Intelligent diagnosis method of vehicle and related device

Abstract

The invention discloses an intelligent diagnosis method of a vehicle and a related device. The method comprises the following steps: receiving an instrument panel image of the vehicle uploaded by a user; extracting vehicle features of the instrument panel image; acquiring state information of the vehicle in a first time period; determining a target fault type according to the state information and the vehicle features when detecting that the vehicle features include a first feature representing vehicle attributes, a second feature representing a vehicle fault state and a third feature representing a vehicle operation state; determining a target analysis agent according to the target fault type; determining a fault diagnosis report of the vehicle according to a target analysis agent and the vehicle characteristics; and generating a fault processing strategy according to the fault diagnosis report. The diagnosis cost can be reduced, and the diagnosis efficiency can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of fault diagnosis, and in particular to an intelligent diagnosis method and related devices for a vehicle. Background Art

[0002] The evolution of automotive fault diagnosis technology reflects a profound shift from traditional manual testing to intelligent diagnosis. Currently, diagnostics are primarily performed through on-board diagnostics (OBD) equipment or remote diagnosis by experienced technicians. However, OBD equipment is expensive and requires specialized personnel, making it difficult for ordinary users to use it. Remote diagnosis, on the other hand, relies on manual experience, lacks real-time vehicle status, and is cumbersome, making it difficult to ensure the accuracy and efficiency of repair recommendations. Summary of the Invention

[0003] The embodiments of the present application provide a vehicle intelligent diagnosis method and related devices to reduce diagnosis costs and improve diagnosis efficiency.

[0004] In a first aspect, an embodiment of the present application provides an intelligent vehicle diagnostic method, comprising:

[0005] Extracting vehicle features from the dashboard image of the vehicle uploaded by the user;

[0006] Acquiring status information of the vehicle within a first time period, where the first time period includes a first time period before a fault moment and a second time period after the fault moment;

[0007] detecting that the vehicle characteristics include a first characteristic characterizing a vehicle attribute, a second characteristic characterizing a vehicle fault state, and a third characteristic characterizing a vehicle operating state, and determining a target fault type based on the state information and the vehicle characteristics;

[0008] Determining a target analysis agent according to the target fault type;

[0009] Determining a fault diagnosis report for the vehicle based on a target analysis agent and characteristics of the vehicle;

[0010] A fault handling strategy is generated according to the fault diagnosis report.

[0011] Wherein, determining the target fault type according to the state information and the vehicle characteristics includes:

[0012] determining at least one first fault type according to the first feature and the second feature;

[0013] determining at least one second fault type according to the second feature and the third feature;

[0014] The target fault type is determined from among the at least one first fault type and the at least one second fault type according to the state information.

[0015] The determining, according to the state information, the target fault type from the at least one first fault type and the at least one second fault type includes:

[0016] determining a driving characteristic of the user;

[0017] determining a matching degree between the driving characteristic and each first fault type of the at least one first fault type to obtain at least one first matching degree; and determining a matching degree between the driving characteristic and each second fault type of the at least one second fault type to obtain at least one second matching degree;

[0018] Determining a degree of matching between the state information and each of the first fault types to obtain at least one third degree of matching; and determining a degree of matching between the state information and each of the second fault types to obtain at least one fourth degree of matching;

[0019] The target fault type is determined according to the at least one first matching degree, the at least one second matching degree, the at least one third matching degree, and the at least one fourth matching degree.

[0020] The determining of the target fault type according to the at least one first matching degree, the at least one second matching degree, the at least one third matching degree, and the at least one fourth matching degree includes:

[0021] obtaining maintenance data of the vehicle;

[0022] determining, based on the maintenance data, a first fault frequency of each first fault type and a second fault frequency of each second fault type in a preset historical period;

[0023] Determining a probability value of each first fault type based on the first fault frequency, the at least one third matching degree, and the at least one first matching degree; and determining a probability value of each second fault type based on the second fault frequency, the at least one second matching degree, and the at least one fourth matching degree;

[0024] The target fault type is determined according to the probability value of each first fault type and the probability value of each second fault type.

[0025] The method further comprises:

[0026] detecting that the vehicle features include the second feature and the third feature, then receiving the vehicle identification image uploaded by the user;

[0027] Extracting a feature of the vehicle identification image to obtain a fourth feature;

[0028] The target fault type is determined according to the fourth feature, the second feature, the third feature, and the status information.

[0029] Before receiving the vehicle identification image uploaded by the user, the method further includes:

[0030] determining at least one photographic location of the vehicle;

[0031] determining a shooting strategy for each of the at least one shooting location;

[0032] Guidance information is generated according to each shooting position and the shooting strategy corresponding to each shooting position, where the guidance information is used to guide the user to upload the vehicle identification image.

[0033] The step of determining a fault diagnosis report for the vehicle based on a target analysis agent and the vehicle characteristics includes:

[0034] Acquiring driving data and maintenance data of the vehicle;

[0035] Analyzing the vehicle characteristics, the driving data, and the maintenance data using the target analysis agent to obtain an analysis result;

[0036] If the analysis result indicates that the analysis is successful, generating the fault diagnosis report;

[0037] If the parsing result indicates that the parsing failed, multiple failure reasons are output.

[0038] In a second aspect, an embodiment of the present application provides an intelligent diagnostic device for a vehicle, comprising:

[0039] An extraction unit, configured to extract vehicle features from a vehicle dashboard image uploaded by a user;

[0040] an acquiring unit, configured to acquire status information of the vehicle within a first time period, the first time period including a first time period before a fault moment and a second time period after the fault moment;

[0041] a first determining unit configured to detect that the vehicle characteristics include a first characteristic representing a vehicle attribute, a second characteristic representing a vehicle fault state, and a third characteristic representing a vehicle operating state, and then determine a target fault type based on the state information and the vehicle characteristics;

[0042] A second determining unit, configured to determine a target analysis agent according to the target fault type;

[0043] a third determining unit, configured to determine a fault diagnosis report of the vehicle based on a target analysis agent and characteristics of the vehicle;

[0044] A generating unit is used to generate a fault handling strategy according to the fault diagnosis report.

[0045] In a third aspect, an embodiment of the present application provides an electronic device comprising a memory, a processor, and an executable program code stored in the memory and runnable on the processor, wherein the processor executes the steps of the method described in the first aspect when executing the executable program code.

[0046] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which executable program code is stored. The executable program code includes execution instructions, and the execution instructions are used to execute the steps of the method described in the first aspect.

[0047] In a fifth aspect, embodiments of the present application provide a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program, wherein the computer program is operable to cause a computer to perform some or all of the steps described in the first aspect of the embodiments of the present application. The computer program product may be a software installation package.

