Fault detection method and device, electronic equipment and storage medium

Abstract

The invention discloses a fault detection method and device, electronic equipment and a storage medium, and relates to the technical field of vehicles, and the main technical scheme comprises the following steps: in response to a received fault detection instruction, extracting a keyword in the fault detection instruction, searching a corresponding relation between the keyword and a preset fault category, and determining the fault category according to the corresponding relation; determining a target fault category corresponding to the fault detection instruction; obtaining target vehicle signal information associated with the target fault category in the to-be-detected vehicle; and performing fault analysis on the target vehicle signal information through a target diagnosis algorithm, and determining an obtained analysis result as a fault detection result of the vehicle. According to the invention, after the fault type to be detected is obtained, the target vehicle signal information corresponding to the fault type is obtained, and the target vehicle signal information is detected, so that targeted self-inspection of the vehicle data is realized, the detection efficiency is improved, fault diagnosis can be carried out on the vehicle anytime and anywhere, and the vehicle use cost of a user is reduced.

Description

Technical Field

[0001] The present disclosure relates to the technical field of vehicles, and in particular, to a method and device for detecting faults, an electronic device, and a storage medium. Background Art

[0002] During the startup and driving of a vehicle, if abnormal conditions occur in the vehicle, such as excessive energy consumption, swaying from side to side during driving, etc., the driver needs to diagnose the vehicle's faults to ensure driving safety.

[0003] In traditional vehicle fault diagnosis, to diagnose a vehicle's faults, the driver has to move the abnormal vehicle to a specialized repair point. The staff at the repair point connects a professional scanning device to the OBD (On-Board Diagnostics) interface of the abnormal vehicle to read the vehicle signal information of each module in the abnormal vehicle. Then, based on the working condition information indicated by the read vehicle signal information of each module, they manually locate the cause of the vehicle's abnormality, and further determine whether the vehicle is faulty, as well as the fault location and solution in case of vehicle faults. Although this method can achieve the diagnosis of vehicle faults, it requires a fixed location and professional personnel to use professional scanning equipment to read and analyze the vehicle signal information of the vehicle, which is very inconvenient. Moreover, even if the vehicle's abnormality is only a minor problem, it is necessary to read and analyze the vehicle signal information of each module in the vehicle, which is time-consuming and laborious. Summary of the Invention

[0004] The present disclosure provides a method, device, electronic device, and storage medium for detecting faults. Its main purpose is to solve the problem that the diagnosis of vehicle faults is time-consuming and laborious.

[0005] According to a first aspect of the present disclosure, there is provided a method for detecting faults, including:

[0006] Obtain the vehicle signal information of the vehicle to be detected;

[0007] In response to receiving a fault detection instruction, extract the keywords in the fault detection instruction, and search for the corresponding relationship between the keywords and preset fault categories. If found, determine the found preset fault category as the target fault category corresponding to the fault detection instruction; the keywords are terms related to the vehicle field;

[0008] Obtain the target vehicle signal information associated with the target fault category from the vehicle signal information; wherein, the target vehicle signal information is used to detect whether there is a fault corresponding to the target fault category in the vehicle;

[0009] Select the target diagnostic algorithm corresponding to the target fault category, perform fault analysis on the target vehicle signal information through the target diagnostic algorithm, and determine the obtained analysis result as the fault detection result of the vehicle.

[0010] Optionally, before selecting the target diagnostic algorithm corresponding to the target fault category, the method further includes:

[0011] Based on a preset name table, standardize the signal names of the target vehicle signal information to eliminate the name differences of the same target vehicle signal information in different vehicle models; wherein, the preset name table contains the names of the same target vehicle signal information in different vehicle models.

[0012] Optionally, the performing fault analysis on the target vehicle signal information through the target diagnostic algorithm includes:

[0013] Match the values of each signal in the target vehicle signal information with the preset standard values corresponding to each signal, and determine the signals with abnormal matching as abnormal signals;

[0014] Search for the fault information corresponding to each abnormal signal, and / or search for the fault information corresponding to the combination of different abnormal signals, and determine the found abnormal information as the fault analysis result.

[0015] Optionally, the target diagnostic algorithm is the fault diagnosis algorithm in a trained fault analysis model;

[0016] The performing fault analysis on the target vehicle signal information through the target diagnostic algorithm includes:

[0017] Input the target vehicle signal information into the fault analysis model;

[0018] Through the fault diagnosis algorithm in the fault analysis model, calculate the similarity between the target vehicle signal information and the fault sample signal information corresponding to the target fault category;

[0019] In the case where there is target fault sample signal information with a similarity greater than a preset threshold, the fault information corresponding to the target fault sample signal information is output by the fault analysis model, and the fault information corresponding to the target fault sample signal information is determined as the fault analysis result.

[0020] Optionally, the method further includes:

[0021] In the case where the fault detection result indicates that the vehicle fault is related to the parameter settings of the vehicle, generate a recommended handling measure for changing the parameter settings, and perform the correction of the parameter settings of the vehicle to be detected when receiving an instruction to agree to change the parameter settings.

[0022] Optionally, after determining that the found preset fault category is the target fault category corresponding to the fault detection instruction, the method further includes:

[0023] Search for the harm level of the target fault category, and determine the processing priority corresponding to the harm level as the priority of the fault detection instruction;

[0024] If it is determined that the priority of the fault detection instruction is higher than the preset standard priority, allocate the computing power of the current vehicle to preferentially process the fault detection;

[0025] If the priority of the fault detection instruction is not higher than the preset standard priority, allocate the computing power of the current vehicle to preferentially meet the computing power requirements of the user for vehicle use.

