Intelligent generation method and system for automobile maintenance record list

Abstract

The invention discloses an intelligent generation method and system for an automobile maintenance record list, and aims to improve the generation efficiency and accuracy of the maintenance record list. The method comprises the steps of collecting automobile basic information input by an automobile owner, querying maintenance standard items and periods, receiving maintenance actual items and maintenance results, generating a record list, then storing the record list in a database, and providing querying and printing functions. Intelligent generation and management of the automobile maintenance record list are realized through information technologies such as natural language processing, machine learning, image recognition, natural language generation, big data storage, mobile pushing, data mining and a cloud database. According to the method and the system, the maintenance record list manufacturing process is simplified, the efficiency and the accuracy are improved, management and inquiry of vehicle owners and service personnel are facilitated, and meanwhile, more commercial opportunities are created for vehicle-mounted market service providers.

Description

Technical Field

[0001] The present invention relates to the field of automobile maintenance services, serving the after-market of automobiles, and particularly relates to a method and system for intelligently generating an automobile maintenance record form. Background Art

[0002] With the development of the economy and the improvement of people's living standards, automobiles have become the daily means of transportation for many families. The number of automobiles in use has been increasing year by year, and the automobile maintenance market has also been expanding day by day. The maintenance of automobiles has always been the focus of concern for the majority of vehicle owners and has also become an important business area for after-market service providers. During the automobile maintenance process, the maintenance record form is an important document for recording the automobile maintenance situation, which records information such as the time of automobile maintenance, maintenance items, replaced parts, etc. The traditional automobile maintenance record form relies on manual filling, which is inefficient and error-prone, and is also not conducive to service providers providing a more intelligent recommendation service with better experience.

[0003] Therefore, it is necessary to study a method and system for intelligently generating an automobile maintenance record form to help maintenance personnel improve the production efficiency and accuracy of the automobile maintenance record form and provide an intelligent push service for vehicle owners. Summary of the Invention

[0004] The object of the present invention is to provide a method and system for intelligently generating an automobile maintenance record form to solve the problems existing in the prior art.

[0005] An automobile maintenance record form intelligent generation method includes the following steps:

[0006] S101: Collecting basic automobile information and maintenance information, receiving and processing the basic automobile information and maintenance information input by the vehicle owner, including license plate number, vehicle model, purchase time, etc., and receiving the automobile maintenance history record input by the vehicle owner.

[0007] S102: Querying maintenance standard items and cycles, according to the collected basic automobile information, querying the corresponding automobile maintenance standard items and cycles from the database, and automatically recommending maintenance items according to the driving mileage or time of the automobile.

[0008] S103: Receiving actual maintenance items and maintenance results: receiving the actual maintenance items and maintenance results of the automobile input by the automobile maintenance service personnel.

[0009] S104: Generating an automobile maintenance record form, matching the actual maintenance items with the maintenance standard items in the database, generating an automobile maintenance record form, and auditing and confirming the generated automobile maintenance record form.

[0010] S105: Store and provide query and printing functions. The generated vehicle maintenance record sheet is stored in the database, and functions such as query and printing are provided for users. The vehicle owner is notified of the generation and storage of the vehicle maintenance record sheet via email or text message.

[0011] S106: Big data analysis of vehicle maintenance. Conduct big data mining and analysis on vehicle maintenance records to provide vehicle owners with vehicle maintenance suggestions and predict future maintenance needs, enhancing the personalization and foresight of services.

[0012] S107: Real-time push of reminders and updated information. Through the vehicle owner's mobile application, the system real-time pushes vehicle maintenance reminders and updated information of the record sheet, improving the vehicle owner's maintenance awareness, service transparency, and user experience.

[0013] Furthermore, step S101 also includes using natural language processing (NLP) technology to collect the vehicle owner's input information through speech recognition or text input. It includes the following steps:

[0014] S201: The NLP technology identifies and analyzes the vehicle owner's input, extracting key information such as license plate number, vehicle model, purchase time, etc.

[0015] S202: Store the identified and analyzed result information in the database.