[0048] It can be seen that in an embodiment of the present application, first, a dashboard image of a vehicle uploaded by a user is received; then, the vehicle features of the dashboard image are extracted; then, the status information of the vehicle within a first time period is obtained, and the first time period includes a first time period before the fault moment and a second time period after the fault moment; then, it is detected that the vehicle features include a first feature characterizing the vehicle attributes, a second feature characterizing the vehicle fault state, and a third feature characterizing the vehicle operating state, and then, based on the status information and the vehicle features, a target fault type is determined; then, a target analysis agent is determined based on the target fault type; then, a fault diagnosis report of the vehicle is determined based on the target analysis agent and the vehicle features; finally, a fault handling strategy is generated based on the fault diagnosis report.

[0049] It can be seen that in the application, the user only needs to provide an image of the vehicle's dashboard and extract vehicle features in multiple dimensions through image recognition to quickly obtain fault diagnosis results without relying on professional equipment or technician experience, thereby reducing diagnostic costs and improving diagnostic efficiency and user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0051] Figure 1 This is a system architecture diagram of an intelligent diagnostic system provided by an embodiment of the present application;

[0052] Figure 2 This is a flow chart of a vehicle intelligent diagnosis method provided by an embodiment of the present application;

[0053] Figure 3 is a schematic diagram of a diagnostic operation interface provided in an embodiment of the present application;

[0054] Figure 4 is a schematic diagram of a fault diagnosis interface provided in an embodiment of the present application;

[0055] Figure 5 This is a schematic diagram of a prompt window provided in an embodiment of the present application;

[0056] Figure 6 This is a block diagram of the functional units of an intelligent diagnostic device for a vehicle provided in an embodiment of the present application;

[0057] Figure 7 This is a block diagram of the functional units of another vehicle intelligent diagnostic device provided by an embodiment of the present application;

[0058] Figure 8 This is a structural diagram of an electronic device proposed in an embodiment of the present application. DETAILED DESCRIPTION

[0059] In order to enable those skilled in the art to better understand the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0060] The terms "first," "second," and the like in the specification and claims of this application and the accompanying drawings are used to distinguish between different objects, not to describe a particular order. Furthermore, the terms "including," "having," and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or elements is not limited to the listed steps or elements but may optionally include steps or elements not listed, or may optionally include other steps or elements inherent to the process, method, product, or apparatus.

[0061] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0062] Existing vehicle diagnostic methods rely heavily on OBD (on-board diagnostic) devices or other specialized diagnostic equipment, or remote diagnosis by experienced technicians. However, diagnostic equipment is expensive and requires specialized personnel, making it difficult for ordinary users to use on their own. Furthermore, remote diagnosis relies on both manual experience and real-time data transmission from devices, neither of which can provide real-time insights into the vehicle's dynamic operating status, resulting in delays in the diagnostic process.

[0063] At the same time, existing diagnostic methods are primarily based on preset fault codes or manual experience, making it difficult to fully utilize the vehicle's multi-source data, such as images, sensor data, and historical operating data, for comprehensive analysis. This results in limited diagnostic accuracy and is prone to misjudgments or missed diagnosis.

[0064] Whether it is OBD-based diagnosis or remote diagnosis, it relies on experienced technicians to operate and interpret the diagnostic results. Ordinary car owners find it difficult to complete vehicle fault diagnosis independently due to lack of professional knowledge and experience.

[0065] In response to the above problems, an embodiment of the present application provides an intelligent diagnosis method and related devices for a vehicle. The embodiment of the present application is described in detail below with reference to the accompanying drawings.

[0066] See also Figure 1 , Figure 1 This is a system architecture diagram of an intelligent diagnostic system provided by an embodiment of the present application. Figure 1 As shown, the intelligent diagnosis system 100 includes a data acquisition layer 101, an image processing and recognition layer 102, a diagnosis and analysis layer 103, and a user interaction layer 104. The data acquisition layer 101, the image processing and recognition layer 102, the diagnosis and analysis layer 103, and the user interaction layer 104 are interconnected.

[0067] The data acquisition layer 101 is responsible for acquiring relevant vehicle image data, such as dashboard images. Users can use their mobile phones or other devices to take photos of relevant vehicle parts. This relevant image data will serve as the basis for diagnosis. It supports a variety of shooting devices, such as mobile phones and tablets. It also provides shooting guides to help users obtain high-quality image data. It supports local storage and upload of image data.

[0068] The image processing and recognition layer 102 receives the vehicle image data collected by the data acquisition layer 101 and performs operations such as denoising, enhancement, and cropping on the image to ensure that the image quality meets recognition requirements. A deep learning model is then used to extract key features from the image, such as the shape, color, and location of vehicle components. Furthermore, the system identifies and records instrument panel fault codes, warning light status, and any abnormalities in the vehicle's exterior and interior.

[0069] Among them, the diagnostic analysis layer 103 is used to receive the image recognition results output by the image processing and recognition layer 102. Based on the image recognition results, combined with the vehicle's historical data and operating status, and the fault information appearing on the dashboard, the fault is accurately located and analyzed through the intelligent agent, and then the fault component and fault type are accurately located, and a fault diagnosis report and fault handling strategy are generated, which are then transmitted to the user.

[0070] The user interaction layer 104 provides a user-friendly interface, allowing users to upload images and receive diagnostic results via mobile software or a webpage. It also supports real-time feedback and personalized recommendations to help users better understand the vehicle's status.

[0071] Based on this, the present application provides an intelligent diagnosis method and related devices for a vehicle, which will be described in detail below with reference to the accompanying drawings.

[0072] See also Figure 2 , Figure 2 This is a flow chart of a vehicle intelligent diagnosis method provided by an embodiment of the present application, such as Figure 2 As shown, the method includes the following steps:

[0073] S210: Extract vehicle features from the vehicle dashboard image uploaded by the user.

[0074] Users can access the vehicle diagnostic system through mobile software or the web. If accessing through mobile software, users first search for the corresponding vehicle diagnostic software in the phone's app store, then download and install it. After installation, open the software, register an account, fill in relevant personal information, and set a password. After successful registration, log in to the software using the registered account and password. Third-party account login is also supported, making it convenient for users to log in quickly. After logging in, connect the phone to the associated vehicle in a specific way, bind the associated vehicle, and obtain various sensor data, positioning data, and maintenance data of the vehicle.

[0075] Among them, the various sensors may include engine running time sensor, door switch sensor, key insertion / start sensor, etc. These sensors can record information such as the number of vehicle starts, engine running time, number of door openings and closings, etc., and thus infer the frequency of vehicle use.

[0076] After successful binding, the user enters the main interface of the vehicle diagnostic system. In the main interface, the user can see various function options such as fault diagnosis, real-time data monitoring, historical diagnostic information, etc., which is simple and intuitive.

