Vehicle fault early warning method and device, vehicle-mounted terminal, vehicle and storage medium

Abstract

The invention relates to a vehicle fault early warning method and device, a vehicle-mounted terminal, a vehicle and a storage medium. The method comprises the following steps: acquiring historical vehicle operation and maintenance data of a target vehicle, vehicle operation data acquired by an internal sensor of the target vehicle in a current time period, and driving behavior data of a driver in the target vehicle in the current time period; inputting the driving behavior data, the historical vehicle operation and maintenance data and the vehicle operation data into a target detection model to obtain a predicted operation state of the target vehicle in a future time period; if the predicted operation state represents that the target vehicle has the target fault in the future time period, fault early warning information is generated; and based on the fault early warning information, performing fault early warning prompt of the target vehicle. According to the scheme, compared with the mode that a diagnostic instrument is connected through an OBD interface for fault detection, the timeliness of vehicle fault detection is improved, and then the driving safety is improved.

Description

Technical Field

[0001] The present application relates to the field of artificial intelligence technology, and in particular to a vehicle fault warning method, device, vehicle-mounted terminal, vehicle and storage medium. Background Art

[0002] When a vehicle is driving, a safety accident may occur due to abnormalities or failures in its components. Therefore, detecting whether the vehicle has any failures helps ensure driving safety.

[0003] In traditional technology, a fault diagnosis instrument is usually used to interface with the vehicle through the on-board diagnostic system (OBD) to perform fault detection on various components of the vehicle. However, there is a problem of untimely vehicle fault detection, which leads to low driving safety. Summary of the invention

[0004] Based on this, it is necessary to provide a vehicle fault warning method, device, vehicle-mounted terminal, vehicle and storage medium to address the above-mentioned technical problems, which can improve the timeliness of vehicle fault detection and thus improve driving safety.

[0005] In a first aspect, the present application provides a vehicle fault warning method, the method comprising:

[0006] Acquire historical vehicle operation and maintenance data of the target vehicle, vehicle operation data collected by internal sensors of the target vehicle during the current period, and driving behavior data of the driver of the target vehicle during the current period;

[0007] Inputting the driving behavior data, the historical vehicle operation and maintenance data, and the vehicle operation data into a target detection model to obtain a predicted operation state of the target vehicle in a future period;

[0008] If the predicted operating state indicates that the target vehicle will have a target fault within the future period, generating fault warning information;

[0009] Based on the fault warning information, a fault warning prompt is given to the target vehicle.

[0010] In one embodiment, if the predicted operating state indicates that the target vehicle will have a target fault within the future period, generating fault warning information includes:

[0011] If the predicted operating state indicates that the target vehicle will have a target fault within the future period, determining a target component in the target vehicle where the target fault occurs;

[0012] Acquire factor data of a target factor affecting the operating state of the target component in the current time period;

[0013] Determining, based on the factor data, a prediction threshold of the target component having the target fault within the future period;

[0014] If the prediction threshold is greater than the set threshold, fault warning information is generated.

[0015] In one embodiment, generating fault warning information includes:

[0016] Based on the geometric information of the target vehicle, a virtual model of the target vehicle is generated; wherein the geometric information includes contour information and component information of the target vehicle;

[0017] Based on the predicted running state, adding corresponding animation parameters to the virtual model to obtain a video animation;

[0018] The video animation is used as the fault warning information.

[0019] In one of the embodiments, the fault warning information includes a predicted fault level and a predicted fault handling method;

[0020] The step of providing a fault warning prompt for the target vehicle based on the fault warning information includes:

[0021] If the predicted fault level is the first level, the predicted fault processing method is used to process the target vehicle and the fault warning information is output;

[0022] If the predicted fault level is the second level, the fault warning information is output; wherein the first level is higher than the second level.

[0023] In one embodiment, the fault warning prompt of the target vehicle based on the fault warning information includes:

[0024] The fault warning information of the target vehicle is sent to other vehicles within a preset range of the target vehicle, so that the other vehicles adjust the driving speed and / or driving trajectory based on the fault warning information.

[0025] In one of the embodiments, the vehicle operation data includes different types of vehicle operation data, and the target detection model includes different types of detection models;

[0026] The step of inputting the driving behavior data, the historical vehicle operation and maintenance data, and the vehicle operation data into a target detection model to obtain a predicted operation state of the target vehicle in a future period includes:

[0027] For each type of vehicle operation data, the driving behavior data, the historical vehicle operation and maintenance data, and the vehicle operation data are input into a corresponding target detection model to obtain a predicted operation state of a vehicle component corresponding to the vehicle operation data in a future period;

[0028] The predicted operating states of the various vehicle components are integrated to obtain the predicted operating states of the target vehicle in a future period.

