Intelligent automobile maintenance auxiliary system

Abstract

The invention provides an intelligent automobile maintenance auxiliary system which comprises a voice interaction module, a retrieval engine module, a judgment priority module, a multi-mode output module and an offline working module, the voice interaction module receives user voice input and converts the user voice input into a text, the retrieval engine module calls the judgment priority module to determine a retrieval strategy, and the multi-mode output module outputs the retrieval strategy to the offline working module. And preferentially querying a local database, supplementing network data, and feeding back a result to the multi-mode output module. Through the voice interaction module, technicians do not need to memorize complex operation instructions or terminologies, accurate maintenance guidance can be obtained only by describing fault phenomena through daily languages, the working efficiency is improved, the technical threshold is reduced, and the reliability of the system is ensured through the offline working capability; the off-line working module ensures reliable operation of the system in various environments, the system can still provide local database support even under the condition that network signals are poor or completely interrupted, and continuity of maintenance work is ensured.

Description

Technical Field

[0001] This invention belongs to the field of automotive repair technology, and in particular relates to an intelligent automotive repair auxiliary system. Background Technology

[0002] In the field of automotive repair technology, traditional fault diagnosis and repair guidance mainly rely on paper repair manuals or electronic technical documents, which has limitations. With the increasing level of automotive electronics, modern vehicles contain up to hundreds of electronic control units (ECUs), leading to an exponential increase in the complexity of fault diagnosis. Technicians often have to manually search through massive amounts of technical documents for relevant information, which is not only inefficient but also prone to overlooking crucial details. Summary of the Invention

[0003] In view of this, in order to solve the problems existing in the technical background, the present invention proposes an intelligent vehicle maintenance assistance system. Specifically, it includes the following:

[0004] An intelligent vehicle maintenance assistance system includes a voice interaction module, a retrieval engine module, a priority judgment module, a multimodal output module, and an offline working module. The voice interaction module receives user voice input and converts it into text. The retrieval engine module calls the priority judgment module to determine the retrieval strategy, first querying the local database and then supplementing it with network data, and then feeding the results back to the multimodal output module.

[0005] Furthermore, the multimodal output module simultaneously generates voice broadcasts and screen visualization content.

[0006] Furthermore, the offline working mode is connected to the voice interaction module, which takes over the retrieval process when the network is interrupted, and the data encryption module ensures information security throughout the process.

[0007] Furthermore, the voice interaction module receives continuous voice input from the user and converts it into text, the retrieval engine module integrates the local maintenance database and the web crawler module, prioritizes the retrieval of local structured maintenance cases and vehicle manual data, and the priority judgment module automatically matches the priority of maintenance solutions based on fault keywords.

[0008] Furthermore, in the priority determination module, local data > publicly available network data.

[0009] Furthermore, the voice interaction module supports multi-turn context-dependent dialogue.

[0010] Furthermore, in the priority judgment module, the vehicle identification code entered by the user's voice or manually is parsed to lock the maintenance specifications for the corresponding model year and configuration. For mechanical faults, local data is directly called, while for electronic faults, online verification is required to check whether there is a software update.

[0011] A smart car maintenance assistance method, characterized by comprising the following steps:

[0012] Step S1: The user wakes up the system through the login-free voice portal. After the voiceprint authentication is successful, a session is established and the system automatically loads the user's historical maintenance preferences.

[0013] Step S2: The voice interaction module parses the continuous questions and extracts the fault entity;

[0014] Step S3: The search engine module locks the vehicle configuration based on the VIN code and searches according to the priority of mechanical faults > electronic faults > network supplements;

[0015] Step S4: The multimodal output module displays AR disassembly and assembly instructions and a tool list via voice broadcast and screen split-screen display, and records the maintenance process to the blockchain log.

[0016] The above technical solution has the following beneficial effects:

[0017] This invention utilizes a voice interaction module, allowing technicians to obtain accurate repair guidance simply by describing the fault symptoms using everyday language, without needing to memorize complex operating instructions or technical jargon. This improves work efficiency, lowers the technical threshold, and ensures both response speed and information comprehensiveness through a combined local and network retrieval strategy. The application of multimodal output makes complex repairs intuitive and simple, while offline operation capability ensures system reliability. The offline module ensures reliable operation of the system in various environments, and even in cases of poor network signal or complete network interruption, the system can still provide local database support, ensuring the continuity of repair work. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the module connection structure of an intelligent vehicle maintenance assistance system according to the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Example 1, see Figure 1As shown, an intelligent vehicle maintenance assistance system is characterized by comprising: a voice interaction module, a retrieval engine module, a priority judgment module, a multimodal output module, and an offline working module. The voice interaction module receives user voice input and converts it into text. The retrieval engine module calls the priority judgment module to determine the retrieval strategy, prioritizes querying the local database, supplements it with network data, and feeds back the results to the multimodal output module.

