Vehicle intelligent diagnosis method based on supplementary data request and related device

Abstract

The invention provides a vehicle intelligent diagnosis method based on a supplementary data request and a related device, and the method comprises the steps: obtaining user voice of a target user for describing a fault state, and converting the user voice into text information; obtaining first diagnosis information corresponding to the fault state, and determining second diagnosis information according to the first diagnosis information and the text information; performing integrity analysis on the second diagnosis information to obtain a target analysis result; if the target analysis result shows that the information is sufficient, generating a first target diagnosis result according to the second diagnosis information; otherwise, determining an information gap corresponding to the second diagnosis information; generating a supplementary data request according to the information gap; and obtaining reference supplementary data fed back by the target user for the supplementary data request, and generating a second target diagnosis result according to the reference supplementary data and the second diagnosis information. According to the embodiment of the invention, the accuracy and process efficiency of vehicle diagnosis can be improved.

Description

Technical Field

[0001] This application relates to the field of vehicle intelligent diagnostic technology, and in particular to a vehicle intelligent diagnostic method and related apparatus based on a request for supplementary information. Background Technology

[0002] Current vehicle intelligent diagnostic technologies generally suffer from a lack of dimensional diagnostic information. Existing solutions often rely solely on historical operating parameters stored in the vehicle's system, such as engine speed and braking frequency, as the core diagnostic basis, without incorporating user-provided fault scenario-based information for targeted supplementation. Furthermore, while some solutions allow users to manually upload images or input additional information, this entirely depends on the user's self-determination of what needs to be supplemented. Users, lacking professional diagnostic knowledge, often cannot provide accurately matching data, resulting in low accuracy and efficiency in data supplementation, hindering the improvement of vehicle diagnostic accuracy and process efficiency.

[0003] Therefore, improving the accuracy and efficiency of vehicle diagnostics is an urgent issue that needs to be addressed. Summary of the Invention

[0004] This application provides a vehicle intelligent diagnostic method and related device based on supplementary information requests. By performing a completeness analysis on the vehicle's diagnostic information, when information is missing, the method identifies the information gap, generates a supplementary information request, and combines the supplementary information provided by the user to improve the diagnostic basis before generating the final diagnostic result, thereby improving the accuracy and efficiency of vehicle diagnosis.

[0005] In a first aspect, embodiments of this application provide a vehicle intelligent diagnostic method based on a request for supplementary information, the method comprising: When the target vehicle is in a faulty state, the user's voice describing the faulty state is obtained and converted into text information. Obtain first diagnostic information corresponding to the fault state, and determine second diagnostic information based on the first diagnostic information and the text information; A completeness analysis is performed on the second diagnostic information to obtain the target analysis results; the target analysis results include whether the information is sufficient or missing. If the target analysis result is sufficient, then a first target diagnosis result is generated based on the second diagnostic information; If the target analysis result is missing information, then the information gap corresponding to the second diagnostic information is determined; Generate a supplementary information request based on the information gap; Obtain the reference supplementary information provided by the target user in response to the supplementary information request, and generate a second target diagnostic result based on the reference supplementary information and the second diagnostic information.

[0006] Secondly, embodiments of this application provide a vehicle intelligent diagnostic device based on a request for supplementary information. The device includes a first acquisition module, a second acquisition module, an analysis module, a first generation module, a determination module, a second generation module, and a third generation module, wherein: The first acquisition module is used to acquire user voice describing the fault state of the target user when the target vehicle is in a fault state, and convert the user voice into text information. The second acquisition module is used to acquire first diagnostic information corresponding to the fault state, and determine second diagnostic information based on the first diagnostic information and the text information; The analysis module is used to perform a completeness analysis on the second diagnostic information to obtain a target analysis result; the target analysis result includes whether the information is sufficient or missing. The first generation module is used to generate a first target diagnosis result based on the second diagnosis information if the target analysis result is sufficient. The determining module is used to determine the information gap corresponding to the second diagnostic information if the target analysis result is missing information. The second generation module is used to generate a supplementary data request based on the information gap; The third generation module is used to obtain the reference supplementary information provided by the target user in response to the supplementary information request, and to generate a second target diagnostic result based on the reference supplementary information and the second diagnostic information.

[0007] Thirdly, embodiments of this application provide an electronic device, including a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor, and the programs include instructions for performing steps in any method of the first aspect of this application.

[0008] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program for electronic data interchange, wherein the computer program causes a computer to perform some or all of the steps described in any method of the first aspect of this application.

[0009] Fifthly, embodiments of this application provide a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps described in any method of the first aspect of this application. The computer program product may be a software installation package.

[0010] By implementing the embodiments of this application, the completeness analysis of vehicle diagnostic information can be performed. When information is missing, the information gap can be identified in a targeted manner, a request for supplementary information can be generated, and the diagnostic basis can be improved by combining the supplementary information provided by the user to generate the final diagnostic result. Compared with the traditional diagnostic method that relies solely on a single fault information or vague description by the user, this method reduces diagnostic bias by fusing multi-source information and avoids invalid information collection by targeted supplementation of missing information, thereby significantly improving the accuracy and efficiency of vehicle diagnosis. Attached Figure Description

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

[0012] Figure 1 This is a system architecture diagram of a vehicle intelligent diagnostic system provided in an embodiment of this application; Figure 2 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application; Figure 3 This is an application scenario diagram of a vehicle intelligent diagnostic system provided in an embodiment of this application; Figure 4 This is a flowchart illustrating a vehicle intelligent diagnostic method based on a request for supplementary information, provided in an embodiment of this application. Figure 5 This is a schematic diagram of a process for determining second diagnostic information provided in an embodiment of this application; Figure 6 This is a schematic diagram of a process for generating a supplementary information request provided in an embodiment of this application; Figure 7 This is a system architecture diagram of the interactive interface of a vehicle intelligent diagnostic system provided in an embodiment of this application; Figure 8 This is a functional module block diagram of a vehicle intelligent diagnostic device based on a request for supplementary information, provided in an embodiment of this application. Detailed Implementation

[0013] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0014] The terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. 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 includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0015] It should be understood that the term "and / or" in this document is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this document indicates that the preceding and following related objects are in an "or" relationship. In the embodiments of this application, "multiple" refers to two or more.

