Vehicle fault diagnosis method and related device

Abstract

The invention provides a vehicle fault diagnosis method and a related device. The method comprises the following steps: acquiring first running state data and environment data of a target vehicle within a preset time period; according to the environment data, to-be-diagnosed parts of the vehicle are determined, a plurality of to-be-diagnosed parts are obtained, and the to-be-diagnosed parts are functional parts associated with the environment and the operation condition in the vehicle; according to the first operation state data and the environment data, determining operation state data corresponding to the plurality of to-be-diagnosed components, and obtaining a plurality of second operation state data; determining fault risk scores corresponding to the plurality of to-be-diagnosed components according to the plurality of second operation state data to obtain a plurality of fault risk scores; determining a plurality of fault diagnosis results according to the plurality of fault risk scores and the plurality of to-be-diagnosed components; and generating a fault diagnosis report according to the plurality of fault diagnosis results. Through the fact analysis of the environmental data, the accuracy of vehicle fault diagnosis in a complex scene can be realized.

Description

Technical Field

[0001] This application relates to the field of automotive diagnostic technologies, and particularly to a vehicle fault diagnosis method and related devices. Background Art

[0002] With the rapid development of the automotive industry, the safety and reliability of vehicle systems have become the focus of user attention. Existing vehicle fault diagnosis technologies are usually based on regular inspections and repairs of vehicles. However, this method has the defect of being unable to monitor the running state of vehicles in real time, making it difficult to discover potential safety hazards in a timely manner. In addition, current detection schemes for vehicle running states usually collect running data based on in-vehicle sensor arrays and analyze the running data. This method has problems with insufficient real-time analysis and perception of environmental data, resulting in inaccurate diagnosis and early warning in real-time vehicle diagnosis in complex scenarios.

[0003] Therefore, how to improve the accuracy of vehicle fault diagnosis urgently needs to be solved. Summary of the Invention

[0004] Embodiments of this application provide a vehicle fault diagnosis method and related devices, which solve the problem of inaccurate diagnosis in real-time vehicle diagnosis in complex scenarios and improve the accuracy of vehicle fault diagnosis.

[0005] In a first aspect, embodiments of this application provide a vehicle fault diagnosis method, and the method includes:

[0006] Obtain first running state data and environmental data of a target vehicle within a preset time period;

[0007] Determine components to be diagnosed of the target vehicle according to the environmental data, and obtain a plurality of components to be diagnosed; the components to be diagnosed are functional components in the vehicle that are associated with the environment and running conditions;

[0008] Determine running state data corresponding to the plurality of components to be diagnosed according to the first running state data and the environmental data, and obtain a plurality of second running state data;

[0009] Determine fault risk scores corresponding to the plurality of components to be diagnosed according to the plurality of second running state data, and obtain a plurality of fault risk scores;

[0010] Determine a plurality of fault diagnosis results according to the plurality of fault risk scores and the plurality of components to be diagnosed;

[0011] Generate a fault diagnosis report according to the plurality of fault diagnosis results.

[0012] In a second aspect, embodiments of this application provide a vehicle fault diagnosis device, which is applied to an electronic device, and the device includes:

[0013] An acquisition unit, configured to acquire first operation state data and environment data of a target vehicle within a preset time period;

[0014] A determination unit, configured to determine components to be diagnosed of the target vehicle according to the environment data, and obtain a plurality of components to be diagnosed; the components to be diagnosed are functional components in the vehicle that are associated with the environment and operating conditions;

[0015] The determination unit is further configured to determine operation state data corresponding to the plurality of components to be diagnosed according to the first operation state data and the environment data, and obtain a plurality of second operation state data;

[0016] A calculation unit, configured to determine fault risk scores corresponding to the plurality of components to be diagnosed according to the plurality of second operation state data, and obtain a plurality of fault risk scores;

[0017] A control unit, configured to determine a plurality of fault diagnosis results according to the plurality of fault risk scores and the plurality of components to be diagnosed;

[0018] The control unit is further configured to generate a fault diagnosis report according to the plurality of fault diagnosis results.

[0019] In a third aspect, an embodiment of the present application provides an electronic device, including a processor, a memory, a communication interface, and one or more programs, where the one or more programs are stored in the memory and are configured to be executed by the processor, and the programs include instructions for executing the steps in any method of the first aspect of the embodiments of the present application.

[0020] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, where the computer-readable storage medium stores a computer program for electronic data exchange, and the computer program enables a computer to execute some or all of the steps described in any method of the first aspect of the embodiments of the present application.

[0021] In a fifth aspect, an embodiment of the present application provides a computer program product, where the computer program product includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to enable a computer to execute some or all of the steps described in any method of the first aspect of the embodiments of the present application. The computer program product may be a software installation package.

[0022] By implementing the embodiments of the present application, the following beneficial effects are achieved:

[0023] A vehicle fault diagnosis method described in this application is applied to an electronic device. By obtaining the first operating state data and environmental data of a target vehicle within a preset time period, the components to be diagnosed of the target vehicle are determined according to the environmental data, and multiple components to be diagnosed are obtained. Among them, the components to be diagnosed are functional components in the vehicle that are associated with the environment and operating conditions. Then, according to the first operating state data and the environmental data, the operating state data corresponding to the multiple components to be diagnosed are determined, and multiple second operating state data are obtained. Then, according to the multiple second operating state data, the fault risk scores corresponding to the multiple components to be diagnosed are determined, and multiple fault risk scores are obtained. Finally, according to the multiple fault risk scores and the multiple components to be diagnosed, multiple fault diagnosis results are determined, and a fault diagnosis report is generated according to the multiple fault diagnosis results. In this way, by real-time detecting the vehicle operating state data, such as speed, engine temperature, brake condition, etc., and at the same time considering the environmental data, such as temperature, humidity, road condition, etc., and real-time analyzing the vehicle operating state data and environmental data for vehicle fault diagnosis, it solves the problem of only analyzing the vehicle operating state data in vehicle fault diagnosis and improves the accuracy of vehicle fault diagnosis. Brief Description of the Drawings

[0024] In order to more clearly illustrate the technical solutions of the embodiments of this application, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are some embodiments of this application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0025] Figure 1 is a system architecture diagram of a vehicle fault diagnosis method provided by an embodiment of this application;

[0026] Figure 2 is a schematic structural diagram of an electronic device provided by an embodiment of this application;

[0027] Figure 3 is a schematic flowchart of a vehicle fault diagnosis method provided by an embodiment of this application;

[0028] Figure 4 is a schematic flowchart of another vehicle fault diagnosis method provided by an embodiment of this application;

[0029] Figure 5 is an interface display diagram of a vehicle fault diagnosis provided by an embodiment of this application;

[0030] Figure 6 is another interface display diagram of a vehicle fault diagnosis provided by an embodiment of this application;

[0031] Figure 7It is an interface display diagram of the operating conditions of a vehicle provided by an embodiment of the present application;

[0032] Figure 8 It is a block diagram of the functional units of a vehicle fault diagnosis device provided by an embodiment of the present application. Detailed implementation manners

[0033] In order to enable those skilled in the art to better understand the solutions of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of the present application.

[0034] The terms "first", "second", etc. in the specification, claims and above-mentioned drawings of the present application are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally further includes steps or units not listed, or optionally further includes other steps or units inherent to these processes, methods, products or devices.

[0035] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B may represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this article indicates that the associated objects before and after are in an "or" relationship. The "multiple" that appears in the embodiments of the present application refers to two or more.

[0036] The "at least one (piece)" or its similar expression in the embodiments of the present application refers to any combination of these items, including any combination of single item (piece) or plural items (pieces), referring to one or more, and multiple refers to two or more. For example, at least one (piece) of a, b or c can represent the following seven situations: a, b, c, a and b, a and c, b and c, a, b and c. Each of a, b, and c can be an element or a set containing one or more elements.

[0037] The "connection" that appears in the embodiments of the present application refers to various connection methods such as direct connection or indirect connection to achieve communication between devices, and the embodiments of the present application do not make any limitations on this.

[0038] References to "embodiments" in this specification mean that a particular feature, structure, or characteristic described in connection with the embodiments can be included in at least one embodiment of the present application. The appearances of this phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are they independent or alternative embodiments mutually exclusive of other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0039] Existing vehicle fault diagnosis technologies are usually based on regular inspections and repairs of vehicles. However, this method has the defect of being unable to monitor the running state of vehicles in real time, making it difficult to discover potential safety hazards in real time. In addition, current detection schemes for vehicle running states usually collect running data based on in-vehicle sensor arrays and analyze the running data. This method has problems with insufficient real-time analysis and perception of environmental data, resulting in inaccurate diagnosis and early warning in real-time vehicle diagnosis in complex scenarios. To solve the above problems, embodiments of the present application provide a vehicle fault diagnosis method and related device, which are applied to an electronic device. By real-time detecting vehicle running state data, such as speed, engine temperature, braking conditions, etc., and simultaneously considering environmental data, such as temperature, humidity, road conditions, etc., and performing real-time analysis on the vehicle running state data and environmental data for vehicle fault diagnosis, it solves the problem of only analyzing vehicle running state data in vehicle fault diagnosis and improves the accuracy of vehicle fault diagnosis.