[0021] The multimodal output module synchronously generates voice broadcasts and screen visualizations. The offline working mode is connected to the voice interaction module and takes over the retrieval process when the network is interrupted. Information security is ensured throughout by the data encryption module. The voice interaction module receives continuous voice input from the user and converts it into text. The retrieval engine module integrates a local repair database and a web crawler module, prioritizing the retrieval of local structured repair cases and vehicle manual data. The priority judgment module automatically matches the priority of repair solutions based on fault keywords. In the priority judgment module, local data > publicly available network data. The voice interaction module supports multi-turn context-based dialogue. In the priority judgment module, the vehicle identification code entered by the user's voice or manually is parsed to lock the repair specifications for the corresponding year and configuration. Mechanical faults directly call local data, while electronic faults require online verification to check for software updates.

[0022] Example 2, based on Example 1, uses a voice interaction module as the entry point for system-user interaction. Employing speech recognition technology, it receives continuous voice input from the user and converts it into text information. This process supports natural descriptions in everyday language and also possesses multi-turn context-based dialogue capabilities. Technicians do not need to memorize complex operating instructions or technical terms; they only need to describe the fault phenomenon step-by-step using natural language, and the system can accurately understand the user's intent, providing accurate basic data for subsequent fault diagnosis and maintenance guidance.

[0023] The speech recognition technology in this embodiment employs an end-to-end deep learning speech recognition model. This model, trained with relevant speech data from the automotive repair field, can recognize continuous user speech input and convert it into text information. During training, speech samples covering various accents, speaking speeds, and expression habits are collected to improve the model's adaptability to different user voices and its recognition accuracy. For example, it can recognize and convert the speech of a technician describing a fault into corresponding text.

[0024] Multi-turn context-sensitive dialogue capability is achieved by building a dialogue management system. This system maintains a dialogue state tracker, recording key information for each round of dialogue, such as descriptions of fault symptoms and information about vehicles mentioned.

[0025] Authentication is performed when a user wakes up the system. The system pre-collects and stores user voiceprint feature templates. When a user wakes up the system through a login-free voice portal, the system collects the user's voice sample in real time, extracts voiceprint features, and compares them with the pre-stored templates. If the matching degree reaches a set threshold, voiceprint authentication is successful, a session is established, and the user's historical repair preferences are automatically loaded, such as previously viewed car models and common fault types, improving the service experience and enhancing user convenience.

[0026] The local repair database is integrated with the web crawler module: The local repair database uses a relational database, such as MySQL, to store key information such as structured repair cases and vehicle manual data. This data is organized and categorized, and an index structure is established for easy retrieval and retrieval. The web crawler module is developed using programming languages ​​such as Python and utilizes a crawling framework such as Scrapy to crawl the latest automotive repair technical data and software update information from the internet according to preset rules. The crawler program runs periodically, storing the crawled data in the local database or a dedicated cache to ensure the timeliness and comprehensiveness of the system information.

[0027] Search Strategy Determination: The search engine module calls the priority determination module to determine the search strategy. After receiving the text information converted by the voice interaction module, it first extracts key information, such as fault keywords and Vehicle Identification Number (VIN). Based on the priority determined by the priority determination module, it prioritizes searching local structured repair cases and vehicle manual data. For mechanical faults, it directly queries relevant repair solutions and guidance information from the local database to improve repair efficiency. For electronic faults, while querying local data, it verifies online for software updates to ensure the accuracy and timeliness of repair solutions. If local data is insufficient, the search engine module supplements the search with online data through a web crawler module, implementing a combined local and online search strategy to ensure response speed and information comprehensiveness.

[0028] Priority rule setting: Priority rules are established, stipulating that local data has higher priority than publicly available online data. During the retrieval process, relevant information is first attempted to be obtained from the local maintenance database. If the local database does not contain the required information, then it is retrieved from the internet using a web crawler module. Different priority orders are set for different types of faults; for example, mechanical faults have higher priority than electronic faults, and electronic faults have higher priority than supplementary online information.

[0029] Vehicle Information Parsing and Matching: The system parses the Vehicle Identification Number (VIN) entered by the user via voice or manual input. Through interaction with a local database or online vehicle information service platform, it identifies the corresponding repair specifications for the vehicle's model year and configuration. For example, it retrieves detailed information such as the engine model and transmission type based on the VIN, and then matches the appropriate repair plan accordingly. For mechanical faults, it directly retrieves repair data matching the vehicle configuration from the local database. For electronic faults, in addition to querying local data, it also verifies online for software updates specific to the vehicle configuration to ensure the accuracy and applicability of the repair plan.

