diagnosis terminal in a system for vehicle fault diagnosis and prediction based on a knowledge graph.
[0018] Figure 3 It is a block diagram of a vehicle fault prediction terminal in a system for vehicle fault diagnosis and prediction based on a knowledge graph.
[0019] Figure 4 It is a block diagram of a knowledge graph terminal in a system for vehicle fault diagnosis and prediction based on a knowledge graph. Embodiment
[0020] Various exemplary embodiments, features, and aspects of the present application will be described in detail below with reference to the accompanying drawings. Identical reference numerals in the drawings denote elements having the same or similar functions. Although various aspects of the embodiments are shown in the drawings, the drawings are not necessarily drawn to scale unless otherwise specified.
[0021] The special term "exemplary" herein means "serving as an example, embodiment, or illustration". Any embodiment described as "exemplary" herein is not necessarily to be construed as superior or better than other embodiments.
[0022] In addition, for a better description of the present application, numerous specific details are given in the following specific embodiments. Those skilled in the art should understand that the present application can be implemented without some of these specific details. In some instances, methods, means, and elements well known to those skilled in the art are not described in detail so as to highlight the gist of the present application. Embodiment
[0023] Please refer to Figure 1 , a system for vehicle fault diagnosis and prediction based on a knowledge graph, which realizes vehicle fault diagnosis and prediction through a knowledge graph, including: a vehicle fault diagnosis terminal, a vehicle fault prediction terminal, and a knowledge graph terminal.
[0024] Vehicle fault diagnosis terminal: Communicates information with the knowledge graph terminal and realizes vehicle fault diagnosis based on the knowledge graph; Vehicle fault prediction terminal: Communicates information with the knowledge graph terminal and realizes vehicle fault prediction based on the knowledge graph; Knowledge graph terminal: Communicates information with the vehicle fault diagnosis terminal and the vehicle fault prediction terminal to ensure fast and accurate diagnosis and prediction of vehicle faults.
[0025] Please refer to Figure 2 , the vehicle fault diagnosis terminal includes: a vehicle fault diagnosis module, a diagnosis information collection module, a diagnosis information processing module, a diagnosis communication module, a diagnosis comparison module, and a vehicle fault diagnosis confirmation module.
[0026] Preferably, in this embodiment, the vehicle fault diagnosis module includes: an acoustic wave vibration diagnosis unit and an image recognition diagnosis unit, which diagnose vehicle faults through acoustic wave vibration and image recognition, and collect relevant diagnosis information through the diagnosis information collection module. After collection, the relevant diagnosis information is processed by the diagnosis information processing module to obtain a preliminary diagnosis result of the vehicle fault.
[0027] Preferably, in this embodiment, the diagnosis communication module is used for information communication between the vehicle fault diagnosis terminal and the knowledge graph terminal, compares the preliminary diagnosis result of the vehicle fault with the knowledge graph through the diagnosis comparison module, and then the vehicle fault diagnosis confirmation module makes a final judgment on the vehicle fault, so as to quickly and accurately diagnose the vehicle fault.
[0028] It should be specifically noted that: by elaborating on the vehicle fault diagnosis terminal, the vehicle fault diagnosis terminal includes: a vehicle fault diagnosis module, a diagnosis information collection module, a diagnosis information processing module, a diagnosis communication module, a diagnosis comparison module, and a vehicle fault diagnosis confirmation module, and diagnoses vehicle faults through acoustic wave vibration and image recognition, and collects relevant diagnosis information through the diagnosis information collection module. After collection, the relevant diagnosis information is processed by the diagnosis information processing module to obtain a preliminary diagnosis result of the vehicle fault, and the diagnosis communication module is used for information communication between the vehicle fault diagnosis terminal and the knowledge graph terminal, compares the preliminary diagnosis result of the vehicle fault with the knowledge graph through the diagnosis comparison module, and then the vehicle fault diagnosis confirmation module makes a final judgment on the vehicle fault, so as to quickly and accurately diagnose the vehicle fault. Embodiment
[0029] Please refer to Figure 1 , a system for vehicle fault diagnosis and prediction based on a knowledge graph, which realizes vehicle fault diagnosis and prediction through the knowledge graph, including: a vehicle fault diagnosis terminal, a vehicle fault prediction terminal, and a knowledge graph terminal.
[0030] Vehicle fault diagnosis terminal: Communicates with the knowledge graph terminal for information and realizes vehicle fault diagnosis based on the knowledge graph; Vehicle fault prediction terminal: Communicates with the knowledge graph terminal for information and realizes vehicle fault prediction based on the knowledge graph; Knowledge graph terminal: Communicates with the vehicle fault diagnosis terminal and the vehicle fault prediction terminal for information to ensure quick and accurate diagnosis and prediction of vehicle faults.
