Method, device and equipment for determining vehicle fault information and storage medium

Abstract

The application discloses a kind of determination method, device and equipment of vehicle fault information and storage medium, belong to vehicle detection technical field.Method includes: obtaining the relevant information of vehicle, the relevant information of vehicle includes driving information and environmental information;Determine the driving scene and driving condition of vehicle based on the relevant information of vehicle;Utilize driving scene and driving condition to determine the first threshold range corresponding to the actual control parameter of vehicle;In the case where actual control parameter is located in the first threshold range, determine the target control parameter of vehicle based on vehicle control model, driving information and environmental information;Utilize actual control parameter, target control parameter and second threshold range to determine the abnormal control information of vehicle;Determine the fault information of vehicle based on abnormal control information and the parameter of reference electronic device, reference electronic device is the device related to abnormal control information in vehicle.The method effectively avoids misjudgment or omissions of fault information, improves the accuracy of fault information.

Description

TECHNICAL FIELD

[0001] Embodiments of the present application relate to the technical field of vehicle detection, and in particular, relate to a method and device for determining vehicle fault information, and a storage medium. BACKGROUND

[0002] With the rapid development of the intelligence of vehicles, the safety and reliability of vehicles during driving are increasingly concerned.

[0003] In related technologies, vehicle fault detection generally relies on comparing parameters contained in driving information of a vehicle with a preset parameter range to obtain vehicle fault information. The determination of vehicle fault information is single, which leads to low accuracy of vehicle fault information. SUMMARY

[0004] Embodiments of the present application provide a method for determining vehicle fault information, which can improve the accuracy of determined vehicle fault information, thereby improving the safety during driving of a vehicle. The technical solution is as follows:

[0005] In one aspect, the present application provides a method for determining vehicle fault information, which comprises:

[0006] obtaining related information of a vehicle, wherein the related information of the vehicle comprises driving information and environmental information, the driving information is used to represent a driving state of the vehicle, and the environmental information is used to represent an external environment in which the vehicle is located;

[0007] determining a driving scene and a driving condition of the vehicle based on the related information of the vehicle;

[0008] determining a first threshold range corresponding to an actual control parameter of the vehicle by using the driving scene and the driving condition;

[0009] determining a target control parameter of the vehicle based on a vehicle control model, the driving information and the environmental information, in a case where the actual control parameter is located in the first threshold range;

[0010] determining abnormal control information of the vehicle by using the actual control parameter, the target control parameter and a second threshold range;

[0011] determining fault information of the vehicle based on the abnormal control information and a parameter of a reference electronic device, wherein the reference electronic device is a device related to the abnormal control information in the vehicle.

[0012] In another aspect, the present application provides a device for determining vehicle fault information, which comprises:

[0013] obtain a related information of a vehicle, the related information of the vehicle comprising driving information and environment information, the driving information being used to represent a driving state of the vehicle, and the environment information being used to represent an external environment in which the vehicle is located;

[0014] determine a driving scene and a driving condition of the vehicle based on the related information of the vehicle;

[0015] The determination module is further configured to determine a first threshold range corresponding to an actual control parameter of the vehicle by using the driving scene and the driving condition.

[0016] The determination module is further configured to determine a target control parameter of the vehicle based on a vehicle control model, the driving information and the environment information, in a case where the actual control parameter is located in the first threshold range.

[0017] The determination module is further configured to determine abnormal control information of the vehicle by using the actual control parameter, the target control parameter and a second threshold range.

[0018] The determination module is further configured to determine fault information of the vehicle based on the abnormal control information and a parameter of a reference electronic device, the reference electronic device being a device related to the abnormal control information in the vehicle.

[0019] In a possible implementation, the apparatus further comprises a control module configured to trigger a safety response mechanism of the vehicle in a case where the fault information indicates that a fault type is a hardware fault, the safety response mechanism comprising at least one of recording the fault information, generating alarm information, performing a fault isolation operation on the hardware that has occurred a fault, or starting a safety parking program.

[0020] In a case where the safety response mechanism indicates to adjust the actual control parameter of the vehicle, a parameter adjustment instruction is generated, and the actual control parameter of the vehicle is adjusted based on the parameter adjustment instruction.

[0021] In a possible implementation, the determination module is configured to obtain an initial threshold range corresponding to the actual control parameter of the vehicle, determine a first adjustment parameter based on the driving scene, determine a second adjustment parameter based on the driving condition, and adjust the initial threshold range by using the first adjustment parameter and the second adjustment parameter to obtain the first threshold range.

[0022] In one possible implementation, the determining module is configured to perform a conversion process on the actual control parameters to obtain converted actual control parameters, wherein the conversion process includes at least one of noise reduction processing or encryption / decryption processing; calculate the difference between the converted actual control parameters and the target control parameters; and determine the abnormal control information based on the actual control parameters corresponding to the difference exceeding the second threshold range.

[0023] In one possible implementation, the determining module is configured to generate constraints corresponding to the vehicle control model using the driving information and the environmental information; generate initial control parameters and performance indicators corresponding to the initial control parameters based on the constraints, the vehicle control model, the driving information, and the environmental information; and determine the initial control parameters whose performance indicators are greater than the indicator threshold as the target control parameters of the vehicle.

[0024] In one possible implementation, the determining module is configured to extract features from the driving information to obtain a first feature; extract features from the environmental information to obtain a second feature; fuse the first feature and the second feature to obtain a fused feature; and use the fused feature to determine the driving scenario and the driving condition.

[0025] On the other hand, embodiments of this application provide a computer device, the computer device including a processor and a memory, the memory storing at least one piece of program code, the at least one piece of program code being loaded and executed by the processor, so that the computer device implements any of the above-described methods for determining vehicle fault information.

[0026] On the other hand, a computer-readable storage medium is also provided, wherein at least one piece of program code is stored in the computer-readable storage medium, the at least one piece of program code being loaded and executed by a processor to enable a computer to implement any of the above-described methods for determining vehicle fault information.

[0027] On the other hand, a computer program or computer program product is also provided, wherein the computer program or computer program product stores at least one computer instruction, which is loaded and executed by a processor to enable the computer to implement any of the above-mentioned methods for determining vehicle fault information.