[0030] The voice broadcast uses speech synthesis technology to convert search results into speech. In this embodiment, the speech synthesis engine is from iFlytek, which can generate speech based on text content and offers a variety of voice styles and speeds to choose from. When generating the speech, the tone, speed, and other parameters are adjusted according to the importance and urgency of the repair instructions, allowing technicians to focus on the repair operation while simultaneously obtaining crucial information through hearing, thus improving operational safety.

[0031] Screen Visualization Generation: Utilizing a graphical user interface design, AR disassembly and assembly instructions and a tool list are displayed in a split-screen format. The AR disassembly and assembly instructions employ augmented reality technology, combined with 3D modeling and image recognition. First, a 3D model is created based on the actual condition of the vehicle components, generating a virtual model. Then, image recognition algorithms are used to identify the actual components on the vehicle in real time, combining the virtual repair steps with the actual vehicle parts to provide technicians with disassembly and assembly guidance. The tool list displays the necessary tools and equipment for the repair in a list format, including tool names, specifications, and other information, allowing technicians to prepare in advance and avoid delays due to missing tools.

[0032] This embodiment employs blockchain technology to ensure the traceability and immutability of the repair process. A blockchain platform, such as Ethereum, is selected, and smart contracts are developed to record key information about the repair process, such as repair time, repair steps, and replaced parts. After each repair operation is completed, the relevant information is written to the blockchain log via the smart contract. Once written, this information cannot be modified, providing strong evidence for subsequent repair quality assessment and dispute resolution.

[0033] The offline working module is tightly integrated with the voice interaction module, monitoring network status in real time. When a network interruption is detected, the offline working module automatically takes over the retrieval process, ceasing requests for network data and relying solely on the local repair database for repair guidance services. The local repair database stores critical information such as repair cases and vehicle manual data, meeting the repair needs of most common faults. This ensures that technicians can still access local database support even in cases of poor or complete network signal loss, guaranteeing the continuity of repair work.

[0034] Data encryption ensures information security: Local data is encrypted using data encryption technology to prevent data leakage and unauthorized access. This embodiment uses a symmetric encryption algorithm to encrypt sensitive information in the local database. During data transmission, the SSL / TLS encryption protocol is also used to ensure data security during network transmission. Simultaneously, access control is implemented, allowing only authorized users to access data in the local database, thus protecting the security and privacy of user data.

[0035] Through the above implementation methods, the various modules of the intelligent vehicle repair assistance system of the present invention can work together to provide repair assistance services for the vehicle repair industry.

[0036] The basic principles and main features of the present invention have been described above. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are only illustrative of the principles of the present invention. Various changes and modifications can be made to the present invention without departing from the spirit and scope of the present invention. All such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent vehicle maintenance assistance system, characterized in that, It includes a voice interaction module, a search engine module, a priority judgment module, a multimodal output module, and an offline working module. The voice interaction module receives user voice input and converts it into text. The search engine module calls the priority judgment module to determine the search strategy, first querying the local database and then supplementing it with network data, and then feeding the results back to the multimodal output module.

2. The intelligent vehicle maintenance assistance system according to claim 1, characterized in that, The multimodal output module simultaneously generates voice broadcasts and screen visualizations.

3. The intelligent vehicle maintenance assistance system according to claim 1, characterized in that, The offline working mode is connected to the voice interaction module and takes over the retrieval process when the network is interrupted. The data encryption module ensures information security throughout the process.

4. The intelligent vehicle maintenance assistance system according to claim 1, characterized in that, The voice interaction module receives continuous voice input from the user and converts it into text. The retrieval engine module integrates a local maintenance database and a web crawler module, prioritizing the retrieval of local structured maintenance cases and vehicle manual data. The priority judgment module automatically matches the priority of maintenance solutions based on fault keywords.

5. The intelligent vehicle maintenance assistance system according to claim 1, characterized in that, In the priority determination module, local data > publicly available network data.

6. An intelligent vehicle maintenance assistance system according to claim 1 or 4, characterized in that, The voice interaction module supports multi-turn context-based dialogue.

7. The intelligent vehicle maintenance assistance system according to claim 4, characterized in that, In the priority determination module, the vehicle identification code entered by the user's voice or manually is parsed to lock the maintenance specifications for the corresponding model year and configuration. For mechanical faults, local data is directly called, while for electronic faults, online verification is required to check whether there is a software update.

8. An intelligent vehicle maintenance assistance method, characterized in that, Includes the following steps: Step S1: The user wakes up the system through the login-free voice portal. After the voiceprint authentication is successful, a session is established and the system automatically loads the user's historical maintenance preferences. Step S2: The voice interaction module parses the continuous questions and extracts the fault entity; Step S3: The search engine module locks the vehicle configuration based on the VIN code and searches according to the priority of mechanical faults > electronic faults > network supplements; Step S4: The multimodal output module displays AR disassembly and assembly instructions and a tool list via voice broadcast and screen split-screen display, and records the maintenance process to the blockchain log.

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