Vehicle fault detection method and device and vehicle

Abstract

The invention provides a vehicle fault detection method and device and a vehicle, and the method comprises the steps: transmitting a target instruction to target unmanned aerial vehicle equipment in a vehicle driving process; wherein the target instruction is used for indicating target unmanned aerial vehicle equipment to collect target data; acquiring target data acquired by the target unmanned aerial vehicle equipment and vehicle data acquired by the vehicle; wherein the vehicle data is used for indicating the vehicle state of the vehicle in the driving process; and obtaining a fault detection result of the vehicle based on the target data and the vehicle data. The method can improve the accuracy of vehicle fault detection.

Description

Technical Field

[0001] The present application relates to the field of vehicles, and more specifically, to a vehicle fault detection method, device and vehicle in the field of vehicles. Background Art

[0002] In the prior art, when a vehicle has a fault, a manual detection method is usually used to detect the fault of the vehicle, or data is obtained and detected through a detection device installed on the vehicle. With the rapid development of the automobile industry and the popularization of intelligent technology, the detection accuracy of the traditional manual detection method is low and it is impossible to accurately detect the fault information of the vehicle. Therefore, how to improve the accuracy of vehicle fault detection is a technical problem that needs to be solved at present. Summary of the invention

[0003] The present application provides a vehicle fault detection method, device and vehicle. The method collects target data through a drone, and obtains a vehicle fault detection result based on the target data and vehicle data collected by the vehicle, which can improve the accuracy of vehicle fault detection.

[0004] In a first aspect, a vehicle fault detection method is provided, the method comprising:

[0005] During the driving process of the vehicle, a target instruction is sent to the target UAV device; wherein the target instruction is used to instruct the target UAV device to collect target data; the target data includes a target image; the target image includes an image of the vehicle's body exterior and an image of the vehicle's driving environment within a preset range;

[0006] Acquire target data collected by the target UAV device and vehicle data collected by the vehicle; wherein the vehicle data is used to indicate the vehicle status of the vehicle during driving;

[0007] Based on the target data and the vehicle data, the vehicle fault detection result is obtained.

[0008] In an embodiment of the present application, during the driving of the vehicle, a target instruction is sent to the target UAV device; the target instruction is used to instruct the target UAV device to collect target data, so when the target UAV device receives the target instruction, it starts to collect target data of the vehicle; the vehicle obtains a fault detection result of the vehicle based on the target data collected by the target UAV device and the vehicle data collected by the vehicle; since the target UAV device can fly around the vehicle, it can collect target data including the external image of the vehicle body and the image of the vehicle's driving environment; therefore, this scheme collects the target data and the vehicle data collected by the vehicle through the target UAV device, thereby ensuring that when performing fault detection on the vehicle, fault detection on the vehicle can be performed based on more comprehensive detection data. In addition, in this scheme, fault detection is performed on the exterior of the vehicle body through the vehicle's exterior image and the vehicle's driving environment image, ensuring that fault detection information on the exterior of the vehicle body can be obtained by combining the vehicle's exterior image and the environment image; fault detection is performed on the interior of the vehicle through vehicle data to obtain fault detection information on the interior of the vehicle; since this scheme can obtain both target data on the exterior of the vehicle body and vehicle data on the interior of the vehicle; therefore, more comprehensive detection data can be obtained; thereby, fault detection information on the exterior of the vehicle body and fault detection information on the interior of the vehicle can be generated; ensuring that more comprehensive and accurate fault detection results are obtained; and improving the accuracy of vehicle fault detection.

[0009] In conjunction with the first aspect, in some implementations of the first aspect, the method further includes:

[0010] Acquire first target data and first vehicle data when the vehicle is in a normal state; wherein the first target data includes a first vehicle body external image;

[0011] Obtaining a fault detection result of the vehicle based on the target data and the vehicle data, including:

[0012] Based on the first similarity between the target data and the first target data, the first detection information is obtained; based on the second similarity between the vehicle data and the first vehicle data, the second detection information is obtained; based on the first detection information and the second detection information, the fault detection result is obtained.

[0013] In an embodiment of the present application, first target data and first vehicle data are obtained when the vehicle is in a normal state; that is, if the vehicle is in a normal state, the target data of the vehicle is represented by the first target data, and the vehicle data of the vehicle is represented by the first vehicle data; first detection information is obtained based on a first similarity between the target data and the first target data; second detection information is obtained based on a second similarity between the vehicle data and the first vehicle data; a fault detection result is obtained based on the first detection information and the second detection information; and whether the vehicle is in a normal state is determined by the similarity, thereby determining whether the vehicle has a fault.

[0014] In combination with the first aspect and the above implementation manners, in some implementation manners of the first aspect, the first detection information includes vehicle body exterior detection information; and obtaining the first detection information based on a first similarity between the target data and the first target data includes:

[0015] The target image is compared with the first vehicle body exterior image, and if there is an image region whose similarity is less than a first preset threshold, it is determined that the vehicle body exterior detection information indicates that there is a fault on the vehicle body exterior.

[0016] In combination with the first aspect and the above implementations, in some implementations of the first aspect, the target data includes emission data of the vehicle, the first detection information includes emission detection information; and the method further includes:

[0017] Get the first emission data of the vehicle in normal state

[0018] The first detection information obtained based on the first similarity between the target data and the first target data includes:

[0019] The emission data is compared with the first emission data, and if the similarity between the emission data and the first emission data is less than a second preset threshold, it is determined that the emission detection information indicates that the vehicle has an emission fault.

[0020] In the embodiment of the present application, since the target image is used to detect the vehicle body and obtain the vehicle body exterior detection information; therefore, when comparing the target image with the first vehicle body exterior image, the smaller the similarity, the greater the possibility that there is a fault on the vehicle body exterior; if there is an image area in the target image with a similarity less than a first preset threshold, it means that there is an abnormality in the vehicle body area indicated by the image area, that is, it means that there is a fault on the vehicle body exterior; since the vehicle emission data is used to detect the vehicle emission performance and obtain the vehicle emission detection information; therefore, when comparing the emission data with the first emission data, if the similarity is less than a second preset threshold, it means that there is an emission fault on the vehicle. By comparing the target image and emission data of the vehicle with the first vehicle body exterior image and the first emission data of the vehicle in a normal state, it is ensured that the first detection information including the vehicle body exterior detection information and the emission detection information can be obtained.

[0021] In combination with the first aspect and the above implementations, in some implementations of the first aspect, the following further includes:

[0022] If the vehicle is equipped with a drone device, the drone device is identified as a target drone device;

[0023] If the vehicle is not equipped with a drone device, an indication signal is sent to a drone station within a preset distance from the vehicle, and the identification of the drone device sent by the drone station is received; the drone device corresponding to the identification of the drone device is determined as the target drone device; wherein the indication signal is used to instruct the drone station to dispatch the drone device to perform fault detection on the vehicle.