[0028] The technical solution provided in this application has at least the following beneficial effects:

[0029] The technical solution provided in this exemplary embodiment determines the vehicle's driving scenario and driving condition by using the vehicle's driving information and environmental information. It then uses the driving scenario and driving condition to determine a first threshold range corresponding to the vehicle's actual control parameters. This makes the first threshold range more consistent with the current driving state of the vehicle, resulting in a more reasonable and accurate first threshold range. Therefore, the vehicle's safety status can be accurately determined using the first threshold range. When the vehicle's real-time control parameters are within the first threshold range, abnormal control information is further determined using the vehicle's real-time control parameters, target control parameters, and a second threshold range. This abnormal control information is then used to determine the vehicle's fault information, effectively avoiding misjudgment or omission of vehicle fault information, improving the accuracy of vehicle fault information, and ensuring vehicle driving safety. Attached Figure Description

[0030] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0031] Figure 1 This is a schematic diagram of an implementation environment provided in an embodiment of this application;

[0032] Figure 2 This is a flowchart of a method for determining vehicle fault information provided in an embodiment of this application;

[0033] Figure 3 This is a schematic diagram of a vehicle fault information determination device provided in an embodiment of this application;

[0034] Figure 4 This is a schematic diagram of a vehicle fault information determination device based on an in-vehicle intelligent power supply three-in-one control system provided in an embodiment of this application;

[0035] Figure 5 This is a structural block diagram of a terminal device provided in an embodiment of this application;

[0036] Figure 6 This is a schematic diagram of the structure of a server provided in an embodiment of this application. Detailed Implementation

[0037] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0038] It should be noted that the terms "first," "second," etc., used in this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such terms can be used interchangeably where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0039] Figure 1 This is a schematic diagram of an implementation environment provided in an embodiment of this application. For example... Figure 1 As shown, the implementation environment may include a vehicle 101 and a vehicle control system 102. The vehicle control system 102 is used to control the vehicle 101 to perform corresponding operations. The vehicle control system 102 may be located in the vehicle 101, for example, the vehicle control system 102 is an in-vehicle terminal; the vehicle control system 102 may also be located outside the vehicle 101, for example, the vehicle control system 102 is a cloud control system.

[0040] The vehicle control system 102 can be a single server, or it can be a server cluster consisting of multiple servers that perform different functions, or it can be a cloud computing center.

[0041] Vehicle 101 may be a vehicle with intelligent assisted driving functions, and may have hardware facilities such as cameras, millimeter-wave radar, lidar, positioning sensors, and communication sensors. Vehicle 101 obtains relevant vehicle information through the configured hardware facilities, which may include vehicle driving information and environmental information in which the vehicle is located; vehicle control system 102 can determine the safe driving status of the vehicle based on the relevant vehicle information of vehicle 101, and generate corresponding fault information when the safe driving status of the vehicle is abnormal.

[0042] The vehicle 101 may also have wireless communication capabilities. The vehicle 101 may be equipped with a communication module that supports wireless communication technology or wired communication technology. The vehicle 101 interacts with the vehicle control system 102 through the communication module.

[0043] Based on the above Figure 1 As shown in the implementation environment, this application embodiment provides a method for determining vehicle fault information. Figure 2 As shown, this method can be derived from... Figure 1 The method can be executed by vehicle 101, or it can be executed interactively by vehicle 101 and vehicle control system 102. The method may include steps 201 to 206.

[0044] In step 201, relevant vehicle information is obtained, including driving information and environmental information. Driving information is used to characterize the driving status of the vehicle, and environmental information is used to characterize the external environment in which the vehicle is located.

[0045] In the exemplary embodiments of this application, the vehicle can be a vehicle equipped with an in-vehicle intelligent system. This in-vehicle intelligent system includes an information acquisition device, through which vehicle information can be acquired. For example, the information acquisition device may include, but is not limited to, cameras, millimeter-wave radar, lidar, and multiple sensors, including inertial sensors, speed sensors, acceleration sensors, engine speed sensors, and temperature sensors. Through the information acquisition device installed in the vehicle, real-time vehicle driving information and environmental information can be acquired. Driving information may include, but is not limited to, parameters of the vehicle's control system, speed, acceleration, position, engine speed, torque, and temperature; environmental information includes, but is not limited to, the temperature, humidity, obstacles, and lighting conditions of the external environment in which the vehicle is located. It should be noted that the vehicle driving information and environmental information described in this application are illustrative examples, and the information acquired can be set based on the actual situation of the vehicle; this application does not impose any limitations on this.

[0046] In step 202, the driving scenario and driving conditions of the vehicle are determined based on relevant vehicle information.

[0047] In an exemplary embodiment of this application, the process of determining the driving scenario and driving conditions of a vehicle based on relevant vehicle information includes steps 2021 to 2024.

[0048] In step 2021, feature extraction is performed on the driving information to obtain the first feature.

[0049] For example, driving information is preprocessed to remove noise and outliers. Then, the preprocessed data is selected to obtain information relevant to the driving condition. For instance, the selected driving information may include speed, acceleration, and engine speed. Feature extraction is then performed on the selected driving information to obtain the first feature. This feature extraction process may include using deep learning models, such as Convolutional Neural Networks (CNNs) or Recurrent Neural Networks (RNNs), to extract features from the driving information.

[0050] In step 2022, feature extraction is performed on the environmental information to obtain the second feature.

[0051] For example, the vehicle's environmental information can come from different information acquisition devices, and the environmental information collected by different devices can be integrated. For instance, radar data collected by lidar and millimeter-wave radar can be integrated with camera data collected by a camera to obtain more comprehensive environmental information. Feature extraction is then performed on the integrated environmental information to obtain a second feature. The method of second feature extraction is similar to that of first feature extraction and will not be described in detail here.

[0052] In step 2023, the first feature and the second feature are fused to obtain the fused feature.

[0053] In an exemplary embodiment of this application, after obtaining the first feature and the second feature, the first feature and the second feature can be aligned temporally and spatially, that is, the first feature and the second feature represent the features of vehicles at the same time and location. Then, the first feature and the second feature are fused based on a feature fusion algorithm to obtain a fused feature. The feature fusion algorithm includes, but is not limited to, direct concatenation, weighted fusion, and fusion based on a fusion model.

