Vehicle fault analysis method, device, equipment, system and storage medium

Abstract

The invention discloses a vehicle fault analysis method, device, equipment and system and a storage medium. The method comprises the following steps: receiving identity information and fault association information of a fault vehicle sent by a work order system end; according to the identity information and the fault association information, reading a first target log of the fault vehicle from the work order system end; a built-in analysis model is utilized to analyze the first target log so as to output fault analysis information to the work order system end, and the fault analysis information comprises a known fault type and a temporary fault removal measure corresponding to the known fault type, and / or unknown fault prompt information. According to the technical scheme provided by the embodiment of the invention, known faults and unknown faults can be accurately and quickly screened out from a large number of vehicle faults, and temporary fault removal measures of the known faults are output, so that the time cost of developers is saved, the integration level is high, and the fault analysis efficiency is greatly improved.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a vehicle fault analysis method, apparatus, equipment, system, and storage medium. Background Technology

[0002] With the continuous development of information technology, vehicles are becoming increasingly powerful. Advanced sensors (such as radar and cameras), controllers, and actuators, through the vehicle's onboard environmental perception system and information terminal, enable information exchange with people, vehicles, and roads, giving vehicles intelligent environmental perception capabilities. This allows them to automatically analyze the safety and danger conditions of vehicle operation and guide the vehicle to its destination according to the driver's wishes.

[0003] When a vehicle malfunctions, fault analysis is crucial. However, in mass production, dealing with vehicle faults that present similar symptoms but have diverse causes requires a significant amount of manpower for repetitive screening and analysis, resulting in low efficiency and high manpower requirements. Summary of the Invention

[0004] This invention provides a vehicle fault analysis method, apparatus, equipment, system, and storage medium to solve the problem of low efficiency in vehicle fault analysis.

[0005] In a first aspect, the present invention provides a vehicle fault analysis method applied to a cloud platform, the method comprising:

[0006] The system receives the identity information and fault association information of the faulty vehicle sent by the work order system. The fault association information includes the fault module information of the faulty vehicle. The fault association information in the work order system is information input by the user.

[0007] Based on the identity information and the fault association information, the first target log of the faulty vehicle is read from the work order system terminal, wherein the identity information and the first target log in the work order system terminal are both obtained from the faulty vehicle;

[0008] The first target log is analyzed using a built-in analysis model to output fault analysis information to the work order system. The fault analysis information includes known fault types and corresponding temporary troubleshooting measures, and / or unknown fault prompts.

[0009] Secondly, this invention provides a vehicle fault analysis system, including a work order system terminal, a cloud platform, and a work order system client, wherein:

[0010] The work order system sends the identity information and fault association information of the faulty vehicle to the cloud platform. The fault association information includes the fault module information of the faulty vehicle. The identity information is obtained from the faulty vehicle through the work order system client. The fault association information is information uploaded by the user through the work order system client.

[0011] The cloud platform receives the identity information and the fault association information, and reads the first target log of the faulty vehicle from the work order system terminal based on the identity information and the fault association information, and analyzes the first target log using the built-in analysis model to output fault analysis information to the work order system terminal. The fault analysis information includes known fault types and corresponding temporary troubleshooting measures for the known fault types, and / or unknown fault prompt information. The first target log in the work order system terminal is obtained from the faulty vehicle through the work order system client.

[0012] The work order system terminal sends the fault analysis information to the work order system client of the faulty vehicle;

[0013] The work order system client for the faulty vehicle outputs the fault analysis information through the work order interface.

[0014] Thirdly, the present invention provides a vehicle fault analysis device applied to a cloud platform, the device comprising:

[0015] The information receiving module is used to receive the identity information and fault association information of the faulty vehicle sent by the work order system. The fault association information includes the fault module information of the faulty vehicle. The fault association information in the work order system is information input by the user.

[0016] The log reading module is used to read the first target log of the faulty vehicle from the work order system terminal according to the identity information and the fault association information, wherein the identity information and the first target log in the work order system terminal are both obtained from the faulty vehicle;

[0017] The fault analysis result output module is used to analyze the first target log using a built-in analysis model to output fault analysis information to the work order system. The fault analysis information includes known fault types and corresponding temporary troubleshooting measures for the known fault types, and / or unknown fault prompt information.