[0024] In an embodiment of the present application, if the vehicle is equipped with a drone device, the drone device carried by the vehicle is directly determined as the target drone device; if the vehicle is not equipped with a drone device, an indication signal is sent to a drone station within a preset distance, and the drone device dispatched by the drone station is used as the target drone device; ensuring that target data can be collected through the drone device whether the vehicle is equipped with a drone device or not, so as to realize vehicle fault detection.

[0025] In combination with the first aspect and the above implementations, in some implementations of the first aspect, the following further includes:

[0026] If a detection instruction for a target component in the vehicle is detected, a target position corresponding to the target component is determined; wherein the target position is a position where the target UAV device collects target data;

[0027] Send target instructions to the target drone device, including:

[0028] Send target instructions and target positions to the target UAV device so that the target UAV device flies to the target position and collects target data.

[0029] In an embodiment of the present application, fault detection of a vehicle may be fault detection of the entire vehicle, or fault detection of a target component in the vehicle; if a detection instruction for a target component in the vehicle is detected, the target position corresponding to the target component is determined; and the target instruction and target position are sent to the target UAV device, so that the target UAV device flies to the target position and collects target data; so as to realize fault detection of specific target components in the vehicle.

[0030] In combination with the first aspect and the above implementations, in some implementations of the first aspect, the following further includes:

[0031] If the detection instruction corresponds to at least two target components, determining the detection priority of the at least two target components based on the degree of influence of the at least two target components on vehicle safety; wherein the detection priority is positively correlated with the degree of influence;

[0032] Determine the order of testing based on testing priority;

[0033] Send target instructions and target location to the target drone device, including:

[0034] Send target instructions, target positions and detection sequences to the target UAV device so that the target UAV device can obtain target data according to the detection sequence.

[0035] In an embodiment of the present application, if a detection instruction corresponds to at least two target components, a detection order is determined based on the detection priorities of the at least two target components, and the target instruction and the detection order are sent to the target UAV device, so that the target UAV device collects target data in accordance with the detection order when collecting target data; since the detection priority is positively correlated with the degree of influence of the target component on vehicle safety; for example, the detection priority of the vehicle headlights is higher than the detection priority of the vehicle exhaust pipe; therefore, the target data is acquired and fault detection is performed according to the detection priority; ensuring that vehicle components that have a greater degree of influence on vehicle safety can be detected preferentially.

[0036] In combination with the first aspect and the above implementations, in some implementations of the first aspect, the following further includes:

[0037] Determine a solution database for vehicle faults based on a target vehicle model; wherein the solution database includes fault types of vehicle faults and solutions corresponding to the fault types;

[0038] Based on the target fault type indicated by the fault detection result and the solution database, a solution corresponding to the target fault type is determined.

[0039] In an embodiment of the present application, a solution database for vehicle faults is determined based on a target vehicle model. Since the solution database includes vehicle fault types and solutions corresponding to the fault types, it is possible to determine a solution corresponding to the target fault type in the solution database based on the target fault type indicated by the fault detection result. This ensures that when the fault detection result indicates that the vehicle has a fault, the corresponding solution can be obtained.

[0040] In a second aspect, a vehicle fault detection device is provided, the device comprising:

[0041] The sending module is used to send a target instruction to the target UAV device during the driving of the vehicle; wherein the target instruction is used to instruct the target UAV device to collect target data; the target data includes a target image; the target image includes an image of the vehicle's body exterior and an image of the vehicle's driving environment within a preset range;

[0042] An acquisition module is used to acquire target data collected by the target UAV device and vehicle data collected by the vehicle; wherein the vehicle data is used to indicate the vehicle status of the vehicle during driving;

[0043] The detection module is used to obtain vehicle fault detection results based on target data and vehicle data.

[0044] In a third aspect, a vehicle is provided, comprising a memory and a processor, wherein the memory is used to store executable program code, and the processor is used to call and run the executable program code from the memory, so that the vehicle executes the method in the above-mentioned first aspect or any possible implementation of the first aspect.

[0045] In a fourth aspect, a computer program product is provided, comprising: a computer program code, which, when executed on a computer, enables the computer to execute the method in the first aspect or any possible implementation of the first aspect.

[0046] In a fifth aspect, a computer-readable storage medium is provided, which stores instructions. When the instructions are executed on a vehicle, the vehicle executes the method in the above-mentioned first aspect or any possible implementation of the first aspect. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 This is a schematic diagram of a scenario provided by an embodiment of the present application;

[0048] Figure 2 is a schematic flow chart of a vehicle fault detection method provided in an embodiment of the present application;

[0049] Figure 3 is a schematic flow chart of another vehicle fault detection method provided in an embodiment of the present application;

[0050] Figure 4 is a schematic flow chart of another vehicle fault detection method provided in an embodiment of the present application;

[0051] Figure 5 is a structural schematic diagram of a vehicle fault detection device provided in an embodiment of the present application;

[0052] Figure 6 It is a structural schematic diagram of a vehicle provided in an embodiment of the present application. DETAILED DESCRIPTION

[0053] The technical solution in the present application will be described clearly and in detail below in conjunction with the accompanying drawings. In the description of the embodiments of the present application, unless otherwise specified, " / " means or, for example, A / B can mean A or B: "and / or" in the text is only a description of the association relationship of associated objects, indicating that there can be three relationships, for example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, in the description of the embodiments of the present application, "multiple" means two or more than two.

[0054] In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as suggesting or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features.

[0055] In the prior art, when a vehicle has a fault, a manual detection method is usually used to detect the fault of the vehicle, or data is obtained and detected through a detection device installed on the vehicle. With the rapid development of the automobile industry and the popularization of intelligent technology, the detection accuracy of the traditional manual detection method is low and it is impossible to accurately detect the fault information of the vehicle. Therefore, how to improve the accuracy of vehicle fault detection is a technical problem that needs to be solved at present.

[0056] In view of this, the present application provides a vehicle fault detection method, device and vehicle. Through the embodiments of the present application, target data is collected by a drone, and the vehicle fault detection result is obtained based on the target data and the vehicle data collected by the vehicle, which can improve the accuracy of vehicle fault detection.

[0057] Figure 1 It is a schematic diagram of a scenario provided in an embodiment of the present application.

[0058] For example, Figure 1 As shown, scene 100 includes a vehicle 110 and a drone device 120; when the vehicle 110 is driving, a target instruction is sent to the drone device 120; the target instruction is used to instruct the drone device 120 to collect target data; when the drone device 120 receives the target instruction, it starts to collect target data; and sends the collected target data to the vehicle; so that the vehicle can perform fault detection based on the target data and the vehicle data obtained by the vehicle.

[0059] Exemplarily, when the vehicle 110 is in motion, the movement direction of the drone device 120 is consistent with the driving direction of the vehicle 110 .

[0060] Below Figures 2 to 4 The vehicle fault detection method provided in the embodiment of the present application is described in detail.