[0029] In a second aspect, the present application also provides a vehicle fault warning device, the device comprising:

[0030] An acquisition module, used to acquire historical vehicle operation and maintenance data of a target vehicle, vehicle operation data collected by internal sensors of the target vehicle during a current period, and driving behavior data of a driver of the target vehicle during the current period;

[0031] A prediction module, used to input the driving behavior data, the historical vehicle operation and maintenance data, and the vehicle operation data into a target detection model to obtain a predicted operation state of the target vehicle in a future period;

[0032] A generating module, configured to generate fault warning information if the predicted operating state indicates that the target vehicle will have a target fault within the future period;

[0033] The prompt module is used to provide a fault warning prompt for the target vehicle based on the fault warning information.

[0034] In a third aspect, the present application further provides a vehicle-mounted terminal, the vehicle-mounted terminal comprising a transceiver, a memory and a processor, the memory storing a computer program, the transceiver being used to perform an operation of receiving data or sending data under the control of the processor, and the processor implementing the following steps when executing the computer program:

[0035] Acquire historical vehicle operation and maintenance data of the target vehicle, vehicle operation data collected by internal sensors of the target vehicle during the current period, and driving behavior data of the driver of the target vehicle during the current period;

[0036] Inputting the driving behavior data, the historical vehicle operation and maintenance data, and the vehicle operation data into a target detection model to obtain a predicted operation state of the target vehicle in a future period;

[0037] If the predicted operating state indicates that the target vehicle will have a target fault within the future period, generating fault warning information;

[0038] Based on the fault warning information, a fault warning prompt is given to the target vehicle.

[0039] In a fourth aspect, the present application also provides a vehicle, comprising the vehicle-mounted terminal provided in the third aspect above.

[0040] In a fifth aspect, the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0041] Acquire historical vehicle operation and maintenance data of the target vehicle, vehicle operation data collected by internal sensors of the target vehicle during the current period, and driving behavior data of the driver of the target vehicle during the current period;

[0042] Inputting the driving behavior data, the historical vehicle operation and maintenance data, and the vehicle operation data into a target detection model to obtain a predicted operation state of the target vehicle in a future period;

[0043] If the predicted operating state indicates that the target vehicle will have a target fault within the future period, generating fault warning information;

[0044] Based on the fault warning information, a fault warning prompt is given to the target vehicle.

[0045] In a sixth aspect, the present application further provides a computer program product, the computer program product comprising a computer program, which implements the following steps when executed by a processor:

[0046] Acquire historical vehicle operation and maintenance data of the target vehicle, vehicle operation data collected by internal sensors of the target vehicle during the current period, and driving behavior data of the driver of the target vehicle during the current period;

[0047] Inputting the driving behavior data, the historical vehicle operation and maintenance data, and the vehicle operation data into a target detection model to obtain a predicted operation state of the target vehicle in a future period;

[0048] If the predicted operating state indicates that the target vehicle will have a target fault within the future period, generating fault warning information;

[0049] Based on the fault warning information, a fault warning prompt is given to the target vehicle.

[0050] The above-mentioned vehicle fault warning method, device, vehicle-mounted terminal, vehicle and storage medium obtain the historical vehicle operation and maintenance data of the target vehicle, the vehicle operation data collected by the internal sensors of the target vehicle in the current period, and the driving behavior data of the driver in the target vehicle in the current period; and input the driving behavior data, historical vehicle operation and maintenance data and vehicle operation data into the target detection model to obtain the predicted operation state of the target vehicle in the future period; if the predicted operation state indicates that the target vehicle will have a target fault in the future period, then a fault warning information is generated; and then based on the fault warning information, a fault warning prompt for the target vehicle is performed. In the above scheme, the vehicle-mounted terminal determines whether the vehicle will have a fault in the future period, and when determining whether the vehicle may have a fault, more dimensional data is introduced, and based on the target detection model, it can be determined whether the target vehicle will have a fault in the future period. Compared with fault detection through the OBD interface connected to the diagnostic instrument, the embodiment of the present application improves the timeliness of vehicle fault detection, thereby improving driving safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 A schematic diagram of a process flow of a vehicle fault warning method in one embodiment;

[0052] Figure 2 A schematic diagram of a process for generating fault warning information in an embodiment;