[0018] Fourthly, the present invention provides an electronic device comprising:

[0019] At least one processor;

[0020] and memory that is communicatively connected to at least one processor;

[0021] The memory stores a computer program that can be executed by at least one processor, which enables the at least one processor to perform the vehicle fault analysis method of the first aspect described above.

[0022] Fifthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a processor to execute the vehicle fault analysis method of the first aspect described above.

[0023] The vehicle fault analysis scheme provided by this invention receives the identity information and fault association information of a faulty vehicle sent by a work order system. The fault association information includes the fault module information of the faulty vehicle. The fault association information in the work order system is user-inputted information. Based on the identity information and the fault association information, a first target log of the faulty vehicle is read from the work order system. Both the identity information and the first target log in the work order system are obtained from the faulty vehicle. The first target log is analyzed using a built-in analysis model to output fault analysis information to the work order system. The fault analysis information includes known fault types and corresponding temporary troubleshooting measures, and / or unknown fault prompts. By adopting the above technical solution, there is no need to develop scripts for each module or spend a lot of manpower on fault analysis. After a vehicle malfunctions, when faced with vehicle malfunctions that have similar symptoms but different causes, the cloud platform configured with the analysis model can automatically perform repetitive fault screening based on vehicle data containing fault module information. It can accurately and quickly filter out known and unknown faults from a large number of vehicle malfunctions, and output temporary troubleshooting measures for known faults. This not only saves developers' time and costs, but also has a high degree of integration and greatly improves the efficiency of fault analysis.

[0024] It should be understood that the description in this section is not intended to identify key or essential features of the invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 This is a flowchart of a vehicle fault analysis method provided in Embodiment 1 of the present invention;

[0027] Figure 2 This is a flowchart of a vehicle fault analysis method provided in Embodiment 2 of the present invention;

[0028] Figure 3 This is a fault analysis flowchart provided according to Embodiment 2 of the present invention;

[0029] Figure 4 This is a schematic diagram of information interaction of a vehicle fault analysis system according to Embodiment 3 of the present invention;

[0030] Figure 5 This is a schematic diagram of the structure of a vehicle fault analysis device according to Embodiment 4 of the present invention;

[0031] Figure 6 This is a schematic diagram of the structure of an electronic device provided in Embodiment 5 of the present invention. Detailed Implementation

[0032] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0033] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. In the description of this invention, unless otherwise stated, "a plurality of" means two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist; for example, A and / or B can represent: A alone, A and B simultaneously, and B alone. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0034] Example 1

[0035] Figure 1 The flowchart of a vehicle fault analysis method is provided in Embodiment 1 of the present invention. This embodiment is applicable to the situation of diagnosing vehicle faults on a cloud platform. The method can be executed by a vehicle fault analysis device, which can be implemented in hardware and / or software and can be configured in an electronic device.

[0036] like Figure 1 As shown, the vehicle fault analysis method provided in Embodiment 1 of the present invention specifically includes the following steps:

[0037] S101. Receive the identity information and fault association information of the faulty vehicle sent by the work order system terminal, wherein the fault association information includes the fault module information of the faulty vehicle, and the fault association information in the work order system terminal is information input by the user.

[0038] In this embodiment, a work order system and a cloud platform can be pre-deployed. The cloud platform can receive the identity information and fault association information of the faulty vehicle through a communication interface with the work order system. The identity information includes the vehicle's VIN (Vehicle Identification Number), and the fault association information includes at least the fault module information of the faulty vehicle. The fault module type includes software modules and hardware modules configured in the vehicle, such as functional modules. The cloud platform can be understood as a cloud-based system, which deploys a vehicle cloud server module that requires constant maintenance. The work order system can be understood as a work order system platform, which can be configured with multiple work order system clients. These clients can be configured in a preset location, such as in the vehicle's infotainment system. The fault association information may also include at least one of the following: fault occurrence time, fault end time, and fault log type.

[0039] S102. Based on the identity information and the fault association information, read the first target log of the faulty vehicle from the work order system terminal, wherein the identity information and the first target log in the work order system terminal are both obtained from the faulty vehicle.

[0040] In this embodiment, the cloud platform can filter out the (first) target log of the faulty vehicle from the work order system terminal, which stores logs of multiple vehicles, based on identity information and fault association information. The first target log is the log associated with the fault association information. The identity information, fault association information, and target log (including the first target log of the faulty vehicle) in the work order system terminal can all be obtained from the faulty vehicle through the work order system client.

