Automobile intelligent diagnosis method, system and electronic device
By combining an automatic diagnostic system, an intelligent diagnostic model, and a big data decision-making model, the problem of after-sales personnel being unable to quickly locate the cause of a fault has been solved, achieving the effect of accurate location and provision of professional repair solutions.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- JIANGLING MOTORS
- Filing Date
- 2024-10-29
- Publication Date
- 2026-06-02
AI Technical Summary
After-sales personnel are unable to quickly pinpoint the cause of a car malfunction during the repair process, resulting in an inability to provide an accurate and professional repair solution.
By acquiring vehicle fault information, and using an automatic diagnostic system and intelligent diagnostic big data model combined with a big data decision-making model, the final cause of the fault is determined, and a matching repair solution is obtained based on the fault repair knowledge base.
It improves the accuracy of fault diagnosis, enables the rapid provision of accurate repair solutions, and enhances repair efficiency and accuracy.
Smart Images
Figure CN119439952B_ABST
Abstract
Description
Technical Field
[0001] This application belongs to the field of automotive technology, specifically relating to an intelligent vehicle diagnostic method, an intelligent vehicle diagnostic system, and an electronic device. Background Technology
[0002] With the rapid development of the automotive industry and the continuous increase in car ownership, vehicle malfunctions have become a major challenge for OEMs. Currently, vehicle fault diagnosis relies primarily on traditional diagnostic equipment. This equipment often only outputs large amounts of raw data or codes, requiring technicians with high levels of expertise and extensive experience to interpret them accurately. This undoubtedly increases the complexity and uncertainty of diagnosis. Furthermore, the limited expertise and insufficient repair experience of after-sales personnel make it difficult to quickly pinpoint the cause of the fault and provide accurate and professional repair solutions during after-sales maintenance. Summary of the Invention
[0003] The purpose of this application is to provide an intelligent diagnostic method, system, and electronic device for automobiles, which solves the technical problem that after-sales personnel cannot quickly locate the cause of the fault and provide an accurate and professional repair solution during after-sales maintenance.
[0004] To solve the above-mentioned technical problems, this application is implemented as follows:
[0005] In a first aspect, embodiments of this application provide a method for intelligent vehicle diagnostics, the method comprising:
[0006] Obtain fault information of the target vehicle, including fault codes and fault snapshot information;
[0007] The fault information is input into the automatic diagnostic system for fault diagnosis, and the first fault cause of the target vehicle is output. The automatic diagnostic system includes multiple fault diagnosis modules.
[0008] Based on the fault characteristic description information of the target vehicle, the second fault cause of the target vehicle is output through the intelligent diagnostic big model, which is constructed based on historical maintenance big data and decision tree algorithm.
[0009] When the first cause of failure is different from the second cause of failure, the big data decision model determines that one of the first cause of failure and the second cause of failure is the final cause of failure.
[0010] Based on the fault repair knowledge base, obtain a repair solution that matches the final cause of the fault.
[0011] Optionally, the steps of inputting fault information into the automatic diagnostic system for fault diagnosis and outputting the first fault cause of the target vehicle specifically include: in response to the fault code, triggering the automatic diagnostic system to enter the analysis state; simulating the fault-free operation state of the target vehicle through the automatic diagnostic system based on preset fault-free information; determining diagnostic information through multiple fault diagnostic modules in response to fault snapshot information; and determining the first fault cause based on the diagnostic information.
[0012] Optionally, when the first fault cause differs from the second fault cause, the step of determining the final fault cause through a big data decision model specifically includes: performing probability analysis on the first and second fault causes based on historical maintenance big data and generating probability analysis results; performing expert decision-making on the first and second fault causes based on the expert system in the big data decision model and generating expert decision results; and determining the final fault cause through the first and second fault causes based on the probability analysis results and the expert decision results.
[0013] Optionally, the steps of making expert decisions on the first and second causes of failure based on the expert system in the big data decision-making model and outputting the expert decision results specifically include: performing reasoning analysis on the first and second causes of failure based on the expert knowledge base and reasoning algorithm in the expert system to obtain the expert decision results.
[0014] Optionally, after the step of reasoning and analyzing the first and second causes of the fault based on the expert knowledge base and reasoning algorithm in the expert system to obtain the expert decision results, the method further includes: generating expert maintenance suggestions that match the expert decision results based on the expert knowledge base.