[0053] Figure 3 A schematic diagram of another process of generating fault warning information in one embodiment;

[0054] Figure 4 A schematic diagram of a process for obtaining a predicted operating state of a target vehicle in one embodiment;

[0055] Figure 5 A schematic diagram of a flow chart of another vehicle fault warning method in one embodiment;

[0056] Figure 6 is a structural block diagram of an XXX device in an embodiment;

[0057] Figure 7 FIG. 4 is a diagram showing the internal structure of a vehicle-mounted terminal in one embodiment. DETAILED DESCRIPTION

[0058] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0059] The vehicle fault warning method provided in the embodiment of the present application can be applied to the application scenario of providing fault warning prompts to the vehicle. The method can be executed by a vehicle-mounted terminal.

[0060] In one embodiment, Figure 1 As shown, a vehicle fault warning method is provided, which is described by taking the method applied to a vehicle-mounted terminal as an example, and includes the following steps:

[0061] S101, acquiring historical vehicle operation and maintenance data of a target vehicle, vehicle operation data collected by internal sensors of the target vehicle in a current period, and driving behavior data of a driver in the target vehicle in a current period.

[0062] For example, the historical vehicle operation and maintenance data of a vehicle may include maintenance time, maintenance parts, part models, etc.; the vehicle operation data may include engine data, brake data, tire pressure data, engine coolant temperature data, and oil temperature data, etc.; the driving behavior data may include the driver's acceleration, braking, and steering data. The vehicle operation data and driving behavior data may be collected by sensors, and the data collected by the sensors may be transmitted to the vehicle terminal by wireless transmission or wired transmission. The specific transmission method is not limited here.

[0063] S102, input driving behavior data, historical vehicle operation and maintenance data, and vehicle operation data into a target detection model to obtain a predicted operation state of the target vehicle in a future period.

[0064] Furthermore, the driving behavior data, historical vehicle operation and maintenance data, and vehicle operation data can be input into the target detection model, which can be trained based on sample driving behavior data, sample vehicle operation and maintenance data, sample vehicle operation data, and vehicle operation status label data; the target detection model predicts the predicted operation status of the target vehicle in the future period based on the input driving behavior data, historical vehicle operation and maintenance data, and vehicle operation data. The future period can be set according to actual conditions.

[0065] S103: If the predicted operating state indicates that the target vehicle will have a target fault in a future period, fault warning information is generated.

[0066] If the predicted running state indicates that the target vehicle will have a target fault in the future period, fault warning information is generated, wherein the fault warning information may include the fault type, fault location, fault level and fault handling method.

[0067] S104, based on the fault warning information, a fault warning prompt is given to the target vehicle.

[0068] Furthermore, based on the fault warning information, a fault warning prompt can be given to the display screen of the target vehicle. Specifically, the prompt can be given in the form of text, voice or video; or a prompt message can be sent to the driver's terminal device, such as the vehicle application in the mobile phone, or prompts can be given in the form of text messages, voice, etc.

[0069] The above vehicle fault warning method obtains the historical vehicle operation and maintenance data of the target vehicle, the vehicle operation data collected by the internal sensors of the target vehicle in the current period, and the driving behavior data of the driver in the target vehicle in the current period; and inputs the driving behavior data, historical vehicle operation and maintenance data and vehicle operation data into the target detection model to obtain the predicted operation state of the target vehicle in the future period; if the predicted operation state indicates that the target vehicle will have a target fault in the future period, a fault warning information is generated; and then based on the fault warning information, a fault warning prompt for the target vehicle is performed. In the above scheme, the vehicle-mounted terminal determines whether the vehicle will fail in the future period, and introduces more dimensional data when determining whether the vehicle may fail. Moreover, based on the target detection model, it can be determined whether the target vehicle will fail in the future period. Compared with fault detection through the OBD interface connected to the diagnostic instrument, the embodiment of the present application improves the timeliness of vehicle fault detection, thereby improving driving safety.

[0070] In some optional implementations, see Figure 2 , Figure 2 A schematic diagram of a process for generating fault warning information is provided, which specifically includes the following steps:

[0071] S201, if the predicted operating state indicates that a target fault will occur in the target vehicle within a future period, determine a target component in the target vehicle where the target fault will occur.