[0016] In the embodiments of this application, "at least one item" or its similar expression refers to any combination of these items, including any combination of a single item or a plurality of items. "One or more" means one or more, while "multiple" means two or more. For example, "at least one item" of a, b, or c can represent the following seven cases: a, b, c; a and b; a and c; b and c; a, b, and c. Each of a, b, and c can be an element or a set containing one or more elements.

[0017] In this application embodiment, "connection" refers to various connection methods such as direct connection or indirect connection to realize communication between devices. This application embodiment does not limit this in any way.

[0018] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0019] Current vehicle intelligent diagnostic technologies generally suffer from a lack of dimensional diagnostic information. Existing solutions often rely solely on historical operating parameters stored in the vehicle's system, such as engine speed and braking frequency, as the core diagnostic basis, without incorporating user-provided fault scenario-based information for targeted supplementation. Furthermore, while some solutions allow users to manually upload images or input additional information, this entirely depends on the user's self-determination of what needs to be supplemented. Users, lacking professional diagnostic knowledge, often cannot provide accurately matching data, resulting in low accuracy and efficiency in data supplementation, hindering the improvement of vehicle diagnostic accuracy and process efficiency.

[0020] Therefore, improving the accuracy and efficiency of vehicle diagnostics is an urgent issue that needs to be addressed.

[0021] To address the aforementioned issues, this application provides a vehicle intelligent diagnostic method and related apparatus based on supplementary information requests. When a target vehicle is in a fault state, the method involves acquiring user voice describing the fault state and converting the voice into text; acquiring first diagnostic information corresponding to the fault state and determining second diagnostic information based on the first diagnostic information and the text; performing a completeness analysis on the second diagnostic information to obtain a target analysis result; the target analysis result includes whether the information is sufficient or missing; if the target analysis result indicates sufficient information, a first target diagnostic result is generated based on the second diagnostic information; if the target analysis result indicates missing information, an information gap corresponding to the second diagnostic information is determined; a supplementary information request is generated based on the information gap; and reference supplementary information provided by the target user in response to the supplementary information request is acquired, and a second target diagnostic result is generated based on the reference supplementary information and the second diagnostic information. By performing a completeness analysis on the vehicle's diagnostic information, identifying information gaps and generating supplementary information requests when information is missing, and combining the supplementary information provided by the user to refine the diagnostic basis before generating the final diagnostic result, the accuracy and efficiency of vehicle diagnosis are improved.

[0022] For easier understanding, please refer to Figure 1 , Figure 1 This is a system architecture diagram of a vehicle intelligent diagnostic system provided in an embodiment of this application. The vehicle intelligent diagnostic system includes an information acquisition unit, a data analysis unit, a data supplementation unit, a fault diagnosis unit, and a user interaction unit.

[0023] The information acquisition unit can collect the target user's voice description of the vehicle's fault status through the vehicle's built-in microphone or the microphone associated with the user's mobile terminal, and simultaneously complete noise reduction, echo cancellation and effective voice segment extraction to ensure the quality of the voice signal; it can also connect to the vehicle's electronic control unit, vehicle sensor network and vehicle control system to obtain vehicle operating parameters, component status data, fault history records and other information in real time to obtain the first diagnostic information.

[0024] The data analysis unit can convert pre-processed speech signals into structured text information based on a pre-trained speech recognition model, and complete the standardization of colloquial expressions, extraction and annotation of fault keywords; then, it can extract features from the text information and perform dimensional alignment and fusion with the first diagnostic information to generate complete second diagnostic information; then, it can verify the status, timing and parameter dimensions of the second diagnostic information one by one according to the preset diagnostic information standard library to determine whether the second diagnostic information is sufficient, and if the information is missing, it can locate the specific gap type and content.

[0025] The data supplementation unit can classify information gaps (such as state gaps, timing gaps, and parameter gaps), determine the information collection elements corresponding to the gap type based on fault diagnosis requirements, and then determine the information collection objects and standards based on the information collection elements. It generates structured supplementary data requests based on the information collection objects and standards, and pushes them to the target user through the user interaction unit. It can also receive reference supplementary data from the target user, complete data type identification and preprocessing (such as image feature extraction and text structuring), generate standardized target supplementary data, and feed it back to the data analysis unit.

[0026] The fault diagnosis unit is equipped with a fault diagnosis model trained on historical fault cases, which supports fault feature matching, association rule reasoning and confidence calculation; the fault diagnosis model can be used to analyze diagnostic information and obtain corresponding diagnostic results.

[0027] The user interaction unit can convert the final diagnostic results into easily understandable voice and text prompts for the target user, and match the corresponding emergency symbols and display styles according to the fault risk level. It also supports the target user to submit supplementary information, provide feedback on the diagnostic results, and trigger manual diagnostic requests. It can also record the target user's feedback on the diagnostic results and update the fault case library in a synchronous manner to optimize the accuracy of the fault diagnosis model.

[0028] It is evident that by verifying the completeness of information and generating supplementary information requests, the problems of incomplete diagnostic information and insufficient targeted supplementary information from users are solved. At the same time, the confidence verification mechanism of the fault diagnosis model ensures the reliability of the diagnostic results, and the multi-form result output of the user interaction unit further enhances the user experience.

[0029] The following is combined Figure 2 The electronic devices in the embodiments of this application will be described. Figure 2 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application, such as... Figure 2 As shown, the electronic device includes one or more processors, a memory, a communication interface, and one or more programs. The processor is connected to the memory and the communication interface via an internal communication bus.

[0030] The processor can be used for: When the target vehicle is in a faulty state, the user's voice describing the faulty state is obtained and converted into text information. Obtain first diagnostic information corresponding to the fault state, and determine second diagnostic information based on the first diagnostic information and the text information; A completeness analysis is performed on the second diagnostic information to obtain the target analysis results; the target analysis results include whether the information is sufficient or missing. If the target analysis result is sufficient, then a first target diagnosis result is generated based on the second diagnostic information; If the target analysis result is missing information, then the information gap corresponding to the second diagnostic information is determined; Generate a supplementary information request based on the information gap; Obtain the reference supplementary information provided by the target user in response to the supplementary information request, and generate a second target diagnostic result based on the reference supplementary information and the second diagnostic information.

[0031] The one or more programs are stored in the aforementioned memory and configured to be executed by the aforementioned processor, and the one or more programs include instructions for performing any step in the above method embodiments.

[0032] The processor can be a central processing unit (CPU), a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, cells, and circuits described in conjunction with the disclosure of this application. The processor can also be a combination that implements computational functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc. The communication unit can be a communication interface, transceiver, transceiver circuit, etc., and the storage unit can be a memory.