[0054] In step 2024, the driving scenario and driving conditions are determined using fused features.

[0055] For example, machine learning or deep learning algorithms are used to process the fused features to obtain the vehicle's driving scenario and driving conditions. Driving conditions may include constant speed driving, acceleration driving, deceleration driving, emergency stopping driving, and turning driving; driving conditions may include road type, traffic conditions, weather conditions, and the location and dynamics of surrounding obstacles. For example, road type may include highways, urban roads, rural roads, etc.; weather conditions may include rainy days, snowy days, sunny days, and foggy days; traffic conditions may include congested states and free-flowing states, and optionally, congestion states and free-flowing states may also include congestion levels and free-flowing levels.

[0056] The technical solution provided in the exemplary embodiments of this application extracts and fuses features from vehicle driving information and environmental information, and uses the fused features to obtain the vehicle's driving scenario and driving condition. This can improve the accuracy of the determined driving scenario and driving condition, thereby improving the accuracy of subsequently using the relevant content of the driving scenario and driving condition to determine the vehicle's fault information.

[0057] In step 203, the first threshold range corresponding to the actual control parameters of the vehicle is determined by using the driving scenario and driving conditions.

[0058] In an exemplary embodiment of this application, the process of determining the first threshold range corresponding to the actual control parameters of the vehicle using the driving scenario and driving conditions may include steps 2032 to 2033.

[0059] In step 2031, the initial threshold range corresponding to the actual control parameters of the vehicle is obtained.

[0060] For example, the actual control parameters of a vehicle may include power control parameters, engine control parameters, and relevant parameters of various electronic devices. For instance, a vehicle can be equipped with an onboard intelligent power supply three-in-one control system, which integrates a Battery Management System (BMS), a Microcontroller Unit (MCU), and a Direct Current-Direct Current Converter (DC-DC Converter). This system enables the management and control of the vehicle. The onboard intelligent power supply three-in-one control system controls multiple electronic devices, obtains the factory safety standard range for each device, and determines this factory safety standard range as the initial threshold range for each device. This initial threshold range includes, but is not limited to, the input voltage, input current, output current, and temperature of the electronic devices.

[0061] In step 2032, a first adjustment parameter is determined based on the driving scenario, and a second adjustment parameter is determined based on the driving conditions.

[0062] For example, based on a pre-trained parameter adjustment model, parameters corresponding to the driving scenario, an initial threshold range, and real-time control parameters can be input into the parameter adjustment model. The parameter adjustment model can identify the optimal parameter range for real-time control parameters under different driving scenarios, compare the optimal parameter range with the initial threshold range, and determine the first adjustment parameter. For instance, the parameter adjustment model can determine the first adjustment parameter corresponding to multiple electronic devices controlled in the vehicle intelligent power supply three-in-one control system based on the driving scenario. The first adjustment parameter can be an adjustment of at least one of the current or voltage of the electronic device. The process of determining the second adjustment parameter based on the driving condition is similar to that of determining the first adjustment parameter based on the driving scenario, and will not be elaborated further here. The first and second adjustment parameters can be specific values ​​adjusted relative to the initial threshold range, or they can be adjustment ratios.

[0063] It should be noted that the method for determining the first adjustment parameter and the second adjustment parameter in this application is illustrative only. The first adjustment parameter and the second adjustment parameter can also be determined by looking up a table or calculating a formula. This application does not limit this method.

[0064] In step 2033, the initial threshold range is adjusted using the first adjustment parameter and the second adjustment parameter to obtain the first threshold range.

[0065] In an exemplary embodiment of this application, after obtaining the first adjustment parameter and the second adjustment parameter, the initial threshold range can be adjusted by using a weighted average or by multiple iterations to obtain the first threshold range.

[0066] The technical solution provided by the exemplary embodiments of this application determines a first adjustment parameter and a second adjustment parameter based on the vehicle's driving scenario and driving conditions. The initial threshold range is adjusted using the first adjustment parameter and the second adjustment parameter to obtain a first threshold range, making the first threshold range more consistent with the current driving state of the vehicle, and the determined first threshold range is more reasonable and accurate.

[0067] In step 204, if the actual control parameters are within the first threshold range, the target control parameters of the vehicle are determined based on the vehicle control model, driving information, and environmental information.

[0068] In an exemplary embodiment of this application, after determining the first threshold range, the actual control parameters can be compared with the first threshold range. If the actual control parameters are within the first threshold range, it is possible to further detect whether the vehicle has a fault. If the actual control parameters are outside the first threshold range, it is possible to directly determine that the vehicle has a fault and the fault information of the vehicle can be directly determined.

[0069] The following example illustrates the process of determining the target control parameters of a vehicle, taking the actual control parameters as falling within the first threshold range. The process may include: generating constraints corresponding to the vehicle control model using driving and environmental information; generating initial control parameters and corresponding performance indices based on the constraints, vehicle control model, driving and environmental information; and determining the initial control parameters whose performance indices exceed the threshold as the vehicle's target control parameters.

[0070] For example, the conditions for safe driving of the current vehicle, i.e., constraints, are determined by comprehensively considering the current vehicle's driving information and environmental information. These constraints may include the vehicle's safe driving speed, the safe distance between the current vehicle and other vehicles, and vehicle stability. Under these constraints, the current vehicle's driving information and environmental information are input into the vehicle control model. The vehicle control model is then used to optimize the vehicle's control parameters, resulting in initial control parameters and corresponding performance indicators. The threshold values ​​corresponding to these performance indicators are obtained, and the initial control parameters whose performance indicators exceed these threshold values ​​are determined as the vehicle's target control parameters. These threshold values ​​can be set based on different electronic devices, and this application does not impose any restrictions on this.

[0071] Taking the initial control parameters of a vehicle as the voltages of electronic devices in an onboard intelligent power supply three-in-one control system, the performance index as a score, and the index threshold as a score threshold as an example, the vehicle control model can output the initial control voltage of the electronic devices under constraints and the corresponding score of the initial control voltage. The voltage with a score higher than the score threshold of the initial control voltage is determined as the target control voltage.