[0072] For example, the predicted operating state can directly reflect the operating state of the vehicle in the future period, including state data such as engine temperature, brake normality, tire pressure, engine coolant temperature and oil temperature. If there is abnormal data in the state data such as engine temperature, brake normality, tire pressure, engine coolant temperature and oil temperature, it is considered that the predicted operating state indicates that the target vehicle will have a target fault in the future period. At this time, the target component with the target fault in the target vehicle can be determined. Specifically, the component with abnormal state data is used as the target component.

[0073] S202, obtaining factor data of a target factor affecting the operating state of a target component in a current period.

[0074] Furthermore, factor data of target factors affecting the operating state of target components in the current time period are obtained. Taking the component as an engine as an example, the target factors affecting the operating state of the engine include but are not limited to fuel, ignition system, cylinder, intake system, wear of mechanical components, cooling system, etc. At this time, the operating data of fuel, ignition system, cylinder, intake system, wear of mechanical components, and cooling system in the current time period can be obtained.

[0075] S203, determining a prediction threshold of a target fault occurring in a target component in a future period according to the factor data.

[0076] Furthermore, the prediction threshold of the target failure of the target component in the future period can be determined based on the factor data of the target factor affecting the operating state of the target component in the current period. Exemplarily, the prediction model can be used to obtain the prediction threshold of the target failure of the target component in the future period based on the factor data, and the prediction threshold can be understood as a probability.

[0077] S204: If the predicted threshold is greater than the set threshold, fault warning information is generated.

[0078] For example, the threshold value can be set according to the importance of the target component. For example, if the target component is a brake system, the threshold value can be set to 30%, and if the target component is an engine system, the threshold value can be set to 40%. This is only an example and is not used to limit the threshold value. If the predicted threshold value is greater than the set threshold value, it is believed that the target component will fail in the future period, and a fault warning message can be generated to prompt the driver to take measures to avoid the failure and ensure driving safety.

[0079] In the embodiment of the present application, by acquiring factor data of the target factor affecting the operating state of the target component in the current time period, further determining whether the target component will fail in the future time period based on the factor data, the accuracy of predicting the failure of the target component is improved.

[0080] In some optional implementations, see Figure 3 , Figure 3 Another flow chart for generating fault warning information is provided, which specifically includes the following steps:

[0081] S301, generating a virtual model of the target vehicle based on geometric information of the target vehicle.

[0082] Exemplarily, the geometric information of the target vehicle includes the outline information and component information of the target vehicle, the outline of the vehicle includes information such as the vehicle body size and body shape, and the component information includes information such as the size and shape of each component of the target vehicle. Furthermore, a virtual model of the target vehicle can be generated based on the geometric information of the target vehicle using a three-dimensional modeling method.

[0083] S302, based on the predicted running state, adding corresponding animation parameters to the virtual model to obtain a video animation.

[0084] Exemplarily, the animation parameters of the virtual model can be determined based on the predicted operating state, for example, the location of the change in the virtual model and the magnitude of the change can be determined; then, based on animation rendering technology, corresponding animation parameters can be added to the virtual model to obtain a video animation.

[0085] S303, using the video animation as fault warning information.

[0086] Furthermore, the video animation may be used as a warning message, for example, the video animation may be displayed on a display screen in a vehicle, and the video animation may be sent to a driver's mobile phone to prompt the driver to take appropriate measures to avoid a malfunction.

[0087] In some optional implementations, the fault warning information may include a predicted fault level and a predicted fault handling method. Based on this, the fault levels and fault handling methods corresponding to various faults may be preset, wherein the first level is higher than the second level.

[0088] If the predicted fault level in the fault warning information is the first level, the predicted fault handling method can be directly used to handle the target vehicle's fault and output the fault warning information. For example, if the fault type is abnormal tire pressure data, which may cause a tire blowout, this type of fault is more dangerous, so this type of fault can be set to the first level. If a tire pressure data failure is predicted, the corresponding fault handling method can be to stop the car and change the tire. At this time, the car can be stopped while ensuring safety, and the driver can be prompted to change the tire.

[0089] If the predicted fault level is the second level, the fault warning information can be directly outputted to prompt the driver to handle the upcoming fault based on the fault handling method in the fault warning information.

[0090] In the embodiment of the present application, different fault warning information prompts are provided according to different fault levels, which can provide prompts more flexibly and can further improve driving safety.

[0091] Furthermore, after a target vehicle fails, the repair progress of the target component of the failure can be detected, and according to the repair progress of the target component, the fault handling method is updated and fed back to the driver in real time to speed up the fault elimination.

[0092] The actual processing method for eliminating the fault of the target component can also be stored, so as to enrich the fault processing method for the target component when the target component fails again.

