Vehicle maintenance strategy determination method and device, electronic equipment and storage medium
By acquiring information about the vehicle model to be repaired and the fault description, and using a pre-built vehicle repair database generation strategy, the problem of vehicle repair technicians being unable to repair all vehicle models is solved, enabling rapid diagnosis and efficient repair.
Patent Information
- Application Number
- CN202510854477.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-06-23
- Publication Date
- 2025-10-21
AI Technical Summary
Vehicle repair technicians cannot repair all vehicle models, resulting in low repair difficulty and efficiency.
By acquiring information about the vehicle model to be repaired and the fault description, a vehicle repair strategy is generated using a pre-built vehicle repair database, and strategy suggestions are provided to the user.
Rapidly reduce diagnostic time, lower the technical threshold, enable novices to handle faults, reduce trial and error and excessive repairs, reduce costs, and ensure consistent quality and efficiency.
Smart Images
Figure CN120823655A_ABST
Abstract
Description
Technical Field
[0001] The embodiments of the present disclosure relate to the field of artificial intelligence technology, and specifically to a method, device, electronic device, and computer-readable storage medium for determining a vehicle maintenance strategy. Background Art
[0002] With the increasing number of vehicle manufacturers and models, the difficulty faced by vehicle maintenance technicians has increased significantly. Whether new to the vehicle maintenance industry or seasoned veterans, it's impossible for them to repair every vehicle model, and they all have gaps in their skills in certain vehicles. Therefore, expanding the range of vehicles that maintenance technicians can repair and improving maintenance efficiency are currently pressing challenges. Summary of the Invention
[0003] The embodiments of the present disclosure provide a method, device, electronic device, and computer-readable storage medium for determining a vehicle maintenance strategy, aiming to solve at least one of the technical problems in the related art to a certain extent.
[0004] In a first aspect, an embodiment of the present disclosure provides a method for determining a vehicle maintenance strategy, the method comprising:
[0005] Obtain the currently input vehicle type to be repaired and the fault description information corresponding to the vehicle type to be repaired;
[0006] Generate a vehicle maintenance strategy based on a pre-built vehicle maintenance database, the vehicle model to be repaired, and the fault description information;
[0007] The vehicle maintenance strategy is replied to the user.
[0008] In a second aspect, an embodiment of the present disclosure further provides a device for determining a vehicle maintenance strategy, the device comprising:
[0009] An acquisition module is used to obtain the currently input vehicle type to be repaired and the fault description information corresponding to the vehicle type to be repaired;
[0010] A generation module, configured to generate a vehicle maintenance strategy based on a pre-built vehicle maintenance database, the vehicle model to be repaired, and the fault description information;
[0011] The reply module is used to reply the vehicle maintenance strategy to the user.
[0012] In a third aspect, an embodiment of the present disclosure further provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, the steps in the above-mentioned method for determining the vehicle maintenance strategy are implemented.
[0013] In a fourth aspect, an embodiment of the present disclosure further provides a computer-readable storage medium having a computer program stored thereon, which implements the steps in the above-mentioned method for determining a vehicle maintenance strategy when the computer program is executed by a processor.
[0014] In a fifth aspect, embodiments of the present disclosure further provide a computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of an electronic device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the electronic device to perform the methods provided in various optional implementations of the embodiments of the present disclosure.
[0015] In the disclosed embodiment, the currently input vehicle type to be repaired and the fault description information corresponding to the vehicle type to be repaired can be obtained first, and then a vehicle maintenance strategy can be generated based on the pre-built vehicle maintenance database, the vehicle type to be repaired and the fault description information, and then the vehicle maintenance strategy can be replied to the user. Therefore, by obtaining the vehicle type to be repaired and the fault information, and combining it with the pre-built vehicle maintenance database to generate and reply strategies, the diagnosis time can be quickly compressed, the technical threshold can be lowered, and novices can also handle faults. This model relies on historical data to prioritize the recommendation of high-success rate solutions, reduce trial and error and excessive repairs, and reduce costs. At the same time, standardized service processes ensure consistent quality, and the database forms experience iterations. Improve efficiency and reliability.
