A fault code processing method and device, electronic equipment and storage medium

Abstract

The application discloses a fault code processing method and device, electronic equipment and a storage medium. The method comprises the following steps: in response to a diagnosis operation of a diagnosis device on a target vehicle, obtaining at least one fault code of the target vehicle and target data associated with the target vehicle; determining the basic fault causes corresponding to each fault code; generating target fault causes corresponding to each basic fault cause according to the target data and the basic fault causes of each fault code; for each fault code, generating fault description content corresponding to the fault code based on the target fault causes corresponding to the basic fault causes of the fault code; and generating and displaying a response result for the diagnosis operation based on the fault description content corresponding to each fault code. In this way, the target fault causes are generated according to the target data of the vehicle and the basic fault causes of the fault codes contained in the vehicle, so that the intelligibility of the fault description content of the output fault codes is improved.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to a fault code processing method, apparatus, electronic device, and storage medium. Background Technology

[0002] With the increasing level of automotive electronics, on-board diagnostics (OBD) systems have become an important technical means for vehicle fault detection and repair. Currently, traditional automotive fault code reading tools mainly establish a communication connection with the vehicle's electronic control unit (ECU) to read the diagnostic fault codes (DTCs) stored in the ECU and output standardized glossary entries corresponding to the fault codes.

[0003] However, since standardized terminology explanations are fixed and the output format is only engineering terminology geared towards professional repair personnel, non-professional repair personnel may find it difficult to understand these standardized terminology explanations. This results in poor comprehensibility of the standardized terminology explanations output by existing automotive fault code reading tools. Summary of the Invention

[0004] This application provides a fault code processing method, apparatus, electronic device, and storage medium. By generating a targeted fault cause (i.e., for the vehicle) based on the vehicle's target data and the basic fault causes of the fault codes contained in the vehicle, the understandability of the fault description content of the output fault codes is improved.

[0005] In a first aspect, embodiments of this application provide a fault code processing method applied to diagnostic equipment, the method comprising: In response to a diagnostic operation performed on a target vehicle using a diagnostic device, at least one fault code of the target vehicle and target data associated with the target vehicle are obtained, wherein the target data is used to assist in expanding the causes of the faults in the target vehicle. Based on the correspondence between preset fault codes and preset basic fault causes, determine the basic fault cause corresponding to each fault code; Based on the target data and the basic fault causes of each fault code, generate the target fault causes corresponding to each basic fault cause. For each fault code, based on the target fault cause corresponding to the basic fault cause of the fault code, generate the fault description content corresponding to the fault code. Based on the fault description corresponding to each fault code, generate and display the response results for the diagnostic operation.

[0006] Secondly, embodiments of this application provide a fault code processing device applied to diagnostic equipment, the device comprising: The data acquisition module is configured to, in response to a diagnostic operation performed on the target vehicle by the diagnostic device, acquire at least one fault code of the target vehicle and target data associated with the target vehicle, wherein the target data is used to assist in expanding the causes of the faults of the target vehicle. The cause acquisition module is used to determine the basic fault cause corresponding to each fault code based on the correspondence between the preset fault codes and the preset basic fault causes. The cause generation module is used to generate target fault causes corresponding to each of the basic fault causes based on the target data and the basic fault causes of each fault code; The content generation module is used to generate fault description content corresponding to each fault code based on the target fault cause corresponding to the basic fault cause of the fault code. The response module is used to generate and display the response result for the diagnostic operation based on the fault description content corresponding to each fault code.

[0007] Furthermore, embodiments of this application also provide a controller, including one or more processors and a memory, wherein the memory stores a computer program, and the processor is used to run the computer program in the memory to implement any of the fault code processing methods provided in embodiments of this application.

[0008] Furthermore, this application embodiment also provides a storage medium storing a computer program. When the computer program is run on a controller, the computer program is used to cause the controller to execute any of the fault code handling methods provided in this application embodiment.

[0009] Furthermore, this application also provides a computer program product, including a computer program or instructions, which, when executed by a processor, implement any of the fault code handling methods provided in this application.

[0010] In this embodiment, in response to a diagnostic operation performed on a target vehicle by a diagnostic device, at least one fault code of the target vehicle and target data associated with the target vehicle are obtained. The target data is used to assist in expanding the causes of the target vehicle's faults. Based on the correspondence between preset fault codes and preset basic fault causes, the basic fault cause corresponding to each fault code is determined. Based on the target data and the basic fault causes of each fault code, a target fault cause corresponding to each basic fault cause is generated. For each fault code, a fault description is generated based on the target fault cause corresponding to the basic fault cause of the fault code. Based on the fault descriptions corresponding to each fault code, a response result for the diagnostic operation is generated and displayed. Thus, by generating targeted (i.e., vehicle-specific) target fault causes based on the vehicle's target data and the basic fault causes of the fault codes contained in the vehicle, the understandability of the fault descriptions of the output fault codes is improved. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] To gain a more complete understanding of this application and its beneficial effects, the following description will be provided in conjunction with the accompanying drawings, wherein the same reference numerals in the following description denote the same parts.

[0013] Figure 1 This is a schematic diagram of an implementation scenario provided in the embodiments of this application; Figure 2 This is a flowchart illustrating the fault code handling method provided in the embodiments of this application; Figure 3 This is a flowchart illustrating a specific embodiment of the fault code handling method provided in this application. Figure 4 This is a schematic diagram of the architecture of a specific embodiment of the fault code handling method provided in this application. Figure 5 This is another schematic diagram of the architecture of a specific embodiment of the fault code handling method provided in this application; Figure 6 This is a timing diagram of a specific embodiment of the fault code handling method provided in this application. Figure 7 This is a schematic diagram of the fault code processing device provided in the embodiments of this application; Figure 8 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0014] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0015] Furthermore, in the embodiments of this application, "multiple" refers to two or more. The terms "first" and "second," etc., in the embodiments of this application are used for distinguishing descriptions and should not be construed as implying relative importance.

[0016] With the rapid development of automotive electronic control systems, modern vehicles are generally equipped with a large number of electronic control units (ECUs) to control various functions such as the engine, transmission, braking system, and vehicle stability system. These systems typically output standardized diagnostic trouble codes (DTCs) through OBD-II (On-Board Diagnostics, second-generation vehicle diagnostic system) to indicate potential vehicle malfunctions or abnormal conditions. Despite the emergence of various diagnostic tools, users (such as ordinary car owners, auto mechanics, and vehicle service providers) face increasingly prominent challenges in fault identification and repair decisions. For example, traditional code readers usually only provide fixed fault code interpretations, and the meanings of fault codes differ between different brands and models. Simultaneously, the need for multi-source integration of sensor data and user input information is becoming increasingly complex, making it difficult for non-professional users to understand the causes and solutions to faults, while professional technicians also face limitations in diagnostic efficiency and high learning costs.