[0093] In some optional implementations, in order to avoid a collision between other vehicles and the target vehicle due to a malfunction of the target vehicle, the fault warning information of the target vehicle can be sent to other vehicles within a preset range of the target vehicle through the vehicle-road cloud system, so that other vehicles can adjust their driving speed or driving trajectory, or adjust their driving speed and driving trajectory based on the fault warning information to avoid a collision with the target vehicle.

[0094] In an embodiment of the present application, a feasible method for sending fault warning information of a target vehicle to other vehicles is provided to avoid collision of other vehicles with the target vehicle due to a fault of the target vehicle, thereby further improving driving safety.

[0095] In some optional implementations, the vehicle operation data may include different types of vehicle operation data, and the target detection model may include different types of detection models. For example, the data types of the vehicle operation data may be engine data, brake data, tire pressure data, engine coolant temperature data, and oil temperature data, etc.; the types of the target detection model may be a neural network model, a random forest model, and a support vector machine model.

[0096] Based on this, see Figure 4 , Figure 4 A flowchart for obtaining a predicted operating state of a target vehicle in a future period is provided, which specifically includes the following steps:

[0097] S401, for each type of vehicle operation data, the driving behavior data, historical vehicle operation and maintenance data, and vehicle operation data are input into the corresponding target detection model to obtain the predicted operation status of the vehicle components corresponding to the vehicle operation data in the future time period.

[0098] Exemplarily, for each type of vehicle operation data, we can first filter out the target detection model corresponding to that type of vehicle operation data, and then input the driving behavior data, historical vehicle operation and maintenance data, and vehicle operation data into the corresponding target detection model to obtain the predicted operating status of the vehicle components corresponding to the vehicle operation data in the future time period.

[0099] Among them, the target detection model corresponding to each type of vehicle operation data can be determined based on experimental data. For example, different target detection models are trained using the same sample vehicle operation data, and the target detection model with the best performance is used as the target detection model corresponding to this type of vehicle operation data.

[0100] S402, the predicted operating states of various vehicle components are integrated to obtain the predicted operating state of the target vehicle in a future period.

[0101] Furthermore, the predicted operating states of various vehicle components can be integrated to obtain the predicted operating states of the target vehicle in the future period.

[0102] In an embodiment of the present application, by adopting a target detection model corresponding to this type of vehicle operation data, the predicted operation state of the vehicle components corresponding to this type of vehicle operation data in the future time period is determined, so that the determined predicted operation state can be made more accurate.

[0103] In some optional implementations, see Figure 5 , Figure 5 A flow chart of another vehicle fault warning method is provided, which specifically includes the following steps:

[0104] S501, acquiring historical vehicle operation and maintenance data of the target vehicle, vehicle operation data collected by internal sensors of the target vehicle in the current period, and driving behavior data of the driver of the target vehicle in the current period.

[0105] S502, for each type of vehicle operation data, the driving behavior data, historical vehicle operation and maintenance data, and vehicle operation data are input into the corresponding target detection model to obtain the predicted operation status of the vehicle components corresponding to the vehicle operation data in the future time period.

[0106] S503, integrating the predicted operating states of various vehicle components to obtain the predicted operating state of the target vehicle in a future period.

[0107] S504: If the predicted operating state indicates that the target vehicle will have a target fault in a future period, determine a target component in the target vehicle where the target fault occurs.

[0108] S505: Obtain factor data of a target factor affecting the operating state of a target component in a current period.

[0109] S506: Determine a prediction threshold of a target fault occurring in a target component in a future period according to the factor data.

[0110] S507: If the prediction threshold is greater than the set threshold, a virtual model of the target vehicle is generated based on the geometric information of the target vehicle.

[0111] S508, based on the predicted running state, adding corresponding animation parameters to the virtual model to obtain a video animation.

[0112] S509, using the video animation as fault warning information.

[0113] S510: If the predicted fault level is the first level, a predicted fault processing method is used to process the target vehicle and output fault warning information.

[0114] S511, if the predicted fault level is the second level, output fault warning information; wherein the first level is higher than the second level.

[0115] S512, sending the fault warning information of the target vehicle to other vehicles within a preset range of the target vehicle, so that the other vehicles adjust the driving speed and / or driving trajectory based on the fault warning information.

[0116] The specific process of S501 to S512 can refer to the description of the above method embodiment, and its implementation principle and technical effect are similar, which will not be repeated here.