[0015] Optionally, the step of determining the first fault cause and the second fault cause as the final fault cause based on the probability analysis results and the expert decision results specifically includes: performing a preliminary weighting allocation on the probability analysis results and the expert decision results to generate a preliminary weighting allocation result; and determining the first fault cause and the second fault cause as the final fault cause based on the preliminary weighting allocation result.
[0016] Optionally, based on the fault characteristic description information of the target vehicle, the intelligent diagnostic big data model outputs the second fault cause of the target vehicle. After the step of constructing the intelligent diagnostic big data model based on historical maintenance big data and decision tree algorithm, the following steps are taken: determining whether the first fault cause is the same as the second fault cause; and inputting the first fault cause or the second fault cause into the fault maintenance knowledge base based on the case that the first fault cause is the same as the second fault cause to obtain a maintenance solution that matches the first fault cause or the second fault cause.
[0017] Optionally, after obtaining a repair solution matching the final cause of the fault based on the fault repair knowledge base, the steps include: obtaining after-sales feedback information of the target vehicle and updating the intelligent diagnostic big model.
[0018] Secondly, embodiments of this application provide an intelligent vehicle diagnostic system that diagnoses vehicle faults according to the intelligent vehicle diagnostic method of the first aspect, including:
[0019] The information acquisition module is used to acquire fault information of the target vehicle, including fault codes and fault snapshot information.
[0020] The automatic diagnostic system module inputs fault information into the automatic diagnostic system for fault diagnosis and outputs the first fault cause of the target vehicle. The automatic diagnostic system includes multiple fault diagnosis modules.
[0021] The intelligent diagnostic big model module is used to output the second cause of the target vehicle's fault based on the fault characteristic description information of the target vehicle. The intelligent diagnostic big model is built on historical maintenance big data and decision tree algorithm.
[0022] The big data decision model module is used to determine, based on the fact that the first cause of failure is different from the second cause of failure, the final cause of failure through the big data decision model.
[0023] The fault repair knowledge base module is used to obtain repair methods that match the cause of the fault based on the fault repair knowledge base.
[0024] Thirdly, embodiments of this application provide an electronic device, which includes a processor, a memory, and a program or instructions stored in the memory and executable on the processor. When the program or instructions are executed by the processor, they implement the vehicle intelligent diagnostic method as described in the first aspect.
[0025] In this embodiment, the fault information of the target vehicle is first obtained, including fault codes and fault snapshot information. This fault information is then input into an automatic diagnostic system for fault diagnosis, outputting the first fault cause of the target vehicle. The automatic diagnostic system includes multiple fault diagnosis modules. Next, based on the fault characteristic description information of the target vehicle, a second fault cause is output through an intelligent diagnostic big data model, which is constructed based on historical maintenance big data and a decision tree algorithm. If the first fault cause differs from the second fault cause, a big data decision model determines one of the first and second fault causes as the final fault cause. Subsequently, a repair solution matching the final fault cause is obtained based on a fault repair knowledge base. In other words, by using the automatic diagnostic system and the intelligent diagnostic big data model to diagnose the target vehicle's faults, the fault cause can be accurately located. When the fault causes obtained by the two systems differ, the big data decision module can determine the final fault cause, improving the accuracy of fault cause diagnosis. The fault repair knowledge base enables the acquisition of a repair solution matching the fault cause. Therefore, this solves the technical problem that after-sales personnel cannot quickly locate the fault cause and provide accurate and professional repair solutions during after-sales repairs. Attached Figure Description
[0026] Figure 1 This is a flowchart illustrating an intelligent vehicle diagnostic method provided by some embodiments of this application.
[0027] Figure 2 This is a logical schematic diagram of an intelligent vehicle diagnostic method provided by some embodiments of this application.
[0028] Figure 3 This is a schematic diagram of an automatic diagnostic system provided by some embodiments of this application.
[0029] Figure 4 This is a schematic diagram of another automatic diagnostic system provided by some embodiments of this application.
[0030] Figure 5 This is a schematic diagram of the structure of an intelligent vehicle diagnostic system provided in some embodiments of this application.
[0031] Figure 6 This is a schematic diagram of the structure of an electronic device provided in some embodiments of this application. Detailed Implementation
[0032] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.
[0033] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.