[0040] The following Figure 1 describes the system architecture of a vehicle fault diagnosis method in an embodiment of the present application. Figure 1 FIG. 10 is a system architecture diagram of a vehicle fault diagnosis method provided by an embodiment of the present application. The vehicle fault diagnosis system 100 includes an electronic device 110 and a vehicle 120. Among them, the electronic device 110 includes: a data acquisition module 111, a control module 112, and a fault diagnosis module 113.

[0041] Among them, the electronic device 110 can be an in-vehicle device or a diagnostic device, which is not limited herein. The electronic device 110 is used to real-time detect the running state of the vehicle 120 and the environmental data during the current running, and dynamically adjust the fault diagnosis threshold according to the environmental data, so as to perform fault diagnosis on the vehicle 120. The electronic device 110 realizes comprehensive fault diagnosis of the vehicle 120 through the collaborative work of the data acquisition module 111, the control module 112, and the fault diagnosis module 113.

[0042] Among them, the data acquisition module 111 is used to collect the operation status data and environmental data of the vehicle 120 in real time. The data acquisition module 111 obtains the operation status data and environmental data of the vehicle 120 in real time through the built-in sensor module and external environmental sensors of the vehicle 120. For example, the speed sensor collects the vehicle speed information, the temperature sensor monitors the engine temperature, the humidity sensor obtains the environmental humidity data, and the road condition sensor identifies the degree of road slipperiness. In addition, the data acquisition module 111 is also responsible for preprocessing the collected data, including data cleaning, normalization, and feature extraction, to ensure the quality and consistency of the data.

[0043] Among them, the control module 112 is used to analyze the environmental data, determine the current meteorological conditions, and determine the components to be diagnosed according to the meteorological conditions. The control module 112 analyzes the current environmental data through the preset mapping relationship between environmental data and meteorological conditions to determine the current meteorological conditions. For example, by analyzing the temperature, humidity, and road slipperiness parameters, the control module 112 can determine that the current meteorological conditions are sunny, rainy, or snowy. Then, the control module 112 determines the components to be diagnosed according to the current meteorological conditions. For example, when the weather is moderate rain or heavy rain, the control module 112 will focus on the operation data of the braking system and tires; when the weather is moderate snow, the control module 112 will focus on the performance of the engine and transmission system.

[0044] Among them, the fault diagnosis module 113 is used to adjust the fault determination threshold of the component to be diagnosed according to the environmental data to obtain the target threshold, and analyze the operation data according to the target threshold to determine whether a fault occurs or there is a fault risk. The fault diagnosis module 113 dynamically adjusts the fault determination threshold according to the environmental data through the preset threshold adjustment rules. For example, under the condition of a slippery road, the fault diagnosis module 113 may increase the tire pressure threshold. The fault diagnosis module 113 analyzes the operation data according to the target threshold to determine whether there is a fault risk in the component to be diagnosed.

[0045] In a possible embodiment, the electronic device 110 can efficiently and accurately complete the vehicle 120 fault diagnosis process. The electronic device 110 collects the operation status data and environmental data of the vehicle 120 in real time through the data acquisition module 111. The control module 112 determines the components to be diagnosed according to the environmental data. The fault diagnosis module 113 dynamically adjusts the fault determination threshold according to the environmental data, and analyzes the operation data according to the target threshold to determine whether there is a fault risk in the component to be diagnosed.

[0046] It can be seen that the vehicle fault diagnosis system 100 effectively improves the accuracy and timeliness of fault diagnosis by real-time monitoring of the vehicle operation status and environmental data and dynamically adjusting the fault diagnosis threshold in combination with environmental factors.

[0047] The following combinesFigure 2 Describe the electronic device in the embodiments of the present application. Figure 2 As shown in the following figure, it is a schematic structural diagram of an electronic device provided by an embodiment of the present application. Figure 2 As shown, the electronic device 200 includes one or more processors 210, a memory 220, a communication interface 230, and one or more programs 221. The processor 210 is communicatively connected to the memory 220 and the communication interface 230 through an internal communication bus.

[0048] Among them, the processor 210 is mainly used for:

[0049] Obtain the first operating state data and environmental data of the target vehicle within a preset time period;

[0050] Determine the components to be diagnosed of the target vehicle according to the environmental data, and obtain a plurality of components to be diagnosed; the components to be diagnosed are functional components in the vehicle that are associated with the environment and operating conditions;

[0051] Determine the operating state data corresponding to the plurality of components to be diagnosed according to the first operating state data and the environmental data, and obtain a plurality of second operating state data;

[0052] Determine the failure risk scores corresponding to the plurality of components to be diagnosed according to the plurality of second operating state data, and obtain a plurality of failure risk scores;

[0053] Determine a plurality of fault diagnosis results according to the plurality of failure risk scores and the plurality of components to be diagnosed;

[0054] Generate a fault diagnosis report according to the plurality of fault diagnosis results.

[0055] Among them, the processor 210 may be a central processing unit (CPU), a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute various exemplary logical blocks, units, and circuits described in connection with the disclosure of the present application. The processor may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, and so on. The communication unit may be a communication interface, a transceiver, a transceiver circuit, etc., and the storage unit may be a memory.

[0056] Among them, the memory 220 can be a volatile memory or a non-volatile memory, or can include both volatile and non-volatile memories. Among them, the non-volatile memory can be a read-only memory (ROM), a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), or a flash memory. The volatile memory can be a random access memory (RAM), which is used as an external cache. By way of example but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchlink DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0057] Among them, the one or more programs 221 are stored in the above-mentioned memory 220 and are configured to be executed by the above-mentioned processor 210. The one or more programs 221 include instructions for executing any step in the following embodiments of a data processing method.

[0058] It can be understood that the electronic device 200 may include more or fewer structural elements than those in the above structural block diagram. For example, it includes a power module, physical buttons, a Wi-Fi module, a speaker, a Bluetooth module, sensors, a display module, etc., which are not limited herein. It can be understood that the electronic device 200 can be equipped with a system architecture such as Figure 1 described in a vehicle fault diagnosis method.

[0059] After understanding the software and hardware architecture of the present application, the following will be combined with Figure 3 to describe a vehicle fault diagnosis method in an embodiment of the present application. Figure 3 is a schematic flowchart of a vehicle fault diagnosis method provided by an embodiment of the present application, which specifically includes the following steps:

[0060] Step S310: Obtain the first operating state data and environmental data of the target vehicle within a preset time period.

[0061] Among them, the first operating state data is real-time data generated during the vehicle's operation, including vehicle speed, engine temperature, brake status, tire pressure, engine speed, etc., which are not limited here. This data is collected in real time through the vehicle's built-in sensor module and can reflect the operating conditions of various vehicle components. The environmental data refers to the external environmental information where the vehicle is located, including temperature, humidity, road conditions (such as wetness, road surface flatness), etc., which are not limited here. Among them, the environmental data can be obtained through the environmental sensors outside the vehicle or through external devices (such as weather stations, road monitoring systems) connected to the vehicle through the network.

[0062] Specifically, when the vehicle enters the fault diagnosis process, the vehicle's built-in sensor module starts to collect the first operating state data in real time. For example, the speed sensor collects vehicle speed information, the temperature sensor monitors the engine temperature, the humidity sensor obtains environmental humidity data, and the road condition sensor identifies the wetness of the road, etc. At the same time, the vehicle conducts data interaction with external environmental monitoring devices or cloud servers through the vehicle-mounted communication module (such as CAN bus, 4G / 5G module) to obtain real-time environmental data. The collected data is encapsulated and transmitted through a preset communication protocol to ensure the integrity and accuracy of the data, and the data is preliminarily cleaned and calibrated to eliminate outliers or noise data to improve the accuracy of subsequent analysis.

[0063] It should be noted that during the data collection process, some special situations may affect the data collection and processing. For example, sensor failures, communication signal interruptions, missing environmental data, etc. When such situations occur, the backup data source will be automatically activated or the data interpolation algorithm will be used to complete the data, ensuring the continuity of the diagnosis process. In addition, the system will also perform intelligent prediction and correction on the missing or abnormal data according to historical data and vehicle operation rules to further improve the reliability of the data. If the problem cannot be solved through automatic processing, the system will prompt the user for manual intervention, such as checking the sensor connection, restarting the communication module, etc., to ensure the integrity and accuracy of the data collection.

[0064] Step S320: Determine the components to be diagnosed of the target vehicle according to the environmental data to obtain multiple components to be diagnosed; the components to be diagnosed are functional components in the vehicle that are associated with the environment and operating conditions.

[0065] Among them, the environmental data includes temperature, humidity, road conditions (such as slipperiness, road surface flatness), etc. These data directly affect the operating status and performance of various vehicle components. For example, a high-temperature environment may cause the engine to overheat, a low-temperature environment may affect the battery performance, and a slippery road may increase the load on the braking system. The components to be diagnosed refer to those vehicle functional components that are vulnerable to influence under specific environmental conditions, such as the braking system, tires, engine, transmission system, etc. By analyzing the environmental data, the system can intelligently identify the components that may face a relatively high risk of failure in the current environment, thereby achieving targeted fault diagnosis.

[0066] Among them, the process of determining the components to be diagnosed is based on a preset association rule between environmental data and vehicle components. This association rule can be obtained through a large amount of experimental data and statistical analysis, or can be predicted according to machine learning algorithms, which is not limited here. It reflects the fault probability and performance change rules of various vehicle components under different environmental conditions. For example, under rainy or slippery road conditions, the risk of failure of the braking system and tires increases significantly; in a low-temperature environment, the starting performance of the battery and engine may be affected.