[0117] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0118] Based on the same inventive concept, the embodiment of the present application also provides a vehicle fault warning device for implementing the vehicle fault warning method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more vehicle fault warning device embodiments provided below can refer to the limitations of the vehicle fault warning method above, and will not be repeated here.

[0119] In one embodiment, Figure 6 As shown, a vehicle fault warning device is provided, comprising:

[0120] The acquisition module 10 is used to acquire the historical vehicle operation and maintenance data of the target vehicle, the vehicle operation data collected by the internal sensors of the target vehicle in the current period, and the driving behavior data of the driver in the target vehicle in the current period;

[0121] The prediction module 20 is used to input the driving behavior data, historical vehicle operation and maintenance data, and vehicle operation data into the target detection model to obtain the predicted operation status of the target vehicle in the future period;

[0122] A generating module 30, for generating fault warning information if the predicted running state indicates that the target vehicle will have a target fault in a future period;

[0123] The prompt module 40 is used to provide a fault warning prompt for the target vehicle based on the fault warning information.

[0124] The above-mentioned vehicle fault warning device obtains the historical vehicle operation and maintenance data of the target vehicle, the vehicle operation data collected by the internal sensors of the target vehicle in the current period, and the driving behavior data of the driver in the target vehicle in the current period; and inputs the driving behavior data, historical vehicle operation and maintenance data and vehicle operation data into the target detection model to obtain the predicted operation state of the target vehicle in the future period; if the predicted operation state indicates that the target vehicle will have a target fault in the future period, a fault warning information is generated; and then based on the fault warning information, a fault warning prompt for the target vehicle is performed. In the above-mentioned scheme, the vehicle-mounted terminal determines whether the vehicle will have a fault in the future period, and introduces more dimensional data when determining whether the vehicle may have a fault, and based on the target detection model, it can be determined whether the target vehicle will have a fault in the future period. Compared with fault detection through the OBD interface connected to the diagnostic instrument, the embodiment of the present application improves the timeliness of vehicle fault detection, thereby improving driving safety.

[0125] In one embodiment, the generating module 30 is specifically used for:

[0126] If the predicted operating state indicates that the target vehicle will have a target fault in a future time period, the target component in the target vehicle where the target fault occurs is determined; the factor data of the target factor affecting the operating state of the target component in the current time period is obtained; based on the factor data, the predicted threshold of the target fault occurring in the target component in the future time period is determined; if the predicted threshold is greater than the set threshold, fault warning information is generated.

[0127] In one embodiment, the generating module 30 is specifically used for:

[0128] Based on the geometric information of the target vehicle, a virtual model of the target vehicle is generated; wherein the geometric information includes the contour information and component information of the target vehicle; based on the predicted operating state, corresponding animation parameters are added to the virtual model to obtain a video animation; and the video animation is used as fault warning information.

[0129] In one embodiment, the fault warning information includes a predicted fault level and a predicted fault handling method; the prompt module 40 is specifically used to:

[0130] If the predicted fault level is the first level, the predicted fault handling method is used to handle the fault of the target vehicle and output fault warning information; if the predicted fault level is the second level, the fault warning information is output; wherein the first level is higher than the second level.

[0131] In one embodiment, the prompt module 40 is specifically used for:

[0132] The fault warning information of the target vehicle is sent to other vehicles within a preset range of the target vehicle, so that other vehicles adjust their driving speed and / or driving trajectory based on the fault warning information.

[0133] In one embodiment, the prediction module 20 is specifically used for:

[0134] For each type of vehicle operation data, the driving behavior data, historical vehicle operation and maintenance data, and vehicle operation data are input into the corresponding target detection model to obtain the predicted operation status of the vehicle components corresponding to the vehicle operation data in the future time period; the predicted operation status of each vehicle component is integrated to obtain the predicted operation status of the target vehicle in the future time period.

[0135] Each module in the above vehicle fault warning device can be implemented in whole or in part by software, hardware or a combination thereof. Each module can be embedded in or independent of the processor in the vehicle terminal in the form of hardware, or can be stored in the memory in the vehicle terminal in the form of software, so that the processor can call and execute the corresponding operations of each module.

[0136] In one embodiment, a vehicle-mounted terminal is provided, and its internal structure diagram can be as follows: Figure 7 As shown. The vehicle-mounted terminal includes a processor, a memory and a network interface connected through a system bus. Among them, the processor of the vehicle-mounted terminal is used to provide computing and control capabilities. The memory of the vehicle-mounted terminal includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the vehicle-mounted terminal is used to store historical vehicle operation and maintenance data and fault warning information. The network interface of the vehicle-mounted terminal is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a vehicle fault warning method is implemented.