[0031] Please refer to Figure 2, the vehicle fault diagnosis terminal includes: a vehicle fault diagnosis module, a diagnosis information collection module, a diagnosis information processing module, a diagnosis communication module, a diagnosis comparison module, and a vehicle fault diagnosis confirmation module.
[0032] Preferably, in this embodiment, the vehicle fault diagnosis module includes: an acoustic vibration diagnosis unit and an image recognition diagnosis unit, which diagnose vehicle faults through acoustic vibration and image recognition, and collect relevant diagnosis information through the diagnosis information collection module. After collection, the diagnosis information processing module processes the relevant diagnosis information to obtain a preliminary diagnosis result of the vehicle fault.
[0033] Preferably, in this embodiment, the diagnosis communication module is used for information communication between the vehicle fault diagnosis terminal and the knowledge graph terminal, and compares the preliminary diagnosis result of the vehicle fault with the knowledge graph through the diagnosis comparison module. Then, the vehicle fault diagnosis confirmation module makes a final judgment on the vehicle fault, so as to quickly and accurately diagnose the vehicle fault.
[0034] Please refer to Figure 3 , the vehicle fault prediction terminal includes: a vehicle fault information collection module, a vehicle fault information processing module, a prediction communication module, a prediction comparison module, and a vehicle fault prediction module.
[0035] Preferably, in this embodiment, the vehicle fault information collection module is used to collect relevant information about vehicle faults, and the vehicle fault information processing module processes the information. The prediction communication module is used to achieve information communication between the vehicle fault prediction terminal and the knowledge graph terminal, and compares the processed vehicle fault information with the knowledge graph through the prediction comparison module. Then, the vehicle fault prediction module completes the rapid and accurate prediction of vehicle faults.
[0036] It should be specifically noted that: compared with the first embodiment, this embodiment details the vehicle fault prediction terminal. The vehicle fault prediction terminal includes: a vehicle fault information collection module, a vehicle fault information processing module, a prediction communication module, a prediction comparison module, and a vehicle fault prediction module. The vehicle fault information collection module is used to collect relevant information about vehicle faults, and the vehicle fault information processing module processes the information. The prediction communication module is used to achieve information communication between the vehicle fault prediction terminal and the knowledge graph terminal, and compares the processed vehicle fault information with the knowledge graph through the prediction comparison module. Then, the vehicle fault prediction module completes the rapid and accurate prediction of vehicle faults. Embodiment
[0037] Please refer to Figure 1, a system for vehicle fault diagnosis and prediction based on a knowledge graph, which realizes vehicle fault diagnosis and prediction through the knowledge graph, including: a vehicle fault diagnosis terminal, a vehicle fault prediction terminal, and a knowledge graph terminal.
[0038] Vehicle fault diagnosis terminal: Communicates information with the knowledge graph terminal and realizes vehicle fault diagnosis based on the knowledge graph; Vehicle fault prediction terminal: Communicates information with the knowledge graph terminal and realizes vehicle fault prediction based on the knowledge graph; Knowledge graph terminal: Communicates information with the vehicle fault diagnosis terminal and the vehicle fault prediction terminal to ensure fast and accurate diagnosis and prediction of vehicle faults.
[0039] Please refer to Figure 2 , the vehicle fault diagnosis terminal includes: a vehicle fault diagnosis module, a diagnosis information collection module, a diagnosis information processing module, a diagnosis communication module, a diagnosis comparison module, and a vehicle fault diagnosis confirmation module.
[0040] Preferably, in this embodiment, the vehicle fault diagnosis module includes: an acoustic vibration diagnosis unit and an image recognition diagnosis unit, which diagnose vehicle faults through acoustic vibration and image recognition, and collect relevant diagnosis information through the diagnosis information collection module. After collection, the relevant diagnosis information is processed by the diagnosis information processing module to obtain a preliminary diagnosis result of the vehicle fault.
[0041] Preferably, in this embodiment, the diagnosis communication module is used for information communication between the vehicle fault diagnosis terminal and the knowledge graph terminal, and the preliminary diagnosis result of the vehicle fault is compared with the knowledge graph through the diagnosis comparison module. Then, the vehicle fault diagnosis confirmation module makes a final judgment on the vehicle fault, so as to quickly and accurately realize the diagnosis of vehicle faults.
[0042] Please refer to Figure 3 , the vehicle fault prediction terminal includes: a vehicle fault information collection module, a vehicle fault information processing module, a prediction communication module, a prediction comparison module, and a vehicle fault prediction module.
