Vehicle fault processing method and device, electronic equipment and computer program product

Abstract

The invention discloses a vehicle fault processing method and device, electronic equipment and a computer program product. The method comprises the steps of obtaining running state data of a vehicle; performing fault detection according to the running state data to obtain vehicle fault data; generating a fault tree according to the vehicle fault data, and analyzing fault precauses of the vehicle according to the fault tree to obtain a fault precause analysis result; and determining a fault processing scheme of the vehicle according to the vehicle fault data and the pre-fault cause analysis result, and processing the fault of the vehicle by using the fault processing scheme. According to the method, fault detection analysis is carried out on the real-time running state data of the vehicle, the fault tree is constructed in real time, the accuracy and adaptability of fault diagnosis are improved, fault precauses are traced based on the fault tree constructed in real time, and the trial and error process in a traditional method is avoided. According to the current running state and the fault condition of the vehicle, the fault processing scheme is dynamically adjusted in real time, and the flexibility and the processing efficiency of vehicle fault processing are improved.

Description

Technical Field

[0001] The present application relates to the technical field of vehicle fault handling, and in particular to a vehicle fault handling method, device, electronic equipment, and computer program product. Background Art

[0002] With the development of intelligent transportation systems, real-time processing and response of vehicle fault information has become particularly important. The existing vehicle fault processing solutions are mainly the following:

[0003] 1) Fixed rule processing: Based on pre-set fault handling rules, when a certain fault is detected, a fixed processing flow is executed. However, fixed diagnostic processes and processing strategies lack flexibility, are difficult to adapt to changing fault types, and cannot cope with complex and changing fault scenarios.

[0004] 2) Expert system: The fault handling model is constructed using expert experience. The system automatically matches the handling plan according to the fault characteristics. Although this method improves the flexibility of processing, it relies on the accumulation of expert experience and has high maintenance costs.

[0005] 3) Big data analysis: Analyze historical fault data through big data technology to predict and handle faults. This method relies on a large amount of historical data, responds slowly to new faults, and has high data processing complexity.

[0006] It can be seen that traditional fault handling methods often rely on fixed logic and preset solutions, and cannot effectively deal with changing fault types and complex fault causes. This not only affects the efficiency of fault handling, but may also lead to unnecessary maintenance and waste of resources. Every time the fault type is updated or changed, the system needs to be reprogrammed and deployed, and the update cost is high; when faced with complex or unknown faults, users may not get timely and effective help, resulting in a poor user experience. Therefore, there is an urgent need for a method that can flexibly and efficiently process vehicle-side fault information to improve the accuracy and efficiency of fault handling. Summary of the invention

[0007] The embodiments of the present application provide a vehicle fault handling method, device, electronic device, and computer program product to improve the flexibility and efficiency of vehicle fault handling.

[0008] The present application embodiment adopts the following technical solutions:

[0009] In a first aspect, an embodiment of the present application provides a vehicle fault handling method, wherein the vehicle fault handling method includes:

[0010] Obtain vehicle operating status data;

[0011] Perform fault detection according to the operating status data to obtain vehicle fault data;

[0012] Generate a fault tree according to the vehicle fault data, and analyze the antecedent causes of the vehicle fault according to the fault tree to obtain an antecedent cause analysis result;

[0013] A vehicle fault handling solution is determined based on the vehicle fault data and the fault antecedent analysis result, and the vehicle fault is handled using the fault handling solution.

[0014] Optionally, performing fault detection according to the running status data to obtain vehicle fault data includes:

[0015] Preprocessing the operating status data to obtain preprocessed operating status data;

[0016] Performing fault detection on the preprocessed running status data using a preset fault detection strategy to obtain vehicle fault data;

[0017] Among them, the preset fault detection strategy includes at least one of an abnormal pattern recognition strategy and an abnormal value recognition strategy, and the vehicle fault data includes at least one of a fault occurrence time, a fault occurrence location, a fault type and a fault severity level.

[0018] Optionally, generating a fault tree according to the vehicle fault data includes:

[0019] Based on the fault occurrence time in the vehicle fault data, obtaining vehicle fault data within a preset time period before and after the fault occurrence time;

[0020] Determine multiple possible fault antecedents corresponding to the current fault of the vehicle according to the vehicle fault data within a preset time period before and after the fault occurs;

[0021] A fault tree corresponding to the current fault of the vehicle is generated according to multiple possible fault antecedents corresponding to the current fault of the vehicle and the causal relationship between the multiple possible fault antecedents.