[0026] According to a second aspect of the present disclosure, there is provided a fault detection device, including:

[0027] A determination unit, configured to, in response to receiving a fault detection instruction, extract keywords in the fault detection instruction, search for a corresponding relationship between the keywords and preset fault categories, and if found, determine the found preset fault category as the target fault category corresponding to the fault detection instruction; the keywords are terms related to the vehicle field;

[0028] An acquisition unit, configured to acquire vehicle signal information of a vehicle to be detected; and acquire target vehicle signal information associated with the target fault category from the vehicle signal information; wherein, the target vehicle signal information is used to detect whether there is a fault corresponding to the target fault category in the vehicle;

[0029] An analysis unit, configured to select a target diagnostic algorithm corresponding to the target fault category, perform fault analysis on the target vehicle signal information through the target diagnostic algorithm, and determine the obtained analysis result as the fault detection result of the vehicle.

[0030] Optionally, the device further includes:

[0031] A processing unit, configured to, before the analysis unit selects the target diagnostic algorithm corresponding to the target fault category, perform standardization processing on the signal names of the target vehicle signal information based on a preset name table to eliminate name differences of the same target vehicle signal information in different vehicle models; wherein, the preset name table includes the names of the same target vehicle signal information in different vehicle models.

[0032] Optionally, the analysis unit is further configured to:

[0033] Match the values of each signal in the target vehicle signal information with the preset standard value corresponding to each signal, and determine the signal with abnormal matching as an abnormal signal;

[0034] Search for the fault information corresponding to each abnormal signal, and / or search for the fault information corresponding to the combination of different abnormal signals, and determine the found abnormal information as the fault analysis result.

[0035] Optionally, the target diagnostic algorithm is the fault diagnosis algorithm in the trained fault analysis model;

[0036] The analysis unit is further configured to:

[0037] Input the target vehicle signal information into the fault analysis model;

[0038] Calculate the similarity between the target vehicle signal information and the fault sample signal information corresponding to the target fault category through the fault diagnosis algorithm in the fault analysis model;

[0039] In the case where there is target fault sample signal information with a similarity greater than the preset threshold, the fault information corresponding to the target fault sample signal information is output by the fault analysis model, and the fault information corresponding to the target fault sample signal information is determined as the fault analysis result.

[0040] Optionally, the device further includes:

[0041] A generation unit, configured to generate a recommended handling measure for changing the parameter setting when the fault detection result indicates that the vehicle fault is related to the parameter setting of the vehicle, and execute the correction of the parameter setting of the vehicle to be detected when receiving an instruction to agree to change the parameter setting.

[0042] Optionally, the device further includes:

[0043] A search unit, configured to search for the harm degree of the target fault category after the determination unit determines the target fault category corresponding to the fault detection instruction, and determine the processing priority corresponding to the harm degree as the priority of the fault detection instruction;

[0044] An allocation unit, configured to allocate the computing power of the current vehicle to preferentially process the fault detection when it is determined that the priority of the fault detection instruction is higher than the preset standard priority;

[0045] The allocation unit is further configured to allocate the computing power of the current vehicle to preferentially meet the computing power requirement of the user for vehicle use when the priority of the fault detection instruction is not higher than the preset standard priority.

[0046] According to a third aspect of the present disclosure, there is provided an electronic device, including:

[0047] at least one processor; and

[0048] a memory communicatively connected to the at least one processor; wherein,

[0049] the memory stores instructions executable by the at least one processor, and when the instructions are executed by the at least one processor, the at least one processor is enabled to execute the method described in the foregoing first aspect.

[0050] According to a fourth aspect of the present disclosure, there is provided a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause the computer to execute the method described in the foregoing first aspect.

[0051] According to a fifth aspect of the present disclosure, there is provided a computer program product including a computer program which, when executed by a processor, implements the method described in the foregoing first aspect.

[0052] The fault detection method, device, electronic device, and storage medium provided by the present disclosure mainly include the following technical solutions: First, in response to receiving a fault detection instruction, extract the keywords in the fault detection instruction, search for the corresponding relationship between the keywords and preset fault categories, and determine the target fault category corresponding to the fault detection instruction; the keywords are terms related to the vehicle field; obtain target vehicle signal information in the vehicle to be detected that is associated with the target fault category; wherein, the target vehicle signal information is used to detect whether there is fault information of the target fault category; select the target diagnostic algorithm corresponding to the target fault category, perform fault analysis on the target vehicle signal information through the target diagnostic algorithm, and determine the obtained analysis result as the fault detection result of the vehicle. Compared with the related art, in the embodiment of the present application, after obtaining the fault category to be detected, the target vehicle signal information corresponding to the fault category is obtained, and the target vehicle signal information is detected to determine whether there is fault information, thereby realizing targeted self-checking of vehicle data, improving the detection efficiency, discovering vehicle fault information through the signal information of the vehicle, enabling fault diagnosis of the vehicle at any time and anywhere, and eliminating the need to use professional equipment for detection at a fixed location, reducing the vehicle use cost for users.