[0017] Current on-board diagnostic methods primarily rely on traditional OBD-II code readers and their accompanying applications. Fault codes are read manually using these tools, and standardized interpretations are retrieved from a fixed database. However, diagnostic tools are specialized tools, and their output often consists of fixed engineering terminology (e.g., "P0301—Ignition fault in cylinder X"), posing a significant understanding and operational challenge for non-professional vehicle users. Furthermore, existing tools struggle to automatically integrate OBD-II fault codes, data from various vehicle sensors, and user input, resulting in fault interpretations and repair suggestions lacking contextual relevance to specific operating conditions and failing to accurately reflect the vehicle's operational status. Therefore, current on-board diagnostic methods generally suffer from unintuitive fault code interpretations, difficulties in integrating multi-source data, and high overall operational complexity.

[0018] Research has revealed that while Large Language Models (LLMs) possess rich knowledge reserves and powerful natural language generation capabilities, and some cloud services or platforms have attempted to introduce them into the field of automotive diagnostics, current applications are mostly limited to providing standard explanations and preliminary analyses based on lightweight rules or small models. In actual diagnostic processes, specific fault interpretations still primarily rely on static databases, making it difficult to combine specific vehicle models, historical fault data, driving environments, and the status of related subsystems for dynamic reasoning and personalized diagnosis. Furthermore, functions such as fault risk level assessment, processing priority ranking, multi-vehicle adaptation, and multi-turn question-and-answer interaction often still require manual judgment or are performed separately in multiple different systems. Therefore, existing solutions applying LLMs to automotive diagnostics mainly focus on improving fault information retrieval efficiency, but still fail to effectively address the following issues: lack of natural language and contextualized expression in fault code interpretation, difficulty in personalizing diagnostic results, insufficient multi-source data integration capabilities, and poor user interaction experience.

[0019] To address at least some of the aforementioned problems, embodiments of this application provide a fault code processing method, apparatus, electronic device, storage medium, and computer program product. The fault code processing apparatus can be integrated into an electronic device, which may be a server, such as a fault code processing system, or a terminal controlled by the fault code processing system.

[0020] The server can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, network acceleration services (Content Delivery Network, CDN), as well as big data and artificial intelligence platforms.

[0021] The terminal can be a smartphone, tablet, laptop, desktop computer, smart speaker, smartwatch, etc., but is not limited to these. The terminal and the server can be connected directly or indirectly through wired or wireless communication, which is not limited herein.

[0022] Please see Figure 1 Taking the integration of fault code processing devices into electronic devices as an example, Figure 1This is a schematic diagram illustrating an implementation scenario of the fault code processing method provided in this application. The electronic device can be a diagnostic device such as a diagnostic instrument. In response to a diagnostic operation performed on a target vehicle by the diagnostic device, it acquires at least one fault code of the target vehicle and target data associated with the target vehicle. The target data is used to assist in expanding the causes of the target vehicle's faults. Based on the correspondence between preset fault codes and preset basic fault causes, it determines the basic fault cause corresponding to each fault code. Based on the target data and the basic fault causes of each fault code, it generates target fault causes corresponding to each basic fault cause. For each fault code, based on the target fault causes corresponding to the basic fault causes of the fault code, it generates a fault description content corresponding to the fault code. Based on the fault description content corresponding to each fault code, it generates and displays the response result to the diagnostic operation. Thus, by generating targeted (i.e., vehicle-specific) target fault causes based on the vehicle's target data and the basic fault causes of the fault codes contained in the vehicle, the understandability of the fault description content of the output fault codes is improved.

[0023] It should be noted that, Figure 1 The illustrated scenario of the fault code handling method is merely an example. The implementation environment of the fault code handling method described in this application is for the purpose of more clearly illustrating the technical solution of this application and does not constitute a limitation on the technical solution provided in this application. Those skilled in the art will understand that with the evolution of data processing and the emergence of new business scenarios, the technical solution provided in this application is also applicable to similar technical problems.

[0024] The solutions provided in this application are specifically illustrated through the following embodiments. It should be noted that the order of description of the following embodiments is not intended to limit the preferred order of the embodiments.

[0025] This embodiment will be described from the perspective of a fault code processing device, which can be integrated into an electronic device, such as a terminal device and / or a server, and this application does not impose any limitations on it.

[0026] Please see Figure 2 , Figure 2 This is a flowchart illustrating a fault code processing method provided in an embodiment of this application. The fault code processing method is applied to a diagnostic device and may include the following steps S101 to S104: Step S101: In response to a diagnostic operation performed on the target vehicle by the diagnostic equipment, at least one fault code of the target vehicle and target data associated with the target vehicle are obtained, wherein the target data is used to assist in expanding the cause of the fault of the target vehicle.

[0027] Diagnostic equipment refers to devices capable of establishing a data communication link with the target vehicle that conforms to standard on-board diagnostic protocols, and performing diagnostic operations such as fault code reading, data requesting, and command sending. The target vehicle refers to the specific vehicle entity that is the object of the current diagnostic operation. Diagnostic equipment includes, but is not limited to, handheld code readers with OBD interface connectivity, mobile terminals (such as smartphones or tablets) that establish wireless communication connections with the vehicle via Bluetooth / Wi-Fi / cellular networks, in-vehicle infotainment systems with integrated diagnostic adapter modules, and remote diagnostic servers deployed in the cloud that interact with the vehicle via remote communication networks; no restrictions are imposed here.

[0028] Diagnostic operation refers to the action of establishing a communication connection between diagnostic equipment and the target vehicle and performing fault reading. Specifically, the diagnostic equipment can establish a data link with the electronic control unit (ECU) of the target vehicle through the OBD interface, CAN bus interface, or wireless communication module (such as Bluetooth / Wi-Fi adapter). In response to the user's read command triggered by the diagnostic equipment, it reads the currently existing diagnostic fault codes (DTCs) from the ECU. It should be noted that the acquisition of fault codes and target data is completed within the same diagnostic session to ensure that the target data and fault codes correspond in terms of timing and operating conditions, avoiding the generation of incorrect fault cause extension results based on outdated or irrelevant vehicle status data.