[0022] Optionally, analyzing the antecedent cause of the vehicle failure according to the fault tree to obtain an antecedent cause analysis result includes:

[0023] Analyze the fault tree layer by layer to determine the fundamental fault antecedent cause corresponding to the current fault of the vehicle;

[0024] A fault antecedent cause analysis result is generated according to the fundamental fault antecedent cause and displayed to the user.

[0025] Optionally, the fault tree corresponding to the current fault of the vehicle includes multiple fault levels, each of the fault levels includes multiple fault antecedent nodes, and the step of analyzing the fault tree layer by layer to determine the fundamental fault antecedent corresponding to the current fault of the vehicle includes:

[0026] Determine the fault severity level corresponding to each of the fault antecedent nodes respectively;

[0027] The fundamental fault antecedent cause corresponding to the current fault of the vehicle is determined according to the fault severity level corresponding to each of the fault antecedent cause nodes.

[0028] Optionally, the respectively determining the fault severity level corresponding to each of the fault antecedent nodes includes:

[0029] Obtaining vehicle fault data corresponding to each of the fault antecedent nodes;

[0030] Determine the fault severity level corresponding to each of the fault antecedent cause nodes according to the vehicle fault data corresponding to each of the fault antecedent cause nodes and the preset fault severity level judgment condition;

[0031] The determining of the fundamental fault antecedent cause corresponding to the current fault of the vehicle according to the fault severity level corresponding to each of the fault antecedent cause nodes includes:

[0032] Determine the fault antecedent with the highest fault severity level according to the fault severity level corresponding to each of the fault antecedent cause nodes;

[0033] The fault antecedent cause with the highest fault severity level is taken as the fundamental fault antecedent cause corresponding to the current fault of the vehicle.

[0034] Optionally, after determining a vehicle fault handling solution according to the vehicle fault data and the fault antecedent analysis result, and handling the vehicle fault using the fault handling solution, the vehicle fault handling method further includes:

[0035] Obtain vehicle fault handling results;

[0036] When the fault handling result is a handling failure, the preset fault detection strategy and / or fault handling solution is optimized.

[0037] In a second aspect, an embodiment of the present application further provides a vehicle fault handling device, wherein the vehicle fault handling device comprises:

[0038] A first acquisition unit, used to acquire running status data of the vehicle;

[0039] A fault detection unit, used to perform fault detection according to the running status data to obtain vehicle fault data;

[0040] A fault analysis unit, used to generate a fault tree according to the vehicle fault data, and analyze the antecedent causes of the vehicle fault according to the fault tree to obtain an antecedent cause analysis result;

[0041] The fault processing unit is used to determine a vehicle fault processing scheme according to the vehicle fault data and the fault antecedent analysis result, and use the fault processing scheme to process the vehicle fault.

[0042] In a third aspect, an embodiment of the present application further provides an electronic device, including:

[0043] Processor; and

[0044] A memory arranged to store computer executable instructions, which, when executed, cause the processor to perform any of the aforementioned vehicle fault handling methods.

[0045] In a fourth aspect, an embodiment of the present application further provides a computer program product, including a computer program or instructions, which, when executed by a processor, implements any of the aforementioned vehicle fault handling methods.

[0046] At least one of the above technical solutions adopted in the embodiment of the present application can achieve the following beneficial effects: the vehicle fault handling method of the embodiment of the present application first obtains the running status data of the vehicle; then performs fault detection according to the running status data to obtain the vehicle fault data; then generates a fault tree according to the vehicle fault data, and analyzes the fault antecedents of the vehicle according to the fault tree to obtain the fault antecedent analysis results; finally determines the fault handling scheme of the vehicle according to the vehicle fault data and the fault antecedent analysis results, and uses the fault handling scheme to handle the fault of the vehicle. The vehicle fault handling method of the embodiment of the present application improves the accuracy and adaptability of fault diagnosis by performing fault detection and analysis on the real-time running status data of the vehicle and constructing a fault tree in real time, further traces the fault antecedents based on the real-time constructed fault tree, avoids the trial and error process in the traditional method, and improves the efficiency of locating the root cause of the fault. In addition, according to the current running status and fault condition of the vehicle, the fault handling scheme can be adjusted dynamically in real time to ensure that the fault can be handled in a timely and effective manner. It supports the configuration of multiple fault handling methods, and can flexibly select applicable handling strategies according to different fault types, severity and impact ranges, thereby improving the flexibility and efficiency of vehicle fault handling. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0048] Figure 1A schematic diagram of a vehicle fault handling method according to an embodiment of the present application;

[0049] Figure 2 This is a structural schematic diagram of a vehicle fault handling device in an embodiment of the present application;

[0050] Figure 3 This is a schematic diagram of the structure of an electronic device in an embodiment of the present application. DETAILED DESCRIPTION

[0051] In order to make the purpose, technical solution and advantages of the present application clearer, the technical solution of the present application will be clearly and completely described below in combination with the specific embodiments of the present application and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present application.