[0034] The intelligent vehicle diagnostic method provided in this application will be described in detail below with reference to the accompanying drawings, through specific embodiments and application scenarios.
[0035] It is worth noting that the intelligent vehicle fault diagnosis method disclosed in this application is designed to quickly locate the cause of the fault and provide accurate repair solutions when a vehicle malfunctions, enabling after-sales personnel to efficiently perform vehicle inspection and repair, thereby improving repair efficiency and accuracy.
[0036] like Figure 1 and Figure 2 The diagram shows the main flowchart and logic diagram of some embodiments of the intelligent vehicle diagnostic method provided in this application. The intelligent vehicle diagnostic method includes the following steps S101 to S105, wherein:
[0037] S101, Obtain fault information of the target vehicle, including fault codes and fault snapshot information.
[0038] In some embodiments of this application, the automatic diagnostic system can be installed on a diagnostic tool and connected to the target vehicle via an on-board diagnostic (OBD) system to read fault information, including fault codes and fault snapshot information. Alternatively, the automatic diagnostic system can be deployed in the cloud, uploading the target vehicle's fault information to the cloud via a telematics box (TBOX), where the automatic diagnostic system deployed on the diagnostic tool or in the cloud analyzes the fault information.
[0039] In this embodiment, fault information of the target vehicle is obtained, including fault codes and fault snapshot information, which are used by the automatic diagnostic system to perform fault diagnosis and output the first fault cause of the target vehicle.
[0040] It is worth noting that fault codes are specific codes automatically generated by the OBD system built into the target vehicle when it detects an anomaly or potential problem. Fault snapshot information includes Controller Area Network (CAN) bus data within a preset time period when the vehicle records the fault, as well as information such as ambient temperature, humidity, and location identified by the vehicle. This information is saved in the form of a snapshot, which helps the automatic diagnostic system to diagnose the fault. In some models, it is also necessary to collect information from the onboard information system (i.e., the vehicle's infotainment system), such as ambient temperature, humidity, and location. This information collected by the vehicle's infotainment system is not included in the CAN bus data and can be used to supplement the fault snapshot information.
[0041] S102, input the fault information into the automatic diagnostic system for fault diagnosis, and output the first fault cause of the target vehicle. The automatic diagnostic system includes multiple fault diagnosis modules.
[0042] In some embodiments of this application, the fault information includes fault codes and fault snapshot information. In response to the fault codes, the automatic diagnostic system is triggered to enter the analysis state. Based on the preset fault-free information, the automatic diagnostic system simulates the fault-free operating state of the target vehicle.
[0043] In this embodiment, the automatic diagnostic system triggers an analysis state after receiving a fault code; based on preset fault-free information, the automatic diagnostic system simulates the fault-free operation state of the target vehicle, enabling the automatic diagnostic system to analyze the collected fault snapshot information under normal timing.
[0044] In some embodiments of this application, the automatic diagnostic system responds to fault snapshot information and can determine diagnostic information through multiple fault diagnostic modules in the automatic diagnostic system. By locating and analyzing the diagnostic information, the first fault cause can be determined.
[0045] In this embodiment, the automatic diagnostic system includes multiple fault diagnosis modules. Each fault diagnosis module can operate in coordination after the import of fault-free data. By performing logical judgment on the fault snapshot information imported in time sequence, the fault diagnosis module related to the cause of the fault will trigger a key signal during the logical judgment process (the diagnostic information includes the triggered key signal and the time of the triggered key signal). When the automatic diagnostic system receives the diagnostic information, it can locate and analyze the diagnostic information to determine the primary cause of the fault in the target vehicle.
[0046] Please see Figure 3 , Figure 3 This is a schematic diagram of an automatic diagnostic system provided in an embodiment of this application. It can be used for fault diagnosis of new energy vehicles. The fault diagnosis modules of this automatic diagnostic system mainly include: VCU fault diagnosis module, DC-DC fault diagnosis module, MCU fault diagnosis module, BMS fault diagnosis module, TBOX fault diagnosis module, IVI fault diagnosis module, ACM fault diagnosis module, AFS fault diagnosis module, AVM fault diagnosis module, BCM fault diagnosis module, BLEM fault diagnosis module, EPS fault diagnosis module, FSCM fault diagnosis module, GW fault diagnosis module, IC fault diagnosis module, IPB fault diagnosis module, IPM fault diagnosis module, MRR fault diagnosis module, PACM fault diagnosis module, PAM fault diagnosis module, PEPS fault diagnosis module, PSDMR fault diagnosis module, RBU fault diagnosis module, RTM fault diagnosis module, SCU fault diagnosis module, SRR fault diagnosis module, and SRS fault diagnosis module.