[0053] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present application, nor is it used to limit the scope of the present application. Other features of the present application will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] The drawings are used to better understand the solution and do not constitute a limitation to the present disclosure. Among them:

[0055] Figure 1 Schematic flowchart of a fault detection method provided by an embodiment of the present disclosure;

[0056] Figure 2 Schematic flowchart of a fault analysis provided by an embodiment of the present disclosure;

[0057] Figure 3 Schematic diagram of a preset diagnostic algorithm example provided by an embodiment of the present application;

[0058] Figure 4 Schematic structural diagram of a fault detection device provided by an embodiment of the present disclosure;

[0059] Figure 5 Schematic structural diagram of a fault detection device provided by an embodiment of the present disclosure;

[0060] Figure 6 Schematic block diagram of an exemplary electronic device provided by an embodiment of the present disclosure. Detailed implementation manners

[0061] The following describes exemplary embodiments of the present disclosure with reference to the accompanying drawings. Various details of the embodiments of the present disclosure are included to assist understanding, and they should be considered merely exemplary. Therefore, those of ordinary skill in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for clarity and conciseness, descriptions of well-known functions and structures are omitted below.

[0062] The following describes a fault detection method, device, electronic device, and storage medium according to embodiments of the present disclosure with reference to the accompanying drawings.

[0063] Figure 1 Schematic flowchart of a fault detection method provided by an embodiment of the present disclosure. The method is applied to a vehicle or a cloud server,

[0064] As Figure 1 shown, the method includes the following steps:

[0065] Step 101, obtain vehicle signal information of a vehicle to be detected.

[0066] In an implementable manner of an embodiment of the present application, the vehicle signal information of the vehicle to be detected can be obtained from a vehicle management system installed on the vehicle to be detected. The vehicle signal information of the vehicle to be detected obtained here includes the vehicle signal information of each module in the vehicle. Specific means for obtaining can refer to related technologies and adopt various means, and the present application is not limited thereto.

[0067] Step 102: In response to receiving a fault detection instruction, extract the keywords in the fault detection instruction, and search for the corresponding relationship between the keywords and the preset fault categories. If found, determine the found preset fault category as the target fault category corresponding to the fault detection instruction; the keywords are terms related to the vehicle field.

[0068] In an implementable manner of the embodiments of the present application, the fault detection instruction can be obtained through various sensors, monitoring devices, or a remote monitoring system or a combination of multiple methods. For example, the audio information of the user can be obtained through an in-vehicle microphone, or the fault detection instruction triggered by the user in the vehicle-mounted system by clicking or other means can be obtained. Specifically, the embodiments of the present application do not limit this.

[0069] In an implementable manner of the embodiments of the present application, in an actual vehicle usage scenario, the user may find that the vehicle is abnormal. For example, if the power consumption is too fast, the fault detection instruction can be "Why is the energy consumption so high". Then, the corresponding relationship between multiple keywords and multiple preset fault categories can be pre-configured in the system. For example, the keyword is: excessive energy consumption, and the corresponding preset fault category can be set to battery failure; specifically, it can be set through the professional field knowledge in the vehicle field, and the embodiments of the present application do not limit this.

[0070] In an implementable manner of the embodiments of the present application, after determining the target fault category according to the keywords in the fault detection instruction, the target fault category can be further identified and classified, and analyzed and judged in terms of the severity, influence range, urgency, etc. of the target fault category to determine the subsequent processing flow. For example, if the fault severity is too high, the vehicle-mounted computing power is preferentially called to query the fault. If the fault severity is not high, the fault is queried under the condition of meeting the user's vehicle usage computing power requirements.

[0071] Step 103: Obtain target vehicle signal information associated with the target fault category from the vehicle signal information; wherein, the target vehicle signal information is used to detect whether there is a fault corresponding to the target fault category in the vehicle.

[0072] In an implementable manner of the embodiments of the present application, before executing this step, the association relationship between the fault category and the target vehicle signal information is first pre-configured in the system. For example, if the fault category is battery failure, the target vehicle signal information can be the target vehicle signal information related to power consumption, such as air conditioning, lights, vehicle speed, driving mode, etc.; if the fault category is tire pressure detection, the target vehicle signal information can be the vehicle body balance degree, tire pressure data, etc. Specifically, it can be configured through professional field knowledge, and the embodiments of the present application do not limit this.

[0073] Continuing the description of the above application embodiments, taking the fault detection instruction "Why is the energy consumption so high" as an example, the target vehicle signal information associated with the fault category can be obtained through in-vehicle sensors, the CAN bus, or other vehicle monitoring devices, such as vehicle speed, engine speed, oil temperature, water temperature, oxygen sensor output, air conditioner settings, driving mode, etc.

[0074] Step 104, select the target diagnostic algorithm corresponding to the target fault category, perform fault analysis on the target vehicle signal information through the target diagnostic algorithm, and determine the obtained analysis result as the fault detection result of the vehicle.

[0075] In an implementable manner of the embodiments of the present application, before performing this step, based on different discrimination methods for different fault categories, different diagnostic algorithms are also set for different fault categories. For example, the diagnostic algorithm corresponding to battery faults discriminates by analyzing whether the current in the target vehicle signal information related to power consumption is too small, or the diagnostic algorithm corresponding to headlight faults determines whether the headlight circuit is normal through the signal information related to the headlights and determines whether the power supply is supplying power to the headlights normally through the signal information related to the power supply, etc. Optionally, different fault analysis models can also be set for different fault categories in this embodiment, and different diagnostic algorithms are set in different fault analysis models.

[0076] After determining the target diagnostic algorithm, perform fault analysis on the target vehicle signal information according to the target diagnostic algorithm. The fault can be identified through the analysis result and the corresponding fault detection result can be determined. For example, according to the changes in engine speed and water temperature, if the water temperature is abnormal, it can be determined that an engine fault is diagnosed and the corresponding fault detection result is obtained.