[0052] The technical solutions provided by various embodiments of the present application are described in detail below in conjunction with the accompanying drawings.

[0053] The present application embodiment provides a vehicle fault processing method, such as Figure 1 As shown, a flow chart of a vehicle fault processing method in an embodiment of the present application is provided, and the vehicle fault processing method at least includes the following steps S110 to S140:

[0054] Step S110, obtaining the running status data of the vehicle.

[0055] When performing vehicle fault processing in the embodiments of the present application, it is necessary to first collect the operating status data of the autonomous driving vehicle in real time. Specifically, the data can be obtained through the fault diagnosis system of the autonomous driving vehicle. When obtaining the vehicle operating status data, the vehicle fault diagnosis system of the autonomous driving vehicle can comprehensively capture key information about the vehicle's hardware and software.

[0056] Hardware operating status data may include the following dimensions, for example:

[0057] 1) Sensor data:

[0058] Camera: Image data, used to identify road signs, pedestrians, vehicles, etc.

[0059] Radar: Measurement data such as distance, speed, and angle to help vehicles perceive their surroundings. LiDAR: 3D point cloud data to provide high-precision environment modeling. Ultrasonic sensor: Short-range obstacle detection data, often used for parking assistance.

[0060] 2) Wire control system data:

[0061] Steer-by-wire: steering wheel angle, steering speed, etc.

[0062] Brake-by-wire: brake pressure, brake status, etc.

[0063] Throttle-by-wire: accelerator pedal position, throttle opening, etc.

[0064] 3) Chassis and power system data:

[0065] Engine Speed: Engine speed.

[0066] Vehicle speed: real-time vehicle speed.

[0067] Braking force: The braking force of the braking system.

[0068] Tire pressure and temperature: tire condition monitoring data.

[0069] 4) Other hardware status:

[0070] Battery status: power, voltage, current, etc.

[0071] Motor status: motor temperature, speed, etc.

[0072] Seat belt status: whether the seat belt is fastened.

[0073] Software operating status data may include, for example, the following dimensions:

[0074] 1) Status of the autonomous driving system:

[0075] System activation status: whether the automatic driving mode is activated.

[0076] Autonomous driving level: The current level of autonomous driving (such as L2, L3, L4, etc.) System response time: The system's response time to sensor input or driver instructions.

[0077] 2) Algorithm and strategy execution:

[0078] Route planning: currently planned driving route data.

[0079] Decision logic: The decision data made by the system based on the environment.

[0080] Control algorithm output: control instructions such as steering angle, throttle opening, etc.

[0081] 3) Software version and configuration:

[0082] Autonomous driving software version: the current running software version number.

[0083] Hardware configuration information: Configuration information that matches the vehicle hardware.

[0084] Of course, it should be noted that the specific dimensions of vehicle operation status data can be flexibly set by technical personnel in this field according to actual needs, and no specific limitation is made here.

[0085] Step S120: performing fault detection according to the operating status data to obtain vehicle fault data.

[0086] After obtaining the vehicle operating status data in the above-mentioned multiple dimensions, a certain fault detection strategy can be adopted to analyze the operating status data, determine whether the autonomous driving vehicle is currently faulty and the specific fault information, and generate vehicle fault data, which may include, for example, the time of the vehicle fault, the location of the fault, the type of fault, and the severity, etc., to provide data support for subsequent fault analysis.

[0087] Step S130, generating a fault tree according to the vehicle fault data, and analyzing the antecedent causes of the vehicle fault according to the fault tree to obtain an antecedent cause analysis result.

[0088] For the vehicle fault data obtained in the above steps, further fault analysis is required. The embodiment of the present application can use a fault tree (FTA) to perform fault analysis. The fault tree is a special inverted tree-like logical causal relationship diagram, which mainly represents the causal relationship between various events in the system through event symbols, logic gate symbols, and transfer symbols. This analysis method helps identify all possible causes of undesirable events (such as system failures or accidents) from top to bottom through reverse reasoning, thereby improving the safety and reliability of the system. In the embodiment of the present application, a fault tree is generated based on the vehicle fault data generated in real time, and a fault antecedent analysis is performed based on the hierarchical relationship of the fault tree, so as to analyze the possible causes and the most fundamental causes of the current failure of the autonomous driving vehicle.