[0072] The technical solution provided in this application determines the constraints of the vehicle control model by using driving information and environmental information. This allows for the acquisition of initial control parameters and performance indicators that better reflect the current driving conditions. The initial control parameters are then further filtered using indicator thresholds to obtain optimal target control parameters. In subsequent processes, these target control parameters are used to determine more accurate abnormal control information, thereby obtaining more accurate fault information and improving vehicle driving safety.

[0073] In step 205, abnormal control information of the vehicle is determined using actual control parameters, target control parameters, and a second threshold range.

[0074] In an exemplary embodiment of this application, an anomaly monitoring system can be used to monitor the actual control parameters of the vehicle, determine whether the deviation between the actual control parameters and the target control parameters is within a safe range, and thus determine the abnormal control information of the vehicle. The process of determining the abnormal control information of the vehicle may include steps 2051 to 2053.

[0075] In step 2051, the actual control parameters are converted to obtain the converted actual control parameters. The conversion process includes at least one of noise reduction processing or encryption / decryption processing.

[0076] For example, the architecture of an anomaly monitoring system may include three layers: a function implementation layer, a function monitoring layer, and a program flow monitoring layer. After obtaining the actual control parameters of the vehicle, the function implementation layer of the anomaly monitoring system monitors the various functions corresponding to the actual control parameters of the vehicle to obtain the monitoring data corresponding to the actual control parameters.

[0077] For example, actual control parameters may include charging parameters and AC / DC conversion parameters, which correspond to the vehicle's fast and slow charging functions. By monitoring the charging parameters and AC / DC conversion parameters, monitoring data of the vehicle's charging process can be obtained. Actual control parameters may also include temperature parameters, voltage parameters, and current parameters. Temperature parameters can characterize the temperature of various electronic devices in the vehicle. By monitoring the temperature parameters, temperature monitoring data during vehicle operation can be obtained. Current and voltage parameters can characterize the current flowing through various electronic devices in the vehicle or the voltage applied to various electronic devices. By monitoring the voltage and current parameters, current monitoring data and voltage monitoring data during vehicle operation can be obtained.

[0078] The monitoring data of the actual control parameters are denoised to obtain the denoised actual control parameters. For example, the electronic circuits in a vehicle may include filters, such as low-pass filters, high-pass filters, and band-pass filters. Based on the filters, spectral analysis is performed on the signals corresponding to the actual control parameters to identify and remove noise from the signals. Then, the denoised actual control parameters are encrypted to obtain the encrypted actual control parameters. The encryption process can be implemented based on encryption algorithms, including but not limited to symmetric encryption algorithms, asymmetric encryption algorithms, and hash algorithms.

[0079] In step 2052, the difference between the actual control parameters after conversion and the target control parameters is calculated.

[0080] For example, the functional implementation layer of the anomaly monitoring system can send the monitoring data of the vehicle's actual control parameters to the functional monitoring layer in real time. The functional monitoring layer decrypts the monitoring data to obtain the vehicle's actual control parameters. The actual control parameters of the same electronic device in the vehicle at the same time are compared with the target control parameters to obtain the difference between the actual control parameters and the target control parameters.

[0081] In step 2053, abnormal control information is determined based on the actual control parameters corresponding to the difference that exceeds the second threshold range.

[0082] In an exemplary embodiment of this application, the difference between the actual control parameter and the target control parameter is compared with a second threshold range. If the difference between the actual control parameter and the target control parameter is within the second threshold range, it indicates that the various functions corresponding to the actual control parameter of the vehicle are operating normally. If the difference between the actual control parameter and the target control parameter is outside the second threshold range, it indicates that the various functions corresponding to the actual control parameter of the vehicle are operating abnormally, thereby generating corresponding abnormal control information. It should be noted that the second threshold range can be set based on the actual situation of the vehicle, and this application does not impose any restrictions on it.

[0083] For example, by monitoring charging parameters and AC / DC conversion parameters, it is possible to confirm whether there are any abnormalities during the vehicle charging process; by monitoring temperature parameters, it is possible to prevent excessively high temperatures from damaging or degrading electronic components, leading to thermal runaway of the vehicle; by monitoring current and voltage parameters, it is possible to prevent overcurrent, overvoltage, and undervoltage from damaging electronic components, leading to loss of vehicle control.

[0084] The technical solution provided in this application is based on the anomaly monitoring system to denoise the actual control parameters. This reduces the impact of noise interference on the comparison results of the difference between the actual control parameters and the target control parameters and the second threshold range, thereby improving the accuracy of determining anomaly control information. When the actual control parameters are transmitted between the functional implementation layer and the functional monitoring layer of the anomaly monitoring system, encryption and decryption processing is performed on the actual control parameters to prevent real-time control parameters from being tampered with or forged, ensuring the data security and integrity of the real-time control parameters during transmission, thereby improving the accuracy of determining anomaly control information.

[0085] In step 206, vehicle fault information is determined based on abnormal control information and parameters of reference electronic devices, where the reference electronic devices are devices in the vehicle related to the abnormal control information.

[0086] In an exemplary embodiment of this application, the anomaly monitoring system may further include a fault database, which may include multiple fault information entries. These fault information entries may include, but are not limited to, fault type, fault cause, involved electronic components, and solutions. The functional monitoring layer of the anomaly monitoring system can monitor real-time control parameters in the functional implementation layer. For example, it can monitor the engine, transmission system, braking system, and electrical system. Upon receiving anomaly control information, the system can compare the anomaly information and the parameters of the electronic components related to the anomaly control information with multiple fault information entries in the fault database to determine the current vehicle fault information.

[0087] For example, after determining the fault information, the vehicle's safety response mechanism can also be triggered based on the fault information. The safety response mechanism includes at least one of recording the fault information, generating alarm information, performing fault isolation operation on the faulty hardware, or starting a safe parking procedure. If the safety response mechanism instructs to adjust the actual control parameters of the vehicle, a parameter adjustment instruction is generated, and the actual control parameters of the vehicle are adjusted based on the parameter adjustment instruction.