[0016]

[0017] Among them, is the predicted basic vehicle information and maintenance information, w i is the weight, x i is the input feature, b is the bias term, and σ is the activation function.

[0018] Furthermore, in step S102 of querying the maintenance standard items and cycles, it also includes using a machine learning algorithm to predict maintenance needs based on the basic vehicle information, including the following steps:

[0019] S301: Predict maintenance needs based on the collected basic vehicle information through the random forest algorithm;

[0020]

[0021] Among them, IG(feature)IG(feature) is the information gain of the feature, used to select the best splitting feature.

[0022] S302: Query the corresponding maintenance standard items and cycles from the database.

[0023] Further, the step S103 of receiving the actual maintenance items and maintenance further includes using image recognition technology to identify and classify the actual maintenance items and results, including the following steps:

[0024] S401: Use convolutional neural network (CNN) technology to identify and classify image data;

[0025]

[0026] Among them, f*g is a convolution operation, f and g are two functions, and n is the position of the convolution kernel.

[0027] S402: Store the actual maintenance items and results in the database.

[0028] Further, the step S104 of generating the vehicle maintenance record form further includes using natural language generation (NLG) technology to generate the maintenance record form according to the actual maintenance items and standard items, including the following steps:

[0029] S501: Use NLG technology to retrieve the maintenance record form template from the database according to the actual maintenance items and standard items.

[0030] f LSTM (x, h prev ) = tanh(W x x + W h h prev + b)

[0031] Among them, f LSTM is the function of the LSTM unit, which calculates the output of the current input and the hidden state at the previous moment.

[0032] S502: Generate the maintenance record form.

[0033] Further, the step S105 of storing and providing query, printing, etc. further includes using big data storage technology to store and query the maintenance record form, including the following steps:

[0034] S601: Store the maintenance record form using the key-value pair or document model of the NoSQL database;

[0035] S602: Provide fast query and printing functions.

[0036] Further, the step S106 of vehicle maintenance big data analysis, etc. further includes using data mining algorithms to analyze and predict the maintenance records, including the following steps:

[0037] S701: Use the data mining algorithm K-means clustering analysis method to analyze and predict the maintenance records;

[0038]

[0039] Among them, J is the within-cluster sum of squares, and S i is the i-th cluster, and μ i is the center point of the i-th cluster.

[0040] S702: Generate car maintenance suggestions for the car owner.

[0041] Furthermore, the step S106 of real-time pushing of reminders and information updates, etc. also includes using mobile push technology to push maintenance reminders and record sheet update information in real time through the car owner's mobile application, including the following steps:

[0042] S701: Push maintenance reminders and record sheet update information in real time through the car owner's mobile application.

[0043] S702: Monitor and calculate the transmission delay of information from the server to the mobile application to ensure that the information can be pushed to the car owner's mobile application in real time.

[0044] According to the above-mentioned intelligent generation method of a car maintenance record sheet, the present invention also provides an information system for operating the intelligent generation method of a car maintenance record sheet, and this system includes the following modules:

[0045] Data information collection module: Responsible for receiving the basic information of the car input by the car owner, such as license plate number, vehicle model, purchase time, etc. Extract key information from the voice or text input of the car owner through natural language processing (NLP) technology, and convert it into a processable data format.

[0046] Maintenance standard query module: According to the basic information of the car, use machine learning algorithms to predict maintenance requirements, and query the corresponding maintenance standard items and cycles from the database.

[0047] Maintenance service input module: Receive the actual items and maintenance results of the car maintenance input by the car maintenance service personnel. Use convolutional neural network (CNN) image recognition technology to identify and classify the actual maintenance items and results, and store them in the database.

[0048] Record sheet generation module: Adopt natural language generation (NLG) technology to retrieve the maintenance record sheet template from the database according to the actual maintenance items and standard items, and generate a maintenance record sheet.

[0049] Data storage and query module: Store the generated car maintenance record sheet in the database, and provide functions such as querying and printing. Specifically, use big data storage technology to build a NoSQL database (MongoDB) to store and retrieve maintenance record sheets to support fast querying and printing.