[0089] Step S140, determining a vehicle fault handling solution according to the vehicle fault data and the fault antecedent analysis result, and using the fault handling solution to handle the vehicle fault.

[0090] Different fault types, different fault severity levels, and different fault antecedents, etc., require different specific fault handling strategies. Therefore, after analyzing the fault antecedents, the embodiments of the present application dynamically configure the current fault handling solution in combination with the fault data such as the type and severity of the vehicle fault and the fault antecedents, and use the fault handling solution to handle the vehicle fault. The fault handling solution here may include automatic handling solutions configured in advance for various common fault types, and may also include fallback handling solutions adopted when uncommon or unknown faults occur, such as manual intervention.

[0091] The vehicle fault handling method of the embodiment of the present application improves the accuracy and adaptability of fault diagnosis by performing fault detection and analysis on the real-time operating status data of the vehicle and constructing a fault tree in real time. It further traces the cause of the fault based on the real-time constructed fault tree, avoids the trial and error process in traditional methods, and improves the efficiency of locating the root cause of the fault. In addition, according to the current operating status and fault condition of the vehicle, the fault handling plan can be adjusted dynamically in real time to ensure that the fault can be handled in a timely and effective manner. It supports the configuration of multiple fault handling methods, and can flexibly select applicable handling strategies according to different fault types, severity and impact ranges, thereby improving the flexibility and efficiency of vehicle fault handling.

[0092] In some embodiments of the present application, performing fault detection based on the operating status data to obtain vehicle fault data includes: preprocessing the operating status data to obtain preprocessed operating status data; performing fault detection on the preprocessed operating status data using a preset fault detection strategy to obtain vehicle fault data; wherein the preset fault detection strategy includes at least one of an abnormal pattern recognition strategy and an abnormal value recognition strategy, and the vehicle fault data includes at least one of a fault occurrence time, a fault occurrence location, a fault type, and a fault severity level.

[0093] When performing fault detection based on the vehicle's operating status data, the embodiments of the present application may first pre-process the collected original operating status data, such as removing noise, filling missing values, standardizing data, etc., to ensure the quality of the data and the accuracy of subsequent analysis. In addition, feature extraction may be performed on the data to highlight key information related to the fault. Of course, the specific pre-processing strategies can be flexibly set by those skilled in the art according to actual needs, and are not specifically limited here.

[0094] After obtaining the preprocessed running status data of the vehicle, a certain fault detection strategy can be adopted to perform fault detection on the preprocessed running status data to obtain vehicle fault data. The fault detection strategy here can mainly include abnormal pattern recognition strategy, abnormal value recognition strategy, etc. The abnormal pattern recognition strategy is based on the analysis of the vehicle running status data to identify whether the current running mode of the vehicle is abnormal. The abnormal value recognition strategy is to determine whether there are abnormal data values ​​that exceed the normal value range or do not meet the threshold requirements. These fault detection strategies can be implemented in a rule-based method, a statistical method, a machine learning algorithm (such as a support vector machine, a neural network, a random forest, etc.) or a deep learning model. How to perform fault detection specifically can be flexibly selected by those skilled in the art according to actual needs, and no specific limitation is made here.

[0095] As mentioned above, the vehicle fault data of the embodiment of the present application may include the time when the fault occurred, the location of the fault, the type of fault, and the severity level of the fault, etc. The time of the fault occurrence is the time point when the fault occurs and the duration of the fault. The location of the fault refers to the location of the autonomous driving vehicle when the fault occurs. The fault type can be flexibly divided according to the scenario and needs, for example, it may include functional failures, driving failures, safety failures, etc. The severity level of the fault can be divided according to the characteristics and impact of the fault, and the severity can be divided into multiple levels from low to high, for example, it can be divided into three levels: controllable (does not affect driving safety), minor (minor impact on driving safety) and severe (severe impact on driving safety). The specific division can be flexibly set by technical personnel in this field according to actual needs, and no specific limitation is made here.

[0096] In some embodiments of the present application, generating a fault tree based on the vehicle fault data includes: based on the fault occurrence time in the vehicle fault data, obtaining vehicle fault data within a preset time period before and after the fault occurrence time; determining multiple possible fault antecedents corresponding to the vehicle's current fault based on the vehicle fault data within a preset time period before and after the fault occurrence time; generating a fault tree corresponding to the vehicle's current fault based on multiple possible fault antecedents corresponding to the vehicle's current fault and the causal relationship between the multiple possible fault antecedents.