[0088] For example, the process monitoring layer of an anomaly monitoring system can also include a safety response mechanism corresponding to fault information. When fault information is generated, the corresponding safety response mechanism can be triggered. The safety response mechanism can include recording fault information, such as recording the time, location, type, and scope of the fault's impact; generating alarm information based on the urgency and scope of the fault information; and alerting the driver through indicator lights on the dashboard, audible prompts, or display on the vehicle's screen.

[0089] When the safety response mechanism indicates that vehicle control parameters need to be adjusted, a corresponding parameter adjustment command can be generated. This command may include, but is not limited to, cutting off power to electronic devices, adjusting engine power, and controlling the vehicle to brake. The actual vehicle control parameters are then adjusted based on the parameter adjustment command.

[0090] For example, if the fault information indicates a hardware failure, the process monitoring layer can generate parameter adjustment instructions to cut off the power supply to the faulty hardware, isolate the faulty hardware, prevent the fault from spreading, and avoid further impact on other hardware or software. If the fault information is more serious and may affect the safe operation of the vehicle, the process monitoring layer can also generate parameter adjustment instructions to start the safe parking procedure and control the vehicle braking based on the parameter adjustment instructions.

[0091] The technical solution provided in this application triggers the vehicle's safety response mechanism through fault information, and adjusts the actual control parameters of the vehicle using the parameter adjustment command corresponding to the safety response mechanism, thereby controlling the vehicle, reducing the impact of faults on vehicle safety, and improving the safety of the vehicle during driving.

[0092] In the exemplary embodiments of this application, during vehicle operation, the program flow monitoring layer of the anomaly monitoring system can also perform program flow checking operations, memory partitioning management operations, and chip selection operations. For example, the program flow monitoring layer monitors real-time control parameters in programs related to vehicle safety to ensure the correct execution of these parameters in each program flow; the program flow monitoring layer can also partition and manage memory resources storing vehicle information to ensure sufficient memory resources for vehicle-related information, real-time control parameters, and the process of determining fault information; the program flow monitoring layer can also select chips corresponding to the security requirement level based on the vehicle's real-time control information to ensure that the chip's data processing performance can meet the vehicle control requirements.

[0093] The technical solution provided in this exemplary embodiment determines the vehicle's driving scenario and driving condition by using the vehicle's driving information and environmental information. It then uses the driving scenario and driving condition to determine a first threshold range corresponding to the vehicle's actual control parameters. This makes the first threshold range more consistent with the current driving state of the vehicle, resulting in a more reasonable and accurate first threshold range. Therefore, the vehicle's safety status can be accurately determined using the first threshold range. When the vehicle's real-time control parameters are within the first threshold range, abnormal control information is further determined using the vehicle's real-time control parameters, target control parameters, and a second threshold range. This abnormal control information is then used to determine the vehicle's fault information, effectively avoiding misjudgment or omission of vehicle fault information, improving the accuracy of vehicle fault information, and ensuring vehicle driving safety.

[0094] This application also provides a device for determining vehicle fault information. Figure 3 This is a schematic diagram of a vehicle fault information determination device provided in an embodiment of this application, such as... Figure 3 As shown, the device includes:

[0095] The acquisition module 301 is used to acquire relevant information about the vehicle, including driving information and environmental information. The driving information is used to characterize the driving status of the vehicle, and the environmental information is used to characterize the external environment in which the vehicle is located.

[0096] The determination module 302 is used to determine the driving scenario and driving conditions of the vehicle based on relevant vehicle information;

[0097] The determining module 302 is also used to determine the first threshold range corresponding to the actual control parameters of the vehicle by utilizing the driving scenario and driving conditions;

[0098] The determination module 302 is also used to determine the target control parameters of the vehicle based on the vehicle control model, driving information and environmental information when the actual control parameters are within the range of the first threshold.

[0099] The determination module 302 is also used to determine abnormal control information of the vehicle using actual control parameters, target control parameters and a second threshold range;

[0100] The determination module 302 is also used to determine the vehicle's fault information based on the abnormal control information and the parameters of the reference electronic device, which is a device in the vehicle related to the abnormal control information.

[0101] In one possible implementation, the device further includes a control module (not shown in the figure), which is used to trigger the vehicle's safety response mechanism when the fault information indicates that the fault type is a hardware fault. The safety response mechanism includes at least one of recording fault information, generating alarm information, performing fault isolation operation on the faulty hardware, or starting a safe parking procedure. When the safety response mechanism indicates that the actual control parameters of the vehicle should be adjusted, a parameter adjustment instruction is generated, and the actual control parameters of the vehicle are adjusted based on the parameter adjustment instruction.

[0102] In one possible implementation, the determining module 302 is used to obtain the initial threshold range corresponding to the actual control parameters of the vehicle; determine a first adjustment parameter based on the driving scenario; determine a second adjustment parameter based on the driving condition; and adjust the initial threshold range using the first adjustment parameter and the second adjustment parameter to obtain the first threshold range.

[0103] In one possible implementation, the determining module 302 is used to perform conversion processing on the actual control parameters to obtain the converted actual control parameters. The conversion processing includes at least one of noise reduction processing or encryption / decryption processing. The module calculates the difference between the converted actual control parameters and the target control parameters. Based on the actual control parameters corresponding to the difference that exceeds the second threshold range, the module determines abnormal control information.

[0104] In one possible implementation, the determining module 302 is used to generate constraints corresponding to the vehicle control model using driving information and environmental information; generate initial control parameters and performance indicators corresponding to the initial control parameters based on the constraints, vehicle control model, driving information and environmental information; and determine the initial control parameters whose performance indicators are greater than the indicator threshold as the target control parameters of the vehicle.

[0105] In one possible implementation, the determining module 302 is used to extract features from driving information to obtain a first feature; extract features from environmental information to obtain a second feature; fuse the first feature and the second feature to obtain a fused feature; and use the fused feature to determine the driving scenario and driving condition.

[0106] The technical solution provided in this exemplary embodiment acquires vehicle driving information and environmental information through an acquisition module, determines the vehicle's driving scenario and driving condition based on the driving and environmental information through a determination module, and uses the driving scenario and driving condition to determine a first threshold range corresponding to the vehicle's actual control parameters. This makes the first threshold range more consistent with the current driving state of the vehicle, and the determined first threshold range is more reasonable and accurate. Therefore, the vehicle's safety status can be accurately determined through the first threshold range. When the vehicle's real-time control parameters are within the first threshold range, abnormal control information of the vehicle is further determined using the real-time control parameters, target control parameters, and a second threshold range. The abnormal control information is used to determine the vehicle's fault information, effectively avoiding misjudgment or omission of vehicle fault information, improving the accuracy of vehicle fault information, and ensuring vehicle driving safety.