[0047] Please see Figure 4 , Figure 4 This is a schematic diagram of another automatic diagnostic system provided in an embodiment of this application, which can be used for fault diagnosis of traditional fuel vehicles. The fault diagnosis modules of this automatic diagnostic system mainly include VCU fault diagnosis module, EMS fault diagnosis module, TCU fault diagnosis module, TBOX fault diagnosis module, IVI fault diagnosis module, FSCM fault diagnosis module, ACM fault diagnosis module, AFS fault diagnosis module, AVM fault diagnosis module, BCM fault diagnosis module, BLEM fault diagnosis module, EPS fault diagnosis module, PAM fault diagnosis module, GW fault diagnosis module, IC fault diagnosis module, IPB fault diagnosis module, IPM fault diagnosis module, MRR fault diagnosis module, PACM fault diagnosis module, SRS fault diagnosis module, PEPS fault diagnosis module, PSDMR fault diagnosis module, RBU fault diagnosis module, RTM fault diagnosis module, SCU fault diagnosis module, and SRR fault diagnosis module.
[0048] S103, based on the fault characteristic description information of the target vehicle, outputs the second fault cause of the target vehicle through the intelligent diagnostic big model, wherein the intelligent diagnostic big model is constructed based on historical maintenance big data and decision tree algorithm;
[0049] In some embodiments of this application, when fault feature description information of the target vehicle is received, the intelligent diagnostic big data model quickly activates its built-in decision tree algorithm. This algorithm, through a series of carefully designed logical branches and judgment conditions, progressively refines and classifies the fault features until a second fault cause is found and output. This process not only relies on the historical maintenance big data learned by the model but also fully utilizes the efficiency and accuracy of the decision tree algorithm, enabling the efficient acquisition of a relatively accurate fault cause.
[0050] In this embodiment, the intelligent diagnostic big data model is constructed based on the decision tree algorithm and historical maintenance big data. It can extract feature parameters of fault performance characteristics from historical maintenance big data to establish nodes; recursively divide each node to generate child nodes; stop dividing when a stopping condition is met, and mark the node as a leaf node. The leaf node represents the cause of the fault. The stopping condition can be that there are no remaining feature parameters that can be used for further division; when fault feature description information is input, the second fault cause can be located according to the logical judgment of the node.
[0051] It is worth noting that the method of inputting fault feature description information into the intelligent diagnostic model depends on the application scenario, and usually includes the following methods: After-sales personnel can manually input the observed fault feature descriptions directly into the intelligent diagnostic model through a keyboard, touch screen or other input devices; After-sales personnel can also import the fault feature description information into the intelligent diagnostic model by uploading files; After-sales personnel can also describe the fault features by voice, and the intelligent diagnostic model can then convert the descriptions into text.
[0052] In some embodiments of this application, after the automatic diagnostic system outputs the first fault cause of the target vehicle and the intelligent diagnostic big model outputs the second fault cause of the target vehicle, there will be a determination process to determine whether the first fault cause is the same as the second fault cause; based on the case that the first fault cause is the same as the second fault cause, the first fault cause or the second fault cause is input into the fault repair knowledge base to obtain a repair solution that matches the first fault cause or the second fault cause.
[0053] In this embodiment, the determination process can rely on the cloud. After the automatic diagnostic system and the intelligent diagnostic big data model determine the cause of the fault, they are uploaded to the cloud. The determination program built in the cloud in advance compares the two causes of the fault to determine whether they are consistent. This step is very important to ensure the accuracy and reliability of the cause of the fault, and helps us to more effectively determine the cause of the fault and obtain the matching repair solution from the fault repair knowledge base.
[0054] S104, if the first cause of failure is different from the second cause of failure, determine the first cause of failure and the second cause of failure as the final cause of failure through a big data decision model;
[0055] In some embodiments of this application, based on historical maintenance big data, a big data decision model is used to perform probability analysis on the first and second causes of the fault, and generate probability analysis results.