[0067] Specifically, first, preprocess the environmental data, including data cleaning, normalization, and feature extraction. Then, input the preprocessed data into a preset association model between environmental data and components. This model can be a rule-based system, a machine learning-based classification model, or a deep learning-based model, which is not limited again. Then, the model outputs a list of vehicle components that may be affected under the current environmental conditions, that is, the components to be diagnosed. Finally, the list of components to be diagnosed will be passed to the subsequent fault diagnosis module for further analysis and processing.

[0068] In a possible embodiment, determining the components to be diagnosed of the target vehicle according to the environmental data, obtaining a plurality of components to be diagnosed, specifically includes the following steps:

[0069] Step 321, determine the temperature and humidity parameters and the road slipperiness parameter in the environmental data;

[0070] Step 322, determine the corresponding meteorological condition of the target vehicle within the preset time period according to the road slipperiness parameter and the temperature and humidity parameters, and obtain the target meteorological condition;

[0071] Step 323, determine the functional components associated with the operating condition in the target vehicle as the components to be diagnosed according to the target meteorological condition, and obtain the plurality of components to be diagnosed.

[0072] Among them, the temperature and humidity parameters refer to the temperature and humidity data of the environment where the vehicle is located, usually collected by temperature sensors and humidity sensors outside the vehicle. The road slipperiness parameter refers to the slipperiness degree of the road on which the vehicle is traveling, usually obtained through in-vehicle road condition sensors or external road monitoring systems connected to the vehicle. In addition, the collection of temperature and humidity parameters and road slipperiness parameters follows specific data formats and communication protocols to ensure the accuracy and consistency of the data. The data format includes the encoding method of the data, the definition of data bits, the verification rules, etc. For example, temperature data is usually in degrees Celsius, humidity data is in percentage, and the road slipperiness parameter is represented by the output value of a specific sensor (e.g., 0 means dry, 1 means slippery). The verification rules are used to detect whether errors occur during data transmission. These verification methods can be parity check, cyclic redundancy check (CRC), etc., which are not limited here. At the same time, the frequency and accuracy of data collection are also set according to the vehicle type and diagnostic requirements to ensure the timeliness and reliability of the data.

[0073] Among them, the target meteorological condition refers to the specific weather conditions at the geographical location where the vehicle is located within a preset time period, such as sunny, rainy, snowy, etc. The determination of the meteorological condition is based on the comprehensive analysis of the temperature and humidity parameters and the road slipperiness parameter. For example, when the humidity parameter is high and the road slipperiness parameter shows that the road surface is slippery, it can be judged that the current meteorological condition is rainy; when the temperature parameter is low and the humidity parameter is high, the system can judge that the current meteorological condition is snowy. The determination process of the meteorological condition can be based on a preset mapping model between environmental data and meteorological conditions. This model is trained with historical environmental data and meteorological data and can predict the meteorological condition according to the current environmental parameters. For example, the model can analyze the temperature, humidity, and road slipperiness parameters through neural network algorithms and output the probability distribution of the current meteorological condition. The model will select the meteorological condition with the highest probability as the target meteorological condition. In addition, the meteorological condition can also be obtained from the server through the vehicle network for the current weather condition.

[0074] Specifically, first, analyze the target meteorological condition to determine its potential impact on each component of the vehicle. Then, according to the preset association rules, screen out the components to be diagnosed that are highly relevant to the current meteorological condition. Finally, pass the list of components to be diagnosed to the subsequent fault diagnosis module for further analysis and processing. Among them, the process of determining the components to be diagnosed includes the following steps: query the preset association database between meteorological conditions and vehicle components according to the target meteorological condition to obtain a list of potential faulty components related to this meteorological condition. Then, according to the current vehicle operation state data, further screen out the components that may actually be affected. For example, if the operation data of the braking system shows that the brake pads are severely worn, the system will list it as a component to be diagnosed; if the operation data of the tire shows that the tire pressure is abnormal, the system will list it as a component to be diagnosed.

[0075] In a possible embodiment, determining the corresponding meteorological condition of the target vehicle within the preset time period according to the road slipperiness parameter and the temperature and humidity parameter to obtain a target meteorological condition specifically includes the following steps:

[0076] Step 3221, obtain historical environmental data;

[0077] Step 3222, construct a relationship prediction model between environmental data and meteorological conditions according to the historical environmental data to obtain a target meteorological model;

[0078] Step 3223, input the road slipperiness parameter and the temperature and humidity parameter into the target meteorological model for prediction to obtain a target meteorological condition.

[0079] Among them, historical environmental data refers to the environmental data collected by the vehicle in the past period of time, including temperature, humidity, road slipperiness parameter, etc. These data are usually stored in the vehicle's data storage module or cloud server and are used to train and optimize the meteorological condition prediction model. The collection of historical environmental data follows specific data formats and communication protocols to ensure the accuracy and consistency of the data. The data format includes the data encoding method, the definition of data bits, the verification rules, etc. The verification rules are used to detect whether errors occur during data transmission. Common verification methods include parity check, CRC check, etc.

[0080] Among them, the prediction model of the relationship between environmental data and meteorological conditions refers to a model constructed through machine learning algorithms or deep learning methods. It can predict the current meteorological conditions (such as sunny, rainy, snowy) based on environmental data (such as temperature, humidity, road slipperiness parameters, etc.). The construction of this model is based on historical environmental data and corresponding meteorological condition labels. Among them, temperature, humidity, and road slipperiness parameters in historical environmental data are used as features, and meteorological conditions (such as sunny, rainy, snowy) are used as labels. Through training, the model can learn the complex relationship between environmental parameters and meteorological conditions, so as to achieve accurate meteorological condition prediction. In the process of constructing the model, first, weather types are extracted from historical environmental data to obtain multiple weather types, and these weather types are used as labels for historical environmental data. Then, the historical environmental data is normalized, including data cleaning, normalization, and feature extraction, to obtain the target historical environmental data. Next, the target historical environmental data is divided into a training set and a test set. The training set is used for model training, and the test set is used for model verification. Finally, based on a preset neural network model, the training set is trained to obtain an initial meteorological model, and the model is verified and optimized through the test set to obtain the target meteorological model. The preset neural network model can be a Long Short-Term Memory (LSTM) model, a Recurrent Neural Network model, or a Gated Recurrent Unit (GRU) neural network, which is not limited here.

[0081] Specifically, first, historical environmental data including temperature, humidity, road slipperiness parameters, etc. is obtained from the vehicle's data storage module or the cloud server. Then, the historical environmental data is normalized to obtain the target historical environmental data. Next, the target historical environmental data is divided into a training set and a test set. Based on a preset neural network model, the training set is trained to obtain an initial meteorological model and the initial meteorological model is verified and optimized through the test set to obtain the target meteorological model. Finally, the current environmental parameters (such as temperature, humidity, road slipperiness parameters) are input into the target meteorological model for prediction to obtain the target meteorological conditions.

[0082] It should be noted that the prediction results of the target meteorological model may be uncertain. For example, there may be noise or errors in the environmental data, resulting in inaccurate meteorological condition prediction. To improve the prediction accuracy, the prediction results will be verified and corrected twice by combining historical environmental data and vehicle operation status data. In addition, the model also supports the manual intervention function, allowing users to manually adjust the prediction results according to actual needs to ensure the reliability and practicality of meteorological condition judgment.

[0083] In a possible embodiment, constructing a prediction model for the relationship between environmental data and meteorological conditions based on the historical environmental data to obtain a target meteorological model specifically includes the following steps:

[0084] Step A1: Extract weather types from the historical environmental data to obtain a plurality of weather types, and use the plurality of weather types as labels for the historical environmental data;

[0085] Step A2: Perform normalization processing on the historical environmental data to obtain target historical environmental data; each weather type in the target historical environmental data corresponds to an environmental data;

[0086] Step A3: Divide the target historical environmental data to obtain a training set and a test set;

[0087] Step A4: Train the training set based on a preset neural network model to obtain a first meteorological model;

[0088] Step A5: Verify the first meteorological model based on the test set according to a preset evaluation index to obtain a first evaluation result;

[0089] Step A6: Optimize the first meteorological model according to the first evaluation result to obtain a target meteorological model.

[0090] Among them, the weather type refers to the specific meteorological conditions of the environment where the vehicle is located, such as sunny, rainy, snowy, etc. The extraction of the weather type is based on key features such as temperature, humidity, and road slipperiness parameters in the historical environmental data. The extraction process of the weather type is based on a preset mapping rule between environmental data and weather types. This rule is obtained through a large amount of experimental data and statistical analysis, reflecting the corresponding relationship between different environmental parameters and weather types, or can be determined according to artificial intelligence algorithms.

[0091] Among them, the normalization process refers to cleaning, normalizing, and feature extraction of historical environmental data to ensure data quality and consistency. Data cleaning includes operations such as removing outliers and filling in missing values; normalization is to scale the data to a unified range, for example, normalizing temperature data to between 0 and 1; feature extraction is to extract features useful for weather condition prediction from the original data, such as temperature, humidity, road slipperiness parameters, etc. The historical environmental data after normalization is called the target historical environmental data, and each weather type corresponds to an environmental data. During the normalization process, first, the historical environmental data is cleaned to remove outliers or noisy data. For example, when the temperature data exceeds a reasonable range (such as -50°C to 50°C), the system marks it as an outlier and processes it. Then, the cleaned data is normalized, scaling data such as temperature, humidity, and road slipperiness parameters to a unified range. Finally, the system extracts features useful for weather condition prediction from the normalized data to obtain the target historical environmental data.