[0061] Figure 2 It is a schematic flow chart of a vehicle fault detection method provided in an embodiment of the present application.

[0062] For example, Figure 2 The illustrated method 200 may be performed by the vehicle 110 ; or may be performed by a processor or chip in the vehicle 110 .

[0063] like Figure 2As shown, the vehicle fault detection method 200 includes S210 to S230, and S210 to S230 are described in detail below.

[0064] S210, sending a target instruction to a target UAV device while the vehicle is traveling.

[0065] Among them, the target instruction is used to instruct the target drone equipment to collect target data; the target data includes the target image; the target image includes the vehicle body external image and the vehicle's driving environment image within a preset range; that is, the target image includes the vehicle body image and the vehicle's driving environment image; wherein the vehicle body image and the vehicle's driving environment image are used to determine whether there are faults in the vehicle body surface and the vehicle's external structure.

[0066] Exemplarily, a high-definition camera for collecting target images is configured on the target UAV device; when the target UAV device receives a target command sent by the vehicle, the target UAV device starts to collect target data; high-speed wireless communication technology in the prior art can be used between the vehicle and the target UAV device to ensure real-time data transmission and command control between the UAV and the vehicle.

[0067] Optionally, a high-definition camera used to capture images may use wide dynamic range technology to ensure that clearer images can be captured in high-contrast environments (such as shadow areas under direct sunlight).

[0068] It should be noted that the drone in the embodiment of the present application may be a drone device mounted on a vehicle or a drone device at a drone site.

[0069] In one implementation, if the vehicle is equipped with a UAV device, the UAV device is determined as a target UAV device;

[0070] If the vehicle is not equipped with a drone device, an indication signal is sent to a drone station within a preset distance from the vehicle, and the identification of the drone device sent by the drone station is received; the drone device corresponding to the identification of the drone device is determined as the target drone device; wherein the indication signal is used to instruct the drone station to dispatch the drone device to perform fault detection on the vehicle.

[0071] Exemplarily, if the vehicle is equipped with a drone device (for example, a vehicle-mounted drone device), the drone device carried by the vehicle is determined as the target drone device; if the vehicle is not equipped with a drone device, when it is necessary to perform fault detection on the vehicle by drone, an indication signal is sent to a drone station within a preset distance (for example, 300m); when the drone station receives the indication signal, it dispatches the drone device to fly to the location of the vehicle and collects target data of the vehicle.

[0072] Exemplarily, the drone site is a management site for drone equipment, and there are multiple drone equipment in the drone site that can perform fault detection on auxiliary vehicles. When the vehicle sends an indication signal to the drone site, the drone site dispatches the drone equipment according to the status information of the drone equipment; at the same time, it sends the identification of the drone equipment to the vehicle; the vehicle communicates with the drone equipment according to the identification of the drone equipment. Among them, the status information of the drone equipment includes the battery level, idle status and other information of the drone equipment; the drone site will give priority to dispatching drone equipment with sufficient battery and in idle status to detect the vehicle.

[0073] In an embodiment of the present application, if the vehicle is equipped with a drone device, the drone device carried by the vehicle is directly determined as the target drone device; if the vehicle is not equipped with a drone device, an indication signal is sent to a drone station within a preset distance, and the drone device dispatched by the drone station is used as the target drone device; ensuring that target data can be collected through the drone device whether the vehicle is equipped with a drone device or not, so as to realize vehicle fault detection.

[0074] Optionally, the structure and performance of the target UAV equipment are improved so that the target UAV equipment can better collect target data; specifically, the target UAV equipment adopts a multi-rotor design to ensure stable flight even in complex environments (such as strong winds and rainy days); the fuselage of the target UAV equipment adopts lightweight and high-strength carbon fiber composite materials to reduce the weight of the target UAV equipment; an anti-collision protective cover and a landing buffer device are designed on the fuselage of the target UAV equipment to ensure the safety of the target UAV equipment; in addition, a navigation system is integrated in the target UAV equipment, combined with visual recognition technology, to achieve high-precision positioning and autonomous navigation.

[0075] Exemplarily, the target UAV equipment adopts standardized interfaces, and all detection devices for acquiring data (for example, high-definition cameras, temperature sensors, etc.) are designed with standardized electrical and data interfaces; and additional interfaces and power supplies are reserved in the target UAV equipment to enable other types of sensors to be installed in the target UAV equipment; such as radar thickness gauges, ultraviolet fluorescence detectors, etc., to meet the fault detection needs of different models and vehicles.

[0076] It should be noted that when performing fault detection on a vehicle, fault detection may be performed on the entire vehicle, or fault detection may be performed on a target component in the vehicle.

[0077] In one implementation, if a detection instruction for a target component in a vehicle is detected, a target position corresponding to the target component is determined; wherein the target position is a position where a target UAV device collects target data; a target instruction and a target position are sent to the target UAV device so that the target UAV device flies to the target position and collects target data.

[0078] Exemplarily, a detection instruction for a target component of the vehicle can be triggered through the vehicle's central control screen, or can be triggered through the driver's voice control instruction; if a detection instruction for a target component in the vehicle is detected, the target position of the target component of the vehicle is determined; and the target instruction and target position are sent to the target UAV device; the target instruction is used to instruct the UAV to collect target data, and the target position is used to indicate the location where the UAV collects target data.

[0079] For example, if the driver chooses to inspect the vehicle's exhaust pipe in the vehicle's central control screen, the vehicle sends a target instruction and the location information of the exhaust pipe to the target UAV device; the target UAV device flies to the location of the exhaust pipe and collects target data of the exhaust pipe; if an inspection instruction for the vehicle's headlights is detected, the vehicle sends the location information of the vehicle's headlights to the target UAV device; the target UAV device flies to the location of the vehicle's headlights and collects target data of the vehicle's headlights.

[0080] It should be noted that the above is an example of the detection component, and this application does not limit this.

[0081] In an embodiment of the present application, fault detection of a vehicle may be fault detection of the entire vehicle, or fault detection of a target component in the vehicle; if a detection instruction for a target component in the vehicle is detected, the target position corresponding to the target component is determined; and the target instruction and target position are sent to the target UAV device, so that the target UAV device flies to the target position and collects target data; thereby realizing the detection of the target component in the vehicle.

[0082] In one implementation, if the detection instruction corresponds to at least two target components, the detection priority of the at least two target components is determined based on the degree of influence of the at least two target components on vehicle safety; wherein the detection priority is positively correlated with the degree of influence;

[0083] Based on the detection priority, the detection order is determined; the target command, target position and detection order are sent to the target UAV device so that the target UAV device obtains the target data according to the detection order.