[0077] Among them, if entering through the web page, the user opens any browser on the computer or mobile device and enters the web page address of the vehicle diagnostic system in the browser's address bar. After entering the login page of the web page, enter the registered account and password to log in. If it is the first visit, first register an account, fill in the relevant information to complete the registration, and then log in. After a successful login, if it is the first time to use it, you may need to bind the associated vehicle first in order to accurately identify the vehicle and provide corresponding diagnostic services. If it is not the first time to use it, the web page enters the main interface of the vehicle diagnostic system. The main interface may also display a list of vehicles associated with the user. If the user clicks on the vehicle icon or name, he can view the historical fault information, historical operation data, etc. of the vehicle.

[0078] In one possible embodiment, see Figure 3 , Figure 3 is a schematic diagram of a diagnostic operation interface provided by an embodiment of the present application, such as Figure 3 As shown, the diagnostic operation interface may include a personal center control, a fault diagnosis control, a data monitoring control, and the like.

[0079] Clicking the Personal Center control takes you to the Personal Information interface, which includes a Personal Information display area that primarily displays the user's basic information, which can be arranged in a list or card format. The Personal Information interface also includes a Vehicle Information Management area, where users can view information about their bound vehicles. For example, multiple vehicle controls may be included, each corresponding to a bound vehicle. Each vehicle control may be named, for example, a vehicle brand or icon. By clicking a single vehicle control, users can view detailed vehicle information, such as the purchase date, mileage, and historical maintenance data.

[0080] Click the fault diagnosis control to enter the fault diagnosis interface. Figure 4 , Figure 4 is a schematic diagram of a fault diagnosis interface provided by an embodiment of the present application, such as Figure 4 As shown, the fault diagnosis interface includes a prompt information generation control and multiple upload controls. Clicking the prompt information generation control generates a prompt message, which prompts the user to capture the vehicle parts, such as the dashboard, exterior, and interior. It also provides shooting guidelines to ensure image quality. Clicking the control with the "+" icon uploads an image.

[0081] Among them, the system supports local storage and instant upload functions.

[0082] Different types of images can correspond to different shooting guidelines. These guidelines can include information such as shooting angle, shooting orientation, and shooting details. For example, if the image is of a dashboard, the shooting guide could be a prompt message that reminds the user to start the vehicle before shooting and to ensure that the dashboard indicators and display are properly illuminated to fully capture their operating status. It also reminds the user to capture the entire dashboard, including the speedometer, tachometer, various indicators, and display.

[0083] In one possible embodiment, the user uses a mobile phone or other terminal to take photos of relevant parts of the vehicle and uploads them to the system through software or a web page.

[0084] After receiving the image uploaded by the user, the system will perform a quality assessment. If the quality assessment fails, a prompt window will pop up on the fault diagnosis interface to prompt that the image quality is unqualified and point out the reason for the failure, such as the image quality is unqualified and the tachometer-related data is unclear.

[0085] In a possible embodiment, the uploaded image may be subjected to clarity analysis, contrast analysis, and integrity analysis to determine whether the image uploaded by the user is qualified.

[0086] Clarity analysis can be performed by calculating the image's information entropy, which measures the richness of information in an image. High-definition images have greater information entropy because they contain more detail and variation. By calculating the information entropy of an image, the overall clarity of the image can be assessed. For example, a clear instrument panel image, where elements such as the speedometer, tachometer, and various indicator lights are clearly displayed, will have a relatively high information entropy. After the calculated information entropy is compared with a first preset threshold, the quality is determined.

[0087] Contrast analysis can be performed by combining local and global contrast. Specifically, for local contrast, for each pixel, a statistic such as the mean or standard deviation of the grayscale difference between the pixel and its neighboring pixels is calculated as the local contrast value for that pixel. For example, using a 3×3 neighborhood window, the grayscale difference between the center pixel and the eight surrounding pixels is calculated and then averaged. A large average value indicates that the local contrast in the area where the pixel resides is high. For global contrast, the mean grayscale value and standard deviation of the grayscale values can be calculated for the entire image. The mean grayscale value reflects the overall brightness of the image, while the standard deviation reflects the dispersion of the grayscale values relative to the mean. A larger standard deviation indicates a higher global contrast. For example, a bright instrument panel image has a high mean grayscale value, but if the standard deviation is also large, it indicates that the image contains both dark and bright areas and has good overall contrast. Finally, weights are determined for each local contrast and global contrast, and a weighted sum is performed to obtain the image contrast result. After the calculated contrast result is compared with a second preset threshold to determine whether it meets the quality requirements.

[0088] The image is checked to see if it fully contains the expected key feature-related elements for completeness analysis. Specifically, based on the task requirements, all necessary key feature-related elements are listed in detail. For example, in the case of an instrument panel image, these elements may include the speedometer, tachometer, various indicator lights, and display screens. The image is then divided into different regions, each corresponding to a set of key feature-related elements. Multi-source image comparison is then performed to determine the completeness of the key feature-related elements within each region. If any key feature-related element is incomplete, the image quality is considered unacceptable.

[0089] In one possible embodiment, the results of the clarity, contrast, and completeness analyses are used to determine whether the user needs to re-upload the image. If clarity, contrast, or a key feature-related element is not satisfactory, the shooting guide is updated based on the analysis results, guiding the user to re-upload a high-quality image to meet the image recognition requirements.

[0090] Among them, clicking on the data monitoring control can enter the data display interface, which includes real-time data controls and historical data controls. If you click on the real-time data control, the data of each vehicle sensor will be displayed in real time; if you click on the historical data control, the user can query the data records of the vehicle over the past period of time. There will be a calendar control at the top of the interface. The user can specify the query time period by selecting a date, accurate to the day or month. After selecting the query time, the data display area below will present the data records within the time period in a list format, including the timestamp of the record, the values of various data parameters, and any anomaly marks. The user can further click on a record to view more detailed data information, such as the data change curve at a specific moment, correlation analysis with other data, etc. When the user clicks on a record to view detailed information, a new window will pop up or detailed data charts and analysis results will be displayed below the original interface.

[0091] In one possible embodiment, pre-processing and feature processing are performed on the image uploaded by the user. Specifically, a quality assessment result of the image can be obtained, and the image can be pre-processed based on the quality assessment result. Exemplary operations such as denoising, enhancement, and cropping can be performed to ensure that the image quality meets recognition requirements.

[0092] In one possible embodiment, if the quality assessment result indicates that the clarity is acceptable but does not meet image recognition requirements, image enhancement processing can be performed. For example, using the Laplacian operator to calculate the difference between each pixel in the image and its surrounding pixels to highlight image edges and details, thereby improving image recognizability. Denoising processing can also be performed. Using mean filtering, the average value of neighboring pixels is used to replace the current pixel value, thereby reducing random noise in the image. Image scaling and cropping can also be performed.

[0093] Among them, deep learning models can be used to extract key features from pre-processed images, such as the shape, color, and location of vehicle components. Fault codes and warning light status on the dashboard, as well as abnormal condition data of the vehicle's exterior and interior, can be identified and recorded.