[0092] Among them, the division of the training set and the test set is to ensure the independence and reliability of the model training and verification processes. The training set is used to train the prediction model for the relationship between environmental data and weather conditions, and the test set is used to verify the prediction performance of the model. Usually, the training set accounts for 70%-80% of the target historical environmental data, and the test set accounts for 20%-30%. The division of the training set and the test set should ensure that each weather type has sufficient samples in both the training set and the test set to avoid overfitting or underfitting of the model.

[0093] Among them, the neural network model refers to a machine learning algorithm used to build a prediction model for the relationship between environmental data and weather conditions. The neural network model can learn the complex relationship between environmental parameters and weather conditions, thereby achieving accurate weather condition prediction. Commonly used neural network models include multi-layer perceptrons, convolutional neural networks, and recurrent neural networks, etc. The training process of the neural network model includes forward propagation and backward propagation. By continuously adjusting the model parameters, the prediction results of the model are made as close as possible to the actual weather type. Among them, the evaluation metrics refer to the metrics used to measure the prediction performance of the model, such as accuracy, precision, recall, and F1 score, etc. Accuracy refers to the proportion of samples correctly predicted by the model in the total samples; precision refers to the proportion of samples actually of a certain weather type among the samples predicted by the model as that weather type; recall refers to the proportion of samples actually of a certain weather type that are correctly predicted by the model; the F1 score is the harmonic mean of precision and recall. The selection of evaluation metrics should be determined according to the specific application scenario and requirements.

[0094] Specifically, first, weather types are extracted from historical environmental data to obtain multiple weather types, and these weather types are used as labels for the historical environmental data. Then, the historical environmental data is normalized to obtain target historical environmental data. Next, the target historical environmental data is divided into a training set and a test set, and the training set is trained based on a preset neural network model to obtain a first meteorological model. The first meteorological model is verified through the test set to obtain a first evaluation result. Finally, the system optimizes the first meteorological model according to the first evaluation result to obtain a target meteorological model.

[0095] In a possible embodiment, the step of determining the functional components associated with the operating conditions in the target vehicle as the components to be diagnosed according to the target meteorological condition to obtain the multiple components to be diagnosed specifically includes the following steps:

[0096] Step 3231, query the vehicle faults associated with the target meteorological condition based on a preset vehicle fault and meteorological association database to obtain multiple potential vehicle faults;

[0097] Step 3232, determine the fault types of the multiple potential vehicle faults to obtain multiple fault types;

[0098] Step 3233, determine the faulty components corresponding to the multiple fault types to obtain multiple faulty components;

[0099] Step 3234, calculate the correlation score between each faulty component in the multiple faulty components and the target meteorological condition to obtain multiple correlation scores;

[0100] Step 3235, determine the faulty components corresponding to the correlation scores greater than a preset first threshold in the multiple correlation scores to obtain the multiple components to be diagnosed.

[0101] Among them, the vehicle fault and meteorological association database refers to a database that stores common vehicle faults under different meteorological conditions. This database is constructed through historical fault data, experimental data, and statistical analysis, reflecting the potential impact of different meteorological conditions on various components of the vehicle. The query process of the vehicle fault and meteorological association database is based on the target meteorological condition. For example, if the target meteorological condition is rainy, the system will query the vehicle faults related to rain in the database, such as brake system failure and tire skidding. The query result is a set of potential vehicle faults that may occur under the current meteorological conditions. The fault type is the specific manifestation form of the vehicle fault, such as brake failure, tire skidding, engine overheating, etc. The determination of the fault type is based on the description information of the potential vehicle fault. The determination process of the fault type is based on a preset fault classification rule, which is obtained through a large amount of experimental data and statistical analysis and reflects the manifestation forms and characteristics of different faults.

[0102] Among them, the faulty components mainly refer to the functional components in the vehicle that may malfunction, such as the braking system, tires, engine, transmission system, etc. The determination of the faulty components is based on the description information of the fault type. For example, if the fault type is "brake failure", the faulty component is the "braking system". Among them, the determination process of the faulty components is based on the preset mapping rules between fault types and components. These rules are obtained through a large amount of experimental data and statistical analysis, reflecting the corresponding relationship between different fault types and vehicle components. For example, brake failure is usually related to the braking system; tire skidding is usually related to the tires. The system determines the specific faulty components according to the description information of the fault type and in combination with the mapping rules.

[0103] Among them, the correlation score refers to the degree of correlation between the faulty component and the target meteorological condition. The higher the score, the greater the probability that the component will malfunction under the current meteorological conditions. The calculation of the correlation score is based on historical fault data and calculated according to a machine learning model. The calculation method of the correlation score includes the following steps: First, extract the fault records related to the target meteorological condition from the historical fault data, and count the fault probability of each faulty component under this meteorological condition. Then, calculate the correlation score of each faulty component based on the fault probability through a machine learning model. The preset first threshold refers to the lowest correlation score used to screen the components to be diagnosed. This threshold is set according to specific application scenarios and requirements, and is usually determined through experimental data and statistical analysis.

[0104] Specifically, first query the vehicle faults associated with the target meteorological condition based on the preset vehicle fault and meteorological association database to obtain multiple potential vehicle faults. Then, determine the fault types of these potential vehicle faults to obtain multiple fault types. Next, determine the faulty components corresponding to these fault types to obtain multiple faulty components and calculate the correlation score between each faulty component and the target meteorological condition to obtain multiple correlation scores. Finally, screen out the faulty components with a correlation score greater than the preset first threshold as the components to be diagnosed.

[0105] Step S330, determine the operating state data corresponding to the multiple components to be diagnosed according to the first operating state data and the environmental data, and obtain multiple second operating state data.

[0106] Among them, the first operating state data refers to the real-time data generated during the vehicle's operation, including: vehicle speed, engine temperature, brake status, tire pressure, engine speed, etc., which are not limited here. This is collected in real time through the vehicle's built-in sensor module and can reflect the operating conditions of various vehicle components. The second operating state data refers to the specific operating state data of the component to be diagnosed under the current environmental conditions. For example, for the braking system, the second operating state data may include brake response time, brake pad wear, brake fluid water content, etc.; for the tires, the second operating state data may include tire pressure, tire temperature, tire tread depth, etc. The determination of the second operating state data is based on the comprehensive analysis of the first operating state data and environmental data. For example, on a rainy day, the system will focus on the operating state data of the braking system and tires; on a snowy day, the system will additionally focus on the operating state data of the engine and transmission system.

[0107] Specifically, first, extract the operating state values corresponding to the components to be diagnosed from the first operating state data. For example, for the braking system, the system extracts data such as brake response time and brake pad wear; for the tires, the system extracts data such as tire pressure and tire temperature. Then, obtain the reference values of these operating state values. For example, for the braking system, set the initial reference threshold to a temperature of 100°C and a pressure of 1500 psi; for the tires, set the initial reference threshold to a tire pressure of 2.5 Bar and a tire temperature of 70°C. Next, determine the influencing factors corresponding to the components to be diagnosed according to the environmental data. For example, if the current environmental temperature is -10°C, the system will adjust the temperature threshold of the braking system from 100°C to 90°C; if the current road condition is slippery, the tire pressure threshold will be increased from 2.5 Bar to 2.7 Bar. Finally, correct the operating state values according to the influencing factors to obtain the second operating state data.

[0108] In a possible embodiment, the determining the operating state data corresponding to the multiple components to be diagnosed according to the first operating state data and the environmental data, and obtaining multiple second operating state data specifically includes the following steps:

[0109] Step 331, determine the operating state values corresponding to the components to be diagnosed in the first operating state data, and obtain multiple operating state values;

[0110] Step 331, obtain the reference values of the multiple operating state values, and obtain multiple first reference values;

[0111] Step 332, determine the influencing factors corresponding to the multiple components to be diagnosed according to the environmental data, and obtain multiple environmental influencing factors;

[0112] Step 333, perform calculations based on the multiple environmental influencing factors and the multiple first reference values to obtain multiple target reference values;

[0113] Step 334, determine the difference between each target reference value among the multiple target reference values and the corresponding operating state value among the multiple operating state values, to obtain multiple first differences;

[0114] Step 335, determine the adjustment parameters corresponding to the multiple first differences according to the mapping relationship between the preset difference and the adjustment parameter, to obtain multiple target adjustment parameters;

[0115] Step 336, adjust the multiple operating state values according to the multiple target adjustment parameters, to obtain multiple second operating state data.

[0116] Among them, the operating state value refers to the specific parameter value of the component to be diagnosed in the current operating state, which is collected in real time by the sensor module built in the vehicle and can reflect the current operating condition of the component to be diagnosed. The determination process of the operating state value is based on the first operating state data, and it is to extract the parameter values related to the component to be diagnosed from the first operating state data. The first reference value refers to the normal operating parameter value of the component to be diagnosed under standard environmental conditions. These reference values are usually obtained based on a large amount of experimental data and statistical analysis, and are used to initially judge whether there is a fault risk for the component to be diagnosed. For example, for the braking system, the initial reference threshold is set to a temperature of 100 °C and a pressure of 1500 psi; for the tire, the initial reference threshold is set to a tire pressure of 2.5 Bar and a tire temperature of 70 °C. The acquisition process of the reference value is based on a preset reference value database, which stores the normal operating parameter values of different vehicle components under standard environmental conditions, and according to the type of the component to be diagnosed, the corresponding reference value is extracted from the database.