[0043] Preferably, in this embodiment, the vehicle fault information collection module is used to collect relevant information about vehicle faults, and the information is processed by the vehicle fault information processing module. The prediction communication module is used to realize information communication between the vehicle fault prediction terminal and the knowledge graph terminal, and the processed vehicle fault information is compared with the knowledge graph through the prediction comparison module. Then, the vehicle fault prediction module completes the fast and accurate prediction of vehicle faults.
[0044] Please refer to Figure 4, the knowledge graph terminal includes: a knowledge graph storage module, a diagnostic communication module, a prediction communication module, a database module, and a control module. The control module is the core module of the entire vehicle fault diagnosis and prediction system, and is used to implement the core control of the vehicle fault diagnosis terminal, the vehicle fault prediction terminal, and the knowledge graph terminal, ensuring the normal operation of the vehicle fault diagnosis terminal, the vehicle fault prediction terminal, and the knowledge graph terminal.
[0045] Preferably, in this embodiment, the knowledge graph storage module is used to store the graph of vehicle fault-related information and store it through the database module.
[0046] Preferably, in this embodiment, the diagnostic communication module is used to realize the information interaction between the vehicle fault diagnosis terminal and the knowledge graph terminal, facilitating the rapid and accurate diagnosis of vehicle faults, and the prediction communication module is used to realize the information interaction between the vehicle fault prediction terminal and the knowledge graph terminal, facilitating the rapid and accurate prediction of vehicle faults.
[0047] It should be specifically noted that: compared with Embodiment 2, this embodiment details that the knowledge graph terminal includes: a knowledge graph storage module, a diagnostic communication module, a prediction communication module, a database module, and a control module, and the diagnostic communication module is used to realize the information interaction between the vehicle fault diagnosis terminal and the knowledge graph terminal, facilitating the rapid and accurate diagnosis of vehicle faults, and the prediction communication module is used to realize the information interaction between the vehicle fault prediction terminal and the knowledge graph terminal, facilitating the rapid and accurate prediction of vehicle faults. Embodiment
[0048] A method for vehicle fault diagnosis and prediction based on a knowledge graph includes the following steps: S1: Vehicle fault diagnosis: Diagnose vehicle faults through acoustic vibration and image recognition, collect the relevant information of the diagnosis through the diagnostic information collection module, process the relevant information of the diagnosis by the diagnostic information processing module after collection, so as to obtain a preliminary diagnosis result of the vehicle fault, compare the preliminary diagnosis result of the vehicle fault with the knowledge graph through the diagnostic comparison module, and then make a final judgment on the vehicle fault by the vehicle fault diagnosis confirmation module, so as to realize the rapid and accurate diagnosis of vehicle faults; S2: Vehicle fault prediction: Collect the relevant information of vehicle faults through the vehicle fault information collection module, process the information by the vehicle fault information processing module, compare the processed vehicle fault information with the knowledge graph through the prediction comparison module, and then complete the rapid and accurate prediction of vehicle faults by the vehicle fault prediction module.
[0049] Specifically, the present invention realizes the diagnosis and prediction of vehicle faults through a knowledge graph, and diagnoses vehicle faults through acoustic wave vibration and image recognition, and collects relevant diagnostic information through a diagnostic information collection module. After collection, the relevant diagnostic information is processed by a diagnostic information processing module to obtain a preliminary diagnosis result of the vehicle fault. Then, the preliminary diagnosis result of the vehicle fault is compared with the knowledge graph through a diagnostic comparison module, and finally, the vehicle fault diagnosis confirmation module makes a final judgment on the vehicle fault, so as to quickly and accurately realize the diagnosis of vehicle faults. In addition, relevant information on vehicle faults is collected through a vehicle fault information collection module, processed by a vehicle fault information processing module, and the processed vehicle fault information is compared with the knowledge graph through a prediction comparison module. Finally, the vehicle fault prediction module completes the rapid and accurate prediction of vehicle faults.
[0050] Moreover, the diagnosis and prediction of vehicle faults in the present invention do not need to rely on human work experience to complete, which saves time and effort, effectively avoids the situation of incorrect fault diagnosis, and is beneficial to the diagnosis and prediction of vehicle faults.
[0051] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and can be implemented in other specific forms without departing from the spirit or basic characteristics of the present invention. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be included in the present invention.
[0052] In addition, it should be understood that although this specification is described according to embodiments, not every embodiment only contains an independent technical solution. This narrative way of the specification is only for clarity. Those skilled in the art should regard the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.