[0097] In order to ensure the comprehensiveness and accuracy of fault analysis, when generating a fault tree based on vehicle fault data, we can first obtain vehicle fault data within a period of time before and after the fault occurrence time based on the fault occurrence time in the vehicle fault data, that is, use the fault time point as the benchmark and trace back the vehicle data for a period of time.

[0098] Based on the vehicle fault data collected within a period of time before and after the fault occurs, a fault tree is generated according to the possible antecedent faults and the causal relationship between multiple possible antecedent faults. Since one fault may correspond to multiple antecedent faults, it is necessary to confirm whether the currently collected fault data contains data on a certain antecedent or multiple antecedent faults.

[0099] For example, assuming that the current fault type of the vehicle is an autonomous driving fault, if autonomous driving is not possible, what can be determined in advance is which modules of the nodes related to autonomous driving, such as the control module, planning module, and positioning module. Then, combined with the vehicle fault data collected within a period of time before and after the fault, it can be determined whether there is data related to the control module, planning module, and positioning module in these vehicle fault data. If only relevant data of the control module and planning module are contained, the control module and planning module can be used as possible fault antecedents corresponding to the current fault.

[0100] In some embodiments of the present application, analyzing the antecedent causes of vehicle failures according to the fault tree to obtain the antecedent cause analysis results includes: analyzing the fault tree layer by layer to determine the fundamental antecedent cause of failure corresponding to the current fault of the vehicle; generating the antecedent cause analysis results according to the fundamental antecedent cause of failure and displaying them to the user.

[0101] When analyzing the antecedents of a fault based on a fault tree, the purpose is to analyze the most fundamental cause of the current vehicle fault. Therefore, the embodiment of the present application can trace and analyze upward step by step based on the hierarchical structure of the fault tree, so as to analyze the fundamental antecedents of the fault corresponding to the current fault. Based on the analysis results, user-friendly fault display information is generated, such as the possible causes of the fault, the fundamental causes of the fault, and the impact of the fault.

[0102] In some embodiments of the present application, the fault tree corresponding to the current fault of the vehicle includes multiple fault levels, each of the fault levels includes multiple fault antecedent nodes, and the fault tree is analyzed layer by layer to determine the fundamental fault antecedent corresponding to the current fault of the vehicle, including: separately determining the fault severity level corresponding to each of the fault antecedent nodes; and determining the fundamental fault antecedent corresponding to the current fault of the vehicle according to the fault severity level corresponding to each of the fault antecedent nodes.

[0103] The fault tree of the embodiment of the present application includes multiple fault levels. The first level is the top event. For example, "autonomous driving vehicle collision" and "autonomous driving system failure" can be used as top events. The corresponding next-level associated nodes include, for example, a control module, a planning module, and a positioning module. Each module such as the control module, the planning module, and the positioning module may also correspond to a next-level node. For example, the next-level node of the positioning module also includes a lidar, a camera, a fusion positioning module, etc.

[0104] When analyzing the fault antecedents layer by layer, the fault severity level corresponding to each fault antecedent node can be determined based on the vehicle fault data. Since the fault severity level is determined based on factors such as the characteristics and impact of the fault, the high or low fault severity level reflects the influence of the fault antecedent node on the current fault of the vehicle. Then, the fundamental fault antecedent corresponding to the current fault of the vehicle can be determined according to the fault severity level corresponding to each fault antecedent node.

[0105] In some embodiments of the present application, respectively determining the fault severity level corresponding to each of the fault antecedent nodes includes: acquiring vehicle fault data corresponding to each of the fault antecedent nodes; determining the fault severity level corresponding to each of the fault antecedent nodes according to the vehicle fault data corresponding to each of the fault antecedent nodes and preset fault severity level judgment conditions; determining the fundamental fault antecedent corresponding to the current fault of the vehicle according to the fault severity level corresponding to each of the fault antecedent nodes includes: determining the fault antecedent with the highest fault severity level according to the fault severity level corresponding to each of the fault antecedent nodes; and using the fault antecedent with the highest fault severity level as the fundamental fault antecedent corresponding to the current fault of the vehicle.