[0137] Those skilled in the art will understand that Figure 7 The structure shown in the figure is only a block diagram of a part of the structure related to the scheme of the present application, and does not constitute a limitation on the vehicle-mounted terminal to which the scheme of the present application is applied. The specific vehicle-mounted terminal may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0138] In one embodiment, a vehicle is provided, which includes the vehicle-mounted terminal provided in the above embodiment.

[0139] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0140] Obtain the historical vehicle operation and maintenance data of the target vehicle, the vehicle operation data collected by the internal sensors of the target vehicle in the current period, and the driving behavior data of the driver in the target vehicle in the current period;

[0141] Input driving behavior data, historical vehicle operation and maintenance data, and vehicle operation data into the target detection model to obtain the predicted operation status of the target vehicle in the future period;

[0142] If the predicted operating state indicates that the target vehicle will have a target fault in the future period, fault warning information is generated;

[0143] Based on the fault warning information, a fault warning prompt is given to the target vehicle.

[0144] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0145] If the predicted operating state indicates that the target vehicle will have a target fault in a future time period, the target component in the target vehicle where the target fault occurs is determined; the factor data of the target factor affecting the operating state of the target component in the current time period is obtained; based on the factor data, the predicted threshold of the target fault occurring in the target component in the future time period is determined; if the predicted threshold is greater than the set threshold, fault warning information is generated.

[0146] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0147] Based on the geometric information of the target vehicle, a virtual model of the target vehicle is generated; wherein the geometric information includes the contour information and component information of the target vehicle; based on the predicted operating state, corresponding animation parameters are added to the virtual model to obtain a video animation; and the video animation is used as fault warning information.

[0148] In one embodiment, the fault warning information includes a predicted fault level and a predicted fault handling method; when the computer program is executed by the processor, the following steps are also implemented:

[0149] If the predicted fault level is the first level, the predicted fault handling method is used to handle the fault of the target vehicle and output fault warning information; if the predicted fault level is the second level, the fault warning information is output; wherein the first level is higher than the second level.

[0150] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0151] The fault warning information of the target vehicle is sent to other vehicles within a preset range of the target vehicle, so that other vehicles adjust their driving speed and / or driving trajectory based on the fault warning information.

[0152] In one embodiment, the vehicle operation data includes different types of vehicle operation data, and the target detection model includes different types of detection models; when the computer program is executed by the processor, the following steps are also implemented:

[0153] For each type of vehicle operation data, the driving behavior data, historical vehicle operation and maintenance data, and vehicle operation data are input into the corresponding target detection model to obtain the predicted operation status of the vehicle components corresponding to the vehicle operation data in the future time period; the predicted operation status of each vehicle component is integrated to obtain the predicted operation status of the target vehicle in the future time period.

[0154] In one embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the following steps:

[0155] Obtain the historical vehicle operation and maintenance data of the target vehicle, the vehicle operation data collected by the internal sensors of the target vehicle in the current period, and the driving behavior data of the driver in the target vehicle in the current period;

[0156] Input driving behavior data, historical vehicle operation and maintenance data, and vehicle operation data into the target detection model to obtain the predicted operation status of the target vehicle in the future period;

[0157] If the predicted operating state indicates that the target vehicle will have a target fault in the future period, fault warning information is generated;

[0158] Based on the fault warning information, a fault warning prompt is given to the target vehicle.

[0159] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0160] If the predicted operating state indicates that the target vehicle will have a target fault in a future time period, the target component in the target vehicle where the target fault occurs is determined; the factor data of the target factor affecting the operating state of the target component in the current time period is obtained; based on the factor data, the predicted threshold of the target fault occurring in the target component in the future time period is determined; if the predicted threshold is greater than the set threshold, fault warning information is generated.

[0161] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0162] Based on the geometric information of the target vehicle, a virtual model of the target vehicle is generated; wherein the geometric information includes the contour information and component information of the target vehicle; based on the predicted operating state, corresponding animation parameters are added to the virtual model to obtain a video animation; and the video animation is used as fault warning information.

[0163] In one embodiment, the fault warning information includes a predicted fault level and a predicted fault handling method; when the computer program is executed by the processor, the following steps are also implemented:

[0164] If the predicted fault level is the first level, the predicted fault handling method is used to handle the fault of the target vehicle and output fault warning information; if the predicted fault level is the second level, the fault warning information is output; wherein the first level is higher than the second level.