[0107] Figure 4 This is a schematic diagram of a vehicle fault information determination device based on an in-vehicle intelligent power supply three-in-one control system, as provided in an embodiment of this application. Figure 4As shown, the device includes an acquisition module, a determination module, a control module, and a power module. The acquisition module may include information acquisition devices, including but not limited to cameras, millimeter-wave radar, lidar, and inertial sensors. The acquisition module collects data about the vehicle itself and its environment, and sends the collected data to the image processing unit, point cloud processing unit, and motion state processing unit of the determination module. The image processing unit and point cloud processing unit process the information collected by the camera, millimeter-wave radar, and lidar to obtain the vehicle's environmental information; the motion state processing unit processes the information collected by the inertial sensor to obtain the vehicle's driving information. The motion processing unit can also acquire the actual power control parameters of the power module. The power module may include an onboard intelligent power supply three-in-one control system, which may include a power control center connected to the integrated power unit (IPU), motor, and power battery. The power control center can control the IPU, motor, and power battery using the actual power control parameters. The motion state processing unit sends driving information and actual power control parameters to the motion state planning unit. The motion state planning unit combines the environmental information, driving information, and actual power control parameters from the data fusion unit to determine the vehicle's safe driving state. After determining that the vehicle is in a safe driving state, the motion state planning unit sends the relevant data to the fault information determination unit. The fault information determination unit combines the data from the data fusion unit and the motion state planning unit to re-determine the vehicle's safe driving state and obtain the vehicle's fault information. The determination module sends the vehicle's fault information to the control module. The control module's control center can trigger a safety response mechanism based on the vehicle's fault information. Using the parameter adjustment commands corresponding to the safety response mechanism, it can control the alarm unit to issue a fault information alarm, store the fault information in the cloud, and perform lateral or longitudinal control of the vehicle.

[0108] It should be noted that, Figure 4 The relevant content has been described in detail in steps 201 to 206. Please refer to the relevant descriptions in steps 201 to 206. We will not go into further detail here.

[0109] It should be understood that the above-described apparatus is only illustrated by the division of the functional modules described above when implementing its functions. In practical applications, the 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. In addition, the apparatus and method embodiments provided in the above embodiments belong to the same concept, and their specific implementation process can be found in the method embodiments, which will not be repeated here.

[0110] Figure 5 This application provides a structural block diagram of a terminal device. The terminal device 1500 can be any electronic device product capable of human-computer interaction with a user through one or more methods such as a keyboard, touchpad, remote control, voice interaction, or handwriting device. Examples include PCs (Personal Computers), mobile phones, smartphones, PDAs (Personal Digital Assistants), wearable devices, PPCs (Pocket PCs), tablet computers, and smart vehicle systems.

[0111] Typically, terminal device 1500 includes a processor 1501 and a memory 1502.

[0112] Processor 1501 may include one or more processing cores, such as a quad-core processor, an octa-core processor, etc. Processor 1501 may be implemented using at least one hardware form selected from DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array). Processor 1501 may also include a main processor and a coprocessor. The main processor, also known as a CPU (Central Processing Unit), is used to process data in the wake-up state; the coprocessor is a low-power processor used to process data in the standby state. In some embodiments, processor 1501 may integrate a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content required to be displayed on the screen. In some embodiments, processor 1501 may also include an AI (Artificial Intelligence) processor, which is used to handle computational operations related to machine learning.

[0113] Memory 1502 may include one or more computer-readable storage media, which may be non-transitory. Memory 1502 may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices or flash memory devices. In some embodiments, the non-transitory computer-readable storage media in memory 1502 is used to store at least one instruction, which is executed by processor 1501 to implement the method for determining vehicle fault information provided in the method embodiments of this application.

[0114] In some embodiments, the terminal device 1500 may also optionally include: a peripheral device interface 1503 and at least one peripheral device. The processor 1501, memory 1502, and peripheral device interface 1503 can be connected via a bus or signal line. Each peripheral device can be connected to the peripheral device interface 1503 via a bus, signal line, or circuit board. Specifically, the peripheral device includes at least one of: a radio frequency circuit 1504, a display screen 1505, a camera assembly 1506, an audio circuit 1507, and a power supply 1508.

[0115] Peripheral interface 1503 can be used to connect at least one I / O (Input / Output) related peripheral device to processor 1501 and memory 1502. In some embodiments, processor 1501, memory 1502 and peripheral interface 1503 are integrated on the same chip or circuit board; in some other embodiments, any one or two of processor 1501, memory 1502 and peripheral interface 1503 can be implemented on separate chips or circuit boards, which is not limited in this embodiment.

[0116] The radio frequency (RF) circuit 1504 is used to receive and transmit RF (Radio Frequency) signals, also known as electromagnetic signals. The RF circuit 1504 communicates with communication networks and other communication devices via electromagnetic signals. The RF circuit 1504 converts electrical signals into electromagnetic signals for transmission, or converts received electromagnetic signals back into electrical signals. Optionally, the RF circuit 1504 includes: an antenna system, an RF transceiver, one or more amplifiers, a tuner, an oscillator, a digital signal processor, a codec chipset, a user identity module card, etc. The RF circuit 1504 can communicate with other terminal devices through at least one wireless communication protocol. This wireless communication protocol includes, but is not limited to: the World Wide Web, metropolitan area networks, intranets, various generations of mobile communication networks (2G, 3G, 4G, and 5G), wireless local area networks, and / or WiFi (Wireless Fidelity) networks. In some embodiments, the RF circuit 1504 may also include circuitry related to NFC (Near Field Communication), which is not limited in this application.