[0016] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure. BRIEF DESCRIPTION OF THE DRAWINGS
[0017] In order to more clearly illustrate the technical solutions in the present disclosure, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.
[0018] Figure 1 is a flowchart of a method for determining a vehicle maintenance strategy provided by an embodiment of the present disclosure;
[0019] Figure 2 is a structural diagram of a vehicle maintenance strategy determination device provided by an embodiment of the present disclosure;
[0020] Figure 3 It is a structural diagram of an electronic device provided by an embodiment of the present disclosure. DETAILED DESCRIPTION
[0021] Some embodiments of the present disclosure will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. Various changes, modifications and equivalents of the methods, devices and / or systems described herein will become apparent after understanding the present disclosure. For example, the order of operations described herein is merely an example and is not limited to those orders set forth herein, but may be changed as becomes apparent after understanding the present disclosure, except for operations that must be performed in a specific order. In addition, for the sake of clarity and brevity, descriptions of features known in the art may be omitted.
[0022] The embodiments described in the following examples of the present disclosure do not represent all embodiments consistent with the present disclosure. Instead, they are merely examples of devices and methods consistent with some aspects of the present disclosure as detailed in the appended claims.
[0023] It should be noted that the execution subject of the vehicle maintenance strategy determination method of this embodiment may be a vehicle maintenance strategy determination device, which may be configured in any type of electronic device and is not limited herein.
[0024] Among them, the electronic device can be a server, or it can be a terminal or other equipment. Among them, the server can be an independent physical server, or it can be a server cluster or distributed system composed of multiple physical servers, or it can be a cloud server that provides cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, network acceleration services (Content Delivery Network, CDN), and basic cloud computing services such as big data and artificial intelligence platforms. The terminal can be a smart phone, tablet computer, laptop computer, desktop computer, smart speaker, smart watch, etc., but is not limited to this. The terminal and the server can be directly or indirectly connected through wired or wireless communication, and this application does not limit this.
[0025] In the embodiment of the present disclosure, the “vehicle maintenance strategy determination device” is used as the execution subject to execute the “vehicle maintenance strategy determination method” for illustration, and no limitation is given here.
[0026] It should be noted that the order of description of the following embodiments does not limit the priority order of the embodiments.
[0027] Figure 1 It is a flowchart of a method for determining a vehicle maintenance strategy provided according to the first embodiment of the present disclosure.
[0028] like Figure 1 As shown, the method includes:
[0029] Step 101: Obtain the currently input vehicle type to be repaired and the fault description information corresponding to the vehicle type to be repaired.
[0030] The vehicle type to be repaired may include the brand, model year, specific model, etc. of the vehicle to be repaired, which are not limited here.
[0031] Among them, the fault description information is used to describe in detail the current problems of the vehicle, such as: "abnormal engine noise when starting, obvious idle vibration", "poor air conditioning cooling effect, small air volume at the air outlet", "steering wheel shakes violently when braking", "the battery fault light on the instrument panel is always on", etc., which are not limited here.
[0032] As a possible implementation method, the user can input the vehicle model to be repaired and the fault description information in the human-machine interface of the device through text, or through voice input, which is not limited here.
[0033] It should be noted that the vehicle type to be repaired input by the user may be one or more types, and the fault description information may describe one fault or multiple faults, which is not limited.
[0034] For example, a dedicated text box could be designed within the repair management software or related applications for electronic devices. The user would enter the make, model year, and specific model of the vehicle being repaired in the "Vehicle Type" text box, and then describe the problem in detail in the "Fault Description" text box, such as "abnormal engine noise during startup and noticeable idle jitter."
[0035] Alternatively, for vehicle model information, a drop-down menu can be pre-loaded with common car brands. When the user selects a brand, a secondary drop-down menu appears showing common vehicle models under that brand. Finally, a text box allows the user to provide specific year and configuration information. The fault description remains in a text box, allowing the user to enter it freely. This improves input efficiency and reduces the error rate in vehicle model input.
[0036] Step 102: Generate a vehicle maintenance strategy based on a pre-built vehicle maintenance database, the vehicle type to be repaired, and the fault description information.