[0117] Among them, the environmental impact factor refers to the degree of influence of environmental data on the operating state of the component to be diagnosed. For example, a low-temperature environment may cause the temperature threshold of the braking system to decrease, and a slippery road may cause the tire pressure threshold to increase. The determination of the environmental impact factor is based on environmental data and preset environmental impact rules. The determination process of the environmental impact factor can be as follows: First, analyze the current environmental data, such as temperature, humidity, road slipperiness, etc. Then, according to the preset environmental impact rules, determine the degree of influence of each environmental parameter on the component to be diagnosed. For example, if the current environmental temperature is -10 °C, the system determines that the temperature impact factor of the braking system is -10%. The target reference value refers to the reference value adjusted according to the environmental impact factor. The calculation of the target reference value is based on the first reference value and the environmental impact factor. The adjustment parameter refers to the parameter used to adjust the operating state value. The determination of the adjustment parameter is based on the first difference and the mapping relationship between the preset difference and the adjustment parameter. For example, if the first difference is +5 °C, the system determines that the adjustment parameter is "reduce the temperature"; if the first difference is -0.25 Bar, the system determines that the adjustment parameter is "increase the tire pressure".

[0118] Step S340: Determine the fault risk scores corresponding to the multiple parts to be diagnosed based on the multiple second operating state data, obtaining multiple fault risk scores.

[0119] Among them, the fault risk score refers to the probability or risk level of a part to be diagnosed having a fault under the current operating state. The calculation of the fault risk score is based on the second operating state data and a preset fault risk scoring model. This model is constructed through historical fault data, experimental data, and statistical analysis, and can predict the fault risk of a part to be diagnosed according to its operating state data. For example, if the operating state data of the braking system shows that the brake pads are severely worn, the system may give a relatively high fault risk score; if the operating state data of the tires shows abnormal tire pressure, a relatively high fault risk score may be given. The process of determining the fault risk score may include: First, preprocess the second operating state data, including data cleaning, normalization, and feature extraction. Then, input the preprocessed data into the preset fault risk scoring model for calculation. This model can be a classification model based on machine learning or a scoring model based on statistical analysis, which is not limited here. The model outputs the fault risk score of each part to be diagnosed, and the scoring range is usually from 0 to 100. The higher the score, the greater the fault risk.

[0120] Specifically, the framework of the fault risk scoring model may include: First, perform data preprocessing, clean, normalize, and extract features from the second operating state data to ensure that the format of the input data is consistent with the data format during model training. For example, normalize data such as brake response time and brake pad wear degree to between 0 and 1; then, perform model input, input the preprocessed data into the fault risk scoring model. This model can be a classification model based on machine learning, such as Support Vector Machine (SVM), Random Forest, or Neural Network, or a scoring model based on statistical analysis; then, perform model calculation, and the model calculates the fault risk score of each part to be diagnosed according to the input data; finally, output the score, and the model outputs the fault risk score of each part to be diagnosed, and the scoring range is usually from 0 to 100. The higher the score, the greater the fault risk. In addition, it also includes a fault warning function and gives maintenance suggestions, generates fault warning information according to the fault risk score, and prompts the user to repair or replace the part to be diagnosed in a timely manner. For example, if the fault risk score of the braking system is 80, the system may prompt the user to check the wear condition of the brake pads. In the stage of maintenance suggestions, generate maintenance suggestions according to the fault risk score to help the user formulate a maintenance plan. At the same time, generate a data analysis report according to the fault risk score to help the user understand the health status of each part of the vehicle. For example, the system can generate a fault risk trend chart of the braking system and tires to help the user predict future fault risks.

[0121] In a possible embodiment, determining the fault risk scores corresponding to the multiple components to be diagnosed according to the multiple second operating state data, and obtaining multiple fault risk scores specifically includes the following steps:

[0122] Step 341, preprocess the multiple second operating state data to obtain preprocessed data; the preprocessing includes data normalization;

[0123] Step 342, input the preprocessed data into a preset fault prediction model for calculation to obtain the multiple first fault risk scores; the preset fault prediction model is a pre-trained fault diagnosis model based on vehicle operating state data;

[0124] Step 343, perform normalization processing on the multiple first fault risk scores to obtain multiple fault risk scores.

[0125] Among them, preprocessing refers to cleaning, normalizing, and feature extraction of the second operating state data to ensure the quality and consistency of the data. Data cleaning includes operations such as removing outliers and filling in missing values; normalization is to scale the data to a unified range, for example, normalizing data such as brake response time and brake pad wear degree to between 0 and 1; feature extraction is to extract features useful for fault prediction from the original data, such as brake response time, brake pad wear degree, tire pressure, and tire temperature. The preprocessed data is called preprocessed data, which can improve the accuracy and stability of the fault prediction model.

[0126] Specifically, first preprocess the second operating state data, including data cleaning, normalization, and feature extraction. Then, input the preprocessed data into a preset fault prediction model for calculation. The model can be a classification model based on machine learning or a scoring model based on statistical analysis. The model outputs the first fault risk score of each component to be diagnosed, and the scoring range is usually from 0 to 1, and the higher the score, the greater the fault risk. Finally, the system performs normalization processing on the first fault risk scores, adjusts the scoring range from 0 to 1 to 0 to 100, and obtains multiple fault risk scores.

[0127] It should be noted that there may be uncertainties in the calculation of the fault risk scores. For example, there may be noise or errors in the second operating state data, resulting in inaccurate calculation of the fault risk scores. To improve the accuracy of the scores, the system will perform secondary verification and correction on the fault risk scores by combining historical fault data and vehicle operating rules.

[0128] Step S350, determine multiple fault diagnosis results according to the multiple fault risk scores and the multiple components to be diagnosed.

[0129] Among them, the fault diagnosis result refers to the fault state or risk level of the component to be diagnosed under the current operating state. The determination of the fault diagnosis result is based on the fault risk score and the preset fault diagnosis rules. The process can be as follows: First, compare the fault risk score of each component to be diagnosed with the preset fault diagnosis threshold. For example, if the fault risk score is greater than 80, the system determines that the component has a high-risk fault; if the fault risk score is between 60 and 80, the system determines that the component has a medium-risk fault; if the fault risk score is less than 60, the system determines that the component has a low-risk fault. Then, generate the fault diagnosis result according to the comparison result. For example, if the fault risk score of the braking system is 85, the system generates a diagnosis result of "the braking system has a high-risk fault"; if the fault risk score of the tire is 70, the system generates a diagnosis result of "the tire has a medium-risk fault". Among them, the specific implementation method of the fault diagnosis rules can include: setting the fault diagnosis thresholds of different risk levels according to historical fault data and statistical analysis; then, comparing the fault risk score of each component to be diagnosed with the preset fault diagnosis threshold. Finally, generate the fault diagnosis result according to the comparison result. For example, if the fault risk score of the braking system is 85, the system generates a diagnosis result of "the braking system has a high-risk fault"; if the fault risk score of the tire is 70, the system generates a diagnosis result of "the tire has a medium-risk fault".

[0130] In a possible embodiment, determining multiple fault diagnosis results according to the multiple fault risk scores and the multiple components to be diagnosed specifically includes the following steps:

[0131] Step 351, obtain the reference risk score threshold corresponding to the target component to be diagnosed to obtain the target reference threshold; the target component to be diagnosed is any one of the multiple components to be diagnosed;

[0132] Step 352, determine the fault diagnosis result of the target component to be diagnosed according to the target reference threshold and the target fault risk score; the target fault risk score corresponds to the fault risk score of the target component to be diagnosed.

[0133] Among them, the reference risk score threshold refers to the score threshold used to judge whether the component to be diagnosed has a fault risk. This threshold is set according to the type of the component to be diagnosed and historical fault data, and is usually determined through experimental data and statistical analysis. The process of obtaining the target reference threshold is based on a preset reference threshold database. This database stores the reference thresholds of different vehicle components at different risk levels. The system extracts the corresponding reference threshold from the database according to the type of the target component to be diagnosed.

[0134] Among them, the fault diagnosis result refers to the fault status or risk level of the target component to be diagnosed under the current operating state. The determination of the fault diagnosis result is based on the comparison between the target fault risk score and the target reference threshold. The determination of this fault diagnosis result includes: First, compare the target fault risk score with the target reference threshold. Then, generate a fault diagnosis result according to the comparison result. For example, if the target fault risk score is 85 and the target reference threshold is 80, the system generates a diagnosis result of "the target component to be diagnosed has a high-risk fault"; if the target fault risk score is 70 and the target reference threshold is 60, the system generates a diagnosis result of "the target component to be diagnosed has a medium-risk fault".