[0033] The memory can be volatile or non-volatile, or a combination of both. Non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. Volatile memory can be random access memory (RAM), used as an external cache. By way of example, but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDR SDRAM), enhanced synchronous DRAM (ESDRAM), synchronous linked DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0034] It is understood that the electronic device may include more or fewer structural elements than those shown in the block diagram above, such as a power module, physical buttons, a Wi-Fi module, a speaker, a Bluetooth module, sensors, a display module, etc., without limitation. It is understood that the electronic device may incorporate elements such as... Figure 1 The system architecture described above.

[0035] For easier understanding, please refer to Figure 3 , Figure 3 This is an application scenario diagram of a vehicle intelligent diagnostic system provided in an embodiment of this application. The target vehicle provides the vehicle intelligent diagnostic system with first diagnostic information corresponding to its fault state (such as operating parameters and component status data stored in the vehicle system, including engine speed and braking frequency). The target user describes the fault state of the target vehicle to the vehicle intelligent diagnostic system via voice (e.g., "abnormal engine noise during startup"). Simultaneously, when the vehicle intelligent diagnostic system needs supplementary information, the target user provides supplementary reference materials (such as pictures of faulty components and audio recordings of abnormal noises). The vehicle intelligent diagnostic system determines second diagnostic information based on the first diagnostic information and the user's voice. If the second diagnostic information lacks key information, it obtains the target user's supplementary reference materials and generates a second target diagnostic result based on the supplementary reference materials and the second diagnostic information, then outputs the second target diagnostic result to the target user.

[0036] After understanding the software and hardware architecture of this application, the following will be combined with... Figure 4 This application describes a vehicle intelligent diagnostic method based on a request for supplementary information, as described in its embodiments. Figure 4 This is a flowchart illustrating a vehicle intelligent diagnostic method based on a request for supplementary information, provided in an embodiment of this application. The method specifically includes the following steps: Step S401: When the target vehicle is in a fault state, acquire the user's voice describing the fault state and convert the user's voice into text information.

[0037] Specifically, when the target vehicle is in a faulty state, the system can receive the user's spoken description of the fault status in real time through a pre-installed voice acquisition module (such as a built-in microphone) or a voice input channel associated with the user's mobile terminal. The acquired user voice is then pre-processed, including noise reduction (filtering out environmental noise and in-vehicle equipment operating noise), voice framing, and endpoint detection (locating valid voice segments and removing invalid silent parts). Next, a pre-trained speech recognition model is used to extract features and perform semantic conversion on the pre-processed voice segments, mapping the voice signal into a text sequence. The converted text sequence is then post-processed and optimized, including typo correction, standardization of colloquial expressions, and keyword enhancement (highlighting core information such as "startup" and "abnormal noise"), ultimately generating structured text information. Simultaneously, the system can record the timestamp of voice acquisition, the identification of the acquisition device, and the clarity score of the voice signal. If the clarity score is lower than a preset threshold, a voice re-acquisition prompt is triggered, guiding the target user to clearly describe the vehicle's fault status again.

[0038] Step S402: Obtain the first diagnostic information corresponding to the fault state, and determine the second diagnostic information based on the first diagnostic information and the text information.

[0039] For easier understanding, please refer to Figure 5 , Figure 5 This is a flowchart illustrating a method for determining second diagnostic information according to an embodiment of this application. The first diagnostic information includes first status information, first timing information, and first parameter information. The specific steps for determining the second diagnostic information based on the first diagnostic information and the text information include: A1. Extract features from the text information to obtain key features; the key features include at least one of the following: component status features, fault timing features, and fault parameter features; A2. Based on the key features, supplement the first state information, the first timing information, and the first parameter information to obtain the second state information, the second timing information, and the second parameter information; A3. Determine the second diagnostic information based on the second status information, the second timing information, and the second parameter information.

[0040] In a specific embodiment, firstly, a preset natural language processing technique can be used to extract features from the structured text information, locating entities related to vehicle components (such as "engine," "brake disc," and "transmission"), state description words (such as "abnormal noise," "vibration," and "malfunction"), time / scene related words (such as "at startup," "during high-speed driving," and "lasting 5 minutes"), and parameter related descriptions (such as "at 3000 RPM" and "brake pedal fully depressed"). Then, the feature extraction results are mapped to standardized key features, which include at least one of the following: component state features, fault timing features, and fault parameter features, without specific limitations. Specifically, component state features correspond to the specific state description of the fault-related component, fault timing features correspond to the time node, duration, and scenario conditions of the fault occurrence, and fault parameter features correspond to the description of vehicle operating parameters at the time of the fault occurrence.

[0041] Next, based on the extracted component status features, the real-time status of the components perceived by the user at the time of the fault (such as "abnormal noise when starting the engine") can be supplemented to the first status information to form the second status information. The first status information is the basic status data of each component of the vehicle pre-stored in the target vehicle (such as component factory parameters and historical maintenance records). Based on the extracted fault timing features, the specific timing of the fault described by the user (such as "shaking occurs 3 seconds after each braking") can be supplemented to the first timing information to form the second timing information. The first timing information is the vehicle running timing data at the time of the fault pre-recorded by the target vehicle. Based on the extracted fault parameter features, the parameter correlation details fed back by the user (such as "fault occurs when the speed exceeds 2500 rpm") can be supplemented to the first parameter information to form the second parameter information. The first parameter information is the basic operating parameters (such as vehicle speed and fuel consumption) collected by the vehicle's onboard sensors at the time of the fault.

[0042] Finally, the second state information, second timing information, and second parameter information are structurally integrated to obtain the second diagnostic information.

[0043] It is evident that by extracting key features from multiple dimensions of text information and supplementing the original diagnostic information with various dimensions, a complete and accurate second diagnostic information can be formed, providing comprehensive and effective data support for subsequent fault diagnosis and improving the accuracy and reliability of the diagnostic results.

[0044] Step S403: Perform a completeness analysis on the second diagnostic information to obtain the target analysis result.

[0045] The target analysis results include whether the information is sufficient or missing.