[0056] In this embodiment, the first and second causes of failure are statistically analyzed based on historical maintenance big data. Then, a probability analysis is performed to generate a probability analysis result. This probability analysis result calculates the probability of occurrence of the first and second causes of failure and analyzes to determine the more likely cause of failure among the first and second causes of failure.
[0057] In some embodiments of this application, based on the expert system in the big data decision-making model, expert decisions are made on the first fault cause and the second fault cause, and expert decision results are generated. The expert decision results are mainly derived by reasoning and analyzing the first fault cause and the second fault cause through the expert knowledge base and reasoning algorithm in the expert system. Based on the expert knowledge base, expert maintenance suggestions that match the expert decision results are generated.
[0058] In this embodiment, the expert knowledge base and inference algorithm within the expert system are fully utilized for in-depth analysis and decision-making regarding the first and second causes of the fault. The expert knowledge base, the foundation of the expert system, stores rich knowledge and experience from experts across various fields. This knowledge and experience is carefully organized and structured for efficient retrieval and retrieval by the computer system. When the automatic diagnostic system outputs the first cause of the fault and the intelligent diagnostic model outputs the second cause, the expert system first extracts relevant expert opinions and historical cases from the expert knowledge base. The inference algorithm then performs logical reasoning and comprehensive analysis of the first and second causes of the fault based on the extracted expert knowledge and experience. The inference algorithm may include rule-based reasoning, case-based reasoning, model-based reasoning, and other methods. These methods can simulate the expert's thought process, dissecting the fault causes layer by layer to reveal the underlying causes and potential development trends. Based on this reasoning, the expert system generates an expert decision result. This result includes selecting the fault cause that best aligns with expert knowledge from the first and second causes, and generating expert maintenance suggestions that match the expert decision result based on the expert knowledge base.
[0059] It is worth noting that the decision results of an expert system are not absolute, but rather the optimal solution based on currently available information and expert knowledge. As new data and knowledge are continuously added, the decision results of the expert system may be adjusted and optimized accordingly.
[0060] In some embodiments of this application, the big data decision model will initially assign weights between the probability analysis results and the expert decision results, and based on the initial weighting results, determine one of the first fault cause and the second fault cause as the final fault cause.
[0061] In this embodiment, the probability analysis results and expert decision results are fused according to a preliminary weighting strategy. This weighting strategy may be based on various factors, such as data reliability, the authority of expert knowledge, and the specificity of the fault phenomenon. Through reasonable weighting settings, it ensures that both results are appropriately reflected in the final decision. During this fusion process, advanced algorithms such as weighted averaging and Bayesian fusion can be used to quantify the probability analysis results and expert decision results and calculate their respective contributions to the final decision. Finally, based on this preliminary weighting result, one of the first and second fault causes is determined as the final fault cause. This method not only improves the accuracy and efficiency of fault diagnosis but also adapts to the complexity of different fault scenarios.
[0062] S105, based on the fault repair knowledge base, obtain a repair solution that matches the final cause of the fault.
[0063] In this embodiment, the fault repair knowledge base can be built in the cloud, including but not limited to repair manuals, repair illustrated guides, tool usage guides, and basic vehicle knowledge. A fault classification system can also be established, categorizing fault causes according to equipment type, fault symptoms, etc., for quick subsequent retrieval. Furthermore, an efficient retrieval mechanism can be built to ensure that after-sales personnel can quickly find a repair solution matching the current fault of the target vehicle simply by inputting the fault cause. By establishing a fault repair knowledge base and designing a reasonable classification system, professional repair solutions matching the finally determined fault cause can be quickly and accurately obtained, providing strong support for after-sales personnel to quickly handle faults during the repair process.
[0064] In some embodiments of this application, after obtaining a matching repair solution, the repair solution can be returned to the diagnostic tool or to the vehicle information system.
[0065] In this embodiment, when after-sales personnel diagnose a fault using a diagnostic tool, after successfully obtaining a repair plan that perfectly matches the vehicle fault through this automotive diagnostic method, they can not only directly return this carefully crafted repair plan to the diagnostic tool for precise guidance in subsequent fault diagnosis and repair work, but also choose to securely return the repair plan to the vehicle's own onboard information system (V2X) via a specific data transmission method. In this way, the vehicle's V2X can receive this targeted repair plan in real time and provide after-sales personnel or vehicle owners with more intuitive and detailed repair information. This flexible and diverse repair plan transmission method not only greatly improves the efficiency and accuracy of repair work, but also brings a more convenient and efficient repair service experience to vehicle owners.
