Vehicle over-the-air upgrade fault diagnosis method, device, equipment and storage medium

Abstract

The application discloses a kind of vehicle OTA upgrade fault diagnosis method, device, equipment and storage medium, belong to artificial intelligence technical field, the method includes: obtaining real-time abnormal data in the process of target vehicle OTA upgrade;According to real-time abnormal data, from OTA upgrade fault special knowledge base, similar historical fault case is obtained;OTA upgrade fault special knowledge base is constructed according to the OTA upgrade data of sample vehicle;According to real-time abnormal data, similar historical fault case and model prompt word, based on OTA special big model, the target fault diagnosis result and target fault repair scheme corresponding to real-time abnormal data are determined.The application improves the accuracy of vehicle OTA upgrade fault diagnosis.

Description

Technical Field

[0001] This invention relates to the field of artificial intelligence technology, and in particular to a method, apparatus, device, and storage medium for diagnosing vehicle OTA upgrade faults. Background Technology

[0002] Over-the-Air (OTA) updates for vehicles are a core means of software iteration, function upgrades, and performance optimization for intelligent connected vehicles. They provide efficient and accurate fault diagnosis for upgrades, enabling timely handling of various upgrade anomalies, reducing the frequency of vehicle shutdowns for repairs, and ensuring the stable operation of vehicle systems.

[0003] However, existing OTA upgrade fault diagnosis methods mostly rely on traditional case libraries or fixed rules to complete fault judgment and handling. They can only identify standardized known faults and are difficult to deal with complex and unknown faults. They are prone to diagnostic bias and result in insufficient overall diagnostic accuracy. Summary of the Invention

[0004] This invention provides a method, apparatus, device, and storage medium for diagnosing vehicle OTA upgrade faults, thereby improving the accuracy of vehicle OTA upgrade fault diagnosis.

[0005] According to one aspect of the present invention, a method for diagnosing vehicle OTA upgrade faults is provided, the method comprising: Acquire real-time anomaly data during the OTA upgrade process of the target vehicle; Based on real-time anomaly data, similar historical fault cases are obtained from the OTA upgrade fault knowledge base; the OTA upgrade fault knowledge base is constructed based on the OTA upgrade data of sample vehicles. Based on real-time abnormal data, similar historical fault cases, and model prompts, and using a dedicated OTA model, the target fault diagnosis results and target fault repair solutions corresponding to the real-time abnormal data are determined.

[0006] According to another aspect of the present invention, a vehicle OTA upgrade fault diagnosis device is provided, the device comprising: The real-time abnormal data acquisition module is used to acquire real-time abnormal data during the OTA upgrade process of the target vehicle. The module for obtaining similar historical fault cases is used to obtain similar historical fault cases from the OTA upgrade fault knowledge base based on real-time abnormal data; the OTA upgrade fault knowledge base is constructed based on the OTA upgrade data of sample vehicles. The OTA upgrade fault diagnosis module is used to determine the target fault diagnosis result and target fault repair solution corresponding to the real-time abnormal data based on the OTA-specific large model, according to real-time abnormal data, similar historical fault cases and model prompt words.

[0007] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising: At least one processor; and a memory communicatively connected to at least one processor; wherein, The memory stores a computer program that can be executed by at least one processor, such that the at least one processor is able to execute the vehicle OTA upgrade fault diagnosis method according to any embodiment of the present invention.

[0008] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the vehicle OTA upgrade fault diagnosis method of any embodiment of the present invention.

[0009] According to another aspect of the present invention, a computer program product is provided, comprising a computer program that, when executed by a processor, implements the vehicle OTA upgrade fault diagnosis method of any embodiment of the present invention.

[0010] The technical solution of this invention involves acquiring real-time abnormal data during the OTA upgrade process of a target vehicle; obtaining similar historical fault cases from an OTA upgrade fault-specific knowledge base based on the real-time abnormal data; constructing the OTA upgrade fault-specific knowledge base based on the OTA upgrade data of the sample vehicle; and determining the target fault diagnosis result and target fault repair solution corresponding to the real-time abnormal data based on the real-time abnormal data, similar historical fault cases, and model prompt words, using a dedicated OTA model. This technical solution, relying on the OTA upgrade fault-specific knowledge base and the dedicated OTA model, analyzes and processes the real-time abnormal data generated during the OTA upgrade process of the target vehicle to obtain the target fault diagnosis result and target fault repair solution corresponding to the real-time abnormal data. The entire solution fully leverages the transfer learning and intelligent reasoning capabilities of the dedicated OTA model, enabling accurate identification of not only common faults but also effective identification of complex and unknown faults. It overcomes the limitations of existing OTA upgrade fault diagnosis methods, achieving effective identification of various faults during the vehicle OTA upgrade process, thereby improving the accuracy of vehicle OTA upgrade fault diagnosis.