[0094] S220: Obtain status information of the vehicle within a first time period.

[0095] The first time period includes a first time period before the fault moment and a second time period after the fault moment.

[0096] Among them, the status information may include driving information, operation information and environmental information before the fault, and may also include vehicle performance change information and driving performance-related information after the fault, such as whether the vehicle's driving speed is affected after the fault occurs, whether there is an inability to accelerate, unstable speed or automatic deceleration, etc.

[0097] S230 , if it is detected that the vehicle characteristics include a first characteristic characterizing a vehicle attribute, a second characteristic characterizing a vehicle fault state, and a third characteristic characterizing a vehicle operating state, a target fault type is determined based on the state information and the vehicle characteristics.

[0098] Among them, when extracting features from the dashboard image, if the first feature, the second feature, and the third feature are extracted, the user does not need to upload additional images for recognition.

[0099] The first feature is used to represent information such as the vehicle's brand, model, and year. The second feature is used to represent information such as fault codes and warning light status. The third feature is used to represent the vehicle's operating status, such as speed and water temperature.

[0100] In a possible embodiment, determining the target fault type based on the status information and the vehicle characteristics includes: determining at least one first fault type based on the first characteristic and the second characteristic; determining at least one second fault type based on the second characteristic and the third characteristic; and determining the target fault type from the at least one first fault type and the at least one second fault type based on the status information.

[0101] In a possible embodiment, the first feature and the second feature are fused to obtain a first comprehensive feature, and then the first comprehensive feature is analyzed to output all possible fault types.

[0102] In a possible embodiment, the third feature and the second feature are fused to obtain a second comprehensive feature, which is then analyzed to output all possible fault types.

[0103] In one possible embodiment, the fault type is preliminarily screened based on the first feature. Brands may have specific issues, and vehicles of different brands may have their own common fault types due to differences in design concepts, technical levels, and quality control. Faults are associated with vehicle model characteristics, and fault types are screened based on the vehicle's purpose and structural design. Model year factors are taken into consideration. As time goes by, vehicle technology will continue to be updated and improved, but vehicles from earlier models may have some design flaws or immature technology applications. For example, the engine fuel injection system design of some older models is not precise enough, which may lead to problems such as severe carbon deposits and excessive fuel consumption.

[0104] The fault type is located using the second characteristic. Specifically, the meaning of the fault code is interpreted. Each fault code has a specific meaning, corresponding to a specific problem with a vehicle system or component. For example, fault code P0301 indicates a fault in the first cylinder ignition system of the engine, possibly a problem with the spark plug, ignition coil, or related circuitry. Fault code P0420 indicates that the catalytic converter efficiency is below the threshold, possibly due to a clogged catalytic converter or a faulty oxygen sensor.

[0105] Among them, after reading the fault code, combined with the warning light status, the possible fault type is determined.

[0106] The various warning lights on a vehicle's dashboard provide a visual indicator of the vehicle's health and potential faults. For example, an illuminated engine warning light may indicate a serious engine malfunction, such as a missing cylinder, excessive water temperature, or low oil pressure. An illuminated brake warning light indicates a brake system problem, such as insufficient brake fluid, excessive brake pad wear, or electrical leakage. Warning lights of different colors and shapes have different meanings. A red warning light typically indicates a serious emergency requiring immediate vehicle stop and inspection. A yellow warning light indicates a fault or abnormality that requires attention but not necessarily an immediate stop.

[0107] In a possible embodiment, the fault type may be identified through the third feature from multiple aspects, including power performance, braking performance, driving stability, etc.

[0108] In terms of power performance, the system determines whether there are any power failures, such as weak acceleration or power loss, and then determines the type of failure that may have occurred. For example, if the vehicle suddenly loses power or even stops moving while driving, it could be a mechanical engine failure or an electrical system failure.

[0109] Regarding braking performance, the system determines whether there are any braking problems, such as increased braking distance, and thus identifies the possible fault type. For example, if braking force feels insufficient and the braking distance is significantly longer, this could be due to insufficient or poor-quality brake fluid, severe brake pad wear, or uneven wear or deformation of the brake disc.

[0110] Regarding driving stability, the system determines whether the vehicle experiences noticeable jitter at idle or while driving, which can help determine the type of fault. For example, jitter at idle could indicate a damaged engine mount, which is unable to effectively cushion engine vibrations. It could also be caused by carbon deposits on the throttle, leading to abnormal throttle opening and affecting intake volume and mixture formation. Jitter while driving could indicate a tire balance issue or an unbalanced drive shaft.

[0111] The fault types selected based on the first feature are cross-checked with the fault types determined based on the second feature. If there are overlaps between the two, then the overlapped fault types have a higher probability.

[0112] The fault types selected based on the third feature are cross-checked with the fault types determined based on the second feature. If there are overlaps between the two, then the overlapped fault types have a higher probability.

[0113] In a possible embodiment, determining the target fault type among the at least one first fault type and the at least one second fault type based on the status information includes: determining the user's driving characteristics; determining the matching degree between the driving characteristics and each first fault type in the at least one first fault type to obtain at least one first matching degree; and determining the matching degree between the driving characteristics and each second fault type in the at least one second fault type to obtain at least one second matching degree; determining the matching degree between the status information and each first fault type to obtain at least one third matching degree; and determining the matching degree between the status information and each second fault type to obtain at least one fourth matching degree; determining the target fault type based on the at least one first matching degree, the at least one second matching degree, the at least one third matching degree, and the at least one fourth matching degree.

[0114] Among them, the user's historical driving data is obtained, features are extracted from the historical driving data, and the user's driving characteristics are output, which may include vehicle operation-related features, such as the frequency of sudden acceleration and the average acceleration amplitude in acceleration behavior, the frequency of sudden braking and the average deceleration amplitude in deceleration behavior, etc.

[0115] Among them, sudden acceleration generally refers to the operation of rapidly increasing the vehicle's speed by pressing the accelerator pedal quickly in a short period of time. The sudden acceleration frequency is the number of sudden accelerations per unit time. The average acceleration amplitude is the average acceleration during each acceleration operation, reflecting the driver's control over the throttle during acceleration. Among them, sudden braking refers to the operation of suddenly and forcefully pressing the brake pedal to quickly slow down or stop the vehicle. The sudden braking frequency records the number of sudden braking operations per unit time. The average deceleration amplitude is the average deceleration during each deceleration operation, reflecting the driver's operating force during the deceleration process.

[0116] Among them, driving characteristics can also include driving trajectory-related characteristics, such as average driving speed, speed change frequency, etc.; among them, average driving speed is the average speed of the user in different sections of the road or the entire journey, reflecting the overall driving rhythm; speed change frequency is the number of times the speed changes in unit time, reflecting the frequency of the driver's speed adjustment during driving.