[0077] Furthermore, according to the fault detection result and the preset processing plan, corresponding recovery suggestions can be generated, including information such as fault handling methods, maintenance suggestions, and urgency levels; through the preset notification method, the generated recovery suggestions are pushed to relevant personnel, such as vehicle managers, maintenance personnel, or drivers. The push methods can include forms such as mobile phone text messages, in-vehicle screen pushes, and voice announcements by the in-vehicle voice assistant, etc., to ensure that relevant personnel can obtain fault handling suggestions in a timely manner.

[0078] In an implementable manner of the embodiments of the present application, the pushed recovery suggestions can be recorded in the in-vehicle system, and according to the actual handling situation, feedback information from relevant personnel is received for optimizing the diagnostic algorithm and improving the accuracy and practicality of the recovery suggestions.

[0079] The fault detection method provided by the present disclosure mainly includes the following technical solutions: First, in response to receiving a fault detection instruction, extract the keywords in the fault detection instruction, search for the corresponding relationship between the keywords and preset fault categories, and determine the target fault category corresponding to the fault detection instruction; the keywords are terms related to the vehicle field; obtain the target vehicle signal information related to the target fault category in the vehicle to be detected; wherein, the target vehicle signal information is used to detect whether there is fault information of the target fault category; select the target diagnostic algorithm corresponding to the target fault category, perform fault analysis on the target vehicle signal information through the target diagnostic algorithm, and determine the obtained analysis result as the fault detection result of the vehicle. Compared with the related art, in the embodiment of the present application, after obtaining the fault category to be detected, the target vehicle signal information corresponding to the fault category is obtained, and the target vehicle signal information is detected to determine whether there is fault information, realizing targeted self-checking of vehicle data, improving the detection efficiency, and discovering vehicle fault information through the signal information of the vehicle, so that the vehicle can be fault-diagnosed anytime and anywhere, without the need to go to a fixed place to use professional equipment for detection, reducing the user's vehicle use cost.

[0080] In a realizable manner of the embodiment of the present application, before performing step 101, if the user wakes up the fault detection by voice, then this embodiment further includes the following steps:

[0081] Collect voice instruction information, and perform voice recognition on the voice instruction information to determine whether it contains the fault detection instruction.

[0082] In a realizable manner of the embodiment of the present application, the voice information of the driver can be collected according to a preset microphone in the vehicle. Specifically, for example, after the user wakes up the voice assistant, the voice information of the user can be collected, and semantic understanding is performed on the voice information to determine whether there is a fault detection instruction in the voice information.

[0083] In a realizable manner of the embodiment of the present application, the target vehicle signal information at least includes at least one of vehicle status information, user settings, driving behavior, and control unit status.

[0084] In a realizable manner of the embodiment of the present application, before selecting the target diagnostic algorithm corresponding to the target fault category, the method further includes:

[0085] Based on a preset name table, standardize the signal names of the target vehicle signal information to eliminate the name differences of the same target vehicle signal information in different vehicle models; wherein, the preset name table contains the names of the same target vehicle signal information in different vehicle models.

[0086] In an implementable manner of the embodiment of the present application, first, a standard signal name table needs to be established in advance, including the standard names and corresponding meanings of various vehicle signal information. This table contains the respective names of the same signal information in different vehicle models, as well as the unified standardized names for different signal information.

[0087] For the signal names of the same component in different vehicle models, establish a mapping relationship to map different names to the standardized name of this component in the standardized signal name table, so as to avoid incorrect fault analysis caused by different names.

[0088] Optionally, the above standardization process can be calculated using the edge computing ability of the vehicle to be detected. The edge computing ability is calculated using the characteristic that the transmission distance of the vehicle and vehicle information signals is relatively close, avoiding the loss of vehicle information signals.

[0089] In an implementable manner of the embodiment of the present application, when performing fault analysis on the target vehicle signal information through the target diagnostic algorithm, it can be determined according to the following steps, including: matching the values of each signal in the target vehicle signal information with the preset standard values corresponding to each signal, and determining the signals with abnormal matching as abnormal signals; searching for the fault information corresponding to each abnormal signal, and / or searching for the fault information corresponding to the combination of different abnormal signals, and determining the found abnormal information as the fault analysis result.

[0090] The above preset standard values are the values of each signal when the vehicle is not faulty. The preset standard value can be a specific value or a range of values. In this embodiment, matching the values of each signal in the target vehicle signal information with the preset standard values corresponding to each signal can be to determine whether the values of each signal are the same as the preset standard values, or to determine whether the values of each signal are within the range of the preset standard values. If there is a signal value that is not the same as the preset standard value or the signal value is not within the range of the preset standard values, it is determined that the matching is abnormal.

[0091] In an implementable manner of the embodiment of the present application, when determining whether there are abnormal signals in the target vehicle signal information, there are two situations. The first is the fault corresponding to a single signal value. For each abnormal target vehicle signal information, a corresponding fault database or mapping relationship can be established. In practical applications, the abnormal target vehicle signal information is respectively matched with the fault database to determine whether there is corresponding fault information for each abnormal target vehicle signal information. For example, according to the abnormal engine speed, relevant information about engine failure is matched; the tire pressure value is too low, resulting in a tire pressure fault, etc. Specifically, the embodiment of the present application does not limit this.