[0011] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present 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

[0012] 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.

[0013] Figure 1 This is a flowchart of a vehicle OTA upgrade fault diagnosis method provided in Embodiment 1 of the present invention; Figure 2 This is a flowchart of a vehicle OTA upgrade fault diagnosis method according to Embodiment 2 of the present invention; Figure 3 This is a schematic diagram of a vehicle OTA upgrade fault diagnosis device according to Embodiment 3 of the present invention; Figure 4 This is a schematic diagram of the structure of an electronic device that implements the vehicle OTA upgrade fault diagnosis method according to an embodiment of the present invention. Detailed Implementation

[0014] 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.

[0015] It should be noted that the terms "target," "first," and "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. 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 apparatus that comprises 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 apparatus.

[0016] Example 1 Figure 1This is a flowchart of a vehicle OTA upgrade fault diagnosis method provided in Embodiment 1 of the present invention. This embodiment is applicable to the diagnosis of real-time faults during the vehicle OTA upgrade process. This method can be executed by a vehicle OTA upgrade fault diagnosis device, which can be implemented in hardware and / or software and can be configured in an electronic device. Figure 1 As shown, the method includes: S101. Obtain real-time abnormal data during the OTA upgrade process of the target vehicle.

[0017] The target vehicle refers to a vehicle undergoing an OTA (Over-The-Air) upgrade. Specifically, real-time anomaly data during the OTA upgrade process can be obtained through the OTA agent built into the target vehicle.

[0018] S102. Based on real-time anomaly data, obtain similar historical fault cases from the OTA upgrade fault knowledge base.

[0019] The "Similar Historical Fault Cases" section refers to historical fault cases in the OTA upgrade fault knowledge base that match real-time anomaly data. This knowledge base is constructed based on the OTA upgrade data of sample vehicles. These sample vehicles are selected based on a comprehensive vehicle model portfolio and industry expert experience. It should be noted that for each sample vehicle, its OTA upgrade data includes data related to in-vehicle terminal upgrades, server upgrades, upgrade network environment data, and historical fault data.

[0020] Among them, the vehicle terminal upgrade-related data refers to the data on the sample vehicle side involved in the OTA upgrade process of the sample vehicle; optionally, the vehicle terminal upgrade-related data includes, but is not limited to, device hardware parameters, software version information, OTA upgrade process logs, and device operating status data. The server upgrade-related data refers to the data on the OTA server side involved in the OTA upgrade process of the sample vehicle; optionally, the server upgrade-related data includes, but is not limited to, OTA upgrade package information, upgrade package push records, upgrade package download records, and upgrade package installation feedback records. The upgrade network environment data refers to the network environment data during the OTA upgrade process of the sample vehicle; optionally, the upgrade network environment data includes, but is not limited to, network bandwidth, network latency, and packet loss rate. Historical fault data refers to fault data that has occurred during the OTA upgrade process of the sample vehicle; optionally, historical fault data includes, but is not limited to, fault type, fault phenomenon, fault root cause, fault diagnosis process, and repair solution.

[0021] Specifically, a retrieval vector can be constructed based on real-time anomaly data, and similar historical fault cases can be obtained from the OTA upgrade fault knowledge base based on the cosine similarity of the retrieval vector.

[0022] S103. Based on real-time abnormal data, similar historical fault cases, and model prompts, determine the target fault diagnosis results and target fault repair solutions corresponding to the real-time abnormal data using the OTA-specific large model.

[0023] Among them, the OTA-specific large model refers to a large language model specifically used to diagnose upgrade faults during the vehicle OTA upgrade process; optionally, the OTA-specific large model is obtained by: acquiring historical vehicle OTA fault data, upgrade fault troubleshooting experience manual, and vehicle OTA upgrade technical specifications; and fine-tuning the preset large model based on the historical vehicle OTA fault data, upgrade fault troubleshooting experience manual, and vehicle OTA upgrade technical specifications to obtain the OTA-specific large model.