[0029] Among them, target data refers to a set of multi-dimensional vehicle status data associated with the target vehicle and used to assist in expanding the causes of the target vehicle's failure.

[0030] In some embodiments, the target data associated with the target vehicle includes one or more of the following: basic vehicle information, historical fault data, vehicle operation data, behavioral information of the person triggering the diagnostic operation, and component association information of various components in the target vehicle. There is no limitation on these aspects. Basic vehicle information refers to the inherent factory attributes and identification information of the target vehicle, including but not limited to the Vehicle Identification Number (VIN), brand, model, year, engine type, transmission type, and powertrain configuration. Historical fault data refers to historical diagnostic fault codes and repair history information recorded in a time sequence and associated with the target vehicle identifier (such as VIN or user account). Historical fault data includes not only the historical fault codes themselves but also the vehicle mileage at the time of the fault occurrence, the frequency of fault occurrence, historical repair operation records, and solutions. Vehicle operation data refers to the dynamic operating parameters of the vehicle collected in real time by onboard sensors and the bus system at the time of diagnostic operation or fault triggering period. This includes, but is not limited to, engine speed, coolant temperature, oil pressure, fuel trim value, air-fuel ratio sensor voltage, transmission gear, vehicle speed, brake pressure, and ABS operating status. Personnel behavior information refers to the driving behavior characteristics, operating habits, and subjective descriptions of fault symptoms of the personnel (including but not limited to vehicle users, maintenance technicians, or remote diagnostic platform operators) who trigger the current diagnostic operation. Behavioral information includes both objectively recorded driving behavior data (such as frequency of rapid acceleration, average daily mileage, common route types, and parking location distribution) and actively inputted fault symptom text (such as "vehicle vibration when accelerating to 80km / h" and "black smoke from the exhaust pipe during cold start"). Component association information refers to the electrical coupling relationships, mechanical connection relationships, and signal dependency paths between various subsystems and components of the target vehicle. Component association information includes both preset vehicle topology data (such as the signal interaction path between the engine management system and the emission control system) and status parameters of associated subsystems read at the time of diagnosis (such as tire pressure, tire temperature, battery voltage, SOC, and electronic control unit operating status).

[0031] Step S102: Determine the basic fault cause corresponding to each fault code based on the correspondence between the preset fault codes and the preset basic fault causes.

[0032] The basic fault cause refers to a standardized fault description that establishes a fixed correspondence with specific diagnostic fault codes based on a preset fault code parsing rule base. This description usually comes from the OBD-II standard protocol or manufacturer technical documents, and only describes the general system abnormality or component failure mode pointed to by the fault code, without involving the specific vehicle's operating condition adaptation or historical evolution analysis. For example, the basic fault cause corresponding to fault code P0301 in the rule base is "misfire detected in cylinder 1," which only indicates that the electronic control unit detected an abnormal combustion in that cylinder, but does not further distinguish whether the misfire is caused by a specific subsystem such as the ignition system, fuel supply system, or mechanical compression system, nor does it combine the vehicle's real-time operating parameters for refined cause attribution.

[0033] The correspondence between preset fault codes and preset basic fault causes refers to the query mapping relationships stored in a pre-built fault code mapping rule base. This rule base uses fault code identifiers as search keys and standardized fault descriptions as basic fault causes, forming one-to-one or many-to-one mapping entries. When the diagnostic equipment reads a fault code from the target vehicle, the system uses that fault code as the query condition to search the rule base and extract the matching basic fault cause, thus completing the conversion from fault code to general fault attribution. This conversion process is static rule matching and does not involve dynamic reasoning based on individual vehicle data.

[0034] Taking fault code P0301 as an example, after the diagnostic equipment reads the fault code through the on-board diagnostic interface, the system queries the preset rule base to determine its basic fault cause as "Misfire detected in the first cylinder, general attribution: ignition, fuel or mechanical system abnormality". This basic fault cause serves as the starting point for subsequent processing. It combines the target data associated with the target vehicle and generates a target fault cause that is adapted to the current vehicle operating condition through extended processing.

[0035] Step S103: Based on the target data and the basic fault causes of each fault code, generate the target fault cause corresponding to each basic fault cause.

[0036] Among them, the target fault cause refers to the vehicle-specific fault cause obtained after performing information expansion processing on the basic fault cause based on the target data.

[0037] There are several ways to expand this information. In some optional embodiments, the step of "generating the target fault cause corresponding to each basic fault cause based on the target data and the basic fault causes of each fault code" may include: encoding the basic fault cause of each fault code to obtain a first feature vector; encoding multiple sub-data in the target data to obtain a second feature vector corresponding to each sub-data, wherein the multiple sub-data includes at least context sub-data used to characterize the context environment of the target vehicle; fusing the first feature vector and each second feature vector to obtain a fused feature vector; and obtaining the target fault cause for the target vehicle under the fault code based on the fused feature vector. The sub-data of the target data may include, but is not limited to, the vehicle basic information of the target vehicle, the historical fault data of the target vehicle, the vehicle operation data of the target vehicle, the behavior information of the personnel who triggered the diagnostic operation, and the component association information of each component in the target vehicle.

[0038] It should be noted that this information expansion processing is not a simple text replacement or template splicing. Instead, the intelligent model expands the basic fault causes based on the target data, such as vehicle operating status parameters and external environment perception data, to adapt to different operating conditions; it corrects the evolution trend based on historical fault time-series data; and it expands the scope of influence based on the status data of related subsystems. After the above processing, the basic fault causes are transformed from fixed standardized descriptions into target fault causes that include fault phenomenon layer elements, fault mechanism layer elements, and system influence path layer elements, making them highly compatible with the current actual operating conditions of the target vehicle.

[0039] Step S104: For each fault code, generate a fault description based on the target fault cause corresponding to the basic fault cause of the fault code.

[0040] The fault description refers to the comprehensive diagnostic output information for the target vehicle under a specific fault code. This information is not a simple list of fault terms, but rather a structured information output to the user terminal after in-depth semantic expansion of the basic fault causes based on the target data.

[0041] In some embodiments, the method further includes: for each fault code, generating a repair suggestion for the target vehicle under the fault code based on the target data and the fault cause of the fault code, wherein the fault cause includes a basic fault cause and a target fault cause.