[0117] Among them, driving characteristics can also include time-related characteristics, such as travel time patterns and vehicle usage frequency.

[0118] Driving characteristics may also include safety-related characteristics, such as the number of speeding violations in the violation record, the number of minor accidents, the number of damaged parts in minor accidents, the number of major accidents, and the number of damaged parts in major accidents in the accident history. The number of speeding violations refers to the number of times the user exceeded the prescribed speed limit while driving; the number of minor accidents refers to the number of minor traffic accidents the user has been involved in, such as scrapes and collisions, and records the damaged parts of the vehicle caused by minor accidents; the number of major accidents refers to the number of major traffic accidents the user has been involved in, such as rollovers and serious collisions, and records the damaged parts of the vehicle caused by major accidents.

[0119] In a possible embodiment, the time-related features, safety-related features, driving trajectory-related features, and vehicle operation-related features are fused to obtain driving features.

[0120] In a possible embodiment, feature fusion may be a simple concatenation of feature vectors from different sources or of different types to form a new feature vector with a higher dimension.

[0121] In a possible embodiment, feature fusion may be weighted fusion, which assigns different weights to different features to obtain driving features.

[0122] In one possible embodiment, before feature fusion, the original features are first screened to remove redundant or irrelevant features, and then the filtered features are fused. This can reduce the dimensionality of the data and improve the efficiency and accuracy of the model. For example, there may be a certain correlation between the average acceleration amplitude and the frequency of sudden acceleration. Using a feature selection algorithm, these redundant features are removed, and the remaining features are then fused.

[0123] In one possible embodiment, each first fault type and each second fault type can be encoded, and different fault types can be mapped to corresponding fault identifiers or vector representations to obtain fault signatures. The distance between the driving signature and the fault signature is then calculated to measure their differences. For example, this can be calculated using methods such as Euclidean distance, Manhattan distance, and cosine similarity. The smaller the distance, the higher the match.

[0124] In one possible embodiment, the degree of matching is determined by calculating the similarity between the driving characteristics and the fault characteristics. For example, the Jaccard similarity coefficient or the Pearson correlation coefficient can be used to measure the similarity between the two sets. A higher similarity indicates a higher degree of matching.

[0125] In one possible embodiment, a machine learning model, such as a random forest or neural network, can be used to learn the mapping relationship between driving characteristics and fault types, and the matching degree can be determined based on the prediction results of the model.

[0126] The status information is preprocessed, starting with data cleaning to remove noise, outliers, and erroneous data. The status information collected before and after the fault is then synchronized to ensure temporal consistency for subsequent analysis. Finally, status information from different sources and dimensions is standardized to ensure comparability.

[0127] This involves feature extraction of pre-fault status information, calculating statistical characteristics of the status information over a period of time before the fault, such as mean, variance, standard deviation, maximum, and minimum values, to reflect the characteristics of the equipment or system under normal operating conditions. Pre-fault status information trends, such as increases, decreases, and fluctuations, are analyzed, and trend characteristics are extracted by calculating trend coefficients and fitting curves. Furthermore, frequency domain analysis of the status information is performed to extract frequency characteristics, such as the main frequency, multiples, and harmonics, which facilitates the detection of periodic faults and vibration faults.

[0128] Among them, feature extraction is performed on the state information after the fault, and the change characteristics are extracted, that is, the change of the state information within a period of time after the fault relative to the state information before the fault is calculated, such as temperature change, pressure change, speed change, etc., to reflect the impact of the fault on the equipment or system.

[0129] The extracted pre-fault and post-fault state information features are combined to form a comprehensive state feature vector. Features from different sources and types can be combined using methods such as splicing and fusion to facilitate subsequent matching calculations.

[0130] In one possible embodiment, the difference between the state features and the fault features can be measured by calculating the distance between them, and the matching degree can be determined by calculating the similarity between the state features and the fault features. Alternatively, a machine learning model can be used to learn the mapping relationship between the state features and the fault types, and the matching degree can be determined based on the prediction results of the model.

[0131] In a possible embodiment, determining the target fault type based on the at least one first matching degree, the at least one second matching degree, the at least one third matching degree and the at least one fourth matching degree includes: obtaining maintenance data of the vehicle; determining the first fault frequency of each first fault type and the second fault frequency of each second fault type in a preset historical period based on the maintenance data; determining the probability value of each first fault type based on the first fault frequency, the at least one third matching degree and the at least one first matching degree; and determining the probability value of each second fault type based on the second fault frequency, the at least one second matching degree and the at least one fourth matching degree; and determining the target fault type based on the probability value of each first fault type and the probability value of each second fault type.

[0132] The maintenance data includes detailed information such as maintenance time, maintenance items, and replaced parts. The frequency of each first fault type and each second fault type is searched in the maintenance data. A third weight for the third matching degree and the first weight for the first matching degree are determined based on the first fault frequency. A second weight for the second matching degree and a fourth weight for the fourth matching degree are determined based on the second fault frequency.

[0133] The first matching degree, the third matching degree, the first weight, and the third weight are weighted and summed to obtain a probability value for each first fault type. The second matching degree, the fourth matching degree, the second weight, and the fourth weight are weighted and summed to obtain a probability value for each second fault type. The fault type with the largest probability value is determined as the target fault type.

[0134] In a possible embodiment, when it is detected that the vehicle characteristics include the second feature and the third feature, a vehicle identification image uploaded by the user is received; the features of the vehicle identification image are extracted to obtain a fourth feature; and the target fault type is determined based on the fourth feature, the second feature, the third feature and the status information.

[0135] If the vehicle brand, model, year, etc. are not identified based on the dashboard image, a prompt window will pop up to prompt the user to upload identification images such as the exterior and interior. Figure 5 , Figure 5 is a schematic diagram of a prompt window provided in an embodiment of the present application, such as Figure 5 As shown, the prompt window includes prompt information for prompting the user that the image is insufficient and whether to continue uploading. When the user clicks the yes control, the user returns to the fault diagnosis interface and continues uploading the image.

[0136] Among them, the identification image uploaded by the user is received, and then image recognition is performed to obtain the fourth feature representing information such as the vehicle brand, model, and year. The target fault type is determined by combining the second feature, the third feature and the status information.

[0137] In one possible embodiment, if the user clicks the No control in the prompt window, the agent can ask multiple rounds of questions to collect basic vehicle information, and the collected information is fed back to the agent for training.

[0138] For example, a multi-round conversation can be:

[0139] Round 1: Brands and models.

[0140] Agent: "Hello, I'd like to get some information about your vehicle. First, could you tell me the make of your vehicle?"

[0141] User: "My car is a Volkswagen."

[0142] Agent: "Okay, Volkswagen is a great brand. So, which model of Volkswagen do you have?"

[0143] User: "It's the Volkswagen Lavida."