[0092] The second is when multiple signal values are abnormal at the same time, corresponding to one fault. Please refer to Figure 3, Figure 3 This is an example diagram of a signal operation expression used to determine fault information in a preset diagnostic algorithm provided by an embodiment of the present application. As Figure 3 shown, a preset standard value is set in advance in the signal operation expression. When the signal values in the Figure 3 preset diagnostic algorithm shown are abnormal at the same time, it is determined that there is fault information corresponding to the preset diagnostic algorithm; it should be noted that different fault information corresponds to different signal operation expressions, which can be set according to actual needs in practical applications, and the embodiments of the present application do not limit this; for example, according to the combination of abnormal engine speed and abnormal water temperature, relevant information about the engine cooling system fault is matched.

[0093] For the determined fault information, comprehensive analysis can also be carried out, including fault causes, possible impacts, handling suggestions, etc. Through comprehensive analysis, the severity and urgency of the fault can be determined, providing a reference for subsequent fault handling.

[0094] According to the fault information, specific smaller fault classifications can be further identified, such as engine faults, transmission system faults, braking system faults, etc., which can be achieved by further classifying and analyzing the fault information, so as to generate more suitable and targeted recovery suggestions for smaller fault classifications. For example, for engine faults, it is recommended to conduct inspections and repairs; for braking system faults, it is recommended to stop immediately and carry out emergency repairs, etc.

[0095] In another implementable manner of the embodiment of the present application, the target diagnostic algorithm can be the fault diagnosis algorithm in a trained fault analysis model. When performing fault analysis on the target vehicle signal information through the target diagnostic algorithm, it can be determined according to the following steps:

[0096] Please refer to Figure 2 , Figure 2 This is a schematic flowchart of a method for detecting a fault provided by an embodiment of the present disclosure, including:

[0097] Step 201, input the target vehicle signal information into the fault analysis model.

[0098] Step 202, calculate the similarity between the target vehicle signal information and the fault sample signal information corresponding to the target fault category through the fault diagnosis algorithm in the fault analysis model.

[0099] In an implementable manner of the embodiment of the present application, the fault sample signal information is the relevant target vehicle signal information in the vehicle when a fault of the fault category occurs.

[0100] Optionally, the similarity between the target vehicle signal information and the fault sample signal information corresponding to the target fault category can be calculated by converting the target vehicle signal information and the fault sample signal information into vectors and then calculating the Euclidean distance between the vectors.

[0101] Step 203, in the case where there is target fault sample signal information with a similarity greater than a preset threshold, the fault information corresponding to the target fault sample signal information is output by the fault analysis model, and the fault information corresponding to the target fault sample signal information is determined as the fault analysis result.

[0102] Similarly, after determining the fault information in this step, specific smaller fault classifications can also be further identified with reference to the embodiments above. The specific process can refer to the above, and will not be elaborated here.

[0103] In an implementable manner of the embodiments of the present application, the priority and urgency of the recovery suggestion can be determined according to the severity and urgency of the fault category. The specific steps include:

[0104] Find the harm degree of the target fault category, and determine the processing priority corresponding to the harm degree as the priority of the fault detection instruction;

[0105] If it is determined that the priority of the fault detection instruction is higher than the preset standard priority, the computing power of the current vehicle is allocated to give priority to processing the fault detection;

[0106] If the priority of the fault detection instruction is not higher than the preset standard priority, the computing power of the current vehicle is allocated to give priority to meeting the computing power requirements of the user for vehicle use.

[0107] In an implementable manner of the embodiments of the present application, before executing this step, the harm degree of the fault category can be determined according to the influence range, urgency, etc. of the fault category. For example, if the fault category is a power fault, it may affect the vehicle use safety, then the harm degree is set to be relatively high. Specifically, the embodiments of the present application do not limit this.

[0108] The preset standard priority is a preset value. Specifically, after the priorities of each fault category are determined, they can be sorted according to the priorities, and the priorities of the fault categories that do not affect driving safety are set as the preset standard priority; it should be noted that this description method is only an exemplary illustration and is not a specific limitation on the setting of the specific preset standard priority. The embodiments of the present application do not limit this.

[0109] In an implementable manner of the embodiments of the present application, the fault causes include parameter settings, driving behaviors, and hardware faults. Generating a recovery suggestion according to the information type of the fault information includes:

[0110] When the fault detection result indicates that the vehicle fault is related to the parameter settings of the vehicle, generate a recommended handling measure for changing the parameter settings, and execute the correction of the parameter settings of the vehicle to be detected when receiving an instruction to agree to change the parameter settings;

[0111] In an implementable manner of the embodiment of the present application, if it can be restored by changing the settings, ask the driver whether they need to change the settings. If the driver agrees, the voice assistant completes the operation through an instruction, or the user can also be prompted to change the corresponding setting parameters when it is convenient. Specifically, the embodiment of the present application does not limit this.

[0112] If the cause of the fault in the fault information is a driving behavior fault, generate a recovery suggestion to change the driving behavior and give a behavior demonstration;

[0113] First, it is necessary to clarify the specific driving behavior fault, such as sudden braking, frequent lane changes, speeding, or long-term parking, etc.; secondly, for the specific driving behavior fault, give corresponding change suggestions, such as reminding the driver to reduce the number of sudden brakings, abide by traffic rules, reduce speeding behaviors, etc.; a specific behavior demonstration can be given, such as showing the correct driving behavior through a video or simulation demonstration. Specifically, the embodiment of the present application does not limit this.

[0114] If the cause of the fault in the fault information is a hardware fault, generate a recovery suggestion to perform manual maintenance.