[0024] The vehicle OTA historical fault data consists of historical fault data for each sample vehicle. The upgrade fault troubleshooting experience manual is a standardized document summarizing the fault cause analysis, on-site troubleshooting steps, and tiered handling plans accumulated by after-sales engineers and R&D personnel during real-world OTA fault handling. The vehicle OTA upgrade technical specification is a standardized technical document recording the constraints, hardware and software compatibility standards, error code interpretations, upgrade package control rules, fault judgment thresholds, and safety rollback requirements for the entire vehicle OTA upgrade process. The preset large model can be determined according to actual business needs; this embodiment of the invention does not impose specific limitations on it.

[0025] Specifically, based on historical vehicle OTA fault data, upgrade fault troubleshooting experience manuals, and vehicle OTA upgrade technical specifications, a pre-set large model is fine-tuned to obtain an OTA-specific large model. This can be achieved by inputting historical vehicle OTA fault data, upgrade fault troubleshooting experience manuals, and vehicle OTA upgrade technical specifications into the pre-set large model, enabling the pre-set large model to learn the characteristic patterns, troubleshooting logic, and repair solutions of vehicle OTA upgrade faults. Then, model fine-tuning techniques, such as LoRA (Low-Rank Adaptation), are used to fine-tune the pre-set large model to obtain an OTA-specific large model.

[0026] Understandably, by fine-tuning the preset large model based on vehicle OTA historical fault data, upgrade fault troubleshooting experience manuals, and vehicle OTA upgrade technical specifications, a dedicated OTA large model is obtained. This avoids the shortcomings of general large models that arbitrarily deduce faults without considering vehicle specifications, and improves the accuracy of the model's output results.

[0027] Here, "Model Prompt Words" refers to the prompt words for inputting the OTA-specific large model, which can be pre-set according to actual business needs. "Target Fault Diagnosis Result" refers to the fault diagnosis result corresponding to the real-time abnormal data; optionally, the target fault diagnosis result includes, but is not limited to, the target fault type, target fault level, and target fault root cause. "Target Fault Type" refers to the fault type corresponding to the real-time abnormal data; optionally, the target fault type can be an upgrade package fault, network fault, device hardware fault, software compatibility fault, or upgrade process fault. "Target Fault Level" refers to the fault level corresponding to the real-time abnormal data; optionally, the target fault level can be a minor fault, a general fault, or a serious fault. "Target Fault Root Cause" refers to the reason why the target vehicle exhibits real-time abnormal data. "Target Fault Repair Solution" refers to the fault repair solution corresponding to the real-time abnormal data; optionally, the target fault repair solution includes automatic repair instructions or manual intervention suggestions.

[0028] Specifically, real-time abnormal data, similar historical fault cases, and model prompts are input into the OTA-specific large model. After processing by the OTA-specific large model, the target fault diagnosis results and target fault repair solutions corresponding to the real-time abnormal data are output.

[0029] The technical solution of this invention involves acquiring real-time abnormal data during the OTA upgrade process of a target vehicle; obtaining similar historical fault cases from an OTA upgrade fault-specific knowledge base based on the real-time abnormal data; constructing the OTA upgrade fault-specific knowledge base based on the OTA upgrade data of the sample vehicle; and determining the target fault diagnosis result and target fault repair solution corresponding to the real-time abnormal data based on the real-time abnormal data, similar historical fault cases, and model prompt words, using a dedicated OTA model. This technical solution, relying on the OTA upgrade fault-specific knowledge base and the dedicated OTA model, analyzes and processes the real-time abnormal data generated during the OTA upgrade process of the target vehicle to obtain the target fault diagnosis result and target fault repair solution corresponding to the real-time abnormal data. The entire solution fully leverages the transfer learning and intelligent reasoning capabilities of the dedicated OTA model, enabling accurate identification of not only common faults but also effective identification of complex and unknown faults. It overcomes the limitations of existing OTA upgrade fault diagnosis methods, achieving effective identification of various faults during the vehicle OTA upgrade process, thereby improving the accuracy of vehicle OTA upgrade fault diagnosis.

[0030] Based on the above embodiments, as an optional embodiment of the present invention, after determining the target fault diagnosis result and target fault repair plan corresponding to the real-time abnormal data, in order to process the real-time abnormal data in a timely and targeted manner, the target fault repair plan can also be sent to the vehicle terminal or operation and maintenance management terminal of the target vehicle according to the target fault level in the target fault diagnosis result.