[0037] The vehicle maintenance database may include fault maintenance data for multiple (eg, 1,000) vehicle models. Each type of vehicle model may have multiple faults, and the vehicle maintenance database may include fault maintenance data corresponding to each fault.
[0038] As a possible implementation method, when constructing a vehicle maintenance database, you can first collect the fault description information corresponding to each fault type of different vehicle models, as well as at least one fault repair method associated with each fault type. Then you can construct a vehicle maintenance database based on the fault description information corresponding to the vehicle model and at least one fault repair method associated with each fault type.
[0039] For example, the process for building a vehicle maintenance database can be as follows: First, collect extensive fault information for various vehicle models, such as various makes, years, and models, including common family sedans, SUVs, and commercial vehicles. Each model may exhibit a variety of fault conditions. For example, a compact sedan may experience unusual engine noise during cold start or brake pedal vibration while driving; a certain brand of SUV may experience issues such as air conditioning failure or an illuminated instrument panel fault light. When collecting these faults, record the specific fault description, such as when the fault occurred (whether it occurred during startup, driving, or idling), the characteristics (type of unusual noise, frequency of vibration, etc.), and any associated abnormalities (such as accompanying fault lights or power changes). For example, a fault description for a mid-size sedan might be "severe steering wheel vibration during high-speed driving, particularly noticeable above 100 km / h, accompanied by slight body resonance." For each fault, collect at least one corresponding repair method. These repair methods may include replacing specific parts, such as spark plugs, ignition coils, or brake pads; or they may involve inspecting, adjusting, or repairing specific components, such as checking circuitry, adjusting brake system clearances, or cleaning fuel injectors. For example, for the above-mentioned high-speed steering wheel shaking fault, a common maintenance method may be to perform dynamic balancing calibration on the wheels, or check whether the connecting rods and ball joints of the suspension system are loose, and replace damaged parts if necessary. Integrating all the collected car models, fault descriptions and corresponding repair methods together forms the basic content of the vehicle maintenance database. This database covers a variety of car models (for example, thousands of types), each of which has different fault types, and each fault type is associated with a specific repair method, thereby providing reference and guidance for vehicle maintenance.
[0040] There are many ways to generate vehicle maintenance strategies based on the pre-built vehicle maintenance database, the vehicle model to be repaired, and the fault description information, which can be as follows:
[0041] When there is a vehicle model to be repaired in the vehicle maintenance database, a first vehicle maintenance strategy that matches the vehicle model to be repaired and the fault description information is screened out from the vehicle maintenance database; when there is no vehicle model to be repaired in the vehicle maintenance database, the similarity between the vehicle model to be repaired and each candidate vehicle model in the vehicle maintenance database is compared; based on the similarity and a preset similarity threshold, at least one reference vehicle model is screened out from each candidate vehicle model; a second vehicle maintenance strategy that matches the at least one reference vehicle model and the fault description information is obtained from the vehicle maintenance database, and the second vehicle maintenance strategy includes identification information of the reference vehicle model.
[0042] The first vehicle maintenance strategy may be a vehicle maintenance strategy directly corresponding to the current vehicle model to be repaired and the fault description information, and is used to resolve the fault of the vehicle model to be repaired. The first vehicle maintenance strategy may also include cost, success rate, and time, which are not limited here.
[0043] Optionally, a mapping relationship between different vehicle models to be repaired, fault descriptions, and vehicle maintenance strategies can be pre-recorded. The corresponding first vehicle maintenance strategy can then be found based on the currently entered vehicle model and fault description. For example, if the vehicle model to be repaired already exists in the database, the database uniquely identifies the target vehicle based on its brand, series, year, and configuration. The user enters a fault description (e.g., "frequent interruptions during charging"), and the database filters all fault records for that vehicle model by keyword matching (e.g., "charging," "interruption") or fault code association to identify the corresponding fault type (e.g., "charging control system failure"). The database then directly retrieves the repair solutions associated with that fault type, sorting them by historical success rate, cost, and other priorities to output the first vehicle maintenance strategy. For example, if the vehicle model to be repaired is vehicle A, and the fault description is "charging interruptions," the first vehicle maintenance strategy for the fault "poor charging port contact" in the database for that vehicle model could be replacing the charging port seal.