[0084] Exemplarily, if the detection instruction corresponds to at least two target components; based on the degree of influence of the at least two target components on vehicle safety, determine the detection priority of the at least two target components; that is, the greater the degree of influence of the target component on vehicle safety, the higher the detection priority of the target component; determine the detection order based on the detection priority. For example, the degree of influence of the vehicle headlight on vehicle safety is greater than the degree of influence of the vehicle exhaust pipe on vehicle safety; therefore, if the target components are the vehicle headlight and the vehicle exhaust pipe, the detection priority of the vehicle headlight is higher than the detection priority of the vehicle exhaust pipe; obtain target data and perform fault detection according to the detection priority; ensure that vehicle components with a higher degree of influence on vehicle safety can be detected first.

[0085] S220, acquiring target data collected by the target UAV device and vehicle data collected by the vehicle.

[0086] Among them, the target data can be the target data of the entire vehicle, or the target data of the target component in the vehicle; the vehicle data is used to indicate the vehicle status of the vehicle during driving; for example, the vehicle data includes: engine-related data, driving and braking data, transmission and chassis data, electrical and electronic system data, body and safety data, etc.

[0087] Specifically, engine-related data include: engine speed, torque, ignition status and fuel injection status; driving and braking data include vehicle speed, mileage, and brake system status; transmission and chassis data include gear information, transmission oil temperature, oil pressure, tire data, and the status of the shock absorber of the suspension system; electrical and electronic system data include battery status, lighting system status, and air conditioning system status; body and safety data include: seat belt usage, airbag status, and the status of the vehicle's anti-lock braking system.

[0088] S230, obtaining a vehicle fault detection result based on the target data and the vehicle data.

[0089] Exemplarily, based on the target data collected by the drone and the vehicle data collected by the vehicle, fault detection is performed on the vehicle to obtain a fault detection result; the process of obtaining the fault detection result is described in detail below.

[0090] Exemplarily, the target data includes an image of the exterior of the vehicle, an image of the driving environment within a preset range of the vehicle, and emission data of the vehicle; wherein the image of the exterior of the vehicle and the image of the driving environment within a preset range of the vehicle are used to perform fault detection on the exterior of the vehicle; and the emission data and vehicle data are used to perform fault detection on the interior of the vehicle.

[0091] For example, the vehicle's driving environment image may affect the fault detection on the exterior of the vehicle body; for example, there may be areas on the vehicle's body surface that are obscured by shadows, such as shadows of trees; if fault detection on the exterior of the vehicle body is performed based on the vehicle's exterior image, the shadow area may be misjudged as a faulty area; therefore, based on the vehicle's driving environment image and the vehicle's exterior image, it is ensured that image information on the vehicle's body surface can be obtained more accurately, thereby more accurately performing fault detection on the vehicle's exterior.

[0092] In one implementation, first target data and first vehicle data are acquired when the vehicle is in a normal state;

[0093] Based on the target data and vehicle data, the vehicle fault detection results are obtained, including:

[0094] Based on the first similarity between the target data and the first target data, the first detection information is obtained; based on the second similarity between the vehicle data and the first vehicle data, the second detection information is obtained; based on the first detection information and the second detection information, the fault detection result is obtained.

[0095] Exemplarily, first target data and first vehicle data are obtained when the vehicle is in a normal state; that is, if the vehicle is in a normal state, the target data of the vehicle is represented by the first target data, and the vehicle data of the vehicle is represented by the first vehicle data; first detection information is obtained based on a first similarity between the target data and the first target data; second detection information is obtained based on a second similarity between the vehicle data and the first vehicle data; a fault detection result is obtained according to the first detection information and the second detection information; that is, whether the vehicle is in a normal state is determined by the similarity, thereby determining whether the vehicle has a fault; if the similarity is higher, the possibility of a vehicle fault is lower, and if the similarity is lower, the possibility of a vehicle fault is higher.

[0096] Exemplarily, since the first detection information is used to represent the fault detection information outside the vehicle body, and the second detection information is used to represent the fault detection information inside the vehicle; therefore, the internal and external faults of the vehicle are comprehensively determined through the first detection information and the second detection information.

[0097] Optionally, the first detection information and the second detection information can be directly determined as the fault detection result of the vehicle; that is, the external fault detection result of the vehicle and the internal fault detection result of the vehicle are determined respectively; or, the first detection information and the second detection information are comprehensively analyzed to determine the fault detection result of the vehicle.

[0098] For example, the first detection information indicates that the vehicle has an emissions fault; if the second detection information indicates that the vehicle's fuel injection is abnormal, it is inferred that the vehicle's fault detection result is a vehicle emissions fault caused by a problem with the vehicle's fuel system; if the second detection information indicates that the vehicle's engine speed or torque is abnormal; it is inferred that the vehicle's fault detection result is a vehicle emissions fault caused by an engine mechanical fault.

[0099] In one implementation, the first detection information includes vehicle body exterior detection information; based on a first similarity between the target data and the first target data, the first detection information is obtained, including:

[0100] The target image is compared with the first vehicle body exterior image, and if there is an image region whose similarity is less than a first preset threshold, it is determined that the vehicle body exterior detection information indicates that there is a fault on the vehicle body exterior.

[0101] Exemplarily, the similarity can be determined by the pixel values ​​of the image, and the pixel values ​​in the target image and the first vehicle body external image are compared; wherein the pixel value refers to the brightness and color information of the pixels constituting the image; for example, the pixel value can be an RGB value; by calculating the similarity of the pixel values ​​in the target image and the first vehicle body external image, the similarity between the target image and the first vehicle body external image is determined.

[0102] Specifically, the target image and the first vehicle body exterior image are first adjusted to the same size, and each pixel of each image is traversed row by row and column by column to obtain the pixel value at each pixel position, that is, the RGB value of each pixel; for pixels at the same position, the difference of each color channel of the pixel value between the target image and the first vehicle body exterior image is calculated; and the difference values ​​at all pixel positions are accumulated; the accumulated difference values ​​are normalized by dividing them by the total number of pixels to obtain a similarity score; the smaller the score, the greater the similarity.

[0103] Optionally, the similarity can be determined by matching the feature information of the images, performing feature extraction on the target image and the first vehicle body external image, identifying different areas and feature information in the images; comparing the feature information of different areas in the target image with the feature information of different areas in the first vehicle body external image, and calculating the matching degree of the feature information. The higher the matching degree of the feature information, the higher the similarity; thereby determining the similarity between the target image and the first vehicle body external image.

[0104] It should be noted that the above is an example of calculating image similarity, and the present application does not specifically limit the method for calculating image similarity.

[0105] In one implementation, the target data includes emission data of the vehicle, and the first detection information includes emission detection information; the first emission data of the vehicle in a normal state is obtained; the emission data is compared with the first emission data, and if the similarity between the emission data and the first emission data is less than a second preset threshold, it is determined that the emission detection information indicates that the vehicle has an emission fault.