[0046] The specific steps for performing integrity analysis on the second diagnostic information to obtain the target analysis result include: B1. Obtain a preset diagnostic information standard library; the diagnostic information standard library includes status information standards, time sequence information standards, and parameter information standards; B2. Analyze the second state information according to the state information standard to obtain the first analysis result; B3. Analyze the second time series information according to the time series information standard to obtain the second analysis result; B4. Analyze the second parameter information according to the parameter information standard to obtain the third analysis result; B5. If the first analysis result, the second analysis result, and the third analysis result are all information-sufficient, then the target analysis result is determined to be information-sufficient. B6. If at least one of the first analysis result, the second analysis result, and the third analysis result is missing information, then the target analysis result is determined to be missing information.

[0047] In a specific embodiment, a pre-defined diagnostic information standard library can be obtained first. This library includes state information standards, time-series information standards, and parameter information standards. The state information standards define the core state dimensions (e.g., engine faults require the inclusion of abnormal noise type, vibration level, and whether oil leakage is present) and timeliness requirements for diagnosing faults in different vehicle components. The time-series information standards clarify the necessary time-series elements for fault diagnosis (e.g., the time of first occurrence of the fault, frequency of occurrence, and temporal correlation with vehicle operation behavior). The parameter information standards specify the key parameter ranges, accuracy requirements, and data integrity thresholds corresponding to various types of faults (e.g., brake faults require the inclusion of brake pedal travel parameters and brake pressure parameters). It should be noted that this diagnostic information standard library can be dynamically updated based on historical fault cases and industry diagnostic standards.

[0048] Next, the second state information is compared one by one with the core state dimensions corresponding to the fault type in the state information standard to verify whether the second state information covers all necessary dimensions and whether the information in each dimension is clear and effective (e.g., whether it clearly states the specific location of the abnormal noise rather than a vague description). If the second state information fully meets the requirements of the state information standard, the first analysis result is "sufficient information"; if there are missing dimensions, vague information, or invalid descriptions, the first analysis result is "information missing".

[0049] Next, the second timing information is verified according to the timing information standard to check whether it contains the key timing elements of the fault occurrence, whether the element descriptions are accurate (e.g., whether it is clearly stated as "within 5 minutes after cold start" instead of "after start"), and whether the timing logic is consistent (e.g., whether the fault occurrence time conflicts with the vehicle operation stage). If the second timing information fully complies with the timing information standard, the second analysis result is "sufficient information"; if there are missing elements, vague descriptions, or logical contradictions, the second analysis result is "information missing".

[0050] Then, based on the parameter information standard, check whether the second parameter information covers all the key parameters of the corresponding fault type, whether the parameter values ​​are within the valid range, and whether the parameter acquisition time matches the fault occurrence time. If the second parameter information meets the completeness and validity requirements of the parameter information standard, the third analysis result is "sufficient information"; if there are problems such as missing parameters, abnormal values, or time mismatch, the third analysis result is "information missing".

[0051] Finally, if the first, second, and third analysis results are all information-sufficient, then the second diagnostic information is determined to cover all the key dimensions required for fault analysis and has complete diagnostic basis. Therefore, the target analysis result is determined to be "information-sufficient". If at least one of the first, second, and third analysis results is information-deficient, then the second diagnostic information is determined to have a key gap and cannot support accurate fault diagnosis. Therefore, the target analysis result is determined to be "information-deficient".

[0052] It is evident that by verifying each component of the second diagnostic information one by one through the preset diagnostic information standard library, it is possible to accurately determine whether the information is complete, identify information gaps, and provide a clear basis for subsequent targeted supplementary information or direct diagnosis, thus avoiding diagnostic bias caused by one-sided information.

[0053] Step S404: If the target analysis result is sufficient, then generate a first target diagnosis result based on the second diagnostic information.

[0054] Specifically, firstly, a pre-set fault diagnosis model is retrieved. This model is trained based on historical fault cases, vehicle component association rules, and fault evolution logic. Then, the second diagnostic information is input into the fault diagnosis model. This model uses a pre-set feature matching algorithm to accurately compare the second state information, second time sequence information, and second parameter information in the second diagnostic information with a pre-set fault feature library, identifying the fault type with the highest matching degree and outputting the fault cause, core associated components, and fault severity level. Next, combined with the fault severity level, the corresponding repair suggestion library is retrieved to generate standardized repair guidelines that include emergency handling measures, professional repair solutions, parts replacement suggestions, and estimated repair costs. Finally, the fault type, fault cause, associated components, severity, and repair suggestions are integrated to form a structured first-target diagnostic result.

[0055] Step S405: If the target analysis result is missing information, then determine the information gap corresponding to the second diagnostic information.

[0056] Specifically, the first, second, and third analysis results in the target analysis are analyzed. If the first analysis result is "information missing," it is marked as a status information gap, and the specific missing component status dimensions are refined (e.g., engine noise type is unclear, brake disc wear degree is not described, etc.). If the second analysis result is "information missing," it is marked as a timing information gap, and the specific missing timing elements are refined (e.g., the time of the first occurrence of the fault is not stated, the time correlation between the fault and vehicle operation is unclear, etc.). If the third analysis result is "information missing," it is marked as a parameter information gap, and the specific missing parameter items are refined (e.g., brake pedal travel parameters are missing, engine speed correlation data is insufficient, etc.). Finally, the corresponding annotation information of the first, second, and third analysis results is integrated to obtain the information gap corresponding to the second diagnostic information.

[0057] Step S406: Generate a supplementary information request based on the information gap.

[0058] For easier understanding, please refer to Figure 6 , Figure 6 This is a flowchart illustrating the process of generating a supplementary information request according to an embodiment of this application. The specific steps of generating the supplementary information request based on the information gap include: C1. Determine the gap type corresponding to the information gap; the gap type includes at least one of the following: state information gap, timing information gap, parameter information gap; C2. Determine the information collection elements based on the gap type; C3. Determine the information collection objects and information collection standards based on the aforementioned information collection elements; C4. Generate the supplementary information request based on the information collection object and information collection standards.

[0059] In a specific embodiment, firstly, the type of information gap is determined, where the gap type includes at least one of the following: status information gap, timing information gap, and parameter information gap. Then, preset core acquisition elements are matched for different gap types: if it is a status information gap, the acquisition elements include the specific status description dimensions of the fault-related components (such as abnormal noise type, vibration level, whether leakage occurs, component appearance characteristics, etc.); if it is a timing information gap, the acquisition elements include the time node of the fault occurrence, duration, frequency of occurrence, and correlation with vehicle operation behavior, etc.; if it is a parameter information gap, the acquisition elements include the specific missing parameter name, parameter acquisition time range, parameter accuracy requirements, etc.