[0106] When determining the fault severity level corresponding to each fault antecedent node, it can be obtained from the vehicle fault data corresponding to each fault antecedent node. For example, the current fault of the autonomous driving vehicle is the inability to perform autonomous driving, and the corresponding fault antecedent nodes are the control module and the planning module. According to the relevant operating status data of the control module, the severity level of the control module fault can be determined to be a severe level or a high level. According to the relevant operating status data of the planning module, the severity level of the planning module fault can be determined to be a controllable level or a low level. That is, the fault level of the control module is higher than the fault level of the planning module, indicating that the control module plays a greater role in the fault that causes the vehicle's autonomous driving function to fail. Therefore, the control module fault can be regarded as the fundamental fault antecedent corresponding to the current fault of the vehicle.

[0107] It should be noted that if the fault levels of multiple fault antecedent nodes are the same, then the multiple fault antecedent nodes can be regarded as the most fundamental fault antecedents and all need to be considered as key factors when determining the fault handling solution.

[0108] Based on the vehicle fault data such as fault type and fault severity obtained in the above embodiments, as well as information such as fault antecedent analysis results, a suitable fault handling solution is dynamically selected and configured, where the fault handling solution may include, for example, automatic processing, prompting the user with processing steps, or suggesting going to a repair station, etc. When configuring the fault handling solution, the embodiment of the present application can dynamically configure the handling solution in a more targeted and flexible manner in combination with the specific vehicle fault data and fault antecedent analysis results.

[0109] For example, assuming that the camera module normally sends 10 frames of data per second, when a data frame drop fault occurs, for example, there are only 5 frames of data in 1 second, but it does not affect the preset unavailable value. In this case, the fault handling solution can only be a warning reminder. However, if there is only 1 frame of data in 1 minute, which exceeds the preset unavailable value, then a solution of stopping the vehicle for inspection is required.

[0110] In some embodiments of the present application, after determining a vehicle fault handling solution based on the vehicle fault data and the fault antecedent analysis results, and using the fault handling solution to handle the vehicle fault, the vehicle fault handling method further includes: obtaining a vehicle fault handling result; and when the fault handling result is a processing failure, optimizing a preset fault detection strategy and / or fault handling solution.

[0111] After executing the selected fault handling solution, the embodiment of the present application can monitor the processing effect in real time, such as whether the vehicle fault is successfully eliminated or resolved. According to the processing results, the preset fault detection strategy, fault handling solution library, etc. can be optimized. For example, the strategy of abnormal pattern recognition in the fault detection stage, the range of abnormal values, etc., are optimized, thereby optimizing the cause analysis results. The processing solutions can also be updated or added, thereby improving the system's self-learning ability and fault handling efficiency.

[0112] In summary, the vehicle fault handling method of the present application has achieved at least the following technical effects:

[0113] 1) The fault tree is constructed through real-time data, which improves the accuracy and adaptability of fault diagnosis;

[0114] 2) Using fault trees for root cause analysis avoids the trial-and-error process of traditional methods;

[0115] 3) Provide personalized user interface and information display according to the root causes of different faults;

[0116] 4) Dynamically configure processing strategies according to different fault types to improve the pertinence and efficiency of fault handling solutions.

[0117] 5) During the troubleshooting process, monitor the processing results in real time to ensure that the fault is effectively resolved.

[0118] The present application also provides a vehicle fault processing device 200, such as Figure 2 As shown, a schematic diagram of the structure of a vehicle fault processing device in an embodiment of the present application is provided, and the vehicle fault processing device 200 includes:

[0119] A first acquisition unit 210 is used to acquire the running status data of the vehicle;

[0120] A fault detection unit 220, configured to perform fault detection according to the operating status data to obtain vehicle fault data;

[0121] A fault analysis unit 230 is used to generate a fault tree according to the vehicle fault data, and analyze the antecedent causes of the vehicle fault according to the fault tree to obtain an antecedent cause analysis result;

[0122] The fault processing unit 240 is used to determine a vehicle fault processing scheme according to the vehicle fault data and the fault antecedent analysis result, and use the fault processing scheme to process the vehicle fault.

[0123] In some embodiments of the present application, the fault detection unit 220 is specifically used to: preprocess the operating status data to obtain preprocessed operating status data; perform fault detection on the preprocessed operating status data using a preset fault detection strategy to obtain vehicle fault data; wherein the preset fault detection strategy includes at least one of an abnormal pattern recognition strategy and an abnormal value recognition strategy, and the vehicle fault data includes at least one of a fault occurrence time, a fault occurrence location, a fault type, and a fault severity level.

[0124] In some embodiments of the present application, the fault analysis unit 230 is specifically used to: obtain vehicle fault data within a preset time period before and after the fault occurrence time based on the fault occurrence time in the vehicle fault data; determine multiple possible fault antecedents corresponding to the vehicle's current fault based on the vehicle fault data within a preset time period before and after the fault occurrence time; generate a fault tree corresponding to the vehicle's current fault based on multiple possible fault antecedents corresponding to the vehicle's current fault and the causal relationship between the multiple possible fault antecedents.