[0066] In some embodiments of this application, it is also necessary to obtain after-sales feedback information provided by after-sales personnel after repairing the target vehicle using the repair plan provided by the automotive intelligent system. By inputting the after-sales feedback information into the intelligent diagnostic big model, the intelligent diagnostic big model can be periodically trained and updated.
[0067] In this embodiment, after-sales feedback information includes fault symptoms, whether the system accurately identifies the fault, whether the repair method provided by the system is accurate, and whether the fault has been resolved. Collecting after-sales feedback information and periodically training the intelligent diagnostic model can continuously improve the efficiency and accuracy of fault diagnosis and repair.
[0068] In this embodiment, the fault information of the target vehicle is first obtained, including fault codes and fault snapshot information. This fault information is then input into an automatic diagnostic system for fault diagnosis, outputting the first fault cause of the target vehicle. The automatic diagnostic system includes multiple fault diagnosis modules. Next, based on the fault characteristic description information of the target vehicle, a second fault cause is output through an intelligent diagnostic big data model, which is constructed based on historical maintenance big data and a decision tree algorithm. If the first fault cause differs from the second fault cause, a big data decision model determines one of the first and second fault causes as the final fault cause. Subsequently, a repair solution matching the final fault cause is obtained based on a fault repair knowledge base. In other words, by using the automatic diagnostic system and the intelligent diagnostic big data model to diagnose the target vehicle's faults, the fault cause can be accurately located. When the fault causes obtained by the two systems differ, the big data decision module can determine the final fault cause, thereby improving the accuracy of fault cause diagnosis. Furthermore, the fault repair knowledge base enables the acquisition of a repair solution matching the fault cause, solving the technical problem that after-sales personnel cannot quickly locate the fault cause and provide accurate and professional repair solutions during after-sales repairs. This provides strong support for after-sales personnel to quickly handle faults during the repair process.
[0069] It should be noted that the vehicle intelligent diagnostic system provided in this application embodiment can be executed by the vehicle intelligent diagnostic system itself, or by a control module within that system for executing the vehicle intelligent diagnostic method. This application embodiment uses the execution of the vehicle intelligent diagnostic method by the vehicle intelligent diagnostic system as an example to illustrate the vehicle intelligent diagnostic method provided in this application embodiment.
[0070] The automotive diagnostic system in this application embodiment can be a system, or a component, integrated circuit, or chip in a terminal. The system can be a mobile electronic device or a non-mobile electronic device. For example, mobile electronic devices can be in-vehicle electronic devices, wearable devices, etc., while non-mobile electronic devices can be servers, network attached storage (NAS), personal computers (PCs), etc. This application embodiment does not impose specific limitations.
[0071] The intelligent vehicle diagnostic system provided in this application embodiment can achieve... Figure 1 To avoid repetition, the various processes implemented in the method embodiments will not be described again here.
[0072] Please see Figure 5 , Figure 5 The diagram shown is a structural schematic of an automotive diagnostic system provided in an embodiment of the second aspect of the present invention. The system includes:
[0073] The information acquisition module 201 is used to acquire fault information of the target vehicle. The fault information includes fault codes and fault snapshot information.
[0074] In this embodiment, the information acquisition module 201 can acquire fault information from the vehicle, including fault codes and fault snapshot information. It can also acquire vehicle-mounted information collected from the vehicle's infotainment system to supplement the fault snapshot information, which is beneficial for acquiring fault information and provides a data foundation for the diagnosis of the automatic diagnostic system.
[0075] Automatic diagnostic system module 202 is used to input fault information into the automatic diagnostic system for fault diagnosis and output the first fault cause of the target vehicle; wherein, the automatic diagnostic system includes multiple fault diagnosis modules;
[0076] In this embodiment, fault information is input from the information acquisition module 201, and the automatic diagnosis system module 202 responds to the fault code and triggers the automatic diagnosis system to enter the analysis state; based on the preset fault-free information, the automatic diagnosis system simulates the fault-free operation state of the target vehicle; in response to the fault snapshot information, the diagnostic information is determined through multiple fault diagnosis modules; based on the diagnostic information, the first fault cause of the target vehicle is determined.