[0115] If the cause of the fault is some reasons such as mechanical or electronic components and cannot be directly restored through system settings, it is recommended that the user go to the 4S store for maintenance, and when the impact of the fault is relatively large, remind the user to pull over to the side of the road in time and stop driving.

[0116] For example, for safety-related faults, provide suggestions for emergency stopping and maintenance; for general faults, provide suggestions for maintenance when it is convenient.

[0117] In an implementable manner of the embodiment of the present application, the vehicle-mounted system can record the diagnosis results and recovery suggestions of each fault, so as to optimize the generation process of the recovery suggestions in combination with historical fault data and user feedback information. For example, understand the common solutions for specific fault causes through historical data, or continuously improve the accuracy and practicality of the recovery suggestions according to user feedback.

[0118] In an implementable manner of the embodiments of the present application, the present application can perform fault detection in the cloud or in the vehicle. Cloud diagnosis can quickly iterate the diagnostic algorithm, and has a large scope and high accuracy in diagnosing faults, but it needs to be used when the vehicle is connected to the network. Vehicle-end diagnosis requires pre-installed diagnostic algorithms and can be used without networking, but there are problems with untimely data updates and low diagnostic accuracy in vehicle-end diagnosis; therefore, vehicle-end diagnosis can be used when the vehicle is not connected to the network, and cloud diagnosis can be used when the vehicle is connected to the network; specifically, the embodiments of the present application do not make any limitations.

[0119] It should be noted that the embodiments of the present disclosure may include multiple steps. For the convenience of description, these steps are numbered, but these numbers are not intended to limit the execution time slots and execution orders between the steps; these steps can be implemented in any order, and the embodiments of the present disclosure do not make any limitations in this regard.

[0120] Corresponding to the above-mentioned fault detection method, the present invention also proposes a fault detection device. Since the device embodiments of the present invention correspond to the above-mentioned method embodiments, details not disclosed in the device embodiments can be referred to the above-mentioned method embodiments, and will not be elaborated in the present invention.

[0121] Figure 4 It is a schematic structural diagram of a fault detection device provided by an embodiment of the present disclosure, as Figure 4 shown, including:

[0122] A determination unit 31, configured to, in response to receiving a fault detection instruction, extract keywords in the fault detection instruction, search for the corresponding relationship between the keywords and a preset fault category, and if found, determine the found preset fault category as the target fault category corresponding to the fault detection instruction; the keywords are terms related to the vehicle field;

[0123] An acquisition unit 32, configured to acquire vehicle signal information of the vehicle to be detected; and acquire target vehicle signal information associated with the target fault category from the vehicle signal information; wherein, the target vehicle signal information is used to detect whether there is a fault corresponding to the target fault category in the vehicle;

[0124] An analysis unit 33, configured to select a target diagnostic algorithm corresponding to the target fault category, perform fault analysis on the target vehicle signal information through the target diagnostic algorithm, and determine the obtained analysis result as the fault detection result of the vehicle.

[0125] The fault detection device provided by the present disclosure mainly includes the following technical solutions: First, in response to receiving a fault detection instruction, extract the keywords in the fault detection instruction, search for the corresponding relationship between the keywords and the preset fault categories, and determine the target fault category corresponding to the fault detection instruction; the keywords are terms related to the vehicle field; obtain the target vehicle signal information related to the target fault category in the vehicle to be detected; wherein, the target vehicle signal information is used to detect whether there is fault information of the target fault category; select the target diagnostic algorithm corresponding to the target fault category, perform fault analysis on the target vehicle signal information through the target diagnostic algorithm, and determine the obtained analysis result as the fault detection result of the vehicle. Compared with the related art, in the embodiment of the present application, after obtaining the fault category to be detected, the target vehicle signal information corresponding to the fault category is obtained, and the target vehicle signal information is detected to determine whether there is fault information, realizing the self-check of vehicle data, discovering vehicle fault information through the signal information of the vehicle, and enabling fault diagnosis of the vehicle at any time and place, reducing the user's vehicle use cost.

[0126] Further, in a possible implementation manner of this embodiment, as Figure 5 shown, the device further includes:

[0127] A processing unit 34, configured to, before the analysis unit 33 selects the target diagnostic algorithm corresponding to the target fault category, based on a preset name table, standardize the signal names of the target vehicle signal information to eliminate the name differences of the same target vehicle signal information in different vehicle models; wherein, the preset name table contains the names of the same target vehicle signal information in different vehicle models.

[0128] Further, in a possible implementation manner of this embodiment, the analysis unit 33 is further configured to:

[0129] Match the values of each signal in the target vehicle signal information with the preset standard values corresponding to the respective signals, and determine the signals with abnormal matching as abnormal signals;

[0130] Search for the fault information corresponding to each abnormal signal, and / or search for the fault information corresponding to the combination of different abnormal signals, and determine the found abnormal information as the fault analysis result.

[0131] Further, in a possible implementation manner of this embodiment, the target diagnostic algorithm is the fault diagnostic algorithm in a trained fault analysis model;

[0132] The analysis unit 33 is further configured to:

[0133] Input the target vehicle signal information into the fault analysis model;

[0134] Calculate the similarity between the target vehicle signal information and the fault sample signal information corresponding to the target fault category through the fault diagnosis algorithm in the fault analysis model;

[0135] In the case where there is target fault sample signal information with a similarity greater than a preset threshold, the fault information corresponding to the target fault sample signal information is output by the fault analysis model, and the fault information corresponding to the target fault sample signal information is determined as the fault analysis result.