[0044] The candidate vehicle models may be various vehicle models in a vehicle maintenance database.
[0045] When the vehicle model to be repaired does not exist in the vehicle maintenance database, the similarity between the vehicle model to be repaired and the candidate models in the vehicle maintenance database can be compared. For example, you can first look at the power type. For example, if the vehicle model to be repaired is a pure electric vehicle, then first look for pure electric models in the database. Then look at the vehicle model's purpose and structure. For example, if the vehicle model to be repaired is a mid-size SUV, give priority to mid-size SUV models in the database. The chassis and suspension structures of sedans and SUVs are different, so direct reference may have large deviations. Then you can look at the brand and technical background. For example, some new brands and old brands belong to the same group and may have technical commonalities, or some models use the same chassis platform and powertrain. Although the brands are different, the fault principles and repair methods may be similar. Then you can look at the commonalities of the fault phenomenon. For example, if the vehicle model to be repaired is "sudden power failure while driving", you can check whether there are other models in the vehicle maintenance database with similar electrical faults to see if the fault manifestations are similar.
[0046] In other words, starting with the vehicle's core attributes, the "match" between the vehicle model to be repaired and candidate models in the vehicle repair database can be determined. For example: Powertrain type: Prioritize matching with the same powertrain type (e.g., pure electric, fuel-powered, hybrid), as the fault logic of different powertrains can vary significantly (e.g., battery failure in an electric vehicle is completely different from engine failure in a fuel-powered vehicle). Vehicle platform and structure: Consider vehicles with the same or similar chassis platforms and body types (e.g., compact sedans, mid-size SUVs), as the component layout and circuitry of vehicles on the same platform are likely highly common. Brand and technology origin: Focus on models from the same manufacturer or technology alliance, as their fault codes and repair procedures may be more similar. Commonality of fault symptoms: Even if the models are different, if the fault symptoms are similar (e.g., "brake squeal" or "air conditioning not cooling"), reference the troubleshooting logic for similar faults, but be mindful of component differences. For example, if a brand-new pure electric SUV (not yet in the database) is used, the powertrain supplier, chassis structure, and common fault types of other pure electric SUVs in the database can be compared. If a mainstream brand pure electric SUV uses the same motor and battery supplier, its powertrain repair solution can be highly valuable, even if the brand is different. If the vehicle to be repaired is a gasoline-powered sedan, and the database only contains data for electric vehicles or trucks, even if the fault symptoms are similar, caution should be exercised in referencing the data because the core systems differ significantly. A vehicle model with a similarity above a preset threshold can then be selected as a reference model, and its fault repair data can be retrieved as the second vehicle repair strategy. The reference model information (such as brand and model) is also annotated to remind maintenance personnel of possible accessory adaptation or process adjustments (such as different wiring harness interfaces or software versions) that may result from vehicle model differences.
[0047] Optionally, the data in the vehicle maintenance database can be vectorized to obtain a vector library corresponding to the vehicle maintenance database. Then, the initial vehicle maintenance strategy model can be trained based on the vector library until the initial vehicle maintenance strategy model is trained to be a usable target vehicle maintenance strategy model. After that, the vehicle model to be repaired and the fault description information can be input into the target vehicle maintenance strategy model to obtain the vehicle maintenance strategy.
[0048] Among them, the initial vehicle maintenance strategy model is a machine learning model that has not been trained or has only been trained with a small amount of data. It is the initial model of the target vehicle maintenance strategy model. It has a basic algorithm framework (such as neural networks and decision trees), but has not yet learned the inherent rules in vehicle maintenance data.
[0049] Among them, the initial vehicle maintenance strategy model has defined the architecture of the input layer (vehicle type / fault vector), hidden layer (feature calculation), and output layer (maintenance strategy vector), but the parameters between each layer (such as weights and biases) are random initial values or default values. Since it is impossible to accurately match the maintenance strategy, it needs to be optimized through large amounts of data training. For example, the initial vehicle maintenance strategy model may incorrectly associate "pure electric vehicle failure" with "fuel vehicle maintenance strategy." As the basis for model iteration, by inputting vector library data, the parameters are gradually adjusted to approximate the real data distribution.