[0077] The intelligent diagnostic big model module 203 is used to output the second cause of the target vehicle's fault based on the fault characteristic description information of the target vehicle through the intelligent diagnostic big model.
[0078] In this embodiment, based on the fault feature description information obtained by the information acquisition module 201, the second cause of the target vehicle's fault is determined through logical judgment by an intelligent diagnostic system based on historical big data and decision tree algorithms. Furthermore, the system can periodically train and update the intelligent diagnostic model based on input after-sales feedback information.
[0079] The big data decision module 204 is used to determine, through a big data decision model, the final fault cause when the first fault cause is different from the second fault cause.
[0080] In this embodiment, the first and second fault causes are obtained from the automatic diagnostic system module 202 and the intelligent diagnostic big data model module 203. Based on historical maintenance big data, a big data decision model is used to perform probability analysis on the first and second fault causes, generating probability analysis results. Based on the expert system within the big data decision model, expert decisions are made on the first and second fault causes, generating expert decision results. These expert decision results are mainly derived through reasoning and analysis of the first and second fault causes using the expert knowledge base and reasoning algorithms within the expert system. Based on the expert knowledge base, expert maintenance suggestions matching the expert decision results are generated. The big data decision model performs a preliminary weighting allocation between the probability analysis results and the expert decision results. Based on this preliminary weighting allocation, one of the first and second fault causes is determined as the final fault cause.
[0081] The fault repair knowledge base module 205 is used to obtain a repair solution that matches the final cause of the fault based on the fault repair knowledge base.
[0082] In this embodiment, the fault repair knowledge base module 205 can quickly and accurately obtain repair solutions that match the finally determined fault cause, providing strong support for after-sales personnel to quickly handle faults during the repair process.
[0083] In this embodiment, the fault information of the target vehicle is first obtained, including fault codes and fault snapshot information. This fault information is then input into an automatic diagnostic system for fault diagnosis, outputting the first fault cause of the target vehicle. The automatic diagnostic system includes multiple fault diagnosis modules. Next, based on the fault characteristic description information of the target vehicle, a second fault cause is output through an intelligent diagnostic big data model, which is constructed based on historical maintenance big data and a decision tree algorithm. If the first fault cause differs from the second fault cause, a big data decision model determines one of the first and second fault causes as the final fault cause. Subsequently, a repair solution matching the final fault cause is obtained based on a fault repair knowledge base. In other words, by using the automatic diagnostic system and the intelligent diagnostic big data model to diagnose the target vehicle's faults, the fault cause can be accurately located. When the fault causes obtained by the two systems differ, the big data decision module can determine the final fault cause, thereby improving the accuracy of fault cause diagnosis. Furthermore, the fault repair knowledge base enables the acquisition of a repair solution matching the fault cause, solving the technical problem that after-sales personnel cannot quickly locate the fault cause and provide accurate and professional repair solutions during after-sales repairs. This provides strong support for after-sales personnel to quickly handle faults during the repair process.
[0084] An embodiment of the third aspect of this application also provides an electronic device, such as... Figure 6As shown, the electronic device includes a processor 301, a memory 302, and a program or instruction 303 stored in the memory and executable on the processor. When the program or instruction is executed by the processor, it implements the various processes of the above-mentioned vehicle intelligent diagnostic method embodiment and can achieve the same technical effect. To avoid repetition, it will not be described again here.
[0085] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, it should be noted that the scope of the methods and apparatuses in the embodiments of this application is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.
[0086] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, 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 is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.
[0087] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.
Claims
1. An automobile intelligent diagnosis method, characterized in that, The method includes: Obtain fault information of the target vehicle, including fault codes and fault snapshot information; wherein, the fault snapshot information includes CAN bus data, vehicle ambient temperature, humidity and location information within a preset time period when the fault occurred; The fault information is input into the automatic diagnostic system for fault diagnosis, and the first fault cause of the target vehicle is output. The automatic diagnostic system includes multiple fault diagnosis modules. Based on the fault characteristic description information of the target vehicle, the second fault cause of the target vehicle is output through the intelligent diagnostic big data model, wherein the intelligent diagnostic big data model is constructed based on historical maintenance big data and decision tree algorithm; When the first cause of failure is different from the second cause of failure, the first cause of failure and the second cause of failure are determined as the final cause of failure through a big data decision model. Based on the fault repair knowledge base, obtain a repair solution that matches the final cause of the fault; Obtain after-sales feedback information for the target vehicle and update the intelligent diagnostic model accordingly.