[0125] In some embodiments of the present application, the fault analysis unit 230 is specifically used to: analyze the fault tree layer by layer to determine the fundamental fault antecedent corresponding to the current fault of the vehicle; generate a fault antecedent analysis result based on the fundamental fault antecedent and display it to the user.

[0126] In some embodiments of the present application, the fault tree corresponding to the current fault of the vehicle includes multiple fault levels, each of the fault levels includes multiple fault antecedent nodes, and the fault analysis unit 230 is specifically used to: respectively determine the fault severity level corresponding to each of the fault antecedent nodes; determine the fundamental fault antecedent corresponding to the current fault of the vehicle according to the fault severity level corresponding to each of the fault antecedent nodes.

[0127] In some embodiments of the present application, the fault analysis unit 230 is specifically used to: obtain vehicle fault data corresponding to each of the fault antecedent nodes; determine the fault severity level corresponding to each of the fault antecedent nodes according to the vehicle fault data corresponding to each of the fault antecedent nodes and preset fault severity level judgment conditions; determine the fault antecedent with the highest fault severity level according to the fault severity level corresponding to each of the fault antecedent nodes; and use the fault antecedent with the highest fault severity level as the fundamental fault antecedent corresponding to the current fault of the vehicle.

[0128] In some embodiments of the present application, the vehicle fault handling device also includes: a second acquisition unit, used to determine the vehicle fault handling solution based on the vehicle fault data and the fault antecedent analysis result, and obtain the vehicle fault handling result after using the fault handling solution to handle the vehicle fault; an optimization unit, used to optimize the preset fault detection strategy and / or fault handling solution when the fault handling result is a processing failure.

[0129] It can be understood that the above-mentioned vehicle fault handling device can implement the various steps of the vehicle fault handling method provided in the aforementioned embodiment, and the relevant explanations about the vehicle fault handling method are applicable to the vehicle fault handling device and will not be repeated here.

[0130] Figure 3 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present application. Figure 3 At the hardware level, the electronic device includes a processor, and optionally also includes an internal bus, a network interface, and a memory. The memory may include a memory, such as a high-speed random access memory (RAM), and may also include a non-volatile memory (non-volatile memory), such as at least one disk storage. Of course, the electronic device may also include hardware required for other services.

[0131] The processor, network interface and memory can be interconnected through an internal bus, which can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0132] The memory is used to store the program. Specifically, the program may include a program code, and the program code includes a computer operation instruction. The memory may include a memory and a non-volatile memory, and provides instructions and data to the processor.

[0133] The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it, forming a vehicle fault processing device at the logical level. The processor executes the program stored in the memory and is specifically used to perform the following operations:

[0134] Obtain vehicle operating status data;

[0135] Perform fault detection according to the operating status data to obtain vehicle fault data;

[0136] Generate a fault tree according to the vehicle fault data, and analyze the antecedent causes of the vehicle fault according to the fault tree to obtain an antecedent cause analysis result;

[0137] A vehicle fault handling solution is determined based on the vehicle fault data and the fault antecedent analysis result, and the vehicle fault is handled using the fault handling solution.

[0138] The above application Figure 1 The method performed by the vehicle fault processing device disclosed in the illustrated embodiment can be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in the processor or an instruction in the form of software. The above processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in the embodiments of the present application can be directly embodied as being executed by a hardware decoding processor, or being executed by a combination of hardware and software modules in a decoding processor. The software module can be located in a storage medium mature in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0139] The present application also provides a computer program product, which stores one or more programs, wherein the one or more programs include instructions, which, when executed by an electronic device including multiple application programs, enable the electronic device to execute Figure 1The method executed by the vehicle fault processing device in the illustrated embodiment is specifically used to execute:

[0140] Obtain vehicle operating status data;

[0141] Perform fault detection according to the operating status data to obtain vehicle fault data;

[0142] Generate a fault tree according to the vehicle fault data, and analyze the antecedent causes of the vehicle fault according to the fault tree to obtain an antecedent cause analysis result;

[0143] A vehicle fault handling solution is determined based on the vehicle fault data and the fault antecedent analysis result, and the vehicle fault is handled using the fault handling solution.