[0050] Among them, the target vehicle maintenance strategy model can be a mature model formed after the initial vehicle maintenance strategy model is fully trained, and has the ability to accurately match the vehicle maintenance strategy. The target vehicle maintenance strategy model receives the vehicle model text (such as "2024 XXMars pure electric SUV") and fault description (such as "power interruption") input by the user, and automatically converts it into a vector. Retrieve the "vehicle model-fault-repair strategy" combination that is most similar to the input vector in the vector library, and generate a recommended strategy based on the weights and association rules calculated by the model parameters. If there is a directly matching vehicle model data, output a precise maintenance strategy (such as "replace the charging port seal"); if the vehicle model is not included, output a strategy that refers to similar models (such as "refer to the motor controller inspection steps of BYD Seal"), and mark the similarity and reference source.
[0051] Optionally, the heterogeneous data in the vehicle maintenance database can first be vectorized. Vehicle model information (brand, series, year, powertrain, etc.), fault descriptions (phenomenon, triggering conditions, associated fault codes, etc.), and maintenance strategies (operation steps, required parts, tools and equipment, etc.) are converted into computer-processable numerical vector representations. Encoding techniques are used to map categorical data into unique digital identifiers. Natural language processing (NLP) methods are used to convert textual fault descriptions into word vectors. Structured coding is then used to decompose maintenance strategies into sequences of operation codes, ultimately constructing a standardized vector library. An initial vehicle maintenance strategy model can then be iteratively trained based on this vector library. By inputting a large number of "vehicle model-fault description-repair strategy" vector samples, a deep learning algorithm is used to mine potential relationships between the data, enabling the model to learn the optimal maintenance strategy mapping rules for different vector combinations. After multiple rounds of training and optimization, when the model reaches the preset accuracy standard, a target vehicle maintenance strategy model is formed, which can be directly applied. This model is capable of accepting user-input vehicle model and fault information and automatically completing vectorization conversion, vector similarity matching, and outputting maintenance strategies.
[0052] Optionally, the vehicle maintenance database may be updated according to a specified period, and then the model parameters in the target vehicle maintenance strategy model may be modified based on the updated vehicle maintenance database.
[0053] Model parameters are learnable variables in a machine learning model that describe the mapping between data features and outputs. During training, these parameters are continuously adjusted using optimization algorithms (such as gradient descent) to ensure that the model's predictions are consistent with the real data.
[0054] It's important to note that after the user enters the vehicle model to be repaired and the fault description, the target vehicle maintenance strategy model first converts this information into a corresponding vector form. It then performs a similarity search within the vector library and generates maintenance strategy recommendations based on the matching results. For new models not included in the database, the model uses a similarity metric in vector space to recommend maintenance plans for vehicles with similar structural or technical characteristics, and annotates the reference source for maintenance personnel's evaluation.
[0055] To maintain model effectiveness, a data update mechanism can be established. New vehicle model data, new fault cases, and optimized maintenance strategies are added to the database on a regular basis (e.g., monthly or quarterly). The target model is retrained based on this updated data, and model parameters are dynamically adjusted to correct for matching errors caused by data changes. This ensures that the model remains adaptable to the latest maintenance knowledge and that recommendations are accurate.
[0056] Step 103: Respond to the user with the vehicle maintenance strategy.
[0057] Optionally, the vehicle maintenance strategy may be replied to the user in text form and / or voice form.
[0058] It should be noted that vehicle maintenance strategies can be communicated to users via text or voice. For example, when a user inquires about a vehicle fault, the system will first generate maintenance recommendations based on the vehicle model and fault information. These recommendations can be displayed in text on a mobile phone, computer, or maintenance equipment screen. The content can be detailed step-by-step instructions, such as which component to check first and what part to replace, as well as accessory prices and precautions. Alternatively, these maintenance recommendations can be converted into voice and played out, such as through a mobile phone voice assistant, car audio, or voice equipment at the maintenance site. This way, users can understand the maintenance plan without staring at the screen, which is suitable for situations where both hands are busy or eyesight is not good during maintenance. Text and voice can be used together, such as voice broadcasting of key steps while detailed text and pictures are displayed on the screen, making it easier for users to understand and operate. Whether they are professional maintenance personnel or ordinary car owners, they can choose the appropriate method to obtain maintenance information according to their needs, making the entire maintenance process more convenient and efficient.