[0144] Round 2: Vehicle Identification Number.

[0145] Agent: "Thank you very much for the information. Next, I need your Vehicle Identification Number. Do you know where it is?"

[0146] User: “Not sure.”

[0147] Agent: "Sure, let me tell you. You can see the VIN on the front left of your vehicle's dashboard. It's a set of numbers and letters. Or, if you have your vehicle registration certificate or insurance policy, it will also be on it. Please find it and tell me."

[0148] User: "Found it, it's this number: 3VW5D7AXE2S4521456."

[0149] Round 3: Purchase date and mileage.

[0150] Agent: "Thank you very much for the detailed information. Finally, I'd like to know your vehicle's purchase date and current mileage, which will help us more accurately assess the condition of your vehicle. Could you please tell me?"

[0151] User: "I bought my car in 2019 and have driven it about 20,000 kilometers so far."

[0152] Agent: "Thank you very much for providing this information. According to your description, your vehicle is a Volkswagen Lavida purchased in 2019, with a vehicle identification number of 3VW5D7AXE2S123456 and a current mileage of approximately 20,000 kilometers. We will provide you with more accurate service based on this information. If you have any other questions or need further assistance, please let me know."

[0153] Therefore, basic vehicle information is collected based on multiple rounds of questions.

[0154] In a possible embodiment, before receiving the vehicle identification image uploaded by the user, the method further includes: determining at least one shooting position of the vehicle; determining a shooting strategy for each of the at least one shooting position; and generating guidance information based on each shooting position and the shooting strategy corresponding to each shooting position, wherein the guidance information is used to guide the user to upload the vehicle identification image.

[0155] Among them, if the user clicks the control in the prompt window, he needs to continue uploading the image, which can be an interior image or an exterior image, etc.

[0156] Shooting locations can include the front of the vehicle, showcasing key features such as the vehicle's overall shape, grille style, headlight shape, and brand logo. This also includes the rear, showcasing information such as the brand logo, model name, and taillight style. It also includes the side of the vehicle, showcasing details such as the body lines, window outlines, wheel styles, and door handles. It also includes interior details such as the steering wheel, center console, and seats.

[0157] Each shooting position corresponds to a shooting strategy. For example, the shooting strategy includes shooting distance, shooting angle, and shooting precautions, etc., so as to facilitate uploading high-quality images.

[0158] S240: Determine a target analysis agent according to the target fault type.

[0159] Once the fault type is diagnosed, the system will call the corresponding analysis agent module to provide detailed fault analysis and repair suggestions. For example, the analysis agent can be AI Copilot.

[0160] The analysis agents are AI programs trained based on extensive vehicle knowledge, maintenance experience, and fault case data. Each analysis agent focuses on a specific fault type or vehicle system, such as engine or brake failure, and includes detailed fault analysis logic and a rich database of repair recommendations.

[0161] Once the fault type is determined, the intelligent diagnostic system automatically calls the appropriate analysis agent based on the fault type. For example, if an engine fault is identified, the system will call the engine fault-related analysis agent to provide the user with the most accurate and relevant fault analysis and repair recommendations.

[0162] S250, determining a fault diagnosis report of the vehicle according to the target analysis agent and the vehicle characteristics.

[0163] In a possible embodiment, determining the fault diagnosis report of the vehicle based on the target analysis agent and the vehicle characteristics includes: obtaining the driving data and maintenance data of the vehicle; parsing the vehicle characteristics, the driving data and the maintenance data through the target analysis agent to obtain a parsing result; if the parsing result is used to indicate a successful parsing, generating the fault diagnosis report; if the parsing result is used to indicate a failed parsing, outputting multiple fault causes.

[0164] After receiving vehicle characteristics, driving data, and maintenance data, the invoked target analysis agent conducts an in-depth analysis of the fault, taking into account factors such as the meaning of the fault code, vehicle operating data, fault frequency, and environmental conditions. Through this comprehensive analysis, the module can provide a detailed explanation of the fault's cause, helping users understand the mechanism of the fault.

[0165] The fault diagnosis report includes information such as the fault location, fault cause, and severity.

[0166] Among them, if the fault analysis fails or the knowledge base lacks relevant information, the system will provide the user with multiple possible causes of the fault and mark the picture to send to the back-end maintenance personnel.

[0167] For fault images that cannot be interpreted, experienced maintenance personnel conduct a secondary analysis. After determining the fault location, cause, severity, and repair recommendations, this data is fed back to the analysis agent for self-learning. Through continuous learning, the analysis agent can more quickly and accurately locate faults when encountering similar images in the future.

[0168] For faults that the analysis agent can identify, the system provides real-time diagnostic reports to the user via software or a webpage. These reports are presented in a combination of text and images for easy understanding. If a fault cannot be identified, the system presents multiple potential faults and corresponding reports.

[0169] In one possible embodiment, the system continuously optimizes diagnostic models and algorithms based on user feedback and new diagnostic data to improve system accuracy and user experience.

[0170] In one possible embodiment, a user feedback module can be developed in the system to collect user opinions to optimize system functions. The intelligent agent diagnosis model can be regularly updated to improve the diagnosis capability by combining the latest data.

[0171] S260: Generate a fault handling strategy based on the fault diagnosis report.

[0172] After the fault analysis is complete, specific repair recommendations can be provided based on the analysis results. These recommendations include repair steps, a list of parts that need to be replaced, tools and technical requirements that may be required during the repair process, and repair costs.

[0173] Users can interact with the analytical agent and ask further questions about faults and repairs. The analytical agent will then provide more detailed answers and guidance based on the user's questions. After the repair is completed, users can also provide feedback to the system, helping it continuously optimize the accuracy and practicality of its fault analysis and repair recommendations.

[0174] It can be seen that the present application fills the gap of missing data and ensures the comprehensiveness, reliability and accuracy of diagnostic maintenance suggestions. Dynamic learning capabilities and multi-source data fusion enable the system to more accurately match fault codes and maintenance suggestions, solving the problem of insufficient accuracy caused by limited data and fixed rules in the existing technology. By continuously learning new fault modes and maintenance experience, the system can dynamically optimize the diagnostic logic and adapt to the ever-changing fault phenomena, solving the problem of lack of dynamic learning capabilities in the existing technology. According to the operating habits of different operators and the positioning coordinates, the personalized output of simple, intuitive and content-rich maintenance suggestions can avoid users' aesthetic fatigue of the single content of the maintenance suggestions and improve the user's intuitive experience.

[0175] It can be seen that in this application, users only need to use their mobile phones to take pictures of the dashboard to identify faults. This is low-cost, highly efficient, and easy to operate. The more image data there is, the more accurate the diagnostic results will be.