[0041] S103. Analyze the first target log using the built-in analysis model to output fault analysis information to the work order system terminal, wherein the fault analysis information includes known fault types and corresponding temporary troubleshooting measures for the known fault types, and / or unknown fault prompt information.

[0042] In this embodiment, the built-in analysis model can be used to analyze the first target log, and the resulting calculation results, i.e., fault analysis information, can be sent to the work order system for output. Specifically, if the output includes a known fault type and corresponding temporary troubleshooting measures, it indicates that the fault type of the current malfunctioning vehicle is known. The output known fault type allows for rapid screening and classification of vehicle faults, and the output temporary troubleshooting measures can help the user troubleshoot the vehicle. If the output includes an unknown fault prompt, it indicates that the fault type of the current malfunctioning vehicle is unknown, and the user can diagnose the unknown fault by analyzing the first target log. If the output includes a known fault type, corresponding temporary troubleshooting measures, and an unknown fault prompt, it indicates that the current malfunctioning vehicle has both known and unknown faults.

[0043] The vehicle fault analysis method provided in this embodiment of the invention receives the identity information and fault association information of a faulty vehicle sent by a work order system. The fault association information includes fault module information of the faulty vehicle. The fault association information in the work order system is user-inputted information. Based on the identity information and the fault association information, a first target log of the faulty vehicle is read from the work order system. Both the identity information and the first target log in the work order system are obtained from the faulty vehicle. The first target log is analyzed using a built-in analysis model to output fault analysis information to the work order system. The fault analysis information includes known fault types and corresponding temporary troubleshooting measures for the known fault types, and / or unknown fault prompt information. The technical solution of this invention eliminates the need for script development for each module and avoids the need for extensive manpower for fault analysis. When a vehicle malfunctions, even with similar symptoms but potentially different causes, a cloud platform equipped with an analysis model can automatically perform repetitive fault screening based on vehicle data containing fault module information. This accurately and quickly identifies known and unknown faults and provides temporary troubleshooting measures for known faults. This not only saves developers' time and has a high degree of integration but also greatly improves the efficiency of fault analysis.

[0044] Optionally, the fault analysis information may further include at least one of the following: the fault occurrence scenario corresponding to the known fault type, the fault occurrence frequency, long-term troubleshooting measures, the software version used to resolve the fault, log analysis fields used to determine the fault, and the full logs for the fault period. The advantage of this configuration is that receiving the aforementioned fault association information ensures the accuracy of the output fault analysis information.

[0045] Example 2

[0046] Figure 2 This is a flowchart of a vehicle fault analysis method provided in Embodiment 2 of the present invention. The technical solution of the present invention is further optimized based on the above optional technical solutions, and provides a specific way to diagnose vehicle faults.

[0047] Optionally, the above method further includes: if it is determined that the work order system is offline, receiving the second target log of the faulty vehicle and the fault association information sent offline through a preset interface, wherein the number of faulty vehicles is at least one, and the number of second target logs for each faulty vehicle is at least one; filtering out the first target logs associated with the fault association information from the second target logs, wherein multiple first target logs contain different fault module information; analyzing the first target logs using the analysis model, so as to output the fault analysis information to the work order system when the work order system is online. The advantage of this setting is that, for a batch of vehicle modules that require long-term and high-efficiency monitoring, by receiving target logs through a preset interface, the cloud platform can use cloud computing power to perform large-scale fault screening operations, further improving the speed of fault analysis and screening.

[0048] Optionally, the analysis model includes a large language model.

[0049] Optionally, after analyzing the first target log using the built-in analysis model to output fault analysis information to the work order system, the method further includes: if the fault analysis information includes the unknown fault prompt information, then training the large language model using the first target log associated with the unknown fault prompt information to obtain an updated large language model. The advantage of this setup is that the large language model can quickly identify and output fault analysis information, and by training the large language model using the first target log associated with the unknown fault prompt information, the breadth of fault analysis capabilities of the large language model can be rapidly improved.

[0050] Optionally, the above method further includes: outputting the fault analysis information to mobile terminals and web pages via the target interface. The advantage of this setup is that it enables multi-channel sharing of fault analysis information using the target interface.