[0060] Then, based on the information collection elements, the information collection objects and standards are determined. The information collection objects are the specific content carriers (such as text descriptions, images, audio, numerical data, etc.) that the target user needs to provide. For example, the "part appearance characteristics" in the status information gap corresponds to "actual photos of the faulty component" as the collection object; the "fault occurrence frequency" in the time-series information gap corresponds to "textual descriptions of the fault" as the collection object; and the "brake pressure parameter" in the parameter information gap corresponds to "screenshots of vehicle sensor values," without specific limitations here. The information collection standards are the specific requirements that the collection objects must meet, including image shooting angle / clarity, the level of detail in the text description, and the format / units of the numerical data, without specific limitations here.

[0061] Finally, the information collection targets, information collection standards, and corresponding guidance texts for the gap types are integrated to generate a structured supplementary information request. For example, "Currently, key information on the engine status is missing. Please provide: the specific type of abnormal engine noise and actual photos of the abnormal engine parts."

[0062] It is evident that by classifying information gaps and determining their corresponding information collection elements, objects, and standards, precise requests for supplementary information can be generated, guiding target users to provide effective supplementary information, avoiding blindly supplementing information, and improving the accuracy and efficiency of information supplementation.

[0063] Step S407: Obtain the reference supplementary information provided by the target user in response to the supplementary information request, and generate a second target diagnostic result based on the reference supplementary information and the second diagnostic information.

[0064] The reference supplementary information includes at least one of the following: status data, time series data, and parameter data. The specific steps for generating a second target diagnostic result based on the reference supplementary information and the second diagnostic information include: D1. Determine the data type corresponding to the reference supplementary data; the data type includes at least one of the following: status data, time series data, and parameter data; D2. Determine the preprocessing scheme corresponding to the data type; D3. Preprocess the reference supplementary data according to the preprocessing scheme to obtain the target supplementary data; D4. Generate the second target diagnosis result based on the target supplementary information and the second diagnostic information.

[0065] In a specific embodiment, firstly, the supplementary reference information provided by the target user can be identified and categorized to determine the corresponding data type. This data type includes at least one of the following: status data, time-series data, and parameter data, without specific limitations. Specifically, if the supplementary reference information is a description of the status of a fault-related component (e.g., "the engine noise is a metallic friction sound, accompanied by oil leakage"), a real-life photo of the component, or a video of its appearance inspection, it is determined to be status data; if the supplementary information is a detail of the time when the fault occurs (e.g., "the fault occurs every morning during a cold start and disappears after about 1 minute"), or a record of the frequency of occurrence (e.g., "occurs 3-4 times a week, mostly after high-speed driving"), it is determined to be time-series data; if the supplementary information is a value of vehicle operating parameters (e.g., "the brake pedal travel is 5 centimeters"), a screenshot of the parameters, or data collected by sensors, it is determined to be parameter data.

[0066] Then, pre-defined standardized preprocessing rules are applied to different data types. The preprocessing scheme for status data includes keyword extraction and structured transformation of text-based status descriptions, and feature recognition and clarity verification for image / video-based status data. The preprocessing scheme for time-series data includes format standardization of time information, quantization annotation of frequency data, and logical consistency verification. The preprocessing scheme for parameter data includes unit standardization of parameter values, outlier filtering, missing value annotation, and data format standardization; specific limitations are not specified here. Next, the reference supplementary data is preprocessed according to the preprocessing scheme to obtain the target supplementary data. Finally, the second target diagnostic result is generated based on the target supplementary data and the second diagnostic information.

[0067] It is evident that by classifying the reference supplementary materials by type and matching them with a dedicated preprocessing scheme, non-standardized supplementary materials can be transformed into standardized target supplementary materials. These materials are then integrated with the second diagnostic information, and the integrated information is used to generate a second target diagnostic result. This effectively improves the utilization rate of supplementary materials and the accuracy of diagnostic results, thus solving the problem of diagnostic bias caused by missing information.

[0068] The specific steps for generating the second target diagnosis result based on the target supplementary information and the second diagnostic information include: E1. Align the target supplementary information with the second diagnostic information by dimension and fuse the data to generate third diagnostic information; E2. Analyze the third diagnostic information according to the preset fault diagnosis model to obtain a reference diagnostic result; E3. Perform a confidence check on the reference diagnostic results to obtain the target confidence level; E4. If the target confidence level meets the preset conditions, then the reference diagnostic result is determined to be the second target diagnostic result; E5. If the target confidence level does not meet the preset condition, then new target supplementary information from the target user is obtained again, and the reference diagnostic result is updated according to the new target supplementary information until the confidence level corresponding to the updated reference diagnostic result meets the preset condition, and the updated reference diagnostic result is determined as the second target diagnostic result.

[0069] In this embodiment, firstly, a dimensional mapping relationship is established between the target supplementary information and the second diagnostic information. Specifically, the state data in the target supplementary information matches the second state information dimension, the time-series data matches the second time-series information dimension, and the parameter data matches the second parameter information dimension. Then, the target supplementary information is filled into the missing dimensions corresponding to the second diagnostic information. Consistency checks are performed on duplicate dimensional information (e.g., if the supplemented parameter data conflicts with the original parameter data, the latest supplementary information provided by the user takes precedence). Finally, the data is integrated to form the third diagnostic information, ensuring data integrity and logical consistency.

[0070] Next, the third diagnostic information is input into a fault diagnosis model trained on historical fault cases. The fault diagnosis model analyzes the third diagnostic information to obtain a reference diagnostic result. Then, the reference diagnostic result is validated based on a pre-set confidence assessment system. This system can extract the matching depth between fault features and the third diagnostic information in the reference diagnostic result, and then combine this with the accuracy data of similar fault diagnoses in historical cases to calculate the target confidence level of the reference diagnostic result.

[0071] Next, it is determined whether the target confidence level meets the preset conditions. These preset conditions can be that the target confidence level is greater than or equal to a preset confidence threshold (e.g., 85%), without specific limitations. If the target confidence level meets the preset conditions, the reference diagnostic result is determined as the second target diagnostic result. If the target confidence level does not meet the preset conditions, new supplementary target information from the target user is retrieved, and the reference diagnostic result is updated based on this new information until the confidence level of the updated reference diagnostic result meets the preset conditions. The updated reference diagnostic result is then determined as the second target diagnostic result.