[0136] Further, in a possible implementation manner of this embodiment, as Figure 5 shown, the device further includes:

[0137] A generating unit 35, configured to generate a recommended handling measure for changing parameter settings when the fault detection result indicates that the vehicle fault is related to the parameter settings of the vehicle, and execute the correction of the parameter settings of the vehicle to be detected when receiving an instruction to agree to change the parameter settings.

[0138] Further, in a possible implementation manner of this embodiment, as Figure 5 shown, the device further includes:

[0139] A searching unit 36, configured to search for the harm degree of the target fault category after the determining unit 31 determines that the found preset fault category is the target fault category corresponding to the fault detection instruction, and determine the processing priority corresponding to the harm degree as the priority of the fault detection instruction;

[0140] An allocating unit 37, configured to allocate the computing power of the current vehicle to preferentially process the fault detection when it is determined that the priority of the fault detection instruction is higher than a preset standard priority;

[0141] The allocating unit 37 is further configured to allocate the computing power of the current vehicle to preferentially meet the computing power requirement of the user for vehicle use when the priority of the fault detection instruction is not higher than the preset standard priority.

[0142] It should be noted that the foregoing explanation of the method embodiment also applies to the device in this embodiment, and the principle is the same, so it is not limited in this embodiment.

[0143] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0144] Figure 6FIG. 0 shows a schematic block diagram of an exemplary electronic device 400 that may be used to implement embodiments of the present disclosure. The electronic device is intended to represent various forms of digital computers, such as, for example, laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as, personal digital processors, cellular telephones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely exemplary and are not intended to limit the implementations of the present disclosure described and / or claimed herein.

[0145] As Figure 6 shown, the device 400 includes a computing unit 401 that may perform various appropriate actions and processes in accordance with a computer program stored in a ROM (Read-Only Memory) 402 or a computer program loaded from a storage unit 408 into a RAM (Random Access Memory) 403. In the RAM 403, various programs and data required for the operation of the device 400 may also be stored. The computing unit 401, the ROM 402, and the RAM 403 are connected to each other via a bus 404. An I / O (Input / Output) interface 405 is also connected to the bus 404.

[0146] A plurality of components in the device 400 are connected to the I / O interface 405, including: an input unit 406, such as a keyboard, a mouse, etc.; an output unit 407, such as various types of displays, speakers, etc.; a storage unit 408, such as a magnetic disk, an optical disk, etc.; and a communication unit 409, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 409 allows the device 400 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0147] The computing unit 401 may be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 401 include, but are not limited to, a CPU (Central Processing Unit), a GPU (Graphic Processing Units), various dedicated AI (Artificial Intelligence) computing chips, various computing units running machine learning model algorithms, a DSP (Digital Signal Processor), and any suitable processor, controller, microcontroller, etc. The computing unit 401 executes the various methods and processes described above, such as the fault detection method. For example, in some embodiments, the fault detection method may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as the storage unit 408. In some embodiments, part or all of the computer program may be loaded and / or installed onto the device 400 via the ROM 402 and / or the communication unit 409. When the computer program is loaded into the RAM 403 and executed by the computing unit 401, one or more steps of the method described above may be executed. Alternatively, in other embodiments, the computing unit 401 may be configured to execute the aforementioned fault detection method in any other suitable manner (e.g., by means of firmware).

[0148] The various embodiments of the systems and techniques described above in this document may be implemented in digital electronic circuitry, integrated circuit systems, FPGAs (Field Programmable Gate Arrays), ASICs (Application-Specific Integrated Circuits), ASSPs (Application Specific Standard Products), SOCs (System On Chip), CPLDs (Complex Programmable Logic Devices), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include: being implemented in one or more computer programs that may be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a special-purpose or general-purpose programmable processor that receives data and instructions from a storage system, at least one input device, and at least one output device, and transmits the data and instructions to the storage system, the at least one input device, and the at least one output device.

[0149] The program code for implementing the methods of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing devices, such that when the program codes are executed by the processor or controller, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The program codes can be executed entirely on the machine, partially on the machine, executed partially on the machine and partially on a remote machine as an independent software package, or executed entirely on a remote machine or server.

[0150] In the context of the present disclosure, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatuses, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media would include electrical connections based on one or more wires, portable computer disks, hard disks, RAM, ROM, EPROM (Electrically Programmable Read-Only-Memory), or flash memory, optical fibers, CD-ROM (Compact Disc Read-Only Memory), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0151] In order to provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (Cathode-Ray Tube) or an LCD (Liquid Crystal Display) monitor); and a keyboard and a pointing device (e.g., a mouse or a trackball) through which the user can provide input to the computer. Other kinds of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0152] The systems and techniques described herein can be implemented in a computing system including backend components (e.g., as a data server), or a computing system including middleware components (e.g., an application server), or a computing system including frontend components (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described herein), or a computing system including any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected to each other by digital data communication in any form or medium (e.g., a communication network). Examples of communication networks include: LAN (Local Area Network), WAN (Wide Area Network), the Internet, and blockchain networks.

[0153] A computer system can include a client and a server. The client and the server are generally far from each other and usually interact through a communication network. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or a cloud host, which is a host product in the cloud computing service system, and solves the defects of difficult management and weak business scalability existing in traditional physical hosts and VPS services ("Virtual Private Server", or simply "VPS"). The server can also be a server of a distributed system, or a server combined with blockchain.