[0059] In the disclosed embodiment, the currently input vehicle type to be repaired and the fault description information corresponding to the vehicle type to be repaired can be obtained first, and then a vehicle maintenance strategy can be generated based on the pre-built vehicle maintenance database, the vehicle type to be repaired and the fault description information, and then the vehicle maintenance strategy can be replied to the user. Therefore, by obtaining the vehicle type to be repaired and the fault information, and combining it with the pre-built vehicle maintenance database to generate and reply strategies, the diagnosis time can be quickly compressed, the technical threshold can be lowered, and novices can also handle faults. This model relies on historical data to prioritize the recommendation of high-success rate solutions, reduce trial and error and excessive repairs, and reduce costs. At the same time, standardized service processes ensure consistent quality, and the database forms experience iterations. Improve efficiency and reliability.
[0060] To facilitate better implementation of the vehicle maintenance strategy determination method disclosed herein, the present disclosure also provides a vehicle maintenance strategy determination device based on the aforementioned vehicle maintenance strategy determination method. The meanings of the terms herein are the same as those in the aforementioned vehicle maintenance strategy determination method. For specific implementation details, please refer to the description in the method embodiment.
[0061] See also Figure 2 , Figure 2 2 is a schematic diagram of a vehicle maintenance strategy determination device according to an embodiment of the present disclosure. The vehicle maintenance strategy determination device 200 includes:
[0062] The acquisition module 210 is used to obtain the currently input vehicle type to be repaired and the fault description information corresponding to the vehicle type to be repaired;
[0063] A generating module 220 is configured to generate a vehicle maintenance strategy based on a pre-built vehicle maintenance database, the vehicle model to be repaired, and the fault description information;
[0064] The reply module 230 is configured to reply the vehicle maintenance strategy to the user.
[0065] Optionally, the generating module 220 is specifically configured to:
[0066] When the vehicle model to be repaired exists in the vehicle repair database, a first vehicle repair strategy matching the vehicle model to be repaired and the fault description information is screened out from the vehicle repair database;
[0067] When the vehicle model to be repaired does not exist in the vehicle repair database, comparing the similarity between the vehicle model to be repaired and each candidate vehicle model in the vehicle repair database;
[0068] Based on the similarity and a preset similarity threshold, selecting at least one reference model from the candidate models;
[0069] A second vehicle maintenance strategy that matches the at least one reference vehicle model and the fault description information is obtained from the vehicle maintenance database, where the second vehicle maintenance strategy includes identification information of the reference vehicle model.
[0070] Optionally, the generating module 220 is further configured to:
[0071] Collect fault description information corresponding to each fault type of different vehicle models, as well as at least one fault repair method associated with each fault type;
[0072] The vehicle maintenance database is constructed based on each fault description information corresponding to the vehicle model and at least one fault maintenance method associated with each fault type.
[0073] Optionally, the generating module 220 is specifically configured to:
[0074] Vectorizing the data in the vehicle maintenance database to obtain a vector library corresponding to the vehicle maintenance database;
[0075] Based on the vector library, the initial vehicle maintenance strategy model is trained until the initial vehicle maintenance strategy model is trained to be a usable target vehicle maintenance strategy model;
[0076] The vehicle type to be repaired and the fault description information are input into the target vehicle maintenance strategy model to obtain the vehicle maintenance strategy.
[0077] Optionally, the generating module 220 is further configured to:
[0078] updating the vehicle maintenance database according to a specified period;
[0079] The model parameters in the target vehicle maintenance strategy model are modified according to the vehicle maintenance database after data update.
[0080] Optionally, the reply module 230 is specifically configured to:
[0081] The vehicle maintenance strategy is responded to the user in text form and / or voice form.