[0144] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0145] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0146] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0147] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0148] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0149] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0150] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0151] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0152] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0153] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A vehicle fault handling method, wherein: The vehicle fault processing method comprises: Obtain vehicle operating status data; Perform fault detection according to the operating status data to obtain vehicle fault data; Generate a fault tree according to the vehicle fault data, and analyze the antecedent causes of the vehicle fault according to the fault tree to obtain an antecedent cause analysis result; A vehicle fault handling solution is determined based on the vehicle fault data and the fault antecedent analysis result, and the vehicle fault is handled using the fault handling solution.

2. The vehicle fault handling method according to claim 1, wherein: The performing fault detection according to the running status data to obtain vehicle fault data comprises: Preprocessing the operating status data to obtain preprocessed operating status data; Performing fault detection on the preprocessed running status data using a preset fault detection strategy to obtain vehicle fault data; Among them, the preset fault detection strategy includes at least one of an abnormal pattern recognition strategy and an abnormal value recognition strategy, and the vehicle fault data includes at least one of a fault occurrence time, a fault occurrence location, a fault type and a fault severity level.

3. The vehicle fault handling method according to claim 1, wherein: Generating a fault tree according to the vehicle fault data comprises: Based on the fault occurrence time in the vehicle fault data, obtaining vehicle fault data within a preset time period before and after the fault occurrence time; Determine multiple possible fault antecedents corresponding to the current fault of the vehicle according to the vehicle fault data within a preset time period before and after the fault occurs; A fault tree corresponding to the current fault of the vehicle is generated according to multiple possible fault antecedents corresponding to the current fault of the vehicle and the causal relationship between the multiple possible fault antecedents.

4. The vehicle fault handling method according to claim 1, wherein: Analyzing the antecedent causes of vehicle failures according to the fault tree to obtain an antecedent cause analysis result includes: Analyze the fault tree layer by layer to determine the fundamental fault antecedent cause corresponding to the current fault of the vehicle; A fault antecedent cause analysis result is generated according to the fundamental fault antecedent cause and displayed to the user.

5. The vehicle fault handling method according to claim 4, wherein: The fault tree corresponding to the current fault of the vehicle includes multiple fault levels, each of which includes multiple fault antecedent nodes. The fault tree is analyzed layer by layer to determine the fundamental fault antecedent corresponding to the current fault of the vehicle, including: Determine the fault severity level corresponding to each of the fault antecedent nodes respectively; The fundamental fault antecedent cause corresponding to the current fault of the vehicle is determined according to the fault severity level corresponding to each of the fault antecedent cause nodes.

6. The vehicle fault handling method according to claim 5, wherein: The respectively determining the fault severity level corresponding to each of the fault antecedent nodes comprises: Obtaining vehicle fault data corresponding to each of the fault antecedent nodes; Determine the fault severity level corresponding to each of the fault antecedent cause nodes according to the vehicle fault data corresponding to each of the fault antecedent cause nodes and the preset fault severity level judgment condition; The determining of the fundamental fault antecedent cause corresponding to the current fault of the vehicle according to the fault severity level corresponding to each of the fault antecedent cause nodes includes: Determine the fault antecedent with the highest fault severity level according to the fault severity level corresponding to each of the fault antecedent cause nodes; The fault antecedent cause with the highest fault severity level is taken as the fundamental fault antecedent cause corresponding to the current fault of the vehicle.

7. The vehicle fault handling method according to claim 1, wherein: After determining a vehicle fault handling solution according to the vehicle fault data and the fault antecedent analysis result, and handling the vehicle fault using the fault handling solution, the vehicle fault handling method further includes: Obtain vehicle fault handling results; When the fault handling result is a handling failure, the preset fault detection strategy and / or fault handling solution is optimized.

8. A vehicle fault handling device, wherein: The vehicle fault processing device comprises: A first acquisition unit, used to acquire running status data of the vehicle; A fault detection unit, used to perform fault detection according to the running status data to obtain vehicle fault data; A fault analysis unit, used to generate a fault tree according to the vehicle fault data, and analyze the antecedent causes of the vehicle fault according to the fault tree to obtain an antecedent cause analysis result; The fault processing unit is used to determine a vehicle fault processing scheme according to the vehicle fault data and the fault antecedent analysis result, and use the fault processing scheme to process the vehicle fault.

9. An electronic device, comprising: processor; as well as A memory arranged to store computer executable instructions, wherein when the executable instructions are executed, the processor executes the vehicle fault processing method according to any one of claims 1 to 7.

10. A computer program product, comprising a computer program or an instruction, wherein when the computer program or the instruction is executed by a processor, the vehicle fault handling method according to any one of claims 1 to 7 is implemented.

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