[0106] Exemplarily, the target UAV device is equipped with a gas analyzer, which uses a high-precision sensor array and can detect multiple gas components, including but not limited to: carbon monoxide (CO), nitrogen oxides (NOx), hydrocarbons (HC), etc., to ensure that a more comprehensive evaluation of the vehicle's emission performance can be performed. The emission data obtained by the target UAV device can be the concentration of different gas components in the vehicle's exhaust; the vehicle compares the emission data with the first emission data; since the first emission data is the emission data of the vehicle in a normal state; therefore, when the similarity between the emission data and the first emission data is less than the second preset threshold, it indicates that the vehicle has an emission fault.

[0107] Optionally, the vehicle generates an emission trend graph based on the acquired historical emission data; the emission trend graph is used to represent the concentration change trend of each gas component in the emission data, so as to help users monitor changes in vehicle emission performance and discover potential problems in a timely manner.

[0108] In the embodiment of the present application, since the target image is used to detect the vehicle body and obtain the vehicle body exterior detection information; therefore, when comparing the target image with the first vehicle body exterior image, the lower the similarity, the greater the possibility that there is a fault on the vehicle body exterior; if there is an image area in the target image with a similarity less than a first preset threshold, it indicates that there is a fault on the vehicle body exterior; similarly, since the vehicle emission data is used to detect the vehicle emission performance and obtain the vehicle emission detection information; therefore, when comparing the emission data with the first emission data, if the similarity is less than a second preset threshold, it indicates that the vehicle has an emission fault. By comparing the vehicle target image and emission data with the first vehicle body exterior image and the first emission data when the vehicle is in a normal state, it is ensured that the first detection information including the vehicle body exterior detection information and the emission detection information can be obtained.

[0109] In a possible implementation, the target data also includes temperature data, sound data and vibration data; the temperature data of the vehicle is collected by an infrared thermal imager configured in the target UAV device; the sound data and vibration data of the vehicle are obtained by sound and vibration sensors configured in the target UAV device.

[0110] Exemplarily, the first detection information also includes temperature detection information. The vehicle obtains temperature data of the vehicle body area collected by the target drone equipment, compares the temperature data with a preset temperature model of the vehicle, and determines the abnormal temperature area of ​​the vehicle, thereby obtaining temperature detection information of the vehicle.

[0111] Optionally, the temperature data of the vehicle body area is annotated and displayed in the target image to provide the user with a more intuitive fault view and facilitate the identification of the vehicle's temperature abnormality area.

[0112] Exemplarily, the first detection information also includes sound detection information, and the vehicle obtains sound data and vibration data collected by the target drone equipment; filters the sound data and vibration data to remove interference from environmental noise (such as wind noise, road noise, etc.); ensures that the mechanical vibration and abnormal sound of the vehicle during operation can be accurately obtained; uses a preset algorithm (for example, a fast Fourier transform algorithm) to perform spectral analysis on the sound data and vibration data to identify characteristic signals in different frequency bands; and compares the characteristic signals with the characteristic signals of the sound data and vibration data of the vehicle in a normal state to determine whether there are abnormal parts in the vehicle, thereby obtaining sound detection information on the vehicle.

[0113] In an embodiment of the present application, vehicle fault detection is performed through multi-dimensional data to ensure that vehicle fault detection can be performed more comprehensively; when generating fault detection results, the data of each dimension can be analyzed separately to obtain preliminary fault diagnosis conclusions; then, through methods such as weighted summation or Bayesian fusion, multiple preliminary fault diagnosis conclusions are combined to obtain the final fault diagnosis result.

[0114] Optionally, the target data and vehicle data are uploaded to the cloud; the target data and vehicle data are processed by a data center in the cloud; and multiple algorithms such as integrated deep learning and machine learning are used to analyze the target data and vehicle data to automatically identify and generate vehicle fault detection results.

[0115] In one implementation, a solution database for vehicle faults is determined based on a target vehicle model; wherein the solution database includes fault types of vehicle faults and solutions corresponding to the fault types;

[0116] Based on the target fault type indicated by the fault detection result and the solution database, a solution corresponding to the target fault type is determined.

[0117] For example, due to different vehicle models, the corresponding solutions to the faults are different; therefore, based on the target vehicle model, a solution database for vehicle faults is determined; the solution database includes the vehicle fault type and the solution corresponding to the fault type; it is ensured that the solution corresponding to the target fault type can be determined in the solution database based on the target fault type indicated by the fault detection result; it is ensured that when the fault detection result indicates that the vehicle has a fault, the corresponding solution can be obtained. For example, the solution database can be shown in Table 1:

[0118] Table 1

[0119] Fault type Solution Fault 1 Solution 1 Fault 2 Solution 2 …… ……

[0120] Exemplarily, if the target fault type indicated by the fault detection result is fault 1, the corresponding solution is solution 1; if the target fault type indicated by the fault detection result is fault 2, the corresponding solution is solution 2; it should be noted that the solution database is updated as fault data accumulates.

[0121] In the above embodiment, the vehicle obtains the fault detection result of the vehicle according to the target data collected by the target drone device and the vehicle data collected by the vehicle; since the target drone device can fly around the vehicle, it can collect images including the vehicle's external body image, the vehicle's driving environment image and the vehicle; therefore, this solution collects the target data and the vehicle data collected by the target drone device to ensure that when the vehicle is fault-detected, the vehicle can be fault-detected based on more comprehensive detection data. In addition, in this solution, the external body of the vehicle is fault-detected by combining the external image of the vehicle and the driving environment image of the vehicle, ensuring that the external body fault detection information can be obtained by combining the external image of the vehicle and the environment image; the internal body of the vehicle is fault-detected by combining the emission data and the vehicle data of the vehicle, obtaining the internal body fault detection information; since this solution can obtain both the target data of the external body and the internal body vehicle data; therefore, more comprehensive detection data can be obtained; thereby, the external body fault detection information and the internal body fault detection information can be generated; ensuring that more comprehensive and accurate fault detection results are obtained; improving the accuracy of vehicle fault detection.

[0122] Figure 3 It is a schematic flow chart of another vehicle fault detection method provided in an embodiment of the present application.

[0123] For example, Figure 3 The illustrated method 300 may be performed by the vehicle 110 ; or may be performed by a processor or chip in the vehicle 110 .

[0124] like Figure 3As shown, the vehicle fault detection method 300 includes S301 to S310, and S301 to S310 are described in detail below.

[0125] S301, the vehicle sends a target instruction to the target UAV device.

[0126] Optionally, the implementation of S301 can refer to Figure 2 The relevant description of S210 is not repeated here.

[0127] S302, obtaining target data collected by the target UAV device and vehicle data collected by the vehicle.

[0128] Optionally, the implementation of S302 can refer to Figure 2 The relevant description of S220 is not repeated here.

[0129] S303, acquiring first target data and first vehicle data of the vehicle.

[0130] Exemplarily, the first target data and the first vehicle data may be pre-stored in the vehicle; the first target data is target data when the vehicle is in a normal state; the first vehicle data is vehicle data when the vehicle is in a normal state during driving; the first target data includes a first vehicle body external image and first emission data.