[0031] Specifically, if the target fault level in the target fault diagnosis result is a minor or general fault, the target fault repair plan is sent to the vehicle's onboard terminal. The vehicle's built-in OTA agent then executes the automatic repair instructions in the target fault repair plan to repair the real-time abnormal data. If the target fault level in the target fault diagnosis result is a serious fault, the target fault repair plan is sent to the operation and maintenance management terminal. The operation and maintenance management terminal then pushes the target fault repair plan to the operation and maintenance personnel so that they can repair the real-time abnormal data based on the manual intervention suggestions in the target fault repair plan.

[0032] Example 2 Figure 2 This is a flowchart of a vehicle OTA upgrade fault diagnosis method provided in Embodiment 2 of the present invention. Based on the above embodiments, this embodiment further optimizes the step of "obtaining similar historical fault cases from an OTA upgrade fault-specific knowledge base based on real-time abnormal data," providing an optional implementation scheme. It should be noted that parts not detailed in this embodiment can be referred to in the relevant descriptions of other embodiments. For example... Figure 2 As shown, the method includes: S201. Obtain real-time abnormal data during the OTA upgrade process of the target vehicle.

[0033] The real-time anomaly data includes the target vehicle identification number (VIN). The target vehicle identification number is the vehicle identification number (VIN) of the target vehicle, used to uniquely identify the target vehicle.

[0034] S202. Based on real-time abnormal data and a preset search template, generate the target search text.

[0035] The target search text refers to the search text generated based on real-time anomaly data. The preset search template includes at least the following: fault stage, fault manifestation, error code, and key anomaly indicators. Fault stage refers to the stage at which the fault occurs; for example, the fault stage could be the OTA upgrade package download stage, the OTA upgrade package installation stage, or the OTA upgrade package version verification stage. Fault manifestation refers to the external symptoms of the fault; for example, the fault manifestation could be an upgrade progress freeze, an OTA upgrade package download interruption, or an OTA upgrade package installation failure. Error code refers to a pre-configured standardized code used to uniquely identify the fault type, anomaly cause, and problem source, accompanied by official interpretation rules. Key anomaly indicators refer to core monitoring parameters that exceed the compliance range during the vehicle OTA upgrade process and can be used to determine a fault.

[0036] Specifically, the required content can be extracted from the preset search template from real-time abnormal data and filled into the corresponding position in the preset search template to obtain the target search text.

[0037] S203. Embed the target retrieval text to obtain the target retrieval vector.

[0038] Here, the target retrieval vector refers to the vector obtained after embedding the target retrieval text. Specifically, the target retrieval text is input into a preset embedding model, and the target retrieval text is embedded through the preset embedding model to obtain the target retrieval vector.

[0039] S204. Based on the target vehicle identification code and the target retrieval vector, obtain similar historical fault cases from the OTA upgrade fault-specific knowledge base.

[0040] Specifically, based on the target vehicle identification code, the first fault case vector is extracted from the fault case vector library in the OTA upgrade fault special knowledge base; based on the preset similarity threshold, the target retrieval vector and all the obtained first fault case vectors, the first matching result is determined; based on the first matching result, similar historical fault cases are determined.

[0041] The first fault case vector refers to historical fault cases involving the target vehicle identification code in the fault case vector library of the OTA upgrade fault knowledge base. The preset similarity threshold can be pre-set according to actual business needs or expert experience of those skilled in the art; this embodiment of the invention does not specifically limit it. The first matching result refers to the matching result between the target retrieval vector and the first fault case vector.

[0042] More specifically, using the target vehicle identification code as an index, historical fault cases related to the target vehicle identification code are extracted from the fault case vector library in the OTA upgrade fault-specific knowledge base, and used as the first fault case vector; the similarity between the target retrieval vector and each first fault case vector is calculated, and the first matching result is determined based on the similarity being greater than a preset similarity threshold; if the first matching result is a matching first fault case vector, the historical fault cases corresponding to the matching first fault case vector are used as similar historical fault cases; if the first matching result is a non-matching first fault case vector, the target vehicle identification code is parsed to obtain the target vehicle model identifier; based on the target vehicle model identifier, the second fault case vector is extracted from the fault case vector library; based on the preset similarity threshold, the target retrieval vector, and all obtained second fault case vectors, similar historical fault cases are determined.

[0043] The target vehicle model identifier is used to uniquely identify the model of the target vehicle. The second fault case vector refers to the historical fault cases related to the target vehicle model identifier in the fault case vector library of the OTA upgrade fault knowledge base.