[0117] Display screen 1505 is used to display a UI (User Interface). This UI may include graphics, text, icons, videos, and any combination thereof. When display screen 1105 is a touch display screen, display screen 1505 also has the ability to collect touch signals on or above the surface of display screen 1505. These touch signals can be input as control signals to processor 1501 for processing. In this case, display screen 1505 can also be used to provide virtual buttons and / or a virtual keyboard, also known as soft buttons and / or a soft keyboard. In some embodiments, display screen 1505 may be a single display screen disposed on the front panel of terminal device 1500; in other embodiments, display screen 1505 may be at least two, disposed on different surfaces of terminal device 1500 or in a folded design; in still other embodiments, display screen 1505 may be a flexible display screen disposed on a curved or folded surface of terminal device 1500. Furthermore, display screen 1505 may also be configured as a non-rectangular irregular shape, i.e., a non-rectangular screen. The display screen 1505 can be made of materials such as LCD (Liquid Crystal Display) and OLED (Organic Light-Emitting Diode).

[0118] The camera assembly 1506 is used to acquire images or videos. Optionally, the camera assembly 1506 includes a front-facing camera and a rear-facing camera. Typically, the front-facing camera is located on the front panel of the terminal device 1500, and the rear-facing camera is located on the back of the terminal device 1500. In some embodiments, there are at least two rear-facing cameras, which are any one of a main camera, a depth-sensing camera, a wide-angle camera, and a telephoto camera, to achieve background blurring by fusion of the main camera and the depth-sensing camera, panoramic shooting by fusion of the main camera and the wide-angle camera, VR (Virtual Reality) shooting, or other fusion shooting functions. In some embodiments, the camera assembly 1506 may also include a flash. The flash can be a single-color temperature flash or a dual-color temperature flash. A dual-color temperature flash refers to a combination of a warm light flash and a cool light flash, which can be used for light compensation at different color temperatures.

[0119] The audio circuit 1507 may include a microphone and a speaker. The microphone is used to collect sound waves from the user and the environment, converting the sound waves into electrical signals that are input to the processor 1501 for processing, or input to the radio frequency circuit 1504 for voice communication. For stereo sound acquisition or noise reduction purposes, multiple microphones may be used, each located at a different part of the terminal device 1500. The microphone may also be an array microphone or an omnidirectional microphone. The speaker is used to convert electrical signals from the processor 1501 or the radio frequency circuit 1504 into sound waves. The speaker may be a conventional diaphragm speaker or a piezoelectric ceramic speaker. When the speaker is a piezoelectric ceramic speaker, it can convert electrical signals not only into audible sound waves but also into inaudible sound waves for purposes such as distance measurement. In some embodiments, the audio circuit 1507 may also include a headphone jack.

[0120] Power supply 1508 is used to power the various components in terminal device 1500. Power supply 1508 can be AC ​​power, DC power, a disposable battery, or a rechargeable battery. When power supply 1508 includes a rechargeable battery, the rechargeable battery can be a wired rechargeable battery or a wireless rechargeable battery. A wired rechargeable battery is a battery that is charged via a wired line, and a wireless rechargeable battery is a battery that is charged via a wireless coil. The rechargeable battery can also be used to support fast charging technology.

[0121] In some embodiments, the terminal device 1500 further includes one or more sensors 1510. The one or more sensors 1510 include, but are not limited to: an acceleration sensor 1511, a gyroscope sensor 1512, a pressure sensor 1513, an optical sensor 1514, and a proximity sensor 1515.

[0122] Accelerometer 1511 can detect the magnitude of acceleration along the three axes of a coordinate system established by terminal device 1500. For example, accelerometer 1511 can be used to detect the components of gravitational acceleration along the three axes. Processor 1501 can control display screen 1505 to display the user interface in either a landscape or portrait view based on the gravitational acceleration signal acquired by accelerometer 1511. Accelerometer 1511 can also be used for games or for acquiring user motion data.

[0123] The gyroscope sensor 1512 can detect the orientation and rotation angle of the terminal device 1500. The gyroscope sensor 1512, in conjunction with the accelerometer sensor 1511, can collect 3D motion data from the user on the terminal device 1500. Based on the data collected by the gyroscope sensor 1512, the processor 1501 can perform the following functions: motion sensing (e.g., changing the UI based on the user's tilt), image stabilization during shooting, game control, and inertial navigation.

[0124] The pressure sensor 1513 can be disposed on the side bezel of the terminal device 1500 and / or on the lower layer of the display screen 1505. When the pressure sensor 1513 is disposed on the side bezel of the terminal device 1500, it can detect the user's grip signal on the terminal device 1500, and the processor 1501 can perform left / right hand recognition or quick operation based on the grip signal collected by the pressure sensor 1513. When the pressure sensor 1513 is disposed on the lower layer of the display screen 1505, the processor 1501 can control the operable controls on the UI interface based on the user's pressure operation on the display screen 1505. The operable controls include at least one of button controls, scroll bar controls, icon controls, and menu controls.

[0125] Optical sensor 1514 is used to collect ambient light intensity. In one embodiment, processor 1501 can control the display brightness of display screen 1505 based on the ambient light intensity collected by optical sensor 1514. Specifically, when the ambient light intensity is high, the display brightness of display screen 1505 is increased; when the ambient light intensity is low, the display brightness of display screen 1505 is decreased. In another embodiment, processor 1501 can also dynamically adjust the shooting parameters of camera assembly 1506 based on the ambient light intensity collected by optical sensor 1514.

[0126] The proximity sensor 1515, also known as a distance sensor, is typically located on the front panel of the terminal device 1500. The proximity sensor 1515 is used to detect the distance between the user and the front of the terminal device 1500. In one embodiment, when the proximity sensor 1515 detects that the distance between the user and the front of the terminal device 1500 is gradually decreasing, the processor 1501 controls the display screen 1505 to switch from a screen-on state to a screen-off state; when the proximity sensor 1515 detects that the distance between the user and the front of the terminal device 1500 is gradually increasing, the processor 1501 controls the display screen 1505 to switch from a screen-off state to a screen-on state.

[0127] Those skilled in the art will understand that Figure 5 The structure shown does not constitute a limitation on the terminal device 1500, and may include more or fewer components than shown, or combine certain components, or use different component arrangements.