[0072] It is evident that by generating complete third diagnostic information through dimensional alignment and data fusion, and combining fault diagnosis model analysis and confidence verification mechanisms, the final output of the second target diagnostic result is ensured to have high reliability. At the same time, by iteratively supplementing data, the diagnostic conclusion is continuously optimized, effectively avoiding the output of low-confidence diagnostic results and improving the accuracy and reliability of vehicle fault diagnosis.

[0073] In one possible embodiment, after generating the second target diagnostic result based on the reference supplementary information and the second diagnostic information, the method further includes the following steps: F1. Generate broadcast voice and text prompts based on the second target diagnosis result; F2. Determine the fault risk level corresponding to the second target diagnostic result; F3. Determine the broadcast tone and emergency indicator based on the aforementioned fault risk level; F4. Broadcast the announcement in the specified tone and simultaneously display the text prompt and the emergency symbol.

[0074] In this embodiment, firstly, the core content (such as fault type, fault cause, and related components) of the second target diagnostic result can be extracted. Then, according to preset natural language generation rules, this core content is transformed into easily understandable spoken text. Specifically, speech synthesis technology is used to generate the broadcast voice for the spoken text, supporting functions such as speech rate adjustment and dialect adaptation. The text prompts simultaneously integrate core diagnostic information with visual elements (such as component diagrams and repair step instructions) to form layered text content (such as core information at the top and detailed content folded).

[0075] Then, the fault risk level corresponding to the second target diagnostic result is determined according to the preset fault risk level classification standard. This fault risk level classification standard is based on the degree of impact of the fault on vehicle driving safety, the irreversibility of component damage, and the urgency of repair, and is divided into three levels: high-risk level, medium-risk level, and low-risk level. High-risk level directly affects driving safety (e.g., brake failure, engine failure), requiring immediate stopping and repair; medium-risk level affects normal vehicle operation and poses safety hazards (e.g., abnormal noise from the transmission, steering system jamming), requiring prompt repair; low-risk level does not affect driving safety, only exhibits functional abnormalities (e.g., window lift malfunction, air conditioning failure), and can be repaired at a later date.

[0076] Next, the broadcast tone and emergency indicator are determined according to the level of fault risk. For example, a high-risk level uses a rapid and emphasized broadcast tone (slightly faster speech and higher pitch), and the emergency indicator is a flashing red icon; a medium-risk level uses a serious and reminding broadcast tone, and the emergency indicator is a yellow icon; a low-risk level uses a calm and informative broadcast tone, and the emergency indicator is a blue icon. No specific restrictions are made here.

[0077] Finally, the generated audio message is played according to the designated tone, while text prompts and emergency indicators are displayed prominently on the interactive interface. It should be noted that this interactive interface can be as follows: Figure 1 The interactive interface of the vehicle intelligent diagnostic system can also be the mobile terminal interactive interface of the target user. The mobile terminal can be the target user's mobile phone or diagnostic-related equipment, and no specific limitation is made here.

[0078] It is evident that by generating various forms of diagnostic result output and combining differentiated broadcast tones and emergency indicators with fault risk levels, the diagnostic information can be delivered intuitively and efficiently, helping target users quickly perceive the urgency of the fault and improving the efficiency of target users in receiving diagnostic results and responding in a timely manner.

[0079] For easier understanding, please refer to Figure 7 , Figure 7This is a system architecture diagram of the interactive interface of a vehicle intelligent diagnostic system provided in this application embodiment. The user interaction unit of the vehicle intelligent diagnostic system includes this interactive interface. The elliptical module (i.e., the voice broadcast) is the audio output part of the interactive interface, used to broadcast diagnostic results to the user in voice form (e.g., voice prompts with different tones matched according to the fault risk level). The rectangular module (i.e., text prompts) is the core information display area of ​​the interactive interface, used to present detailed content of the diagnostic results (e.g., structured text such as fault causes and repair suggestions). The triangular module (i.e., emergency indicators) is a risk warning element of the interactive interface, which usually displays corresponding indicator styles (e.g., different colors, dynamic effects) according to the degree of danger of the fault (e.g., high-risk level, medium-risk level) to help the target user quickly perceive the urgency of the fault. It should be noted that the interactive interface also includes a data upload portal and a feedback interaction module, which are not specifically limited here. The data upload portal, as the core interactive node for target users to provide feedback and supplementary information, can provide upload channels for various types of data such as images, audio, text, and parameter screenshots. The feedback interaction module can support target users to submit evaluations and suggestions on the diagnostic service to optimize the system's interactive experience.

[0080] The above primarily describes the solutions of the embodiments of this application from the perspective of the method execution process. It is understood that, in order to achieve the above functions, the electronic device includes corresponding hardware structures and / or software modules for executing each function. Those skilled in the art should readily recognize that, in conjunction with the units and algorithm steps of the various examples described in the embodiments provided herein, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed by hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0081] This application embodiment can divide the electronic device into functional units according to the above method example. For example, each function can be divided into a separate functional unit, or two or more functions can be integrated into one processing unit. The integrated unit can be implemented in hardware or as a software functional unit. It should be noted that the unit division in this application embodiment is illustrative and only represents one logical functional division. In actual implementation, there may be other division methods.

[0082] When dividing each function into modules according to its corresponding function. Figure 8This is a functional block diagram of a vehicle intelligent diagnostic device based on a request for supplemental information, provided in an embodiment of this application. The vehicle intelligent diagnostic device 800 based on a request for supplemental information includes a first acquisition module 810, a second acquisition module 820, an analysis module 830, a first generation module 840, a determination module 850, a second generation module 860, and a third generation module 870, wherein: The first acquisition module 810 is used to acquire user voice describing the fault state of the target user when the target vehicle is in a fault state, and convert the user voice into text information. The second acquisition module 820 is used to acquire first diagnostic information corresponding to the fault state, and determine second diagnostic information based on the first diagnostic information and the text information; The analysis module 830 is used to perform a completeness analysis on the second diagnostic information to obtain a target analysis result; the target analysis result includes whether the information is sufficient or missing. The first generation module 840 is used to generate a first target diagnosis result based on the second diagnosis information if the target analysis result is sufficient. The determining module 850 is used to determine the information gap corresponding to the second diagnostic information if the target analysis result is missing information; The second generation module 860 is used to generate a supplementary data request based on the information gap; The third generation module 870 is used to obtain the reference supplementary information provided by the target user in response to the supplementary information request, and to generate a second target diagnostic result based on the reference supplementary information and the second diagnostic information.