[0131] S304: Determine a first similarity between the target data and the first target data.

[0132] Exemplarily, the similarity between the target image in the target data and the first vehicle body exterior image, and the similarity between the emission data in the target data and the first emission data are determined.

[0133] For example, the similarity between the target image and the first vehicle body exterior image can be determined by comparing pixel values; first, the target image and the first vehicle body exterior image are adjusted to the same size, and each pixel of each image is traversed row by row and column by column to obtain the pixel value at the position of each pixel, that is, the RGB value of each pixel; for pixels at the same position, the difference between the pixel values ​​of the target image and the first vehicle body exterior image in each color channel is calculated; and the difference values ​​at all pixel positions are accumulated; the accumulated difference values ​​are divided by the total number of pixels for normalization to obtain a similarity score; the smaller the score, the greater the similarity.

[0134] Exemplarily, the similarity between the emission data and the first emission data can be determined by the difference in concentration of each gas component in the emission data and the first emission data; the concentration difference of each gas component is calculated one by one, and the smaller the difference, the greater the similarity.

[0135] It should be noted that the above is an example of a method for calculating similarity, and this application does not limit this.

[0136] S305: Determine first detection information according to the first similarity.

[0137] Exemplarily, the first detection information includes the vehicle's external body detection information and the vehicle's emission detection information; if there is an image area whose similarity is less than a first preset threshold, the external body detection information is determined to indicate that there is a fault on the outside of the vehicle's body; if the similarity between the emission data and the first emission data is less than a second preset threshold, the emission detection information is determined to indicate that the vehicle has an emission fault.

[0138] S306: Determine a second similarity between the vehicle data and the first vehicle data.

[0139] Exemplarily, the difference between each item of data in the vehicle data and each item of data in the first vehicle data is calculated; the smaller the difference, the greater the similarity; for example, the vehicle data includes the oil temperature and oil pressure of the vehicle transmission; then the first oil temperature and the first oil pressure are obtained when the vehicle is in a normal state and at the current vehicle speed; the oil temperature of the vehicle is compared with the first oil temperature, and the oil pressure of the vehicle is compared with the first oil pressure; and the corresponding similarity is determined.

[0140] S307: Determine second detection information according to the second similarity.

[0141] Exemplarily, if the similarity of the data of the target item in the vehicle data is less than the third preset threshold, then it is determined that the second detection information indicates that the device corresponding to the target item has a fault; for example, if the similarity between the oil temperature in the vehicle data and the first oil temperature is less than the third preset threshold, or the similarity between the oil pressure in the vehicle data and the first oil pressure is less than the third preset threshold, then it is determined that the device corresponding to the oil temperature and the oil pressure has a fault; that is, it is determined that the second detection information indicates that the vehicle's gearbox has a fault.

[0142] S308, obtaining a fault detection result according to the first detection information and the second detection information.

[0143] Exemplarily, determine the fault indicated by the first detection information and the fault indicated by the second detection information; comprehensively analyze the fault information to determine the fault detection result; for example, the first detection information indicates that the vehicle has an emissions fault; if the second detection information indicates that the vehicle's fuel injection is abnormal, then it is inferred that the vehicle's fault detection result is a vehicle emissions fault caused by a problem with the vehicle's fuel system; if the second detection information indicates that the vehicle's engine speed or torque is abnormal; then it is inferred that the vehicle's fault detection result is a vehicle emissions fault caused by an engine mechanical fault.

[0144] It should be noted that the above is an example of the fault detection result, and this application does not limit this.

[0145] S309, determining a solution database for vehicle failure.

[0146] Exemplarily, due to different vehicle models, the solutions corresponding to the faults are different; therefore, based on the target vehicle model, a solution database for vehicle faults is determined; the solution database includes vehicle fault types and solutions corresponding to the fault types.

[0147] S310, determining a corresponding solution according to the fault type indicated by the fault detection result.

[0148] Optionally, the implementation of S310 can refer to Figure 2 The relevant description of S230 is not repeated here.

[0149] In an embodiment of the present application, target data collected by a target UAV device and vehicle data collected by a vehicle are obtained to ensure that more comprehensive detection data can be obtained when performing fault detection on the vehicle; and the target data and vehicle data are compared with the target data and vehicle data in a normal state of the vehicle to determine the similarity, thereby determining the fault detection result of the vehicle; if the similarity is smaller, the difference between the vehicle and the normal state of the vehicle is greater, that is, the possibility that the vehicle has a fault is greater; through the similarity, it is ensured that it can be determined whether the vehicle is in a normal state, thereby obtaining the fault detection result of the vehicle.

[0150] Figure 4 It is a schematic flow chart of another vehicle fault detection method provided in an embodiment of the present application.

[0151] For example, Figure 4 The illustrated method 400 may be performed by the vehicle 110 ; or may be performed by a processor or chip in the vehicle 110 .

[0152] like Figure 4 As shown, the vehicle fault detection method 400 includes S401 to S408, and S401 to S408 are described in detail below.

[0153] S401, detecting a detection instruction for a target component in a vehicle.

[0154] Exemplarily, the fault detection of the vehicle may be the fault detection of the entire vehicle, or the fault detection of a target component in the vehicle; if a detection instruction for a target component in the vehicle is detected, it means that the fault detection of the target component in the vehicle is currently being performed. Figure 3 The relevant description of S310 is not repeated here.

[0155] S402, check whether the instruction corresponds to multiple target components; if so, execute S403; if not, execute S405.

[0156] Exemplarily, it is determined whether the detection instruction corresponds to multiple target components; if so, the detection order is determined according to the detection priority of the target components; if not, the target position of the target component is directly obtained.

[0157] S403: Determine a detection order according to the detection priority of the target component.

[0158] Exemplarily, the detection priority is positively correlated with the impact of the target component on vehicle safety; if the impact of the target component on vehicle safety is higher, the detection priority of the target component is higher; for example, the detection priority of the vehicle headlights is higher than the detection priority of the vehicle exhaust pipe.

[0159] S404, sending a detection sequence to the target UAV device.

[0160] Exemplarily, the detection order is used to indicate the order in which the target drone collects target data.

[0161] S405, obtaining a target position of a target component.

[0162] Exemplarily, if the detection instruction corresponds to multiple target components, the target position of each target component in the multiple target components is obtained; if the detection instruction corresponds to one target component, the target position of the target component is obtained.

[0163] S406, sending a target instruction and a target position to the target UAV device.

[0164] Exemplarily, the target instruction is used to instruct the target UAV device to collect target data; the vehicle sends the target instruction and target position to the target UAV device.

[0165] S407, obtaining target data collected by the target UAV device.

[0166] Optionally, the implementation of S407 can refer to Figure 2 The relevant description of S220 is not repeated here.

[0167] S408, obtaining a fault detection result based on the target data and the vehicle data.