[0042] The maintenance recommendations refer to the handling strategies generated based on the fault code's cause (basic fault cause and target fault cause) and target data, tailored to the specific technical condition of the target vehicle. Based on the system impact path in the fault code's cause, the root cause and derivative components are identified, generating a cascaded maintenance operation sequence from root cause repair to downstream verification. Simultaneously, potential degradation risks are assessed using related subsystem status data, and preventative maintenance recommendations for related components are added when necessary. Furthermore, based on current driving scenario parameters and external environment perception data, specific operational precautions adapted to the current driving conditions are generated. Therefore, the maintenance recommendations and the fault code's cause together constitute a complete fault handling guide for the target vehicle.

[0043] Based on this, the step "Generating the fault description content corresponding to the fault code based on the target fault cause corresponding to the basic fault cause of the fault code" includes: generating the fault description content corresponding to the fault code based on the target fault cause and maintenance suggestions corresponding to the basic fault cause of the fault code.

[0044] It should be noted that the fault description may include, but is not limited to, the target fault cause and repair recommendations. The target fault cause refers to the individualized fault cause for the target vehicle. Repair recommendations refer to specific handling strategies for the current state of the target vehicle. Furthermore, based on the depth of diagnosis and user interaction needs, the fault description can be further expanded to include at least one of the following: risk level and handling priority, fault impact path description, related component inspection recommendations, fault evolution prediction, driving scenario constraints, and user interaction control parameters. There are no restrictions on this.

[0045] In some embodiments, a pre-trained intelligent model can be used to generate the target fault cause and repair suggestions for the target vehicle under each fault code, based on the target data and the underlying fault cause of the fault code.

[0046] The intelligent model refers to a model with semantic understanding and generation capabilities, which can be deployed on a cloud server or locally on the diagnostic device, depending on system resource configuration and real-time requirements. When the intelligent model is deployed in the cloud, the diagnostic device uploads fault codes and target data to the cloud, where the intelligent model performs inference calculations and then sends the fault description back to the diagnostic device. When the intelligent model is deployed locally, it can be embedded in the local processing unit of the diagnostic device in a lightweight form, directly completing inference and outputting fault descriptions locally without relying on external network connections, thus meeting data privacy protection requirements or real-time response needs in weak network environments.

[0047] It should be noted that the specific network architecture and training method of the intelligent model can be adaptively selected according to the actual application scenario, and this application embodiment does not limit this. As an optional implementation, the intelligent model can be a pre-trained large language model, which can be further fine-tuned with a vehicle diagnostic corpus to form a vertical domain language model for vehicle fault diagnosis scenarios. As another optional implementation, the intelligent model can also be an enhanced language model, which is equipped with a vehicle diagnostic knowledge base. When generating fault description content, it first retrieves reference diagnostic information that matches the current fault code and target data from the vehicle diagnostic knowledge base, and then incorporates the retrieval results as contextual constraints into the generation process to improve the domain accuracy of the output content.

[0048] In this embodiment, after determining the underlying fault cause corresponding to each fault code, the system uses a pre-trained intelligent model to process each underlying fault cause based on the target data, generating fault descriptions corresponding to each fault code. Specifically, the intelligent model takes both the target data and the underlying fault causes as input, and couples the generalized standard fault attribution with the individualized state information of the target vehicle through semantic fusion and contextual association processing, thereby outputting fault descriptions that are adapted to the current technical state of the vehicle.

[0049] Unlike traditional fault diagnosis methods that rely solely on the mapping between fault codes and fixed terms, the target data in this application serves as a contextual constraint, which can be used subsequently to dynamically expand the basic fault causes and generate maintenance suggestions. Specifically, the basic fault causes typically originate from a pre-defined mapping rule base, providing only standardized, general fault descriptions corresponding to fault codes; while the target data, by incorporating the vehicle's unique operating state, historical traces, and environmental constraints, allows the basic fault causes to be expanded into target fault causes adapted to the current state of the target vehicle, thereby transforming general standardized term interpretations into personalized fault descriptions.

[0050] In some embodiments, the above-described fault code processing method further includes: performing quality analysis on the target fault cause to obtain a quality score; if the quality score is not less than a preset score, performing a step of generating a repair suggestion for the target vehicle under the fault code based on the target data and the fault cause of the fault code; if the quality score is less than the preset score, correcting the target fault cause and returning to the step of performing quality analysis on the target fault cause.

[0051] To ensure the engineering reliability of the target fault causes generated by the intelligent model, the system further performs multi-dimensional quality analysis on the target fault causes after performing information expansion processing. This quality analysis does not rely on subjective judgment based on human experience, but rather on objective quantitative verification of the generated content based on preset technical evaluation rules. Its analysis dimensions include at least data support completeness, logical consistency, factual accuracy, and operating condition adaptability. Specifically, data support completeness is used to assess whether the parameters of each dimension in the target data are sufficient to support the attribution conclusions in the target fault causes. For example, when the target fault causes attribute a cylinder misfire to poor fuel injector atomization, the system needs to verify whether the target data includes fuel trim values, injection pulse widths, or related oxygen sensor data as supporting evidence. If supporting evidence is missing or the data dimension is singular, the data support completeness is determined to not meet the preset threshold. Logical consistency is used to assess whether the attribution directions between the target fault cause and the basic fault cause are compatible, and whether there are causal contradictions among the technical assertions within the target fault cause. For example, if the same target fault cause is determined to be both ignition system failure and misfire due to an overly lean air-fuel mixture, and the primary and secondary relationship between the two is not clearly defined, then a logical consistency anomaly is triggered. Factual accuracy is used to assess whether the vehicle engineering parameters, component working principles, and fault propagation paths involved in the target fault cause conform to the standardized technical facts in the preset vehicle engineering rule base. For example, if the generated content contains component location descriptions that contradict the engine structure of the target vehicle model, or contains attribution logic that violates thermodynamic principles, then the factual accuracy is deemed unsatisfactory. Operating condition fit is used to assess whether the fault mechanism description in the target fault cause matches the current vehicle operating condition mode. For example, if the target data indicates that the vehicle is in a cold start idling condition, but the target fault cause generates a typical failure mode under high-load hot engine conditions, then the operating condition fit is deemed low.