[0051] like Figure 2 As shown, the vehicle fault analysis method provided in Embodiment 2 of the present invention specifically includes the following steps:

[0052] S201. Determine if the work order system is offline. If yes, proceed to step 202; otherwise, proceed to step 203.

[0053] Specifically, the work order system client can be deployed in the vehicle. First, it's necessary to determine if the work order system client is offline. An online work order system client can communicate normally with the work order system client and has established a stable communication connection with the cloud platform, enabling it to reliably send vehicle logs to the cloud platform. Vehicle logs from an offline work order system client cannot be uploaded to the cloud platform over the network.

[0054] S202. Receive the second target log and fault association information of the faulty vehicle sent offline through a preset interface, and filter out the first target log associated with the fault association information from the second target log. The number of faulty vehicles is at least one, the number of second target logs for each faulty vehicle is at least one, and the multiple first target logs contain different fault module information. Execute step 205.

[0055] Specifically, Figure 3 This is a fault analysis flowchart. For example... Figure 3 As shown, for the offline work order system, fault association information can be received through the cloud platform's preset interface, as well as a large number of offline logs (i.e., second target logs) of faulty vehicles, and / or a large batch of second target logs of faulty vehicles can be received. First target logs can then be selected from the second target logs based on the fault association information. The fault module information contained in the fault association information of these first target logs can be different.

[0056] S203. Receive the identity information and fault association information of the faulty vehicle sent by the work order system.

[0057] Specifically, such as Figure 3 As shown, for the online work order system, the system can receive vehicle information (i.e., identity information and fault association information) of the faulty vehicle sent by the work order system via the network.

[0058] Optionally, the faulty vehicle includes a vehicle that is on the production line.

[0059] Specifically, for production lines, this method avoids situations where excessive vehicle congestion due to inefficient fault analysis can disrupt normal production.

[0060] S204. Based on the identity information and the fault association information, read the first target log of the faulty vehicle from the work order system.

[0061] Specifically, the cloud platform can read the first target log of the faulty vehicle through the work order system based on the identity information and fault association information.

[0062] S205. Analyze the first target log using the built-in large language model to output fault analysis information to the work order system terminal, wherein the fault analysis information includes known fault types and corresponding temporary troubleshooting measures for the known fault types, and / or, unknown fault prompt information.

[0063] Specifically, the cloud platform has a built-in large language model, such as Figure 3 As shown, during cloud platform analysis, this large language model can be used to analyze the first target log to obtain fault analysis information.

[0064] Optionally, the method for determining the large language model includes: generating known fault training samples using known fault work orders and known fault logs; and using the known fault training samples to perform multiple rounds of iterative training on the initial large language model to obtain the trained large language model.

[0065] Specifically, the cloud platform can generate a cloud platform database in the form of fields for training and updating the initial large language model within the cloud platform. This database includes underlying code for expanding the training samples of the large language model, as well as data such as work order data for known faults, known fault logs, and system documents, which can be used for training and learning by the initial large language model. Using the above data, known fault training samples can be generated. By using the known fault training samples to perform multiple rounds of iterative training on the initial large language model, a fully trained large language model is generated.

[0066] S206. Output the fault analysis information to the mobile terminal and web page through the target interface.

[0067] Specifically, the cloud platform can be configured with various types of target interfaces, such as mobile interfaces and web interfaces, to output fault analysis information to mobile terminals and web pages.

[0068] S207. If the fault analysis information includes unknown fault indication information, then the large language model is trained using the first target log associated with the unknown fault indication information to obtain an updated large language model. Execute step 201.

[0069] Specifically, it can output the first target log associated with the unknown fault prompt information, and receive manually analyzed information through a set interface. The first target log associated with the unknown fault prompt information and the manually analyzed information are then used to train a large language model to obtain an updated large language model. The manually analyzed information includes temporary troubleshooting measures for the unknown fault input by a person analyzing the associated first target log, as well as at least one of the following: fault occurrence time, fault end time, fault log type, fault occurrence scenario, fault occurrence frequency, long-term troubleshooting measures, software version used to resolve the fault, log analysis fields used to determine the fault, and the full log for the fault period.