[0168] For example, the implementation of S408 can be found in Figure 3 The relevant descriptions of the embodiments in S304 to S308 are not repeated here.

[0169] Optionally, after obtaining the fault detection result, a solution database for the vehicle fault is determined based on the vehicle model; and a corresponding solution is determined according to the fault type indicated by the fault detection result and the solution database.

[0170] In the embodiment of the present application, the number of target components indicated by the detection instruction is determined; target data is obtained according to the position of the target components; and it is ensured that the components in the vehicle can be detected. Since the detection priority is positively correlated with the degree of influence of the target components on the safety of the vehicle when there are multiple target components; therefore, the target data is obtained and fault detection is performed according to the detection priority; and it is ensured that the vehicle components with a higher degree of influence on the safety of the vehicle can be detected preferentially.

[0171] It should be noted that the target UAV device in this solution can use a high-energy-density lithium polymer battery to provide long-term battery life; when acquiring target data, the flight strategy (such as flight speed, altitude, route, etc.) is automatically adjusted according to the flight mission and remaining power of the target UAV device, thereby optimizing energy utilization efficiency; if the target UAV device is low on power, the target UAV device can be quickly charged through a vehicle to improve operational efficiency.

[0172] Combination of the above Figures 1 to 4 The vehicle fault detection method provided by the embodiment of the present application is described in detail; Figure 5 and Figure 6 The device embodiments of the present application are described in detail. It should be understood that the device in the embodiments of the present application can execute the various methods of the aforementioned embodiments of the present application, that is, the specific working processes of the following various products can refer to the corresponding processes in the aforementioned method embodiments.

[0173] Figure 5 It is a structural schematic diagram of a vehicle fault detection device provided in an embodiment of the present application.

[0174] For example, Figure 5 As shown, the vehicle fault detection device 500 includes:

[0175] The sending module 510 is used to send a target instruction to the target UAV device during the driving of the vehicle; wherein the target instruction is used to instruct the target UAV device to collect target data; the target data includes a target image; the target image includes an image of the vehicle's body exterior and an image of the vehicle's driving environment within a preset range;

[0176] The acquisition module 520 is used to acquire the target data collected by the target UAV device and the vehicle data collected by the vehicle; wherein the vehicle data is used to indicate the vehicle status of the vehicle during driving;

[0177] The detection module 530 is used to obtain a vehicle fault detection result based on the target data and the vehicle data.

[0178] Optionally, as an embodiment, the acquisition module 520 is further used to: acquire first target data and first vehicle data when the vehicle is in a normal state; wherein the first target data includes a first vehicle body external image;

[0179] The detection module 530 is specifically used to: obtain first detection information based on a first similarity between the target data and the first target data; obtain second detection information based on a second similarity between the vehicle data and the first vehicle data; and obtain a fault detection result based on the first detection information and the second detection information.

[0180] Optionally, as an embodiment, the detection module 530 is specifically used to: compare the target image with the first vehicle body exterior image, and if there is an image area whose similarity is less than a first preset threshold, determine that the vehicle body exterior detection information indicates that there is a fault on the vehicle body exterior.

[0181] Optionally, as an embodiment, the acquisition module 520 is further used to: acquire first emission data when the vehicle is in a normal state;

[0182] The detection module 530 is specifically used to compare the emission data with the first emission data, and if the similarity between the emission data and the first emission data is less than a second preset threshold, determine that the emission detection information indicates that the vehicle has an emission fault.

[0183] Optionally, as an embodiment, it also includes a determination module, which is specifically used to: if the vehicle is equipped with a drone device, determine the drone device as a target drone device; if the vehicle is not equipped with a drone device, send an indication signal to a drone site within a preset distance of the vehicle, and receive an identification of the drone device sent by the drone site; determine the drone device corresponding to the identification of the drone device as the target drone device; wherein the indication signal is used to instruct the drone site to dispatch the drone device to perform fault detection on the vehicle.

[0184] Optionally, as an embodiment, the determination module is further used to: if a detection instruction for a target component in the vehicle is detected, determine a target position corresponding to the target component; wherein the target position is a position where the target drone device collects target data;

[0185] The sending module 510 is specifically used to send a target instruction and a target position to a target UAV device, so that the target UAV device flies to the target position and collects target data.

[0186] Optionally, as an embodiment, the determination module is further used to: if the detection instruction corresponds to at least two target components, determine the detection priority of the at least two target components based on the impact degree of the at least two target components on the vehicle safety; wherein the detection priority is positively correlated with the impact degree; and determine the detection order based on the detection priority;

[0187] The sending module 510 is specifically used to send a target instruction, a target position and a detection sequence to the target UAV device, so that the target UAV device obtains the target data according to the detection sequence.

[0188] Optionally, as an embodiment, the determination module is also used to: determine a solution database for vehicle faults based on a target vehicle model; wherein the solution database includes fault types of vehicle faults and solutions corresponding to the fault types; and determine a solution corresponding to the target fault type based on the target fault type indicated by the fault detection result and the solution database.

[0189] It should be noted that the above vehicle fault detection device is embodied in the form of a functional unit. The term "module" here can be implemented in the form of software and / or hardware, and is not specifically limited to this.

[0190] For example, a "module" may be a software program, a hardware circuit, or a combination of the two that implements the above functions. The hardware circuit may include an application specific integrated circuit (ASIC), an electronic circuit, a processor (such as a shared processor, a dedicated processor, or a group processor, etc.) and a memory for executing one or more software or firmware programs, a combined logic circuit, and / or other suitable components that support the described functions.

[0191] Therefore, the units of each example described in the embodiments of the present application can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present application.

[0192] Figure 6 It is a structural schematic diagram of a vehicle provided in an embodiment of the present application.

[0193] Exemplarily, vehicle 600 includes: a processor 610 , a memory 620 , and executable program code 630 .

[0194] Exemplarily, the vehicle 600 includes one or more processors 610, which can support the vehicle 600 to implement the vehicle fault detection method in the method embodiment. The processor 610 can be a general-purpose processor or a special-purpose processor. For example, the processor 610 can be a central processing unit (CPU), a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic devices, such as discrete gates, transistor logic devices, or discrete hardware components.

[0195] Exemplarily, the processor 610 may be used to control the vehicle 600, execute software programs, and process data of the software programs. The vehicle 600 may also include a communication unit to implement input (reception) and output (transmission) of signals.

[0196] Exemplarily, the vehicle 600 may include one or more memories 620 on which executable program code 630 is stored. The executable program code 630 can be executed by the processor 610 to generate instructions so that the processor 610 executes the vehicle fault detection method described in the above method embodiment according to the instructions.

[0197] Optionally, data may be stored in the memory 620. Optionally, the processor 610 may read data stored in the memory 620, which may be stored at the same storage address as the executable program code 630, or may be stored at a different storage address from the executable program code 630.