[0044] Specifically, based on a preset similarity threshold, the target retrieval vector, and all obtained second fault case vectors, similar historical fault cases are determined. This can be achieved by: calculating the similarity between the target retrieval vector and each second fault case vector, and detecting whether there are second fault case vectors with a similarity greater than the preset similarity threshold; if so, using the second fault case vector with a similarity greater than the preset similarity threshold as the matching fault case vector; obtaining historical fault cases corresponding to the matching fault case vectors from the OTA upgrade fault-specific knowledge base as similar historical fault cases; if not, calculating the similarity between the target retrieval vector and each fault case vector in the fault case vector library of the OTA upgrade fault-specific knowledge base, and using a similarity greater than the preset similarity threshold as the matching criterion, obtaining similar historical fault cases from the OTA upgrade fault-specific knowledge base.

[0045] Understandably, based on the target vehicle identification code and the target retrieval vector, the system prioritizes searching the private fault case vector library corresponding to the target vehicle (i.e., the fault case vector library composed of all first fault case vectors), then searches the fault case vector library of the same vehicle model (i.e., the fault case vector library composed of all second fault case vectors), and finally performs a full-domain search in the fault case vector library in the OTA upgrade fault-specific knowledge base. If no results are found in the local fault case vector library, a fallback search is performed using the fault case vector library in the OTA upgrade fault-specific knowledge base. This ensures the completeness and success rate of similar historical fault case retrieval, while also improving the retrieval efficiency of similar historical fault cases.

[0046] S205. Based on real-time abnormal data, similar historical fault cases, and model prompts, determine the target fault diagnosis results and target fault repair solutions corresponding to the real-time abnormal data using the OTA-specific large model.

[0047] The technical solution of this invention involves acquiring real-time abnormal data during the OTA upgrade process of a target vehicle; generating target retrieval text based on a preset retrieval template using the real-time abnormal data; embedding the target retrieval text to obtain a target retrieval vector; obtaining similar historical fault cases from an OTA upgrade fault-specific knowledge base based on the target vehicle identification code and the target retrieval vector; and determining the target fault diagnosis result and target fault repair solution corresponding to the real-time abnormal data based on the real-time abnormal data, similar historical fault cases, and model prompt words, using an OTA-specific large-scale model. This technical solution, relying on an OTA upgrade fault-specific knowledge base and an OTA-specific large-scale model, analyzes and processes the real-time abnormal data generated during the OTA upgrade process of a target vehicle to obtain the target fault diagnosis result and target fault repair solution corresponding to the real-time abnormal data. The entire solution fully leverages the transfer learning and intelligent reasoning capabilities of the OTA-specific large-scale model, enabling accurate identification of not only conventional faults but also effective identification of complex and unknown faults. It overcomes the limitations of existing OTA upgrade fault diagnosis methods, achieving effective identification of various faults during the vehicle OTA upgrade process, thereby improving the accuracy of vehicle OTA upgrade fault diagnosis.

[0048] Based on the above embodiments, as an optional approach of this invention, after determining the target fault diagnosis result and target fault repair plan corresponding to the real-time abnormal data, in order to achieve dynamic incremental iterative improvement of the OTA upgrade fault-specific knowledge base and continuously optimize the retrieval accuracy of subsequent fault cases and the fault diagnosis capability of the OTA-specific large model, the target fault diagnosis result and target fault repair plan can also be added to the OTA upgrade fault-specific knowledge base.

[0049] Example 3 Figure 3 This is a schematic diagram of a vehicle OTA upgrade fault diagnosis device provided in Embodiment 3 of the present invention. This embodiment is applicable to the diagnosis of real-time faults during the vehicle OTA upgrade process. The device can be implemented in hardware and / or software and can be configured in an electronic device. Figure 3 As shown, the device includes: The real-time abnormal data acquisition module 301 is used to acquire real-time abnormal data during the OTA upgrade process of the target vehicle. The similar historical fault case acquisition module 302 is used to acquire similar historical fault cases from the OTA upgrade fault special knowledge base based on real-time abnormal data; the OTA upgrade fault special knowledge base is constructed based on the OTA upgrade data of the sample vehicles; The OTA upgrade fault diagnosis module 303 is used to determine the target fault diagnosis result and target fault repair solution corresponding to the real-time abnormal data based on the OTA-specific large model, according to real-time abnormal data, similar historical fault cases and model prompt words.