[0176] For the same example as above, please refer to Figure 6 , Figure 6 This is a block diagram of the functional units of a vehicle intelligent diagnostic device provided by an embodiment of the present application, such as Figure 6As shown, the intelligent diagnostic device 60 for a vehicle includes: an extraction unit 61, which is used to extract vehicle features of a dashboard image of a vehicle uploaded by a user; an acquisition unit 62, which is used to acquire status information of the vehicle within a first time period, wherein the first time period includes a first time period before the fault moment and a second time period after the fault moment; a first determination unit 63, which is used to detect that the vehicle features include a first feature characterizing vehicle attributes, a second feature characterizing vehicle fault status, and a third feature characterizing vehicle operating status, and then determine a target fault type based on the status information and the vehicle features; a second determination unit 64, which is used to determine a target analysis agent based on the target fault type; a third determination unit 65, which is used to determine a fault diagnosis report of the vehicle based on the target analysis agent and the vehicle features; and a generation unit 66, which is used to generate a fault handling strategy based on the fault diagnosis report.

[0177] In a possible embodiment, in terms of determining the target fault type based on the status information and the vehicle characteristics, the first determination unit 63 is specifically used to: determine at least one first fault type based on the first characteristic and the second characteristic; determine at least one second fault type based on the second characteristic and the third characteristic; and determine the target fault type from the at least one first fault type and the at least one second fault type based on the status information.

[0178] In a possible embodiment, in terms of determining the target fault type among the at least one first fault type and the at least one second fault type based on the status information, the first determination unit 63 is specifically further used to: determine the driving characteristics of the user; determine the matching degree between the driving characteristics and each first fault type in the at least one first fault type to obtain at least one first matching degree; and, determine the matching degree between the driving characteristics and each second fault type in the at least one second fault type to obtain at least one second matching degree; determine the matching degree between the status information and each first fault type to obtain at least one third matching degree; and, determine the matching degree between the status information and each second fault type to obtain at least one fourth matching degree; determine the target fault type based on the at least one first matching degree, the at least one second matching degree, the at least one third matching degree and the at least one fourth matching degree.

[0179] In one possible embodiment, in terms of determining the target fault type based on the at least one first matching degree, the at least one second matching degree, the at least one third matching degree and the at least one fourth matching degree, the first determination unit 63 is specifically further used to: obtain maintenance data of the vehicle; determine the first fault frequency of each first fault type and the second fault frequency of each second fault type in a preset historical period based on the maintenance data; determine the probability value of each first fault type based on the first fault frequency, the at least one third matching degree and the at least one first matching degree; and determine the probability value of each second fault type based on the second fault frequency, the at least one second matching degree and the at least one fourth matching degree; determine the target fault type based on the probability value of each first fault type and the probability value of each second fault type.

[0180] In a possible embodiment, the vehicle's intelligent diagnostic device 60 is further specifically used to: when it is detected that the vehicle characteristics include the second feature and the third feature, receive the vehicle identification image uploaded by the user; extract the features of the vehicle identification image to obtain a fourth feature; and determine the target fault type based on the fourth feature, the second feature, the third feature and the status information.

[0181] In a possible embodiment, before receiving the vehicle identification image uploaded by the user, the vehicle's intelligent diagnostic device 60 is further specifically used to: determine at least one shooting position of the vehicle; determine a shooting strategy for each of the at least one shooting position; and generate guidance information based on each shooting position and the shooting strategy corresponding to each shooting position, wherein the guidance information is used to guide the user to upload the vehicle identification image.

[0182] In one possible embodiment, in terms of determining the fault diagnosis report of the vehicle based on the target analysis agent and the vehicle characteristics, the third determination unit 65 is specifically used to: obtain the driving data and maintenance data of the vehicle; parse and process the vehicle characteristics, the driving data and the maintenance data through the target analysis agent to obtain a parsing result; if the parsing result is used to indicate that the parsing is successful, generate the fault diagnosis report; if the parsing result is used to indicate that the parsing fails, output multiple fault causes.

[0183] It can be understood that since the method embodiment and the device embodiment are different presentation forms of the same technical concept, the content of the method embodiment part in this application should be synchronously adapted to the device embodiment part and will not be repeated here.

[0184] In the case of integrated units, see Figure 7 , Figure 7 This is a block diagram of the functional units of another vehicle intelligent diagnostic device provided by an embodiment of the present application, such as Figure 7 As shown, the vehicle intelligent diagnostic device 60 includes: a processing module 602 and a communication module 601. The processing module 602 is used to control and manage the actions of the vehicle intelligent diagnostic device 60, for example, executing the steps of the extraction unit 61, the acquisition unit 62, the first determination unit 63, the second determination unit 64, the third determination unit 65 and the generation unit 66, and / or other processes for executing the technology described herein. The communication module 601 is used for the interaction between the vehicle intelligent diagnostic device 60 and other devices. Figure 7 As shown, the vehicle intelligent diagnostic device 60 may further include a storage module 603 , and the storage module 603 is used to store program codes and data of the vehicle intelligent diagnostic device 60 .

[0185] Among them, the processing module 602 can be a processor or controller, for example, a central processing unit (CPU), a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It can implement or execute the various exemplary logic blocks, modules and circuits described in conjunction with the disclosure of this application. The processor can also be a combination that implements computing functions, for example, a combination of one or more microprocessors, a combination of a DSP and a microprocessor, and the like. The communication module 601 can be a transceiver, an RF circuit or a communication interface, etc. The storage module 603 can be a memory.

[0186] Among them, all relevant contents of each scenario involved in the above method embodiment can be referred to the functional description of the corresponding functional module, and will not be repeated here. The above vehicle intelligent diagnostic device 60 can execute the above Figure 2 The intelligent diagnostic method of the vehicle is shown.

[0187] See also Figure 8 , Figure 8 This is a schematic diagram of the structure of an electronic device proposed in an embodiment of the present application. Figure 8As shown, the electronic device 800 includes a processor 810, a memory 820, a communication interface 830 and one or more programs 821. The one or more programs 821 are stored in the memory and are configured to be executed by the processor. When the program is executed, it includes part or all of the steps of any one of the vehicle intelligent diagnosis methods recorded in the above method embodiments. The processor, memory and communication interface are interconnected and complete communication with each other.

[0188] The memory may be a volatile memory such as a dynamic random access memory (DRAM) or a non-volatile memory such as a mechanical hard disk. The memory is used to store a set of executable program codes, and the processor is used to call the executable program codes stored in the memory to execute some or all of the steps of any vehicle intelligent diagnosis method described in the above-mentioned vehicle intelligent diagnosis method embodiment.