[0135] Specifically, first obtain the reference risk score threshold corresponding to the target component to be diagnosed from the preset reference threshold database. For example, if the target component to be diagnosed is the braking system, the system extracts the reference threshold for high-risk faults as 80, the reference threshold for medium-risk faults as 60, and the reference threshold for low-risk faults as 40; if the target component to be diagnosed is the tire, the system extracts the reference threshold for high-risk faults as 70, the reference threshold for medium-risk faults as 50, and the reference threshold for low-risk faults as 30. Then, compare the target fault risk score with the target reference threshold. For example, if the target fault risk score is 85 and the target reference threshold is 80, the system compares it with the reference threshold for high-risk faults; if the target fault risk score is 70 and the target reference threshold is 60, the system compares it with the reference threshold for medium-risk faults. Finally, generate a fault diagnosis result according to the comparison result. For example, if the target fault risk score is 85 and the target reference threshold is 80, a diagnosis result of "the target component to be diagnosed has a high-risk fault" is generated; if the target fault risk score is 70 and the target reference threshold is 60, the system generates a diagnosis result of "the target component to be diagnosed has a medium-risk fault".

[0136] Step S360, generate a fault diagnosis report according to the multiple fault diagnosis results.

[0137] Among them, the fault diagnosis report refers to a report that summarizes and analyzes the fault diagnosis results of each component to be diagnosed in the vehicle. The content of the fault diagnosis report includes the name of the component to be diagnosed, the fault risk score, the fault diagnosis result, the fault risk level, the recommended maintenance measures, etc. The generation of the fault diagnosis report is based on multiple fault diagnosis results and can provide users with a comprehensive analysis of the vehicle's health status and maintenance suggestions. Among them, the specific content of the fault diagnosis report can include the following aspects: information on components to be diagnosed, the fault diagnosis report will list the names and types of all components to be diagnosed, such as the braking system, tires, engine, etc.; fault risk score, the fault diagnosis report will show the fault risk score of each component to be diagnosed, and the score range is usually from 0 to 100. The higher the score, the greater the fault risk; the fault diagnosis result will show the fault diagnosis result of each component to be diagnosed, such as "high-risk fault", "medium-risk fault", "low-risk fault", etc.; the fault diagnosis report will show the fault risk level. According to the fault risk score and the fault diagnosis result, the fault risk level of each component to be diagnosed is determined, such as "high risk", "medium risk", "low risk", etc.; in addition, the fault diagnosis report will also show maintenance suggestions: according to the fault diagnosis result and the fault risk level, maintenance suggestions for each component to be diagnosed are generated, such as "immediately check the braking system", "adjust the tire pressure or replace the tires", etc., and data analysis is carried out on the fault diagnosis results to generate a fault risk trend chart, a fault distribution chart, etc., to help users understand the health status of each component of the vehicle.

[0138] Specifically, first, multiple fault diagnosis results are summarized to generate a preliminary fault diagnosis report. Then, the system analyzes the fault diagnosis results to determine the fault risk level and the recommended maintenance measures for each component to be diagnosed. For example, if the fault diagnosis result of the braking system is "high-risk fault", it may be recommended that the user immediately check the braking system; if the fault diagnosis result of the tires is "medium-risk fault", it may be recommended that the user adjust the tire pressure or replace the tires. Finally, the analysis results and maintenance suggestions are integrated into the fault diagnosis report to generate the final fault diagnosis report.

[0139] For ease of understanding, please refer to Figure 4 , Figure 4It is a schematic flowchart of another vehicle fault diagnosis method provided by an embodiment of the present application. This method aims to improve the accuracy and timeliness of vehicle fault diagnosis by real-time monitoring of vehicle operating status and environmental data and dynamically adjusting the fault diagnosis threshold in combination with environmental factors. During the vehicle fault diagnosis process, the operating status data and environmental data of the target vehicle are collected in real time through the sensor module. The operating status data includes vehicle speed, engine temperature, brake status, etc., and the environmental data includes temperature, humidity, road conditions, etc. Then, the environmental data is analyzed to determine the current meteorological conditions. For example, by analyzing temperature, humidity, and road slipperiness parameters, it can be determined that the current meteorological conditions are sunny, rainy, or snowy. Then, the components to be diagnosed of the target vehicle are determined according to the current meteorological conditions, the operating data of the components to be diagnosed are determined according to the operating status data, and the fault determination threshold of the components to be diagnosed is adjusted according to the environmental data to obtain the target threshold. For example, in a low-temperature environment, the system may lower the temperature threshold of the braking system; in slippery road conditions, the tire pressure threshold may be increased. The adjustment of the target threshold is based on environmental data and preset threshold adjustment rules, which are obtained through experimental data and statistical analysis and can dynamically adjust the fault determination threshold according to environmental conditions, thereby improving the accuracy and timeliness of fault diagnosis. Finally, the operating data is analyzed according to the target threshold to determine whether a fault has occurred or there is a risk of a fault. For example, if the operating data of the braking system shows that the brake response time exceeds the target threshold, the system determines that there is a risk of a fault in the braking system; if the operating data of the tire shows that the tire pressure is lower than the target threshold, the system determines that there is a risk of a fault in the tire. The fault diagnosis result is represented by a fault risk score, and the higher the score, the greater the risk of a fault.

[0140] It can be seen that through Figure 4 the steps provided, the vehicle fault diagnosis process can be completed efficiently and accurately. This method improves the accuracy and timeliness of fault diagnosis effectively by real-time monitoring of vehicle operating status and environmental data and dynamically adjusting the fault diagnosis threshold in combination with environmental factors.

[0141] For ease of understanding, please refer to Figure 5 , Figure 5 which is an interface display diagram of a vehicle fault diagnosis provided by an embodiment of the present application. It can be seen that Figure 5The shown fault diagnosis report contains the following key information: time and weather information; a list of fault details. The list of fault details includes: Faulty component: lists the names of the components to be diagnosed (such as the braking system, tires, engine); Fault risk score: shows the risk values calculated based on the operating state data and environmental data for each component (such as a braking system score of 85 and a tire score of 70); Risk level: divides the risk level according to a preset threshold (such as high risk, medium risk, low risk). For example, a braking system score of 85 (threshold 80) is determined to be high risk, and a tire score of 70 (threshold 60) is determined to be medium risk; Repair suggestions: provide specific suggestions for different risk levels (such as "Immediately check the wear condition of the brake pads", "Adjust the tire pressure to 2.7 Bar").

[0142] For ease of understanding, please refer to Figure 6 , Figure 6 which is another interface display diagram of vehicle fault diagnosis provided by the embodiment of the present application. It can be seen that the interface includes multiple functional modules: First is the time and weather information area. On the left, the diagnosis time is recorded as "xx year xx month xx day xx:xx", and the weather is marked on the right (such as "light snow"), which can provide a reference basis for the subsequent fault attribution analysis from the environmental dimension. The core module of this report is the "list of fault details", which presents the fault codes and specific fault descriptions in tabular form. For example, the fault code "C1200" corresponds to "Braking system fault - abnormal signal of the left front wheel speed sensor", and "B2500" corresponds to "Lighting system fault - the left daytime running light cannot be lit". This recording method of "code + description" combines professionalism and clarity: on the one hand, the standardized fault codes facilitate maintenance personnel to quickly match the maintenance manual and locate the system to which the fault belongs; on the other hand, the detailed fault description avoids the ambiguity of text expression, ensures the accurate transmission of fault information, and improves the efficiency of maintenance diagnosis. In addition, it also includes a virtual button of "Next Page", reflecting its expandable design to ensure adaptation to the detailed recording requirements in complex fault scenarios, ensuring that all fault information is presented completely and avoiding information omission due to page limitations. Through this diagnostic interface display diagram, maintenance personnel can quickly call technical materials based on the fault code, and combined with the fault description and environmental information, accurately analyze the cause of the fault and formulate a maintenance plan. At the same time, the standardized recording form is convenient for the archive management of maintenance files, laying a structured data foundation for vehicle maintenance history tracing and fault statistical analysis, and helping to improve the professionalism and efficiency of maintenance services.

[0143] For ease of understanding, please refer to Figure 7 , Figure 7It is an interface display diagram of the vehicle operation status provided by the embodiments of the present application. It can be seen that in the vehicle driving status data area, core parameters such as tire pressure, engine temperature, and tire temperature are presented in tabular form. Among them, the tire pressure of 2.5 Bar is within the standard range of most household cars (2.2 - 2.5 Bar). In light snow weather in winter, this value helps to balance traction and energy consumption; the engine temperature of 85 °C is within the normal operating range in winter (80 - 90 °C), indicating that the engine cooling system is operating normally; the tire temperature of 65 °C needs to be analyzed in combination with the driving scenario. If it is the data after a long-distance drive, it is a normal manifestation of heat generated by tire friction. If this temperature is reached after a short drive, problems such as abnormal tire wear or brake system jamming need to be investigated. By recording and integrating operation data and environmental information, it provides support for the full-life cycle management of the vehicle. Maintenance personnel can quickly judge the vehicle status based on the parameters, comprehensively analyze the operation trend by combining weather and time factors, and formulate precise maintenance strategies. At the same time, the standardized recording form facilitates operation file management, lays a foundation for historical data traceability and vehicle performance evolution analysis, and improves the scientific nature of maintenance decisions. For vehicle management parties, this mode helps to accumulate operation data, optimize maintenance plans through data mining, predict component wear, promote the standardization and systematization of vehicle operation management, strengthen the reliability of vehicle status monitoring, and provide data-level guarantees for the safe and efficient operation of the vehicle.

[0144] It can be seen that by implementing the above vehicle fault diagnosis method, by real-time detecting vehicle operation status data such as speed, engine temperature, brake condition, etc., and at the same time considering environmental data such as temperature, humidity, road condition, etc., and analyzing the vehicle operation status data and environmental data in real time for vehicle fault diagnosis, the problem of only analyzing vehicle operation status data in vehicle fault diagnosis is solved, and the accuracy of vehicle fault diagnosis is improved.