[0052] When the quality analysis results of any of the above dimensions fail to meet the preset quality threshold, the system triggers a corresponding automatic correction mechanism. Specifically, if the data completeness does not meet the requirements, the system generates a data completion strategy based on the missing data dimensions. This strategy includes pushing follow-up questions to the user terminal regarding specific sensor data or fault symptom descriptions, or controlling the diagnostic equipment to re-collect specified vehicle operating parameters, and re-inputting the completed target data into the intelligent model for secondary information expansion processing. If the logical consistency or factual accuracy does not meet the requirements, the system inputs conflicting information or erroneous conclusions as negative constraints into the intelligent model, while simultaneously activating the preset vehicle engineering rule base to constrain the intelligent model's generation space. This allows the intelligent model to re-execute contextual reasoning within the rule boundaries, generating a corrected version of the target fault cause after rule verification. If the quality still does not meet the preset requirements after multiple iterations of correction, the system executes a downgraded output strategy, replacing the target fault cause with a combination of the basic fault cause and quality risk warning information. The quality risk warning information indicates that the current target data is insufficient to generate a reliable individualized attribution, suggesting that the user supplement the testing or refer to standardized repair manuals, thereby avoiding the risk of misjudgment due to the uncertainty of the intelligent model's output.

[0053] Step S105: Based on the fault description content corresponding to each fault code, generate and display the response results for the diagnostic operation.

[0054] The response result refers to the result displayed on the diagnostic device in at least one form, such as text, images, voice, or operation steps, for the user to view. The response result may include fault descriptions corresponding to all or a preset number of fault codes.

[0055] To enable users to determine which faults should be prioritized, in some embodiments, the fault code processing method further includes: determining risk assessment information and / or priority information corresponding to each fault code based on the fault description content corresponding to each fault code; and determining the display order of each fault code based on the risk assessment information and / or priority information.

[0056] Specifically, the aforementioned intelligent model can be used to determine the risk assessment information and / or priority information corresponding to each fault code based on the fault description content.

[0057] Based on this, the above step "generating and displaying the response results for the diagnostic operation based on the fault description content corresponding to each fault code" can include: generating and displaying the response results for the diagnostic operation based on the display order and fault description content corresponding to each fault code.

[0058] After displaying the response results, users can also engage in multi-round question-and-answer or voice interaction based on the diagnostic device's interactive interface. Specifically, the aforementioned fault code handling method further includes: responding to a first interactive operation based on the diagnostic device's response results, conducting multi-round dialogues based on the session information corresponding to the first interactive operation to complete the diagnostic operation; responding to a second interactive operation based on the diagnostic device's response results, adjusting the intelligent model based on the feedback information corresponding to the second interactive operation, wherein the intelligent model is used to generate target fault causes corresponding to each basic fault cause based on the target data and the basic fault causes of each fault code.

[0059] The first interactive operation refers to the interactive operation that initiates a dialogue. The conversation information corresponding to the first interactive operation includes, but is not limited to, correction information, follow-up questions, and operation information.

[0060] The second interactive operation refers to the feedback interaction. The feedback information corresponding to the second interactive operation can include, but is not limited to, positive and negative feedback. This feedback information is used to update the rule base and fine-tune the intelligent model, enabling self-learning and continuous optimization. The specific adjustment method can be adjusted according to the actual situation. No restrictions are placed here.

[0061] Therefore, the fault code processing method provided in this application embodiment can, in response to a diagnostic operation performed on a target vehicle by a diagnostic device, acquire at least one fault code of the target vehicle and target data associated with the target vehicle, wherein the target data is used to assist in expanding the causes of the target vehicle's faults; determine the basic fault cause corresponding to each fault code based on the correspondence between preset fault codes and preset basic fault causes; generate target fault causes corresponding to each basic fault cause based on the target data and the basic fault causes of each fault code; generate fault description content corresponding to each fault code based on the target fault causes corresponding to the basic fault causes of the fault code; and generate and display the response result for the diagnostic operation based on the fault description content corresponding to each fault code. Thus, by generating targeted (i.e., vehicle-specific) target fault causes based on the vehicle's target data and the basic fault causes of the fault codes contained in the vehicle, the understandability of the fault description content of the output fault codes is improved.

[0062] To better understand the fault code handling method provided in the embodiments of this application, please refer to... Figures 3 to 6 The following is an explanation of a specific embodiment applied to a smartphone app (i.e., diagnostic device), which supports multi-turn question-and-answer, voice interaction and dynamic feedback, enabling users to understand and use diagnostic functions more intuitively and conveniently.

[0063] like Figure 3 and Figure 6As shown, the smartphone app establishes a communication connection with the vehicle via the OBD-II interface or Bluetooth / wireless scanning device to obtain diagnostic fault codes (DTCs). Simultaneously, it collects basic vehicle information (including brand, model, year, and engine type) and real-time sensor operating parameters (such as engine speed, coolant temperature, oil pressure, and geographic location information), and receives additional information such as user-inputted fault symptom descriptions and driving environment. The collected multi-source heterogeneous data is sent to a local processing unit or cloud server for preprocessing operations such as data cleaning, format standardization, and outlier removal. Based on a preset fault code parsing rule base, the DTCs are initially parsed to extract standard fault descriptions and basic fault causes. Then, a pre-trained intelligent model integrates vehicle information, historical fault data, and contextual parameters for semantic reasoning and contextual association processing, generating natural language explanations, target fault causes, and repair suggestions adapted to the current vehicle operating conditions. Based on a fault propagation association model, risk assessments and priority rankings are performed for each fault. Subsequently, the diagnostic results are pushed to the user terminal through the app interface in at least one multimodal format, including text, voice, and operation step guidance, supporting user feedback or multi-round question-and-answer interaction. Ultimately, user feedback data is used to update the preset fault code parsing rule base and optimize the generation parameters of the intelligent model, thereby enabling the diagnostic system to learn and continuously optimize itself.