[0154] It should be noted that artificial intelligence is a discipline that studies how to make a computer simulate certain thinking processes and intelligent behaviors of humans (such as learning, reasoning, thinking, planning, etc.), and it has both hardware-level technologies and software-level technologies. Artificial intelligence hardware technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, and big data processing; artificial intelligence software technologies mainly include several major directions such as computer vision technology, speech recognition technology, natural language processing technology, and machine learning / deep learning, big data processing technology, and knowledge graph technology.

[0155] The various digital numbers such as the first and the second involved in this disclosure are only for the convenience of description and are not used to limit the scope of the embodiments of this disclosure, nor do they represent the order of precedence.

[0156] At least one in the present disclosure may also be described as one or more. The plurality may be two, three, four or more, and the present disclosure does not make a limitation. In the embodiments of the present disclosure, for a technical feature, the technical features in this kind of technical feature are distinguished by "first", "second", "third", "A", "B", "C" and "D", etc. There is no order of precedence or order of magnitude among the technical features described by the "first", "second", "third", "A", "B", "C" and "D".

[0157] It should be understood that various forms of the processes shown above can be used, steps can be reordered, added or deleted. For example, the steps recited in the present disclosure can be executed in parallel, sequentially or in a different order, as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved, and no limitation is made herein.

[0158] The above specific embodiments do not constitute a limitation on the protection scope of the present disclosure. Those skilled in the art should understand that various modifications, combinations, sub - combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principle of the present disclosure shall be included within the protection scope of the present disclosure.

Claims

1. A method for detecting a fault, characterized in that, it includes: Obtain the vehicle signal information of the vehicle to be detected; In response to receiving a fault detection instruction, extract the keywords in the fault detection instruction, search for the corresponding relationship between the keywords and the preset fault categories. If found, determine the found preset fault category as the target fault category corresponding to the fault detection instruction; the keywords are terms related to the vehicle field; Obtain the target vehicle signal information associated with the target fault category from the vehicle signal information; wherein, the target vehicle signal information is used to detect whether there is a fault corresponding to the target fault category in the vehicle; Select the target diagnostic algorithm corresponding to the target fault category, perform fault analysis on the target vehicle signal information through the target diagnostic algorithm, and determine the obtained analysis result as the fault detection result of the vehicle.

2. The method according to claim 1, characterized in that, Before selecting the target diagnostic algorithm corresponding to the target fault category, the method further includes: Based on a preset name table, standardize the signal names of the target vehicle signal information to eliminate the name differences of the same target vehicle signal information in different vehicle models; wherein, the preset name table contains the names of the same target vehicle signal information in different vehicle models.

3. The method according to claim 1, characterized in that, The performing fault analysis on the target vehicle signal information through the target diagnostic algorithm includes: Match the values of each signal in the target vehicle signal information with the preset standard values corresponding to the respective signals, and determine the signals with abnormal matches as abnormal signals; Search for the fault information corresponding to each abnormal signal, and / or search for the fault information corresponding to the combination of different abnormal signals, and determine the found abnormal information as the fault analysis result.

4. The method according to claim 1, characterized in that, The target diagnostic algorithm is the fault diagnostic algorithm in a trained fault analysis model; The performing fault analysis on the target vehicle signal information through the target diagnostic algorithm includes: Input the target vehicle signal information into the fault analysis model; Through the fault diagnostic algorithm in the fault analysis model, calculate the similarity between the target vehicle signal information and the fault sample signal information corresponding to the target fault category; In the case where there is a target fault sample signal information with a similarity greater than a preset threshold, the fault analysis model outputs the fault information corresponding to the target fault sample signal information, and determines the fault information corresponding to the target fault sample signal information as the fault analysis result.

5. The method according to claim 1, characterized in that, The method further includes: In the case where the fault detection result indicates that the vehicle fault is related to the parameter settings of the vehicle, generate a recommended handling measure for changing the parameter settings, and in the case of receiving an instruction to agree to change the parameter settings, perform the correction of the parameter settings of the vehicle.

6. The method according to claim 1, characterized in that, After determining that the preset fault category found is the target fault category corresponding to the fault detection instruction, the method further includes: Finding the harm degree of the target fault category, and determining the processing priority corresponding to the harm degree as the priority of the fault detection instruction; If it is determined that the priority of the fault detection instruction is higher than the preset standard priority, allocate the computing power of the current vehicle to preferentially process the fault detection; If the priority of the fault detection instruction is not higher than the preset standard priority, allocate the computing power of the current vehicle to preferentially meet the computing power requirements of the user for vehicle use.

7. A fault detection device, Characterized in that, It includes: A determination unit, configured to, in response to receiving a fault detection instruction, extract keywords in the fault detection instruction, search for the corresponding relationship between the keywords and preset fault categories, and if found, determine the found preset fault category as the target fault category corresponding to the fault detection instruction; the keywords are terms related to the vehicle field; An acquisition unit, configured to acquire vehicle signal information of the vehicle to be detected; and acquire target vehicle signal information associated with the target fault category from the vehicle signal information; wherein, the target vehicle signal information is used to detect whether there is a fault corresponding to the target fault category in the vehicle; An analysis unit, configured to select a target diagnostic algorithm corresponding to the target fault category, perform fault analysis on the target vehicle signal information through the target diagnostic algorithm, and determine the obtained analysis result as the fault detection result of the vehicle.

8. A vehicle, Characterized in that, The vehicle includes a fault detection device as described in claim 7.

9. An electronic device, Characterized in that, It includes: At least one processor; And A memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method according to any one of claims 1-6.

10. A non-transitory computer-readable storage medium storing computer instructions, Characterized in that, The computer instructions are used to cause the computer to execute the method according to any one of claims 1-6.

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