[0050] The technical solution of this invention involves acquiring real-time abnormal data during the OTA upgrade process of a target vehicle; obtaining similar historical fault cases from an OTA upgrade fault-specific knowledge base based on the real-time abnormal data; constructing the OTA upgrade fault-specific knowledge base based on the OTA upgrade data of the sample vehicle; and determining the target fault diagnosis result and target fault repair solution corresponding to the real-time abnormal data based on the real-time abnormal data, similar historical fault cases, and model prompt words, using a dedicated OTA model. This technical solution, relying on the OTA upgrade fault-specific knowledge base and the dedicated OTA model, analyzes and processes the real-time abnormal data generated during the OTA upgrade process of the target vehicle to obtain the target fault diagnosis result and target fault repair solution corresponding to the real-time abnormal data. The entire solution fully leverages the transfer learning and intelligent reasoning capabilities of the dedicated OTA model, enabling accurate identification of not only common faults but also effective identification of complex and unknown faults. It overcomes the limitations of existing OTA upgrade fault diagnosis methods, achieving effective identification of various faults during the vehicle OTA upgrade process, thereby improving the accuracy of vehicle OTA upgrade fault diagnosis.

[0051] Optionally, the real-time anomaly data may include the target vehicle identification number; The similar historical fault case acquisition module 302 includes: The target retrieval text generation unit is used to generate target retrieval text based on real-time anomaly data and a preset retrieval template. The preset retrieval template includes at least the following: fault stage, fault manifestation, error code, and key anomaly indicators. The target retrieval vector determination unit is used to embed the target retrieval text to obtain the target retrieval vector; The similar historical fault case acquisition unit is used to acquire similar historical fault cases from the OTA upgrade fault-specific knowledge base based on the target vehicle identification code and the target retrieval vector.

[0052] Optional, the similar historical fault case acquisition unit includes: The first fault case vector extraction subunit is used to extract the first fault case vector from the fault case vector library in the OTA upgrade fault-specific knowledge base based on the target vehicle identification code. The first matching result determination subunit is used to determine the first matching result based on the preset similarity threshold, the target retrieval vector, and all obtained first fault case vectors; The similar historical fault case acquisition sub-unit is used to determine similar historical fault cases based on the first matching result.

[0053] Optionally, a sub-unit for obtaining similar historical fault cases is used specifically for: If the first matching result is a first fault case vector that has a match, then the historical fault case corresponding to the first fault case vector is taken as a similar historical fault case. If the first matching result is a first fault case vector that does not have a match, then the target vehicle identification code is parsed to obtain the target vehicle model identifier; Based on the target vehicle model identifier, extract the second fault case vector from the fault case vector library; Based on the preset similarity threshold, the target retrieval vector, and all obtained second fault case vectors, similar historical fault cases are determined.

[0054] Optionally, the device also includes an OTA-specific large model determination module, which is specifically used for: Acquire historical vehicle OTA fault data, upgrade fault troubleshooting experience manuals, and vehicle OTA upgrade technical specifications; Based on historical vehicle OTA fault data, upgrade fault troubleshooting experience manual, and vehicle OTA upgrade technical specifications, the preset large model is fine-tuned to obtain a dedicated OTA large model.

[0055] Optionally, the device may also include: The OTA upgrade fault-specific knowledge base update module is used to add the target fault diagnosis results and target fault repair solutions to the OTA upgrade fault-specific knowledge base after determining the target fault diagnosis results and target fault repair solutions corresponding to the real-time abnormal data.

[0056] Optionally, the device may also include: The fault repair solution sending module is used to send the target fault repair solution to the vehicle terminal or operation and maintenance management terminal of the target vehicle according to the target fault level in the target fault diagnosis result after determining the target fault diagnosis result and target fault repair solution corresponding to the real-time abnormal data.

[0057] The vehicle OTA upgrade fault diagnosis device provided in this embodiment of the invention can execute the vehicle OTA upgrade fault diagnosis method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects for executing each vehicle OTA upgrade fault diagnosis method.

[0058] According to embodiments of the present invention, the present invention also provides an electronic device, a readable storage medium, and a computer program product.

[0059] Example 4 Figure 4A schematic diagram of an electronic device 10, which 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 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.

[0060] like Figure 4 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded into the RAM 13 from storage unit 18. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0061] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0062] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 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 11 performs the various methods and processes described above, such as vehicle OTA upgrade fault diagnosis methods.