[0082] In the disclosed embodiment, the currently input vehicle type to be repaired and the fault description information corresponding to the vehicle type to be repaired can be obtained first, and then a vehicle maintenance strategy can be generated based on the pre-built vehicle maintenance database, the vehicle type to be repaired and the fault description information, and then the vehicle maintenance strategy can be replied to the user. Therefore, by obtaining the vehicle type to be repaired and the fault information, and combining it with the pre-built vehicle maintenance database to generate and reply strategies, the diagnosis time can be quickly compressed, the technical threshold can be lowered, and novices can also handle faults. This model relies on historical data to prioritize the recommendation of high-success rate solutions, reduce trial and error and excessive repairs, and reduce costs. At the same time, standardized service processes ensure consistent quality, and the database forms experience iterations. Improve efficiency and reliability.
[0083] In addition, the present disclosure also provides an electronic device, such as Figure 3 , which shows a schematic structural diagram of the electronic device involved in the present disclosure, specifically:
[0084] The electronic device may include one or more processors 301 of processing cores, one or more computer-readable storage media memories 302, a power supply 303, an input unit 304 and other components. Those skilled in the art will appreciate that Figure 3 The electronic device structure shown in the figure does not constitute a limitation of the electronic device, and may include more or fewer components than shown in the figure, or combine certain components, or arrange components differently.
[0085] The processor 301 is the control center of the electronic device. It connects all parts of the electronic device using various interfaces and lines. By running or executing software programs and / or modules stored in the memory 302 and accessing data stored in the memory 302, it performs various functions of the electronic device and processes data, thereby monitoring the electronic device as a whole. 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 primarily processes the operating system, user interface, and application programs, while the modem processor primarily handles wireless communications. It is understood that the modem processor may not be integrated into the processor 301.
[0086] The memory 302 can be used to store software programs and modules. The processor 301 executes various functional applications and data processing by running the software programs and modules stored in the memory 302. The memory 302 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area may store data created according to the use of the electronic device, etc. In addition, the memory 302 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other volatile solid-state storage device. Accordingly, the memory 302 may also include a memory controller to provide the processor 301 with access to the memory 302.
[0087] The electronic device also includes a power supply 303 for supplying power to various components. Preferably, the power supply 303 can be logically connected to the processor 301 via a power management system, thereby enabling the power management system to manage charging, discharging, and power consumption. The power supply 303 can also include one or more DC or AC power supplies, a recharging system, a power supply device debugging circuit, a power converter or inverter, a power status indicator, and other arbitrary components.
[0088] The electronic device may further include an input unit 304, which may be configured to receive input digital or character information and generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function control.
[0089] Although not shown, the electronic device may further 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 one or more application processes into the memory 302 according to the following instructions, and the processor 301 runs the application stored in the memory 302, thereby implementing the steps of any of the vehicle maintenance strategy determination methods provided in the embodiments of the present disclosure.
[0090] In the disclosed embodiment, the currently input vehicle type to be repaired and the fault description information corresponding to the vehicle type to be repaired can be obtained first, and then a vehicle maintenance strategy can be generated based on the pre-built vehicle maintenance database, the vehicle type to be repaired and the fault description information, and then the vehicle maintenance strategy can be replied to the user. Therefore, by obtaining the vehicle type to be repaired and the fault information, and combining it with the pre-built vehicle maintenance database to generate and reply strategies, the diagnosis time can be quickly compressed, the technical threshold can be lowered, and novices can also handle faults. This model relies on historical data to prioritize the recommendation of high-success rate solutions, reduce trial and error and excessive repairs, and reduce costs. At the same time, standardized service processes ensure consistent quality, and the database forms experience iterations. Improve efficiency and reliability.
[0091] The specific implementation of the above operations can be found in the previous embodiments and will not be repeated here.
[0092] Those skilled in the art will appreciate that all or part of the steps in the various methods of the above embodiments may be accomplished by instructions, or by controlling related hardware through instructions. The instructions may be stored in a computer-readable storage medium and loaded and executed by a processor.
[0093] To this end, the present disclosure provides a computer-readable storage medium having a computer program stored thereon. The computer program can be loaded by a processor to execute the steps in any one of the vehicle maintenance strategy determination methods provided in the present disclosure.