[0165] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0166] The fault warning information of the target vehicle is sent to other vehicles within a preset range of the target vehicle, so that other vehicles adjust their driving speed and / or driving trajectory based on the fault warning information.

[0167] In one embodiment, the vehicle operation data includes different types of vehicle operation data, and the target detection model includes different types of detection models; when the computer program is executed by the processor, the following steps are also implemented:

[0168] For each type of vehicle operation data, the driving behavior data, historical vehicle operation and maintenance data, and vehicle operation data are input into the corresponding target detection model to obtain the predicted operation status of the vehicle components corresponding to the vehicle operation data in the future time period; the predicted operation status of each vehicle component is integrated to obtain the predicted operation status of the target vehicle in the future time period.

[0169] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0170] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0171] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0172] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A vehicle fault warning method, characterized in that: The method comprises: Acquire historical vehicle operation and maintenance data of the target vehicle, vehicle operation data collected by internal sensors of the target vehicle during the current period, and driving behavior data of the driver of the target vehicle during the current period; Inputting the driving behavior data, the historical vehicle operation and maintenance data, and the vehicle operation data into a target detection model to obtain a predicted operation state of the target vehicle in a future period; If the predicted operating state indicates that the target vehicle will have a target fault within the future period, generating fault warning information; Based on the fault warning information, a fault warning prompt is given to the target vehicle.

2. The method according to claim 1, characterized in that If the predicted operating state indicates that the target vehicle will have a target fault within the future period, generating fault warning information includes: If the predicted operating state indicates that the target vehicle will have a target fault within the future period, determining a target component in the target vehicle where the target fault occurs; Acquire factor data of a target factor affecting the operating state of the target component in the current time period; Determining, based on the factor data, a prediction threshold of the target component having the target fault within the future period; If the prediction threshold is greater than the set threshold, fault warning information is generated.

3. The method according to claim 1, characterized in that The generating of fault warning information comprises: Based on the geometric information of the target vehicle, a virtual model of the target vehicle is generated; wherein the geometric information includes contour information and component information of the target vehicle; Based on the predicted running state, adding corresponding animation parameters to the virtual model to obtain a video animation; The video animation is used as the fault warning information.

4. The method according to claim 1, characterized in that: The fault warning information includes the predicted fault level and the predicted fault handling method; The step of providing a fault warning prompt for the target vehicle based on the fault warning information includes: If the predicted fault level is the first level, the predicted fault processing method is used to process the target vehicle and the fault warning information is output; If the predicted fault level is the second level, the fault warning information is output; wherein the first level is higher than the second level.

5. The method according to claim 1, characterized in that The step of providing a fault warning prompt for the target vehicle based on the fault warning information includes: The fault warning information of the target vehicle is sent to other vehicles within a preset range of the target vehicle, so that the other vehicles adjust the driving speed and / or driving trajectory based on the fault warning information.

6. The method according to claim 1, characterized in that The vehicle operation data includes different types of vehicle operation data, and the target detection model includes different types of detection models; The step of inputting the driving behavior data, the historical vehicle operation and maintenance data, and the vehicle operation data into a target detection model to obtain a predicted operation state of the target vehicle in a future period includes: For each type of vehicle operation data, the driving behavior data, the historical vehicle operation and maintenance data, and the vehicle operation data are input into a corresponding target detection model to obtain a predicted operation state of a vehicle component corresponding to the vehicle operation data in a future period; The predicted operating states of the various vehicle components are integrated to obtain the predicted operating states of the target vehicle in a future period.

7. A vehicle fault warning device, characterized in that: The device comprises: An acquisition module, used to acquire historical vehicle operation and maintenance data of a target vehicle, vehicle operation data collected by internal sensors of the target vehicle during a current period, and driving behavior data of a driver of the target vehicle during the current period; A prediction module, used to input the driving behavior data, the historical vehicle operation and maintenance data, and the vehicle operation data into a target detection model to obtain a predicted operation state of the target vehicle in a future period; A generating module, configured to generate fault warning information if the predicted operating state indicates that the target vehicle will have a target fault within the future period; The prompt module is used to provide a fault warning prompt for the target vehicle based on the fault warning information.

8. A vehicle-mounted terminal, comprising a transceiver, a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented, and the transceiver is used to perform the operation of receiving data or sending data under the control of the processor.

9. A vehicle, characterized in that: The vehicle includes the vehicle-mounted terminal according to claim 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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