[0070] The vehicle fault analysis method provided in this invention targets a large number of vehicle modules that require long-term and high-efficiency monitoring. By receiving target logs through a preset interface, the cloud platform can utilize cloud computing power to perform large-scale fault screening operations, further improving the speed of fault analysis and screening. Furthermore, the large language model can quickly identify and output fault analysis information. By training the large language model with the first target log associated with unknown fault prompt information, the breadth of fault analysis capabilities of the large language model can be rapidly improved. Additionally, the target interface enables multi-channel sharing of fault analysis information.

[0071] Example 3

[0072] Figure 4 This invention provides a schematic diagram of information interaction for a vehicle fault analysis system according to Embodiment 3. The fault analysis system 30 can be used to diagnose vehicle faults and can be implemented in hardware and / or software.

[0073] like Figure 4 As shown, the vehicle fault analysis system provided in Embodiment 3 of the present invention includes a work order system terminal 301, a work order system client 302, and a cloud platform 302, wherein:

[0074] The work order system client 301 sends the identity information and fault association information of the faulty vehicle to the cloud platform 302. The fault association information includes the fault module information of the faulty vehicle. The identity information is obtained from the faulty vehicle through the work order system client 302. The fault association information is information uploaded by the user through the work order system client 302.

[0075] The cloud platform 302 receives the identity information and the fault association information, and reads the first target log of the faulty vehicle from the work order system terminal 301 according to the identity information and the fault association information, and analyzes the first target log using the built-in analysis model to output fault analysis information to the work order system terminal 301. The fault analysis information includes known fault types and corresponding temporary troubleshooting measures for the known fault types, and / or unknown fault prompt information. The first target log in the work order system terminal 301 is obtained from the faulty vehicle through the work order system client 302.

[0076] The work order system terminal 301 sends the fault analysis information to the work order system client 302 of the faulty vehicle;

[0077] The work order system client 302 of the faulty vehicle outputs the fault analysis information through the work order interface.

[0078] The cloud platform in the vehicle fault analysis system provided in this embodiment of the invention can execute the vehicle fault analysis method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects for executing the method. Technical details not described in detail in this embodiment can be found in the vehicle fault analysis method provided in the above embodiments of the invention.

[0079] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0080] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0081] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0082] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0083] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

[0084] Example 4

[0085] Figure 5 This is a schematic diagram of a vehicle fault analysis device provided in Embodiment 4 of the present invention. Figure 5 As shown, the device includes: an information receiving module 401, a log reading module 402, and a fault analysis result output module 403. This device is applied to a cloud platform, wherein:

[0086] The information receiving module is used to receive the identity information and fault association information of the faulty vehicle sent by the work order system. The fault association information includes the fault module information of the faulty vehicle. The fault association information in the work order system is information input by the user.

[0087] The log reading module is used to read the first target log of the faulty vehicle from the work order system terminal according to the identity information and the fault association information, wherein the identity information and the first target log in the work order system terminal are both obtained from the faulty vehicle;

[0088] The fault analysis result output module is used to analyze the first target log using a built-in analysis model to output fault analysis information to the work order system. The fault analysis information includes known fault types and corresponding temporary troubleshooting measures for the known fault types, and / or unknown fault prompt information.

[0089] The vehicle fault analysis device provided in this invention eliminates the need for script development for each module and avoids the need for extensive manpower for fault analysis. When a vehicle experiences a fault, even when faced with similar fault symptoms but potentially different causes, the device utilizes a cloud platform equipped with an analysis model to automatically perform repetitive fault screening based on vehicle data containing fault module information. This accurately and quickly identifies known and unknown faults and provides temporary troubleshooting measures for known faults. This not only saves developers' time and has a high degree of integration but also significantly improves the efficiency of fault analysis.

[0090] Optionally, the device may also include:

[0091] The offline information receiving module is used to receive the second target log of the faulty vehicle and the fault association information sent offline through a preset interface if it is determined that the work order system is offline. The number of the faulty vehicles is at least one, and the number of second target logs for each faulty vehicle is at least one.

[0092] A filtering module is used to filter out first target logs associated with the fault association information from the second target logs, wherein different fault module information exists in multiple first target logs;

[0093] The result output module is used to analyze the first target log using the analysis model, so as to output the fault analysis information to the work order system when the work order system is online.