2. The automobile intelligent diagnosis method according to claim 1, characterized in that, The step of inputting the fault information into the automatic diagnostic system for fault diagnosis and outputting the first fault cause of the target vehicle specifically includes: In response to the fault code, the automatic diagnostic system is triggered to enter the analysis state; Based on preset fault-free information, the automatic diagnostic system simulates the fault-free operating state of the target vehicle. In response to the fault snapshot information, diagnostic information is determined through the plurality of fault diagnosis modules; Based on the diagnostic information, the first cause of the fault is determined.
3. The automobile intelligent diagnosis method according to claim 1, characterized in that, The step of determining, based on the fact that the first fault cause is different from the second fault cause, the final fault cause through a big data decision model specifically includes: Based on the historical maintenance big data, the big data decision-making model is used to perform probability analysis on the first fault cause and the second fault cause, and generate probability analysis results. Based on the expert system in the big data decision-making model, expert decisions are made on the first fault cause and the second fault cause, and expert decision results are generated. Based on the probability analysis results and the expert decision results, one of the first fault cause and the second fault cause is determined as the final fault cause.
4. The automobile intelligent diagnosis method according to claim 3, characterized in that, The step of using the expert system in the big data decision-making model to make expert decisions on the first cause of failure and the second cause of failure, and outputting the expert decision results, specifically includes: Based on the expert knowledge base and reasoning algorithm in the expert system, reasoning analysis is performed on the first fault cause and the second fault cause to obtain expert decision results.
5. The automobile intelligent diagnosis method according to claim 4, characterized in that, After the step of reasoning and analyzing the first fault cause and the second fault cause based on the expert knowledge base and reasoning algorithm in the expert system to obtain the expert decision result, the method further includes: Based on the expert knowledge base, expert maintenance suggestions that match the expert decision results are generated.
6. The automobile intelligent diagnosis method according to claim 3, characterized in that, The step of determining one of the first fault cause and the second fault cause as the final fault cause based on the probability analysis results and the expert decision results specifically includes: The probability analysis results and the expert decision results are initially weighted and assigned to generate preliminary weighting results; Based on the preliminary weighting results, one of the first fault cause and the second fault cause is determined as the final fault cause.
7. The automobile intelligent diagnosis method according to claim 1, characterized in that, The step of outputting the second cause of the target vehicle's fault based on the fault characteristic description information of the target vehicle through the intelligent diagnostic big data model, wherein the intelligent diagnostic big data model is constructed based on historical maintenance big data and decision tree algorithm, includes the following: Determine whether the cause of the first fault is the same as the cause of the second fault; If the first fault cause is the same as the second fault cause, the first fault cause or the second fault cause is input into the fault repair knowledge base to obtain a repair solution that matches the first fault cause or the second fault cause.
8. An intelligent diagnosis system for a vehicle, characterized by, The system includes: The information acquisition module is used to acquire fault information of the target vehicle, including fault codes and fault snapshot information; wherein, the fault snapshot information includes CAN bus data, vehicle ambient temperature, humidity and location information within a preset time period when the fault occurred. An automatic diagnostic system module inputs the fault information into the automatic diagnostic system for fault diagnosis and outputs the first fault cause of the target vehicle. The automatic diagnostic system includes multiple fault diagnosis modules. The intelligent diagnostic big data model module is used to output the second cause of the target vehicle's fault based on the fault characteristic description information of the target vehicle. The intelligent diagnostic big data model is constructed based on historical maintenance big data and decision tree algorithm. The big data decision model module is used to determine, based on the fact that the first fault cause is different from the second fault cause, the final fault cause by using a big data decision model. The fault repair knowledge base module is used to obtain repair solutions that match the cause of the fault based on the fault repair knowledge base; The update module is used to obtain after-sales feedback information of the target vehicle and update the intelligent diagnostic model.
9. An electronic device, comprising: The electronic device includes a processor, a memory, and storage on the memory. And a program or instruction that can run on the processor, wherein the program or instruction, when executed by the processor, implements the vehicle intelligent diagnostic method as described in any one of claims 1 to 7.