[0198] Exemplarily, the processor 610 and the memory 620 may be provided separately or integrated together, for example, integrated on a system on chip (SOC) of the terminal device.

[0199] Exemplarily, the memory 620 can be used to store relevant programs of the vehicle fault detection method provided in the embodiment of the present application, and the processor 620 can be used to call the executable program code 630 stored in the memory 620 when controlling the vehicle to execute the vehicle fault detection method of the embodiment of the present application; for example, during the driving of the vehicle, a target instruction is sent to the target UAV device; wherein the target instruction is used to instruct the target UAV device to collect target data; the target data includes a target image; the target image includes an image of the vehicle's body exterior and an image of the vehicle's driving environment within a preset range; the target data collected by the target UAV device and the vehicle data collected by the vehicle are obtained; wherein the vehicle data is used to indicate the vehicle status of the vehicle during driving; based on the target data and the vehicle data, the vehicle fault detection result is obtained.

[0200] The present application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the vehicle fault detection method of any of the aforementioned embodiments.

[0201] Among them, computer-readable storage media may include but are not limited to any type of disk, including floppy disks, optical disks, digital versatile disks (Digital Video Disc, DVD), compact disc read-only memory (Compact Disc Read-Only Memory, CD-ROM), microdrives and magneto-optical disks, read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), erasable programmable read-only memory (Erasable Programmable Read-Only Memory, EPROM), electrically erasable programmable read only memory (Electrically Erasable Programmable read only memory, EEPROM), dynamic random access memory (Dynamic Random Access Memory, DRAM), video random access memory (Video Random Access Memory, VRAM), flash memory devices, magnetic cards or optical cards, nanosystems (including molecular memory ICs), or any type of medium or device suitable for storing instructions and / or data.

[0202] The present application also provides a computer program product. When the computer program product runs on a computer, the computer executes the above-mentioned related steps to implement a vehicle fault detection method in the above-mentioned embodiment.

[0203] In addition, the electronic device provided in the embodiments of the present application may specifically be a chip, a component or a module, and the electronic device may include a connected processor and a memory; wherein the memory is used to store instructions, and when the electronic device is running, the processor may call and execute instructions so that the chip executes a vehicle fault detection method in the above-mentioned embodiments.

[0204] Among them, the electronic device, computer-readable storage medium, computer program product or chip provided in this application are all used to execute the corresponding vehicle fault detection method provided above. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding vehicle fault detection method provided above, and will not be repeated here.

[0205] Through the description of the above implementation methods, technical personnel in the relevant field can understand that for the convenience and simplicity of description, only the division of the above-mentioned functional modules is used as an example. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0206] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of modules or units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another device, 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, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0207] The above contents are only specific implementation methods of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A vehicle fault detection method, characterized in that: The method comprises: During the driving process of the vehicle, a target instruction is sent to the target UAV device; wherein the target instruction is used to instruct the target UAV device to collect target data; the target data includes a target image; the target image includes an image of the vehicle's body exterior and an image of the driving environment of the vehicle within a preset range; Acquire the target data collected by the target UAV device and the vehicle data collected by the vehicle; wherein the vehicle data is used to indicate the vehicle state of the vehicle during the driving process; Based on the target data and the vehicle data, a fault detection result of the vehicle is obtained.

2. The method according to claim 1, characterized in that Also includes: Acquire first target data and first vehicle data of the vehicle in a normal state; wherein the first target data includes a first vehicle body external image; The obtaining of a fault detection result of the vehicle based on the target data and the vehicle data includes: first detection information obtained based on a first similarity between the target data and the first target data; obtaining second detection information based on a second similarity between the vehicle data and the first vehicle data; The fault detection result is obtained based on the first detection information and the second detection information.

3. The method according to claim 2, characterized in that The first detection information includes vehicle body exterior detection information; the first detection information is obtained based on the first similarity between the target data and the first target data, including: The target image is compared with the first vehicle body exterior image, and if there is an image region whose similarity is less than a first preset threshold, it is determined that the vehicle body exterior detection information indicates that there is a fault on the vehicle body exterior.

4. The method according to claim 2, characterized in that: The target data includes emission data of the vehicle, and the first detection information includes emission detection information; the method further includes: Acquire first emission data of the vehicle in a normal state; The first detection information obtained based on the first similarity between the target data and the first target data includes: The emission data is compared with the first emission data, and if the similarity between the emission data and the first emission data is less than a second preset threshold, it is determined that the emission detection information indicates that the vehicle has an emission fault.

5. The method according to claim 1, characterized in that Also includes: If the vehicle is equipped with a UAV device, determining the UAV device as the target UAV device; If the vehicle is not equipped with a drone device, an indication signal is sent to a drone station within a preset distance of the vehicle, and an identifier of the drone device sent by the drone station is received; the drone device corresponding to the identifier of the drone device is determined as the target drone device; wherein the indication signal is used to instruct the drone station to dispatch the drone device to perform the fault detection on the vehicle.

6. The method according to any one of claims 1 to 5, characterized in that Also includes: If a detection instruction for a target component in the vehicle is detected, a target position corresponding to the target component is determined; wherein the target position is a position where the target UAV device collects the target data; The sending of a target instruction to the target UAV device includes: The target instruction and the target position are sent to the target UAV device, so that the target UAV device flies to the target position and collects the target data.

7. The method according to claim 6, characterized in that Also includes: If the detection instruction corresponds to at least two of the target components, determining the detection priority of the at least two target components based on the degree of influence of the at least two target components on vehicle safety; wherein the detection priority is positively correlated with the degree of influence; Based on the detection priority, determining a detection order; The sending the target instruction and the target position to the target UAV device includes: The target instruction, the target position and the detection sequence are sent to the target UAV device, so that the target UAV device obtains the target data according to the detection sequence.

8. The method according to any one of claims 1 to 5, characterized in that Also includes: Determine a solution database for vehicle faults based on the target vehicle model; wherein the solution database includes the fault type of the vehicle fault and the solution corresponding to the fault type; Based on the target fault type indicated by the fault detection result and the solution database, a solution corresponding to the target fault type is determined.

9. A vehicle fault detection device, characterized in that: The device comprises: A sending module, used for sending a target instruction to a target UAV device during the driving of the vehicle; wherein the target instruction is used to instruct the target UAV device to collect target data; the target data includes a target image; the target image includes an image of the vehicle's exterior and an image of the vehicle's driving environment within a preset range; An acquisition module, used to acquire the target data collected by the target UAV device and the vehicle data collected by the vehicle; wherein the vehicle data is used to indicate the vehicle state of the vehicle during the driving process; A detection module is used to obtain a fault detection result of the vehicle based on the target data and the vehicle data.

10. A vehicle, characterized in that: The vehicle comprises: A memory for storing executable program codes; A processor is used to call and run the executable program code from the memory so that the vehicle executes the method as claimed in any one of claims 1 to 8.

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