[0094] Optionally, the fault analysis information may also include at least one of the following: the fault occurrence scenario corresponding to the known fault type, the fault occurrence frequency, long-term fault troubleshooting measures, the software version used to resolve the fault, the log analysis fields used to determine the fault, and the full logs during the fault period.

[0095] Optionally, the analysis model includes a large language model; wherein the method for determining the large language model includes: generating known fault training samples using known fault work orders and known fault logs; and performing multiple rounds of iterative training on the initial large language model using the known fault training samples to obtain a trained large language model.

[0096] Optionally, the device may also include:

[0097] The model update module is used to, after analyzing the first target log using the built-in analysis model to output fault analysis information to the work order system, if the fault analysis information includes the unknown fault prompt information, then use the first target log associated with the unknown fault prompt information to train the large language model to obtain the updated large language model.

[0098] Optionally, the faulty vehicle includes a vehicle that is on the production line.

[0099] Optionally, the device may also include:

[0100] A multi-channel output module is used to output the fault analysis information to mobile terminals and web pages through the target interface.

[0101] The vehicle fault analysis device provided in this embodiment of the invention can execute the vehicle fault analysis method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0102] Example 5

[0103] Figure 6 A schematic diagram of an electronic device 50 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as in-vehicle systems, laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0104] like Figure 6As shown, the electronic device 50 includes at least one processor 51 and a memory, such as a read-only memory (ROM) 52 and a random access memory (RAM) 53, communicatively connected to the at least one processor 51. The memory stores computer programs executable by the at least one processor. The processor 51 can perform various appropriate actions and processes based on the computer program stored in the ROM 52 or loaded into the RAM 53 from storage unit 58. The RAM 53 can also store various programs and data required for the operation of the electronic device 50. The processor 51, ROM 52, and RAM 53 are interconnected via a bus 54. An input / output (I / O) interface 55 is also connected to the bus 54.

[0105] Multiple components in electronic device 50 are connected to I / O interface 55, including: input unit 56, such as keyboard, mouse, etc.; output unit 57, such as various types of monitors, speakers, etc.; storage unit 58, such as disk, optical disk, etc.; and communication unit 59, such as network card, modem, wireless transceiver, etc. Communication unit 59 allows electronic device 50 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0106] Processor 51 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 51 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 51 performs the various methods and processes described above, such as vehicle fault analysis methods.

[0107] In some embodiments, the vehicle fault analysis method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 58. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 50 via ROM 52 and / or communication unit 59. When the computer program is loaded into RAM 53 and executed by processor 51, one or more steps of the vehicle fault analysis method described above may be performed. Alternatively, in other embodiments, processor 51 may be configured to perform the vehicle fault analysis method by any other suitable means (e.g., by means of firmware).

[0108] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0109] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0110] The computer equipment provided above can be used to execute the vehicle fault analysis method provided in any of the above embodiments, and has the corresponding functions and beneficial effects.

[0111] Example 6

[0112] In the context of this invention, the computer-readable storage medium may be a tangible medium, and the computer-executable instructions, when executed by a computer processor, are used to perform a first vehicle fault analysis method, specifically including:

[0113] The system receives the identity information and fault association information of the faulty vehicle sent by the work order system. The fault association information includes the fault module information of the faulty vehicle. The fault association information in the work order system is information input by the user.

[0114] Based on the identity information and the fault association information, the first target log of the faulty vehicle is read from the work order system terminal, wherein the identity information and the first target log in the work order system terminal are both obtained from the faulty vehicle;

[0115] The first target log is analyzed using a built-in analysis model to output fault analysis information to the work order system. The fault analysis information includes known fault types and corresponding temporary troubleshooting measures, and / or unknown fault prompts.

[0116] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by, or in conjunction with, an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0117] The computer equipment provided above can be used to execute the vehicle fault analysis method provided in any of the above embodiments, and has the corresponding functions and beneficial effects.

[0118] It is worth noting that in the embodiments of the above-mentioned vehicle fault analysis device, the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of each functional unit are only for easy differentiation and are not used to limit the scope of protection of the present invention.

[0119] Note that the above description is merely a preferred embodiment of the present invention and the technical principles employed. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions can be made without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in detail through the above embodiments, the present invention is not limited to the above embodiments, and may include many other equivalent embodiments without departing from the concept of the present invention, the scope of which is determined by the scope of the appended claims.