[0145] The above mainly introduces the solution of the embodiments of the present application from the perspective of the execution process on the method side. It can be understood that in order for the electronic device to implement the above functions, it includes the corresponding hardware structure and / or software module for executing each function. Those skilled in the art should easily realize that, combined with the units and algorithm steps of each example described in the embodiments provided in this article, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a certain function is executed in the way of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0146] Embodiments of the present application can divide functional units of an electronic device according to the above method examples. For example, each functional unit can be divided corresponding to each function, or two or more functions can be integrated into one processing unit. The above integrated unit can be implemented in the form of hardware or in the form of a software functional unit. It should be noted that the division of units in the embodiments of the present application is illustrative, only a logical function division, and there may be other division methods in actual implementation.

[0147] In the case of dividing each functional module corresponding to each function, Figure 8 is a block diagram of the functional unit composition of a vehicle fault diagnosis device provided by an embodiment of the present application. The vehicle fault diagnosis device 800 is applied to an electronic device, and the device includes:

[0148] An acquisition unit 810, configured to acquire first operation state data and environmental data of a target vehicle within a preset time period;

[0149] A determination unit 820, configured to determine components to be diagnosed of the target vehicle according to the environmental data, and obtain a plurality of components to be diagnosed; the components to be diagnosed are functional components in the vehicle that are associated with the environment and operating conditions;

[0150] The determination unit 820 is further configured to determine operation state data corresponding to the plurality of components to be diagnosed according to the first operation state data and the environmental data, and obtain a plurality of second operation state data;

[0151] A calculation unit 830, configured to determine fault risk scores corresponding to the plurality of components to be diagnosed according to the plurality of second operation state data, and obtain a plurality of fault risk scores;

[0152] A control unit 840, configured to determine a plurality of fault diagnosis results according to the plurality of fault risk scores and the plurality of components to be diagnosed;

[0153] The control unit 840 is further configured to generate a fault diagnosis report according to the plurality of fault diagnosis results.

[0154] In a possible embodiment, when the determination unit 820 determines the components to be diagnosed of the target vehicle according to the environmental data and obtains a plurality of components to be diagnosed, specifically:

[0155] Determine the temperature and humidity parameters and road slipperiness parameters in the environmental data;

[0156] Determine the corresponding meteorological conditions of the target vehicle within the preset time period according to the road slipperiness parameters and the temperature and humidity parameters, and obtain the target meteorological conditions;

[0157] Determine functional components associated with the operating condition in the target vehicle as components to be diagnosed according to the target meteorological condition, and obtain the multiple components to be diagnosed.

[0158] In a possible embodiment, when the determining unit 820 determines the meteorological condition corresponding to the target vehicle within the preset time period according to the road slipperiness parameter and the temperature and humidity parameter to obtain the target meteorological condition, it includes:

[0159] Obtain historical environmental data;

[0160] Construct a relationship prediction model between environmental data and meteorological conditions based on the historical environmental data to obtain a target meteorological model;

[0161] Input the road slipperiness parameter and the temperature and humidity parameter into the target meteorological model for prediction to obtain the target meteorological condition.

[0162] In a possible embodiment, when the determining unit 820 constructs a relationship prediction model between environmental data and meteorological conditions based on the historical environmental data to obtain the target meteorological model, it specifically is used for:

[0163] Extract weather types from the historical environmental data to obtain multiple weather types, and use the multiple weather types as labels of the historical environmental data;

[0164] Perform normalization processing on the historical environmental data to obtain target historical environmental data; each weather type in the target historical environmental data corresponds to an environmental data;

[0165] Divide the target historical environmental data to obtain a training set and a test set;

[0166] Train the training set based on a preset neural network model to obtain a first meteorological model;

[0167] Verify the first meteorological model according to the test set based on a preset evaluation index to obtain a first evaluation result;

[0168] Optimize the first meteorological model according to the first evaluation result to obtain the target meteorological model.

[0169] In a possible embodiment, when the determining unit 820 determines functional components associated with the operating condition in the target vehicle as components to be diagnosed according to the target meteorological condition to obtain the multiple components to be diagnosed, it specifically is used for:

[0170] Query vehicle faults associated with the target meteorological condition based on a preset vehicle fault and preset meteorological association database to obtain multiple potential vehicle faults;

[0171] Determine the fault types of the multiple potential vehicle faults to obtain multiple fault types;

[0172] Determine the faulty components corresponding to the multiple fault types to obtain multiple faulty components; [[ID=??]]

[0173] Calculate the correlation score of each faulty component among the multiple faulty components with the target meteorological condition to obtain multiple correlation scores;

[0174] Determine the faulty components corresponding to the correlation scores greater than a preset first threshold among the multiple correlation scores to obtain the multiple components to be diagnosed.

[0175] In a possible embodiment, the determining unit 820, in determining the operating state data corresponding to the multiple components to be diagnosed according to the first operating state data and the environmental data to obtain multiple second operating state data, specifically is used for:

[0176] Determine the operating state values corresponding to the components to be diagnosed in the first operating state data to obtain multiple operating state values;

[0177] Obtain the reference values of the multiple operating state values to obtain multiple first reference values;

[0178] Determine the influence factors corresponding to the multiple components to be diagnosed according to the environmental data to obtain multiple environmental influence factors;

[0179] Perform calculations based on the multiple environmental influence factors and the multiple first reference values to obtain multiple target reference values;

[0180] Determine the difference between each target reference value among the multiple target reference values and the corresponding operating state value among the multiple operating state values to obtain multiple first differences;

[0181] Determine the adjustment parameters corresponding to the multiple first differences according to the mapping relationship between the preset difference and the adjustment parameters to obtain multiple target adjustment parameters;

[0182] Adjust the multiple operating state values according to the multiple target adjustment parameters to obtain multiple second operating state data.

[0183] In a possible embodiment, the calculating unit 830, in determining the fault risk scores corresponding to the multiple components to be diagnosed according to the multiple second operating state data to obtain multiple fault risk scores, specifically is used for:

[0184] Perform preprocessing on the multiple second operating state data to obtain preprocessed data; the preprocessing includes data normalization;

[0185] It should be noted that there seems to be a missing ID number in the original text for the line "Determine the faulty components corresponding to the multiple fault types to obtain multiple faulty components;". I've marked it as "??" in the translation for clarity.Inputting the pre-processed data into a preset fault prediction model for calculation to obtain the plurality of first fault risk scores; the preset fault prediction model is a pre-trained fault diagnosis model based on vehicle operating status data;

[0186] Normalization is performed on the multiple first fault risk scores to obtain multiple fault risk scores.

[0187] In a possible embodiment, the control unit 840 is specifically configured to determine multiple fault diagnosis results according to the multiple fault risk scores and the multiple components to be diagnosed:

[0188] Obtaining a reference risk score threshold corresponding to a target component to be diagnosed, to obtain a target reference threshold; the target component to be diagnosed is any one of the multiple components to be diagnosed;

[0189] A fault diagnosis result of the target component to be diagnosed is determined according to the target reference threshold and the target fault risk score; the target fault risk score corresponds to the fault risk score of the target component to be diagnosed.

[0190] It should be noted that the specific functional implementation of a vehicle fault diagnosis device 800 is shown in the above Figure 3 The description of a vehicle fault diagnosis method shown in the figure, for example, the acquisition unit 810 is used to implement the relevant content of executing S310, and the control unit 840 is used to implement the relevant content of executing S360, which will not be repeated. The various units or modules in a vehicle fault diagnosis device 800 can be individually or completely merged into one or several other units or modules to form a structure, or one (some) of the units or modules can be further divided into multiple functionally smaller units or modules to form a structure, which can achieve the same operation without affecting the realization of the technical effects of the embodiments of the present invention. The above-mentioned units or modules are divided based on logical functions. In actual applications, the functions of one unit (or module) are implemented by multiple units (or modules), or the functions of multiple units (or modules) are implemented by one unit (or module).

[0191] It can be seen that the vehicle fault diagnosis device described in the embodiment of the present application improves the accuracy of vehicle fault diagnosis by simultaneously analyzing environmental data and vehicle operating status data to ensure the safety of the vehicle and the user.

[0192] An embodiment of the present application also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program for electronic data exchange, and the computer program enables a computer to execute part or all of the steps of any method described in the above method embodiments, and the above computer includes an electronic device.

[0193] An embodiment of the present application also provides a computer program product. The computer program product includes a non-transitory computer-readable storage medium storing a computer program. The computer program is operable to cause a computer to execute some or all of the steps of any of the methods described in the foregoing method embodiments. The computer program product may be a software installation package, and the computer includes an electronic device.

[0194] It should be noted that for the above-mentioned various embodiments, for the sake of simple description, they are all expressed as a series of action combinations. Those skilled in the art should know that the present application is not limited by the described order of actions, because some steps in the embodiments of the present application can be performed in other orders or simultaneously. In addition, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions, steps, modules or units involved are not necessarily essential to the embodiments of the present application.

[0195] In the above embodiments, each embodiment of the present application is described with emphasis. For the parts not detailed in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

[0196] Those of ordinary skill in the art can understand all or part of the processes of implementing the methods in the above embodiments. These processes can be completed by relevant hardware instructed by a computer program. The program can be stored in a computer-readable storage medium. When the program is executed, it may include the processes of the above-mentioned method embodiments. The foregoing storage medium includes: ROM or random access memory RAM, magnetic disk or optical disk and other various media that can store program codes.