[0189] It can be seen that the electronic device 800 described in the embodiment of the present application first receives a dashboard image of a vehicle uploaded by a user; then extracts the vehicle features of the dashboard image; then obtains the status information of the vehicle within a first time period, the first time period including a first time period before the fault moment and a second time period after the fault moment; then detects that the vehicle features include a first feature characterizing vehicle attributes, a second feature characterizing the vehicle fault state, and a third feature characterizing the vehicle operating state, and then determines the target fault type based on the status information and the vehicle features; then determines the target analysis agent based on the target fault type; then determines the fault diagnosis report of the vehicle based on the target analysis agent and the vehicle features; and finally generates a fault handling strategy based on the fault diagnosis report.

[0190] It can be seen that in the application, the user only needs to provide an image of the vehicle's dashboard and extract vehicle features in multiple dimensions through image recognition to quickly obtain fault diagnosis results without relying on professional equipment or technician experience, thereby reducing diagnostic costs and improving diagnostic efficiency and user experience.

[0191] An embodiment of the present application also provides a computer storage medium, wherein the computer storage medium stores a computer program for electronic data exchange, and the computer program enables a computer to execute part or all of the steps of any method described in the above method embodiments, and the above computer includes an electronic device.

[0192] The present application also provides a computer program product comprising a non-transitory computer-readable storage medium storing a computer program, wherein the computer program is operable to cause a computer to perform some or all of the steps of any of the methods described in the above method embodiments. The computer program product may be a software installation package, and the computer may comprise an electronic device.

[0193] It should be noted that for the aforementioned method implementations, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that this application is not limited by the order of the actions described, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the implementations described in the specification are all optional implementations, and the actions and modules involved are not necessarily required for this application.

[0194] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0195] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0196] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of this embodiment.

[0197] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or in the form of software program modules.

[0198] If the integrated unit is implemented in the form of a software program module and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes a number of instructions for enabling a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned memory includes: various media that can store program codes, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk.

[0199] Those skilled in the art will understand that all or part of the steps in the various methods of the above-mentioned embodiments can be completed by instructing the relevant hardware through a program, and the program can be stored in a computer-readable memory, which may include: a flash drive, a read-only memory, a random access memory, a magnetic disk or an optical disk, etc.

[0200] The above is a detailed introduction to the implementation methods of the present application. Specific examples are used herein to illustrate the principles and implementation methods of the present application. The description of the above implementation methods is only used to help understand the method and core idea of the present application. At the same time, for those skilled in the art, based on the ideas of the present application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A vehicle intelligent diagnosis method, characterized in that: include: Extracting vehicle features from the dashboard image of the vehicle uploaded by the user; Acquiring status information of the vehicle within a first time period, where the first time period includes a first time period before a fault moment and a second time period after the fault moment; detecting that the vehicle characteristics include a first characteristic characterizing a vehicle attribute, a second characteristic characterizing a vehicle fault state, and a third characteristic characterizing a vehicle operating state, and determining a target fault type based on the state information and the vehicle characteristics; Determining a target analysis agent according to the target fault type; Determining a fault diagnosis report for the vehicle based on a target analysis agent and characteristics of the vehicle; A fault handling strategy is generated according to the fault diagnosis report.

2. The method according to claim 1, characterized in that The determining of a target fault type according to the state information and the vehicle characteristics includes: determining at least one first fault type according to the first feature and the second feature; determining at least one second fault type according to the second feature and the third feature; The target fault type is determined from among the at least one first fault type and the at least one second fault type according to the state information.

3. The method according to claim 2, characterized in that The determining, according to the state information, the target fault type from the at least one first fault type and the at least one second fault type includes: determining a driving characteristic of the user; determining a matching degree between the driving characteristic and each first fault type of the at least one first fault type to obtain at least one first matching degree; and determining a matching degree between the driving characteristic and each second fault type of the at least one second fault type to obtain at least one second matching degree; Determining a degree of matching between the state information and each of the first fault types to obtain at least one third degree of matching; and determining a degree of matching between the state information and each of the second fault types to obtain at least one fourth degree of matching; The target fault type is determined according to the at least one first matching degree, the at least one second matching degree, the at least one third matching degree, and the at least one fourth matching degree.

4. The method according to claim 3, characterized in that The determining the target fault type according to the at least one first matching degree, the at least one second matching degree, the at least one third matching degree, and the at least one fourth matching degree includes: obtaining maintenance data of the vehicle; determining, based on the maintenance data, a first fault frequency of each first fault type and a second fault frequency of each second fault type in a preset historical period; Determining a probability value of each first fault type based on the first fault frequency, the at least one third matching degree, and the at least one first matching degree; and determining a probability value of each second fault type based on the second fault frequency, the at least one second matching degree, and the at least one fourth matching degree; The target fault type is determined according to the probability value of each first fault type and the probability value of each second fault type.

5. The method according to claim 1, wherein The method further comprises: detecting that the vehicle features include the second feature and the third feature, then receiving the vehicle identification image uploaded by the user; Extracting a feature of the vehicle identification image to obtain a fourth feature; The target fault type is determined according to the fourth feature, the second feature, the third feature, and the status information.

6. The method according to claim 5, characterized in that Before receiving the vehicle identification image uploaded by the user, the method further includes: determining at least one photographic location of the vehicle; determining a shooting strategy for each of the at least one shooting location; Guidance information is generated according to each shooting position and the shooting strategy corresponding to each shooting position, where the guidance information is used to guide the user to upload the vehicle identification image.

7. The method according to claim 1, characterized in that The step of analyzing the intelligent agent and the vehicle characteristics according to the target to determine a fault diagnosis report for the vehicle includes: Acquiring driving data and maintenance data of the vehicle; Analyzing the vehicle characteristics, the driving data, and the maintenance data using the target analysis agent to obtain an analysis result; If the analysis result indicates that the analysis is successful, generating the fault diagnosis report; If the parsing result indicates that the parsing failed, multiple failure reasons are output.

8. An intelligent diagnostic device for a vehicle, characterized in that: include: An extraction unit, configured to extract vehicle features from a vehicle dashboard image uploaded by a user; an acquiring unit, configured to acquire status information of the vehicle within a first time period, the first time period including a first time period before a fault moment and a second time period after the fault moment; a first determining unit configured to detect that the vehicle characteristics include a first characteristic representing a vehicle attribute, a second characteristic representing a vehicle fault state, and a third characteristic representing a vehicle operating state, and then determine a target fault type based on the state information and the vehicle characteristics; A second determining unit, configured to determine a target analysis agent according to the target fault type; a third determining unit, configured to determine a fault diagnosis report of the vehicle based on a target analysis agent and characteristics of the vehicle; A generating unit is used to generate a fault handling strategy according to the fault diagnosis report.

9. An electronic device, characterized in that: The device comprises: A memory, a processor, and an executable program code stored in the memory and executable on the processor, wherein the processor executes the steps of the intelligent diagnosis method for a vehicle as claimed in any one of claims 1 to 7 when executing the executable program code.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores executable program code, which includes execution instructions for executing the steps of the intelligent diagnosis method for a vehicle as described in any one of claims 1 to 7.

Images