[0063] In some embodiments, the vehicle OTA upgrade fault diagnosis method can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the vehicle OTA upgrade fault diagnosis method described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to perform the vehicle OTA upgrade fault diagnosis method by any other suitable means (e.g., by means of firmware).

[0064] 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.

[0065] 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.

[0066] 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.

[0067] 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).

[0068] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or 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.

[0069] 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.

[0070] 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.

[0071] 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.

Claims

1. A method for diagnosing vehicle OTA upgrade faults, characterized in that, include: Acquire real-time anomaly data during the OTA upgrade process of the target vehicle; Based on the real-time anomaly data, similar historical fault cases are obtained from the OTA upgrade fault special knowledge base; the OTA upgrade fault special knowledge base is constructed based on the OTA upgrade data of the sample vehicles; Based on the real-time abnormal data, similar historical fault cases, and model prompts, and using the OTA-specific large model, the target fault diagnosis result and target fault repair solution corresponding to the real-time abnormal data are determined.

2. The method according to claim 1, characterized in that, The real-time anomaly data includes the target vehicle identification code; The step of obtaining similar historical fault cases from the OTA upgrade fault knowledge base based on the real-time anomaly data includes: Based on the real-time anomaly data, a target search text is generated using a preset search template; the preset search template includes at least the following: fault stage, fault manifestation, error code, and key anomaly indicators; The target retrieval text is embedded to obtain the target retrieval vector; Based on the target vehicle identification code and the target retrieval vector, similar historical fault cases are obtained from the OTA upgrade fault-specific knowledge base.

3. The method according to claim 2, characterized in that, The step of obtaining similar historical fault cases from the OTA upgrade fault-specific knowledge base based on the target vehicle identification code and the target retrieval vector includes: Based on the target vehicle identification code, extract the first fault case vector from the fault case vector library in the OTA upgrade fault special knowledge base; The first matching result is determined based on the preset similarity threshold, the target retrieval vector, and all obtained first fault case vectors; Based on the first matching result, similar historical fault cases are identified.

4. The method according to claim 3, characterized in that, The step of determining similar historical fault cases based on the first matching result includes: If the first matching result is a first fault case vector that has a match, then the historical fault case corresponding to the first fault case vector is taken as a similar historical fault case. If the first matching result is a first fault case vector that does not have a match, then the target vehicle identification code is parsed to obtain the target vehicle model identifier; Based on the target vehicle model identifier, extract the second fault case vector from the fault case vector library; Based on the preset similarity threshold, the target retrieval vector, and all obtained second fault case vectors, similar historical fault cases are determined.

5. The method according to claim 1, characterized in that, The OTA-specific large model is obtained in the following way: Acquire historical vehicle OTA fault data, upgrade fault troubleshooting experience manuals, and vehicle OTA upgrade technical specifications; Based on the vehicle's historical OTA fault data, the upgrade fault troubleshooting experience manual, and the vehicle's OTA upgrade technical specifications, the preset large model is fine-tuned to obtain a dedicated OTA large model.

6. The method according to claim 1, characterized in that, After determining the target fault diagnosis result and target fault repair plan corresponding to the real-time abnormal data, the method further includes: The target fault diagnosis results and the target fault repair plan are added to the OTA upgrade fault-specific knowledge base.

7. The method according to claim 1, characterized in that, After determining the target fault diagnosis result and target fault repair plan corresponding to the real-time abnormal data, the method further includes: Based on the target fault level in the target fault diagnosis result, the target fault repair plan is sent to the vehicle terminal or operation and maintenance management terminal of the target vehicle.

8. A vehicle OTA upgrade fault diagnosis device, characterized in that, include: The real-time abnormal data acquisition module is used to acquire real-time abnormal data during the OTA upgrade process of the target vehicle. The similar historical fault case acquisition module is used to acquire similar historical fault cases from the OTA upgrade fault special knowledge base based on the real-time abnormal data; the OTA upgrade fault special knowledge base is constructed based on the OTA upgrade data of the sample vehicles. The OTA upgrade fault diagnosis module is used to determine the target fault diagnosis result and target fault repair plan corresponding to the real-time abnormal data based on the real-time abnormal data, the similar historical fault cases and model prompt words, and the OTA-specific large model.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the vehicle OTA upgrade fault diagnosis method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the vehicle OTA upgrade fault diagnosis method according to any one of claims 1-7.

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