[0197] The steps of the methods or algorithms described in the embodiments of the present application can be implemented in a hardware manner or by a processor executing software instructions. The software instructions can be composed of corresponding software modules. The software modules can be stored in RAM, flash memory, ROM, EPROM, electrically erasable programmable read-only memory (EEPROM), registers, hard disk, removable hard disk, CD-ROM or any other form of storage medium well known in the art. An exemplary storage medium is coupled to the processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can be located in an ASIC. In addition, the ASIC can be located in a terminal device or a management device. Of course, the processor and the storage medium can also exist as discrete components in a terminal device or a management device.

[0198] Those skilled in the art should be able to realize that in one or more of the above examples, the functions described in the embodiments of the present application can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wire (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or wirelessly (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that the computer can access or a data storage device such as a server or data center that includes one or more integrated available media. The available medium can be a magnetic medium (such as a floppy disk, hard disk, magnetic tape), an optical medium (such as a digital video disc (DVD)), or a semiconductor medium (such as a solid state disk (SSD)), etc.

[0199] Each device and product described in the above embodiments includes various modules / units, which can be software modules / units, hardware modules / units, or can be partially software modules / units and partially hardware modules / units. For example, for each device and product applied to or integrated into a chip, each module / unit it includes can be implemented in the form of hardware such as circuits. Or, at least some of the modules / units can be implemented in the form of software programs that run on a processor integrated inside the chip, and the remaining (if any) part of the modules / units can be implemented in the form of hardware such as circuits; for each device and product applied to or integrated into a chip module, each module / unit it includes can be implemented in the form of hardware such as circuits. Different modules / units can be located in the same component (such as a chip, a circuit module, etc.) or different components of the chip module. Or, at least some of the modules / units can be implemented in the form of software programs that run on a processor integrated inside the chip module, and the remaining (if any) part of the modules / units can be implemented in the form of hardware such as circuits; for each device and product applied to or integrated into a terminal device, each module / unit it includes can be implemented in the form of hardware such as circuits. Different modules / units can be located in the same component (such as a chip, a circuit module, etc.) or different components inside the terminal device. Or, at least some of the modules / units can be implemented in the form of software programs that run on a processor integrated inside the terminal device, and the remaining (if any) part of the modules / units can be implemented in the form of hardware such as circuits.

[0200] The specific embodiments described above further elaborate on the purpose, technical solutions, and beneficial effects of the embodiments of the present application. It should be understood that the above description is only the specific embodiments of the embodiments of the present application and is not used to limit the protection scope of the embodiments of the present application. Any modifications, equivalent replacements, improvements, etc. made on the basis of the technical solutions of the embodiments of the present application shall be included in the protection scope of the embodiments of the present application.

Claims

1. A vehicle fault diagnosis method, characterized in that, Applied to an electronic device, the method includes: Obtaining first operating state data and environmental data of a target vehicle within a preset time period; Determining components to be diagnosed of the target vehicle according to the environmental data, obtaining a plurality of components to be diagnosed; the components to be diagnosed are functional components in the vehicle associated with the environment and operating conditions; Determining operating state data corresponding to the plurality of components to be diagnosed according to the first operating state data and the environmental data, obtaining a plurality of second operating state data; Determining failure risk scores corresponding to the plurality of components to be diagnosed according to the plurality of second operating state data, obtaining a plurality of failure risk scores; Determining a plurality of fault diagnosis results according to the plurality of failure risk scores and the plurality of components to be diagnosed; Generating a fault diagnosis report according to the plurality of fault diagnosis results.

2. The method according to claim 1, wherein The step of determining components to be diagnosed of the target vehicle according to the environmental data, obtaining a plurality of components to be diagnosed, includes: Determining temperature and humidity parameters and road slipperiness parameters in the environmental data; Determining the corresponding meteorological condition of the target vehicle within the preset time period according to the road slipperiness parameters and the temperature and humidity parameters, obtaining a target meteorological condition; Determining functional components in the target vehicle associated with the operating condition as components to be diagnosed according to the target meteorological condition, obtaining the plurality of components to be diagnosed.

3. The method according to claim 2, wherein The step of determining the corresponding meteorological condition of the target vehicle within the preset time period according to the road slipperiness parameters and the temperature and humidity parameters, obtaining a target meteorological condition, includes: Obtaining historical environmental data; Constructing a relationship prediction model between environmental data and meteorological conditions according to the historical environmental data, obtaining a target meteorological model; Inputting the road slipperiness parameters and the temperature and humidity parameters into the target meteorological model for prediction, obtaining a target meteorological condition.

4. The method according to claim 3, characterized in that, The step of constructing a relationship prediction model between environmental data and meteorological conditions according to the historical environmental data, obtaining a target meteorological model, includes: Extracting weather types from the historical environmental data, obtaining a plurality of weather types, and using the plurality of weather types as labels of the historical environmental data; Performing normalization processing on the historical environmental data, obtaining target historical environmental data; each weather type in the target historical environmental data corresponds to an environmental data; Dividing the target historical environmental data, obtaining a training set and a test set; Training the training set based on a preset neural network model, obtaining a first meteorological model; Verifying the first meteorological model according to the test set based on preset evaluation indicators, obtaining a first evaluation result; Optimizing the first meteorological model according to the first evaluation result, obtaining a target meteorological model.

5. The method according to claim 2, characterized in that, The step of determining functional components in the target vehicle associated with the operating condition as components to be diagnosed according to the target meteorological condition, obtaining the plurality of components to be diagnosed, includes: Querying vehicle faults associated with the target meteorological condition based on a preset vehicle fault and preset meteorological association database, obtaining a plurality of potential vehicle faults; Determining the fault types of the plurality of potential vehicle faults, obtaining a plurality of fault types; Determine the faulty components corresponding to the multiple fault types to obtain multiple faulty components; Calculate the correlation score of each faulty component among the multiple faulty components with the target meteorological condition to obtain multiple correlation scores; Determine the faulty components corresponding to the correlation scores greater than the preset first threshold among the multiple correlation scores to obtain the multiple components to be diagnosed.

6. The method according to any one of claims 1-5, characterized in that, The determining the operating state data corresponding to the multiple components to be diagnosed according to the first operating state data and the environmental data to obtain multiple second operating state data includes: Determine the operating state values corresponding to the components to be diagnosed in the first operating state data to obtain multiple operating state values; Obtain the reference values of the multiple operating state values to obtain multiple first reference values; Determine the influence factors corresponding to the multiple components to be diagnosed according to the environmental data to obtain multiple environmental influence factors; Perform calculations based on the multiple environmental influence factors and the multiple first reference values to obtain multiple target reference values; Determine the difference between each target reference value among the multiple target reference values and the corresponding operating state value among the multiple operating state values to obtain multiple first differences; Determine the adjustment parameters corresponding to the multiple first differences according to the mapping relationship between the preset difference and the adjustment parameters to obtain multiple target adjustment parameters; Adjust the multiple operating state values according to the multiple target adjustment parameters to obtain multiple second operating state data.

7. The method according to any one of claims 1-4, characterized in that The determining the fault risk scores corresponding to the multiple components to be diagnosed according to the multiple second operating state data to obtain multiple fault risk scores includes: Perform preprocessing on the multiple second operating state data to obtain preprocessed data; the preprocessing includes data normalization; Input the preprocessed data into a preset fault prediction model for calculation to obtain the multiple first fault risk scores; the preset fault prediction model is a pre-trained fault diagnosis model based on vehicle operating state data; Perform normalization processing on the multiple first fault risk scores to obtain multiple fault risk scores.

8. The method according to claim 1, wherein The determining multiple fault diagnosis results according to the multiple fault risk scores and the multiple components to be diagnosed includes: Obtain the reference risk score threshold corresponding to the target component to be diagnosed to obtain the target reference threshold; the target component to be diagnosed is any one of the multiple components to be diagnosed; Determine the fault diagnosis result of the target component to be diagnosed according to the target reference threshold and the target fault risk score; the target fault risk score corresponds to the fault risk score of the target component to be diagnosed.

9. A vehicle fault diagnosis device, characterized in that, The device includes an acquisition unit, a determination unit, a calculation unit, and a control unit, where: The acquisition unit is used to acquire the first operating state data and environmental data of the target vehicle within a preset time period; The determination unit is used to determine the components to be diagnosed of the target vehicle according to the environmental data to obtain multiple components to be diagnosed; the components to be diagnosed are functional components in the vehicle that are associated with the environment and operating conditions; The determining unit is further configured to determine the operation status data corresponding to the multiple components to be diagnosed according to the first operation status data and the environment data, so as to obtain a plurality of second operation status data; The calculating unit is configured to determine the fault risk scores corresponding to the multiple components to be diagnosed according to the plurality of second operation status data, so as to obtain a plurality of fault risk scores; The control unit is configured to determine a plurality of fault diagnosis results according to the plurality of fault risk scores and the multiple components to be diagnosed; The control unit is further configured to generate a fault diagnosis report according to the plurality of fault diagnosis results.

10. An electronic device, characterized in that, including: a processor, a memory, a communication interface, and one or more programs; The one or more programs are stored in the memory and configured to be executed by the processor, and the programs include instructions for performing the steps in the method according to any one of claims 1-8.

Images