Claims
1. A system for vehicle fault diagnosis and prediction based on a knowledge graph, which realizes vehicle fault diagnosis and prediction through the knowledge graph, is characterized in that, including: a vehicle fault diagnosis terminal, a vehicle fault prediction terminal, and a knowledge graph terminal; Vehicle fault diagnosis terminal: Through information communication with the knowledge graph terminal and based on the knowledge graph, it realizes the diagnosis of vehicle faults; Vehicle fault prediction terminal: Through information communication with the knowledge graph terminal and based on the knowledge graph, it realizes the prediction of vehicle faults; Knowledge graph terminal: Through information communication with the vehicle fault diagnosis terminal and the vehicle fault prediction terminal, it ensures the fast and accurate diagnosis and prediction of vehicle faults.
2. The system for vehicle fault diagnosis and prediction based on a knowledge graph according to claim 1, characterized in that, The vehicle fault diagnosis terminal includes: a vehicle fault diagnosis module, a diagnosis information collection module, a diagnosis information processing module, a diagnosis communication module, a diagnosis comparison module, and a vehicle fault diagnosis confirmation module.
3. The system for vehicle fault diagnosis and prediction based on a knowledge graph according to claim 2, characterized in that, The vehicle fault diagnosis module includes: an acoustic wave vibration diagnosis unit and an image recognition diagnosis unit. It diagnoses vehicle faults through acoustic wave vibration and image recognition, and collects the relevant diagnosis information through the diagnosis information collection module. After collection, the diagnosis information processing module processes the relevant diagnosis information to obtain a preliminary diagnosis result of the vehicle fault.
4. The system for vehicle fault diagnosis and prediction based on a knowledge graph according to claim 3, wherein The diagnosis communication module is used for information communication between the vehicle fault diagnosis terminal and the knowledge graph terminal, and compares the preliminary diagnosis result of the vehicle fault with the knowledge graph through the diagnosis comparison module. Then, the vehicle fault diagnosis confirmation module makes a final judgment on the vehicle fault, so as to quickly and accurately realize the diagnosis of vehicle faults.
5. The system for vehicle fault diagnosis and prediction based on a knowledge graph according to claim 1, wherein The vehicle fault prediction terminal includes: a vehicle fault information collection module, a vehicle fault information processing module, a prediction communication module, a prediction comparison module, and a vehicle fault prediction module.
6. The system for vehicle fault diagnosis and prediction based on a knowledge graph according to claim 5, wherein The vehicle fault information collection module is used to collect the relevant information of vehicle faults, and the vehicle fault information processing module processes the information. The prediction communication module is used to realize information communication between the vehicle fault prediction terminal and the knowledge graph terminal, and compares the processed vehicle fault information with the knowledge graph through the prediction comparison module. Then, the vehicle fault prediction module completes the fast and accurate prediction of vehicle faults.
7. The system for vehicle fault diagnosis and prediction based on a knowledge graph according to claim 1, wherein The knowledge graph terminal includes: a knowledge graph storage module, a diagnosis intercommunication module, a prediction intercommunication module, a database module, and a control module. The control module is the core module of the entire vehicle fault diagnosis and prediction system, and is used to realize the core control of the vehicle fault diagnosis terminal, the vehicle fault prediction terminal, and the knowledge graph terminal, ensuring the normal operation of the vehicle fault diagnosis terminal, the vehicle fault prediction terminal, and the knowledge graph terminal.
8. The system for vehicle fault diagnosis and prediction based on a knowledge graph according to claim 7, characterized in that, The knowledge graph storage module is used to store the graph of vehicle fault-related information and store it through the database module.
9. The system for vehicle fault diagnosis and prediction based on a knowledge graph according to claim 8, characterized in that, The diagnosis intercommunication module is used to realize information interaction between the vehicle fault diagnosis terminal and the knowledge graph terminal, facilitating the fast and accurate diagnosis of vehicle faults. The prediction intercommunication module is used to realize information interaction between the vehicle fault prediction terminal and the knowledge graph terminal, facilitating the fast and accurate prediction of vehicle faults.
10. A method for vehicle fault diagnosis and prediction based on a knowledge graph as described in claims 1-9, characterized in that, including the following steps: S1: Vehicle fault diagnosis: Diagnose vehicle faults through acoustic wave vibration and image recognition, collect relevant diagnostic information through the diagnostic information collection module, process the relevant diagnostic information after collection by the diagnostic information processing module to obtain a preliminary diagnosis result of the vehicle fault, compare the preliminary diagnosis result of the vehicle fault with the knowledge graph through the diagnostic comparison module, and finally make a final judgment on the vehicle fault by the vehicle fault diagnosis confirmation module, so as to quickly and accurately diagnose the vehicle fault; S2: Vehicle fault prediction: Collect relevant information on vehicle faults through the vehicle fault information collection module, process the information by the vehicle fault information processing module, compare the processed vehicle fault information with the knowledge graph through the prediction comparison module, and finally complete the quick and accurate prediction of vehicle faults by the vehicle fault prediction module.