[0083] Optionally, the first diagnostic information includes first status information, first timing information, and first parameter information. Regarding determining the second diagnostic information based on the first diagnostic information and the text information, the second acquisition module 820 is specifically used for: Feature extraction is performed on the text information to obtain key features; the key features include at least one of the following: component status features, fault timing features, and fault parameter features; Based on the key features, the first state information, the first timing information, and the first parameter information are supplemented to obtain the second state information, the second timing information, and the second parameter information. The second diagnostic information is determined based on the second status information, the second timing information, and the second parameter information.

[0084] Optionally, in the process of performing a completeness analysis on the second diagnostic information to obtain the target analysis result, the analysis module 830 is specifically used for: Obtain a preset diagnostic information standard library; the diagnostic information standard library includes status information standards, time sequence information standards, and parameter information standards; The second state information is analyzed according to the state information standard to obtain a first analysis result; The second time series information is analyzed according to the time series information standard to obtain the second analysis result; The second parameter information is analyzed according to the parameter information standard to obtain the third analysis result; If the first analysis result, the second analysis result, and the third analysis result are all information-sufficient, then the target analysis result is determined to be information-sufficient. If at least one of the first analysis result, the second analysis result, and the third analysis result is missing information, then the target analysis result is determined to be missing information.

[0085] Optionally, in generating the supplementary information request based on the information gap, the second generation module 860 is specifically used for: Determine the gap type corresponding to the information gap; the gap type includes at least one of the following: state information gap, timing information gap, parameter information gap; The information collection elements are determined based on the type of gap. The information collection objects and information collection standards are determined based on the aforementioned information collection elements; The supplementary information request is generated based on the information collection object and information collection standards.

[0086] Optionally, the reference supplementary information includes at least one of the following: status information, time series information, and parameter information. In generating the second target diagnostic result based on the reference supplementary information and the second diagnostic information, the third generation module 870 is specifically used for: Determine the data type corresponding to the reference supplementary data; the data type includes at least one of the following: status data, time series data, and parameter data; Determine the preprocessing scheme corresponding to the data type; The reference supplementary data is preprocessed according to the preprocessing scheme to obtain the target supplementary data; The second target diagnostic result is generated based on the target supplementary information and the second diagnostic information.

[0087] Optionally, in generating the second target diagnostic result based on the target supplementary information and the second diagnostic information, the third generation module 870 is further specifically used for: The target supplementary information is dimensionally aligned and data fused with the second diagnostic information to generate third diagnostic information; The third diagnostic information is analyzed according to the preset fault diagnosis model to obtain a reference diagnostic result; The confidence level of the reference diagnostic results is verified to obtain the target confidence level. If the target confidence level meets the preset conditions, then the reference diagnostic result is determined to be the second target diagnostic result; If the target confidence level does not meet the preset condition, new target supplementary information from the target user is obtained again, and the reference diagnostic result is updated according to the new target supplementary information until the confidence level corresponding to the updated reference diagnostic result meets the preset condition. The updated reference diagnostic result is then determined as the second target diagnostic result.

[0088] Optionally, after generating the second target diagnostic result based on the supplementary reference information and the second diagnostic information, the third generation module 870 is further specifically used for: Based on the second target diagnosis result, a broadcast voice and text prompt are generated; Determine the fault risk level corresponding to the second target diagnostic result; The broadcast tone and emergency indication are determined based on the aforementioned fault risk level; The announcement is broadcast in the specified tone, and the text prompts and emergency symbols are displayed simultaneously.

[0089] It is evident that by performing a completeness analysis on the vehicle's diagnostic information, identifying information gaps when information is missing, generating requests for supplementary information, and combining supplementary information from user feedback to refine the diagnostic basis before generating the final diagnostic result, the accuracy and efficiency of vehicle diagnosis can be improved.

[0090] It should be noted that the specific implementation of each operation can be described in the corresponding description of the method embodiments shown above. The vehicle intelligent diagnostic device 800 based on the supplementary information request can be used to execute the above method embodiments of this application, and will not be described again here.

[0091] This application also provides a computer-readable storage medium storing a computer program for electronic data interchange, which causes a computer to perform some or all of the steps of any of the methods described in the above method embodiments, wherein the computer includes an electronic device.

[0092] This application also provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps of any of the methods described in the above method embodiments. The computer program product may be a software installation package, and the computer may include an electronic device.

[0093] It should be noted that, for the sake of simplicity, the above embodiments are all described as a series of actions. Those skilled in the art should understand that this application is not limited to the described order of actions, as some steps in the embodiments of this application can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions, steps, modules, or units involved are not necessarily essential to the embodiments of this application.

[0094] In the above embodiments, the descriptions of each embodiment in this application have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0095] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This program can be stored in a computer-readable storage medium, and when executed, it can include the processes described in the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM or random access memory (RAM), magnetic disks, or optical disks.

[0096] The steps of the methods or algorithms described in the embodiments of this application can be implemented in hardware or by a processor executing software instructions. The software instructions can consist of corresponding software modules, which can be stored in RAM, flash memory, ROM, EPROM, electrically erasable programmable read-only memory (EEPROM), registers, hard disk, portable hard disk, read-only optical disk (CD-ROM), or any other form of storage medium well known in the art. An exemplary storage medium is coupled to a processor, enabling the processor to read information from and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and storage medium can reside in an ASIC. Furthermore, the ASIC can reside in a terminal device or management device. Alternatively, the processor and storage medium can exist as discrete components in the terminal device or management device.

[0097] Those skilled in the art will recognize that, in one or more of the examples above, the functions described in the embodiments of this application can be implemented, in whole or in part, by software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. This computer program product includes one or more computer instructions. When these computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. The available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., digital video discs (DVDs)), or semiconductor media (e.g., solid-state disks (SSDs)).

[0098] The modules / units included in the various devices and products described in the above embodiments can be software modules / units, hardware modules / units, or a combination of both. For example, for devices and products applied to or integrated into a chip, all modules / units can be implemented using hardware methods such as circuits, or at least some modules / units can be implemented using software programs that run on a processor integrated within the chip, while the remaining (if any) modules / units can be implemented using hardware methods such as circuits. For devices and products applied to or integrated into a chip module, all modules / units can be implemented using hardware methods such as circuits. Different modules / units can be located in the same component (e.g., chip, circuit module, etc.) or different components of the chip module, or at least some modules / units can be implemented using hardware methods such as circuits. The implementation is achieved through a software program that runs on the processor integrated within the chip module. The remaining modules / units (if any) can be implemented using hardware methods such as circuits. For various devices and products applied to or integrated into terminal equipment, each of their modules / units can be implemented using hardware methods such as circuits. Different modules / units can be located in the same component (e.g., chip, circuit module, etc.) or different components within the terminal equipment. Alternatively, at least some modules / units can be implemented through a software program that runs on the processor integrated within the terminal equipment, while the remaining modules / units (if any) can be implemented using hardware methods such as circuits.