[0064] like Figure 4As shown, the smartphone app adopts a modular architecture, including at least a data acquisition module, a communication module, a rule parsing module, an AI analysis engine, a risk assessment module, a user interface module, and a feedback learning module. The data acquisition module reads Diagnostic Trouble Codes (DTCs) from the vehicle's electronic control unit via the OBD-II interface or wireless communication module, and collects onboard sensor operating parameters and user-inputted fault symptoms and environmental information. The communication module establishes a secure data transmission channel between the local processing unit and the cloud server, enabling the uploading of collected data and the feedback of processing results. The rule parsing module performs preliminary parsing of DTCs based on a preset fault code parsing rule base, extracting standard fault descriptions and basic fault causes. The AI ​​analysis engine calls a pre-trained intelligent model, integrates vehicle operating data, historical fault data, and contextual environmental parameters to perform semantic reasoning and contextual association processing, generating fault descriptions adapted to the current vehicle operating conditions, including the target fault cause and repair suggestions. The risk assessment module constructs a fault propagation correlation model, calculates the impact coefficient of each fault on vehicle operating safety based on current driving scenario parameters, and then scores the severity and urgency of the faults and prioritizes their handling. The user interface module displays diagnostic results via the app interface in at least one multimodal format, including text, audio, and step-by-step instructions, and supports multi-round question-and-answer interactions. The feedback learning module receives user feedback data and updates and optimizes the mapping relationships in the preset rule base and the generation parameters of the intelligent model, enabling the system's self-learning and continuous evolution. Under this system architecture, data flows along the following closed-loop path: the data acquisition module collects multi-source data from the vehicle, which is then uploaded to the processing module via the communication module. This data undergoes static mapping processing by the rule parsing module, dynamic semantic reasoning by the AI ​​analysis engine, and quantitative evaluation by the risk assessment module. The diagnostic results are then pushed to the user terminal via the user interface module. User feedback data received by the user interface module flows back to the rule parsing module and the AI ​​analysis engine via the feedback learning module, driving the collaborative optimization of the rule base and intelligent model, forming a complete technical closed loop from data acquisition, intelligent reasoning, risk ranking, interactive output to model iteration.

[0065] like Figure 5As shown, a data communication link is established with the vehicle's electronic control unit and onboard sensor network through the OBD-II interface and sensor interface to acquire diagnostic fault codes stored in the vehicle, as well as real-time operating status data from sensors such as the engine, door locks, oil pressure, and temperature. The microprocessor / control unit, as the core processing unit of the device, performs preprocessing on the received raw data, performs rule parsing based on a preset fault code parsing rule base, and executes lightweight intelligent inference locally to generate preliminary diagnostic results. The communication module uploads the processed data to a mobile terminal application or cloud server via Wi-Fi, Bluetooth, or cellular network, enabling secure data transmission and collaborative processing between the local processing unit and remote computing resources. The power management module provides stable power to all functional units of the device, ensuring continuous and reliable operation of the system during vehicle diagnostic sessions. The user interface connection establishes a data interaction channel with the mobile terminal application, presenting diagnostic results in graphical and textual form on the user terminal, supporting voice interaction, and receiving user feedback to drive subsequent model optimization and rule base updates.

[0066] To facilitate better implementation of the fault code processing method provided in this application, this application also provides an apparatus based on the above-described fault code processing method. The meanings of the terms used are the same as in the fault code processing method described above, and specific implementation details can be found in the descriptions within the method embodiments.

[0067] For example, such as Figure 7 As shown, this fault code processing device is applied to a terminal device, which may be a diagnostic device. The fault code processing device may include: The data acquisition module 201 is used to acquire at least one fault code of the target vehicle and target data associated with the target vehicle in response to a diagnostic operation of the diagnostic equipment for the target vehicle, wherein the target data is used to assist in expanding the cause of the fault of the target vehicle. The cause acquisition module 202 is used to determine the basic fault cause corresponding to each fault code based on the correspondence between the preset fault codes and the preset basic fault causes. The cause generation module 203 is used to generate the target fault cause corresponding to each basic fault cause based on the target data and the basic fault causes of each fault code. The content generation module 204 is used to generate fault description content corresponding to each fault code based on the target fault cause corresponding to the basic fault cause of the fault code. The response module 205 is used to generate and display the response results for the diagnostic operation based on the fault description content corresponding to each fault code.

[0068] In some embodiments, the cause generation module 203 generates target fault causes corresponding to each basic fault cause based on the target data and the basic fault causes of each fault code, including: The first encoding unit is used to encode the basic fault cause of each fault code to obtain the first feature vector. The second encoding unit is used to encode multiple sub-data in the target data respectively to obtain the second feature vector corresponding to each sub-data, wherein the multiple sub-data includes at least context sub-data used to characterize the context environment of the target vehicle; The fusion unit is used to fuse the first feature vector and each of the second feature vectors to obtain a fused feature vector. The cause generation unit is used to obtain the target fault cause for the target vehicle under the fault code based on the fused feature vector.

[0069] In some embodiments, the above-described fault code processing apparatus further includes: The suggestion generation unit is used to generate repair suggestions for the target vehicle under each fault code based on the target data and the fault cause of the fault code. The fault cause includes the basic fault cause and the target fault cause. Based on this, the content generation module 204 generates the fault description content corresponding to the fault code based on the target fault cause corresponding to the basic fault cause of the fault code; including: The content generation unit is used to generate fault description content corresponding to the fault code based on the target fault cause and maintenance suggestions corresponding to the basic fault cause of the fault code.

[0070] In some embodiments, the above-described fault code processing apparatus further includes: The analysis unit is used to perform quality analysis on the causes of target failures and obtain quality scores. The first processing unit is used to perform the following steps, when the quality score is not less than the preset score, to generate repair suggestions for the target vehicle under the fault code based on the fault cause of the target data and the fault code; The second processing unit is used to correct the cause of the target failure when the quality score is less than the preset score, and then return to the step of performing quality analysis on the cause of the target failure.

[0071] In some embodiments, the above-described fault code processing apparatus further includes: The information determination unit is used to determine the risk assessment information and / or priority information corresponding to each fault code based on the fault description content corresponding to each fault code. The sequence determination unit is used to determine the display order of each fault code based on risk assessment information and / or priority information.

[0072] Based on this, the above-mentioned response module 204 includes: The response unit is used to generate and display the response results for the diagnostic operation based on the display order and fault description content corresponding to each fault code.

[0073] In some embodiments, the target data associated with the target vehicle includes at least one of the following: basic vehicle information of the target vehicle, historical fault data of the target vehicle, vehicle operation data of the target vehicle, behavioral information of the person who triggered the diagnostic operation, and component association information of each component in the target vehicle.

[0074] In some embodiments, the above-described fault code processing apparatus further includes: The first interaction unit is used to respond to the first interaction operation based on the response result of the diagnostic device, and to conduct multi-round dialogue based on the session information corresponding to the first interaction operation in order to complete the diagnostic operation. The second interaction unit is used to respond to the second interaction operation based on the response result of the diagnostic device. Based on the feedback information corresponding to the second interaction operation, the intelligent model is adjusted. The intelligent model is used to generate the target fault cause corresponding to each basic fault cause according to the target data and the basic fault causes of each fault code.