[0128] Figure 6This is a schematic diagram of the server structure provided in the embodiments of this application. The server 1600 can vary considerably due to different configurations or performance. It may include one or more processors 1601 and one or more memories 1602. The one or more memories 1602 store at least one piece of program code, which is loaded and executed by the one or more processors 1601 to implement the vehicle fault information determination method provided in the above-described method embodiments. Of course, the server 1600 may also have wired or wireless network interfaces, a keyboard, and input / output interfaces for input and output. The server 1600 may also include other components for implementing device functions, which will not be elaborated here.

[0129] In an exemplary embodiment, a computer-readable storage medium is also provided, which stores at least one piece of program code that is loaded and executed by a processor to enable a computer to implement any of the above-described methods for determining vehicle fault information.

[0130] Optionally, the aforementioned computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a compact disc read-only memory (CD-ROM), magnetic tape, floppy disk, and optical data storage device, etc.

[0131] In an exemplary embodiment, a computer program or computer program product is also provided, which stores at least one computer instruction, which is loaded and executed by a processor to enable the computer to implement any of the above-described methods for determining vehicle fault information.

[0132] It should be noted that all information (including but not limited to user device information, user personal information, etc.), data (including but not limited to data used for analysis, stored data, displayed data, etc.), and signals involved in this application have been authorized by the user or fully authorized by all parties, and the collection, use, and processing of related data must comply with the relevant laws, regulations, and standards of the relevant countries and regions. For example, the driving information, environmental information, actual control parameters, and fault information involved in this application were all obtained with full authorization.

[0133] It should be understood that "multiple" as used in this article refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. The character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0134] The above description is merely an exemplary embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the principles of this application should be included within the protection scope of this application.

Claims

1. A method of determining vehicle fault information, characterized by, The method comprises: obtaining related information of a vehicle, the related information of the vehicle comprising driving information and environment information, the driving information being used to represent a driving state of the vehicle, and the environment information being used to represent an external environment in which the vehicle is located; determining a driving scene and a driving condition of the vehicle based on the related information of the vehicle; obtaining an initial threshold range corresponding to an actual control parameter of the vehicle; determining a first adjustment parameter based on the driving scene and a second adjustment parameter based on the driving condition; adjusting the initial threshold range by using the first adjustment parameter and the second adjustment parameter to obtain a first threshold range; determining a target control parameter of the vehicle based on a vehicle control model, the driving information and the environment information in a case where the actual control parameter is located in the first threshold range; determining abnormal control information of the vehicle by using the actual control parameter, the target control parameter and a second threshold range; determining fault information of the vehicle based on the abnormal control information and a parameter of a reference electronic device, the reference electronic device being a device related to the abnormal control information in the vehicle.

2. The method of claim 1, wherein, After the determination of the fault information of the vehicle based on the abnormal control information and the parameter of the reference electronic device, the method further comprises: triggering a safety response mechanism of the vehicle based on the fault information, the safety response mechanism comprising at least one of recording the fault information, generating alarm information, performing a fault isolation operation on a hardware that has occurred a fault or starting a safety parking program; generating a parameter adjustment instruction in a case where the safety response mechanism indicates to adjust the actual control parameter of the vehicle, and adjusting the actual control parameter of the vehicle based on the parameter adjustment instruction.

3. The method of claim 1, wherein, The determination of the abnormal control information of the vehicle by using the actual control parameter, the target control parameter and the second threshold range comprises: performing conversion processing on the actual control parameter to obtain a converted actual control parameter, the conversion processing comprising at least one of denoising processing or encryption and decryption processing; calculating a difference value between the converted actual control parameter and the target control parameter; determining the abnormal control information based on the actual control parameter corresponding to the difference value that exceeds the second threshold range.

4. The method according to any one of claims 1 to 3, characterized in that, The determination of the target control parameter of the vehicle based on the vehicle control model, the driving information and the environment information comprises: generating a constraint condition corresponding to the vehicle control model by using the driving information and the environment information; generating an initial control parameter and a performance index corresponding to the initial control parameter based on the constraint condition, the vehicle control model, the driving information and the environment information; determining the initial control parameter with a performance index greater than an index threshold value as the target control parameter of the vehicle.

5. The method according to any one of claims 1 to 3, characterized in that, The determination of the driving scene and the driving condition of the vehicle based on the related information of the vehicle comprises: performing feature extraction on the driving information to obtain a first feature; performing feature extraction on the environment information to obtain a second feature; performing fusion on the first feature and the second feature to obtain a fused feature; The driving scene and the driving working condition are determined by using the fusion feature.

6. A device for determining vehicle failure information, characterized by The device comprises: An acquisition module is configured to acquire relevant information of a vehicle, the relevant information of the vehicle comprising driving information and environment information, the driving information being used to represent a driving state of the vehicle, and the environment information being used to represent an external environment in which the vehicle is located. A determination module is configured to determine a driving scene and a driving working condition of the vehicle based on the relevant information of the vehicle. The acquisition module is further configured to acquire an initial threshold range corresponding to an actual control parameter of the vehicle. The determination module is further configured to determine a first adjustment parameter based on the driving scene and a second adjustment parameter based on the driving working condition. The determination module is further configured to adjust the initial threshold range by using the first adjustment parameter and the second adjustment parameter to obtain a first threshold range. The determination module is further configured to determine a target control parameter of the vehicle based on a vehicle control model, the driving information and the environment information in a case where the actual control parameter is located in the first threshold range. The determination module is further configured to determine abnormal control information of the vehicle by using the actual control parameter, the target control parameter and a second threshold range. The determination module is further configured to determine fault information of the vehicle based on the abnormal control information and a parameter of a reference electronic device, the reference electronic device being a device related to the abnormal control information in the vehicle.

7. A computer device, comprising: The computer device comprises a processor and a memory, and the memory stores at least one program code, which is loaded and executed by the processor, so that the computer device implements the method for determining vehicle fault information according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer readable storage medium stores at least one program code, which is loaded and executed by the processor, so that the computer implements the method for determining vehicle fault information according to any one of claims 1 to 5.

9. A computer program product, characterised in that, The computer program product stores at least one computer instruction, which is loaded and executed by the processor, so that the computer implements the method for determining vehicle fault information according to any one of claims 1 to 5.

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