[0099] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the embodiments of this application. It should be understood that the above descriptions are merely specific embodiments of the embodiments of this application and are not intended to limit the protection scope of the embodiments of this application. Any modifications, equivalent substitutions, improvements, etc., made on the basis of the technical solutions of the embodiments of this application should be included within the protection scope of the embodiments of this application.

Claims

1. A vehicle intelligent diagnostic method based on supplementary data requests, characterized in that, The method includes: When the target vehicle is in a faulty state, the user's voice describing the faulty state is obtained and converted into text information. Obtain first diagnostic information corresponding to the fault state, and determine second diagnostic information based on the first diagnostic information and the text information; A completeness analysis is performed on the second diagnostic information to obtain the target analysis results; the target analysis results include whether the information is sufficient or missing. If the target analysis result is sufficient, then a first target diagnosis result is generated based on the second diagnostic information; If the target analysis result is missing information, then the information gap corresponding to the second diagnostic information is determined; Generate a supplementary information request based on the information gap; Obtain the reference supplementary information provided by the target user in response to the supplementary information request, and generate a second target diagnostic result based on the reference supplementary information and the second diagnostic information.

2. The method as described in claim 1, characterized in that, The first diagnostic information includes first status information, first timing information, and first parameter information. Determining the second diagnostic information based on the first diagnostic information and the text information includes: Feature extraction is performed on the text information to obtain key features; the key features include at least one of the following: component status features, fault timing features, and fault parameter features; Based on the key features, the first state information, the first timing information, and the first parameter information are supplemented to obtain the second state information, the second timing information, and the second parameter information. The second diagnostic information is determined based on the second status information, the second timing information, and the second parameter information.

3. The method as described in claim 2, characterized in that, The completeness analysis of the second diagnostic information to obtain the target analysis results includes: Obtain a preset diagnostic information standard library; the diagnostic information standard library includes status information standards, time sequence information standards, and parameter information standards; The second state information is analyzed according to the state information standard to obtain a first analysis result; The second time series information is analyzed according to the time series information standard to obtain the second analysis result; The second parameter information is analyzed according to the parameter information standard to obtain the third analysis result; If the first analysis result, the second analysis result, and the third analysis result are all information-sufficient, then the target analysis result is determined to be information-sufficient. If at least one of the first analysis result, the second analysis result, and the third analysis result is missing information, then the target analysis result is determined to be missing information.

4. The method as described in claim 3, characterized in that, The step of generating a supplementary data request based on the information gap includes: Determine the gap type corresponding to the information gap; the gap type includes at least one of the following: state information gap, timing information gap, and parameter information gap; The information collection elements are determined based on the type of gap. The information collection objects and information collection standards are determined based on the aforementioned information collection elements; The supplementary information request is generated based on the information collection object and information collection standards.

5. The method as described in claim 2, characterized in that, The reference supplementary information includes at least one of the following: status data, time series data, and parameter data. Generating a second target diagnostic result based on the reference supplementary information and the second diagnostic information includes: Determine the data type corresponding to the reference supplementary data; the data type includes at least one of the following: status data, time series data, and parameter data; Determine the preprocessing scheme corresponding to the data type; The reference supplementary data is preprocessed according to the preprocessing scheme to obtain the target supplementary data; The second target diagnostic result is generated based on the target supplementary information and the second diagnostic information.

6. The method as described in claim 5, characterized in that, The step of generating the second target diagnosis result based on the target supplementary information and the second diagnostic information includes: The target supplementary information is dimensionally aligned and data fused with the second diagnostic information to generate third diagnostic information; The third diagnostic information is analyzed according to the preset fault diagnosis model to obtain a reference diagnostic result; The confidence level of the reference diagnostic results is verified to obtain the target confidence level. If the target confidence level meets the preset conditions, then the reference diagnostic result is determined to be the second target diagnostic result; If the target confidence level does not meet the preset condition, new target supplementary information from the target user is obtained again, and the reference diagnostic result is updated according to the new target supplementary information until the confidence level corresponding to the updated reference diagnostic result meets the preset condition. The updated reference diagnostic result is then determined as the second target diagnostic result.

7. The method according to any one of claims 1-6, characterized in that, After generating the second target diagnostic result based on the supplementary reference information and the second diagnostic information, the method further includes: Based on the second target diagnosis result, a broadcast voice and text prompt are generated; Determine the fault risk level corresponding to the second target diagnostic result; The broadcast tone and emergency indication are determined based on the aforementioned fault risk level; The announcement is broadcast in the specified tone, and the text prompts and emergency symbols are displayed simultaneously.

8. A vehicle intelligent diagnostic device based on supplementary data requests, characterized in that, The device includes a first acquisition module, a second acquisition module, an analysis module, a first generation module, a determination module, a second generation module, and a third generation module, wherein: The first acquisition module is used to acquire user voice describing the fault state of the target user when the target vehicle is in a fault state, and convert the user voice into text information. The second acquisition module is used to acquire first diagnostic information corresponding to the fault state, and determine second diagnostic information based on the first diagnostic information and the text information; The analysis module is used to perform a completeness analysis on the second diagnostic information to obtain a target analysis result; the target analysis result includes whether the information is sufficient or missing. The first generation module is used to generate a first target diagnosis result based on the second diagnosis information if the target analysis result is sufficient. The determining module is used to determine the information gap corresponding to the second diagnostic information if the target analysis result is missing information. The second generation module is used to generate a supplementary data request based on the information gap; The third generation module is used to obtain the reference supplementary information provided by the target user in response to the supplementary information request, and to generate a second target diagnostic result based on the reference supplementary information and the second diagnostic information.

9. An electronic device, characterized in that, include: Processor, memory, communication interface, and one or more programs; The one or more programs are stored in the memory and configured to be executed by the processor, the programs including instructions for performing the steps of the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, the computer program including program instructions that, when executed by a processor, cause the processor to perform the method as described in any one of claims 1-7.

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