[0075] Therefore, the fault code processing device provided in this application embodiment can, in response to a diagnostic operation on a target vehicle based on a diagnostic device, acquire at least one fault code of the target vehicle and target data associated with the target vehicle through the data acquisition module 201. The target data is used to assist in expanding the causes of the target vehicle's faults. The cause acquisition module 202 determines the basic fault cause corresponding to each fault code based on the correspondence between preset fault codes and preset basic fault causes. The cause generation module 203 generates target fault causes corresponding to each basic fault cause based on the target data and the basic fault causes of each fault code. The content generation module 204 generates fault description content corresponding to each fault code based on the target fault cause corresponding to the basic fault cause of the fault code. The response module 205 generates and displays the response result for the diagnostic operation based on the fault description content corresponding to each fault code. Thus, by generating targeted (i.e., vehicle-specific) target fault causes based on the vehicle's target data and the basic fault causes of the fault codes contained in the vehicle, the understandability of the fault description content of the output fault codes is improved.

[0076] In practice, each of the above modules can be implemented as an independent entity or can be combined arbitrarily to be implemented as the same or several entities. For the specific implementation methods and corresponding beneficial effects of each of the above modules, please refer to the previous method embodiments, which will not be repeated here.

[0077] This application also provides an electronic device, such as... Figure 8 As shown, it illustrates a structural schematic diagram of the electronic device involved in the embodiments of this application, specifically: The electronic device may include components such as a processor 301 with one or more processing cores, a memory 302 with one or more storage media, a power supply 303, and an input unit 304. Those skilled in the art will understand that... Figure 8 The electronic device structure shown does not constitute a limitation on the electronic device and may include more or fewer components than shown, or combine certain components, or have different component arrangements. Wherein: The processor 301 is the control center of the electronic device, connecting various parts of the device via various interfaces and lines. It executes computer programs and / or modules stored in the memory 302, and calls data stored in the memory 302 to perform various functions and process data. Optionally, the processor 301 may include one or more processing cores; preferably, the processor 301 may integrate an application processor and a modem processor, wherein the application processor mainly handles the operating system, user interface, and applications, and the modem processor mainly handles wireless communication. It is understood that the modem processor may not be integrated into the processor 301.

[0078] The memory 302 can be used to store computer programs and modules. The processor 301 executes various functional applications and diagnostic schemes by running the computer programs and modules stored in the memory 302. The memory 302 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, computer programs required for at least one function (such as voice prompt function, text input function, voice input function, scheme selection function, etc.), etc.; the data storage area may store data created according to the use of the electronic device. In addition, the memory 302 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device. Accordingly, the memory 302 may also include memory electronics to provide the processor 301 with access to the memory 302.

[0079] The electronic device also includes a power supply 303 that supplies power to various components. Preferably, the power supply 303 can be logically connected to the processor 301 through a power management system, thereby enabling functions such as charging, discharging, and power consumption management through the power management system. The power supply 303 may also include one or more DC or AC power supplies, recharging systems, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components.

[0080] The electronic device may also include an input unit 304, which can be used to receive input digital or character information and generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function control.

[0081] Although not shown, the electronic device may also include a display unit, etc., which will not be described in detail here. Specifically, in this embodiment, the processor 301 in the electronic device loads the executable files corresponding to the processes of one or more computer programs into the memory 302 according to the following instructions, and the processor 301 runs the computer programs stored in the memory 302 to realize various functions, such as: In response to a diagnostic operation performed on a target vehicle using a diagnostic device, at least one fault code of the target vehicle and target data associated with the target vehicle are obtained, wherein the target data is used to assist in expanding the causes of the faults in the target vehicle. Based on the correspondence between preset fault codes and preset basic fault causes, determine the basic fault cause corresponding to each fault code; Based on the target data and the basic fault causes of each fault code, generate the target fault causes corresponding to each basic fault cause. For each fault code, based on the target fault cause corresponding to the basic fault cause of the fault code, generate the fault description content corresponding to the fault code. Based on the fault description corresponding to each fault code, generate and display the response results for the diagnostic operation.

[0082] Therefore, the electronic device provided in this application embodiment can, in response to a diagnostic operation performed on a target vehicle by a diagnostic device, acquire at least one fault code of the target vehicle and target data associated with the target vehicle, wherein the target data is used to assist in expanding the causes of the target vehicle's faults; determine the basic fault cause corresponding to each fault code based on the correspondence between preset fault codes and preset basic fault causes; generate target fault causes corresponding to each basic fault cause based on the target data and the basic fault causes of each fault code; generate fault description content corresponding to each fault code based on the target fault causes corresponding to the basic fault causes of the fault code; and generate and display the response result to the diagnostic operation based on the fault description content corresponding to each fault code. Thus, by generating targeted (i.e., vehicle-specific) target fault causes based on the vehicle's target data and the basic fault causes of the fault codes contained in the vehicle, the understandability of the fault description content of the output fault codes is improved.

[0083] For details on the specific implementation methods and corresponding beneficial effects of each of the above operations, please refer to the detailed description of the fault code handling method above, which will not be repeated here.

[0084] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be performed by a computer program, or by a computer program controlling related hardware. The computer program can be stored in a storage medium and loaded and executed by a processor.

[0085] Therefore, embodiments of this application provide a storage medium storing a computer program that can be loaded by a processor to execute steps in any of the fault code handling methods provided in embodiments of this application. For example, the computer program can execute the following steps: In response to a diagnostic operation performed on a target vehicle using a diagnostic device, at least one fault code of the target vehicle and target data associated with the target vehicle are obtained, wherein the target data is used to assist in expanding the causes of the faults in the target vehicle. Based on the correspondence between preset fault codes and preset basic fault causes, determine the basic fault cause corresponding to each fault code; Based on the target data and the basic fault causes of each fault code, generate the target fault causes corresponding to each basic fault cause. For each fault code, based on the target fault cause corresponding to the basic fault cause of the fault code, generate the fault description content corresponding to the fault code. Based on the fault description corresponding to each fault code, generate and display the response results for the diagnostic operation.