[0094] The specific implementation of the above operations can be found in the previous embodiments and will not be repeated here.
[0095] The computer-readable storage medium may include a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.
[0096] Since the instructions stored in the computer-readable storage medium can execute the steps in any of the vehicle maintenance strategy determination methods provided in the present disclosure, the beneficial effects that can be achieved by any of the vehicle maintenance strategy determination methods provided in the present disclosure can be achieved. Please refer to the previous embodiments for details and will not be repeated here.
[0097] The above is a detailed introduction to the method, device, electronic device and computer-readable storage medium for determining a vehicle maintenance strategy provided by the present disclosure. Specific examples are used herein to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method and core ideas of the present invention. At the same time, for those skilled in the art, according to the ideas of the present invention, there may be changes in the specific implementation methods and application scopes. In summary, the contents of this specification should not be understood as limiting the present invention.
Claims
1. A method for determining a vehicle maintenance strategy, characterized in that: include: Obtain the currently input vehicle type to be repaired and the fault description information corresponding to the vehicle type to be repaired; Generate a vehicle maintenance strategy based on a pre-built vehicle maintenance database, the vehicle model to be repaired, and the fault description information; The vehicle maintenance strategy is replied to the user.
2. The method according to claim 1, characterized in that The generating of a vehicle maintenance strategy based on a pre-built vehicle maintenance database, the vehicle model to be repaired, and the fault description information includes: When the vehicle model to be repaired exists in the vehicle repair database, a first vehicle repair strategy matching the vehicle model to be repaired and the fault description information is screened out from the vehicle repair database; When the vehicle model to be repaired does not exist in the vehicle repair database, comparing the similarity between the vehicle model to be repaired and each candidate vehicle model in the vehicle repair database; Based on the similarity and a preset similarity threshold, selecting at least one reference model from the candidate models; A second vehicle maintenance strategy that matches the at least one reference vehicle model and the fault description information is obtained from the vehicle maintenance database, where the second vehicle maintenance strategy includes identification information of the reference vehicle model.
3. The method according to claim 1, characterized in that Before generating a vehicle maintenance strategy based on the pre-built vehicle maintenance database, the vehicle model to be repaired, and the fault description information, the method further includes: Collect fault description information corresponding to each fault type of different vehicle models, as well as at least one fault repair method associated with each fault type; The vehicle maintenance database is constructed based on each fault description information corresponding to the vehicle model and at least one fault maintenance method associated with each fault type.
4. The method according to claim 3, characterized in that The generating of a vehicle maintenance strategy based on a pre-built vehicle maintenance database, the vehicle model to be repaired, and the fault description information includes: Vectorizing the data in the vehicle maintenance database to obtain a vector library corresponding to the vehicle maintenance database; Based on the vector library, the initial vehicle maintenance strategy model is trained until the initial vehicle maintenance strategy model is trained to be a usable target vehicle maintenance strategy model; The vehicle type to be repaired and the fault description information are input into the target vehicle maintenance strategy model to obtain the vehicle maintenance strategy.
5. The method according to claim 4, characterized in that Also includes: updating the vehicle maintenance database according to a specified period; The model parameters in the target vehicle maintenance strategy model are modified according to the vehicle maintenance database after data update.
6. The method according to claim 1, characterized in that The step of replying to the user about the vehicle maintenance strategy includes: The vehicle maintenance strategy is responded to the user in text form and / or voice form.
7. A device for determining a vehicle maintenance strategy, characterized in that: include: An acquisition module is used to obtain the currently input vehicle type to be repaired and the fault description information corresponding to the vehicle type to be repaired; A generation module, configured to generate a vehicle maintenance strategy based on a pre-built vehicle maintenance database, the vehicle model to be repaired, and the fault description information; The reply module is used to reply the vehicle maintenance strategy to the user.
8. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the memory stores an application program, and the processor is configured to run the application program in the memory to execute the steps of the method according to any one of claims 1 to 6.
9. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.
10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a plurality of instructions, and the instructions are suitable for being loaded by a processor to execute the steps of the method according to any one of claims 1 to 6.