Claims

1. A vehicle failure analysis method characterized by, Applied to a cloud platform, the method comprises: Receiving identity information and fault correlation information of a fault vehicle sent by a ticket system end, wherein the fault correlation information comprises fault module information of the fault vehicle, and the fault correlation information in the ticket system end is information input by a user; Reading first target logs of the fault vehicle from the ticket system end according to the identity information and the fault correlation information, wherein the identity information and the first target logs in the ticket system end are obtained from the fault vehicle; Analyzing the first target logs by using an embedded analysis model to output fault analysis information to the ticket system end, wherein the fault analysis information comprises a known fault type and a temporary fault elimination measure corresponding to the known fault type, and / or unknown fault prompt information.

2. The method of claim 1, wherein, Further comprising: If it is determined that the ticket system end is offline, receiving second target logs of the fault vehicle and the fault correlation information sent offline through a preset interface, wherein the number of the fault vehicles is at least one, and the number of the second target logs of each fault vehicle is at least one; Screening first target logs associated with the fault correlation information from the second target logs, wherein different fault module information exists in the first target logs; Analyzing the first target logs by using the analysis model to output the fault analysis information to the ticket system end when the ticket system end is online.

3. The method of claim 1, wherein, The fault analysis information further comprises at least one of a fault occurrence scenario corresponding to the known fault type, a fault occurrence frequency, a long-term fault elimination measure, a software version for solving the fault, a log analysis field for determining the fault, and full-amount logs in a fault period.

4. The method according to any one of claims 1-3, characterized in that, The analysis model comprises a large language model; wherein the determination method of the large language model comprises: Generating known fault training samples by using known fault tickets and known fault logs; Performing multi-round iterative training on an initial large language model by using the known fault training samples to obtain a trained large language model.

5. The method of claim 4, wherein, After the step of analyzing the first target logs by using the embedded analysis model to output the fault analysis information to the ticket system end, further comprising: If the fault analysis information comprises the unknown fault prompt information, training the large language model by using first target logs associated with the unknown fault prompt information to obtain an updated large language model.

6. The method of claim 1, wherein, The fault vehicle comprises a vehicle on a production line.

7. The method of claim 1, wherein, Further comprising: Outputting the fault analysis information to a mobile terminal and a webpage end through a target interface.

8. A vehicle fault analysis system characterized by, Comprising a ticket system end, a cloud platform, and a ticket system client, wherein: The ticket system end sends identity information and fault correlation information of a fault vehicle to the cloud platform, wherein the fault correlation information comprises fault module information of the fault vehicle, the identity information is obtained from the fault vehicle through the ticket system client, and the fault correlation information is information uploaded by a user through the ticket system client; The cloud platform receives the identity information and the fault association information, reads a first target log of the fault vehicle from the ticket system end according to the identity information and the fault association information, and analyzes the first target log by using an analysis model built-in to output fault analysis information to the ticket system end, wherein the fault analysis information includes a known fault type and a temporary troubleshooting measure corresponding to the known fault type, and / or unknown fault prompt information, and the first target log in the ticket system end is obtained from the fault vehicle by the ticket system client; The ticket system end sends the fault analysis information to the ticket system client of the fault vehicle; The ticket system client of the fault vehicle outputs the fault analysis information through a ticket interface.

9. A vehicle failure analysis apparatus characterized by comprising: The device applied to a cloud platform comprises: An information receiving module configured to receive identity information and fault association information of a fault vehicle sent by a ticket system end, wherein the fault association information comprises fault module information of the fault vehicle, and the fault association information in the ticket system end is user input information; A log reading module configured to read a first target log of the fault vehicle from the ticket system end according to the identity information and the fault association information, wherein the identity information and the first target log in the ticket system end are obtained from the fault vehicle; A fault analysis result output module configured to analyze the first target log by using an analysis model built-in to output fault analysis information to the ticket system end, wherein the fault analysis information includes a known fault type and a temporary troubleshooting measure corresponding to the known fault type, and / or unknown fault prompt information.

10. An electronic device, comprising: The electronic device comprises: at least one processor; and a memory connected with the at least one processor in communication; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the vehicle fault analysis method in any one of claims 1-7.

11. A computer readable storage medium, characterized in that, The computer readable storage medium stores computer instructions for enabling the processor to execute the vehicle fault analysis method in any one of claims 1-7 when executed.

Images