[0086] Therefore, the storage medium provided in this application embodiment can, in response to a diagnostic operation performed on a target vehicle by a diagnostic device, acquire at least one fault code of the target vehicle and target data associated with the target vehicle. The target data is used to assist in expanding the causes of the target vehicle's faults. Based on the correspondence between preset fault codes and preset basic fault causes, the basic fault cause corresponding to each fault code is determined. Based on the target data and the basic fault causes of each fault code, a target fault cause corresponding to each basic fault cause is generated. For each fault code, based on the target fault cause corresponding to the basic fault cause of the fault code, a fault description content corresponding to the fault code is generated. Based on the fault description content corresponding to each fault code, a response result for the diagnostic operation is generated and displayed. Thus, by generating targeted (i.e., vehicle-specific) target fault causes based on the vehicle's target data and the basic fault causes of the fault codes contained in the vehicle, the understandability of the fault description content of the output fault codes is improved.

[0087] For details on the specific implementation methods and corresponding beneficial effects of the above operations, please refer to the previous embodiments, which will not be repeated here.

[0088] The storage medium may include: read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0089] Since the computer program stored in the storage medium can execute the steps in any of the fault code handling methods provided in the embodiments of this application, the beneficial effects that any of the fault code handling methods provided in the embodiments of this application can achieve can be realized. For details, please refer to the previous embodiments, which will not be repeated here.

[0090] According to one aspect of this application, a computer program product or computer program is provided, comprising computer instructions stored in a storage medium. A processor of a computer device reads the computer instructions from the storage medium and executes the computer instructions, causing the computer device to perform the aforementioned fault code handling method.

[0091] The foregoing has provided a detailed description of a fault code processing method, apparatus, electronic device, storage medium, and computer program product provided in the embodiments of this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the embodiments above are only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A fault code processing method, characterized in that, Applied to diagnostic devices, the method includes: In response to a diagnostic operation performed on a target vehicle by the diagnostic device, at least one fault code of the target vehicle and target data associated with the target vehicle are obtained, wherein the target data is used to assist in expanding the causes of the faults in the target vehicle. Based on the correspondence between preset fault codes and preset basic fault causes, determine the basic fault cause corresponding to each fault code; Based on the target data and the basic fault causes of each fault code, generate the target fault cause corresponding to each of the basic fault causes; For each of the aforementioned fault codes, based on the target fault cause corresponding to the basic fault cause of the fault code, a fault description content corresponding to the fault code is generated. Based on the fault description content corresponding to each fault code, a response result for the diagnostic operation is generated and displayed.

2. The fault code processing method according to claim 1, characterized in that, The step of generating target fault causes corresponding to each of the basic fault causes based on the target data and the basic fault causes of each of the fault codes includes: For each of the aforementioned fault codes, the underlying fault cause of the fault code is encoded to obtain a first feature vector; Encode each of the multiple sub-data in the target data to obtain a second feature vector corresponding to each sub-data, wherein the multiple sub-data includes at least context sub-data for characterizing the context environment of the target vehicle; The first feature vector and each of the second feature vectors are fused to obtain a fused feature vector. Based on the fused feature vector, the target fault cause for the target vehicle under the fault code is obtained.

3. The fault code processing method according to claim 1, characterized in that, The method further includes: For each of the aforementioned fault codes, based on the target data and the fault cause of the fault code, a repair suggestion for the target vehicle under the fault code is generated, wherein the fault cause includes the basic fault cause and the target fault cause; Based on the target fault cause corresponding to the basic fault cause of the fault code, a fault description content corresponding to the fault code is generated; including: Based on the basic fault cause corresponding to the fault code and the maintenance suggestion, a fault description content corresponding to the fault code is generated.

4. The fault code processing method according to claim 3, characterized in that, The method further includes: A quality analysis is performed on the causes of the target failure to obtain a quality score; If the quality score is not less than a preset score, the step of generating a repair suggestion for the target vehicle under the fault code based on the target data and the fault code's cause of failure is executed. If the quality score is less than the preset score, the target fault cause is corrected, and the process returns to the step of performing quality analysis on the target fault cause.

5. The fault code processing method according to any one of claims 1 to 4, characterized in that, The method further includes: Based on the fault description content corresponding to each fault code, determine the risk assessment information and / or priority information corresponding to each fault code; The display order of each fault code is determined based on the risk assessment information and / or the priority information. The step of generating and displaying a response result for the diagnostic operation based on the fault description content corresponding to each fault code includes: Based on the display order and fault description content corresponding to each fault code, a response result for the diagnostic operation is generated and displayed.

6. The fault code processing method according to any one of claims 1 to 4, characterized in that, The target data associated with the target vehicle includes at least one of the following: the target vehicle's basic vehicle information, the target vehicle's historical fault data, the target vehicle's vehicle operation data, the behavior information of the person who triggered the diagnostic operation, and the component association information of each component in the target vehicle.

7. The fault code processing method according to any one of claims 1 to 4, characterized in that, The method further includes: In response to a first interactive operation based on the response result of the diagnostic device, a multi-turn dialogue is performed based on the session information corresponding to the first interactive operation to complete the diagnostic operation. In response to a second interactive operation based on the response result of the diagnostic device, the intelligent model is adjusted based on the feedback information corresponding to the second interactive operation. The intelligent model is used to generate target fault causes corresponding to each of the basic fault causes based on the target data and the basic fault causes of each fault code.

8. A fault code processing device, characterized in that, Applied to diagnostic equipment, the device includes: The data acquisition module is configured to, in response to a diagnostic operation performed on the target vehicle by the diagnostic device, acquire at least one fault code of the target vehicle and target data associated with the target vehicle, wherein the target data is used to assist in expanding the causes of the faults of the target vehicle. The cause acquisition module is used to determine the basic fault cause corresponding to each fault code based on the correspondence between the preset fault codes and the preset basic fault causes. The cause generation module is used to generate target fault causes corresponding to each of the basic fault causes based on the target data and the basic fault causes of each fault code; The content generation module is used to generate fault description content corresponding to each fault code based on the target fault cause corresponding to the basic fault cause of the fault code. The response module is used to generate and display the response result for the diagnostic operation based on the fault description content corresponding to each fault code.

9. An electronic device, characterized in that, It includes a processor and a memory, wherein the memory stores a computer program that, when executed by the processor, causes the processor to perform the steps of the fault code handling method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, It includes a computer program that, when run on an electronic device, causes the electronic device to perform the steps of the fault code handling method according to any one of claims 1 to 7.

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