[0050] Information Push Module: Using mobile push technology, it real-time pushes car maintenance reminders and record sheet updates to the owner's mobile application, improving the owner's maintenance awareness, service transparency, and user experience.

[0051] Big Data Analysis Module: Applying data mining algorithms, it analyzes and predicts the data of car maintenance record sheets to provide car maintenance suggestions for the owner and predict future maintenance needs, enhancing the personalization and foresight of the service.

[0052] Cloud Database Module: Utilizing the distributed storage and computing capabilities of the cloud database, it facilitates the owner and the maintenance service provider to access and update the maintenance records anytime and anywhere.

[0053] Data Security and Privacy Protection Module: Implementing measures such as data encryption, access control, and logging to protect data security and user privacy in accordance with national data security laws and regulations.

[0054] Main Control Module: By receiving data and feedback from each module, it monitors and manages the entire system. It coordinates data interaction and task allocation between each module to ensure the stable and efficient operation of the system.

[0055] The advantages of the present invention are as follows:

[0056] Through the intelligent generation method and system of car maintenance record sheets provided by the present invention, it can effectively improve the production efficiency, accuracy, and intelligent push service of car maintenance record sheets, provide a better maintenance experience for car owners, and also provide more business opportunities for service providers. Brief Description of the Drawings

[0057] Figure 1 It is a flowchart of the intelligent generation method of car maintenance record sheets according to the present invention;

[0058] Figure 2 It is a structural diagram of the intelligent generation system of car maintenance record sheets according to the present invention. Detailed Embodiments

[0059] The following further describes the detailed embodiments of the present invention with reference to the drawings. It should be noted here that the description of these embodiments is for helping to understand the present invention, but does not constitute a limitation to the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0060] As Figure 1 shown, the flowchart of the intelligent generation method of car maintenance record sheets of the present invention includes the following steps:

[0061] S101: Collect basic vehicle information and maintenance information, receive and process the basic vehicle information and maintenance information input by the vehicle owner, including license plate number, vehicle model, purchase time, etc., and receive the vehicle maintenance history record input by the vehicle owner.

[0062] In this step, natural language processing (NLP) technology is used to collect the vehicle owner's input information through speech recognition or text input. The specific steps are as follows:

[0063] (1) NLP technology identifies and parses the vehicle owner's input, including steps such as text preprocessing, word segmentation, part-of-speech tagging, named entity recognition, etc., to extract key information such as license plate number, vehicle model, purchase time, etc., to improve the accuracy of information extraction.

[0064]

[0065] Among them, is the predicted basic vehicle information and maintenance information, w i is the weight, x i is the input feature, b is the bias term, and σ is the activation function.

[0066] (2) Convert the extracted key information into structured data, and store the recognized and parsed result information in the database for subsequent processing.

[0067] S102: Query the standard maintenance items and cycles. According to the basic vehicle information collected, query the corresponding standard vehicle maintenance items and cycles from the database, and automatically recommend maintenance items according to the vehicle's mileage or time.

[0068] In this step, machine learning algorithms are used to predict maintenance requirements based on the basic vehicle information. The specific steps are as follows:

[0069] (1): According to the basic vehicle information collected, by applying the random forest algorithm, construct multiple decision trees to improve the prediction accuracy and predict maintenance requirements;

[0070]

[0071] Among them, IG(feature)IG(feature) is the information gain of the feature, which is used to select the best splitting feature.

[0072] (2): The database will store the historical data of the standard vehicle maintenance items for prediction and query. And query the corresponding standard maintenance items and cycles from the database.

[0073] S103: Receive the actual maintenance items and maintenance results: Receive the actual maintenance items and maintenance results of the vehicle input by the vehicle maintenance service personnel.

[0074] In this step, image recognition technology is used to identify and classify the actual maintenance items and results. The specific steps are as follows:

[0075] (1) Automatically learn the features in the maintenance order image and perform classification processing. Use convolutional neural network (CNN) technology to identify and classify the image data;

[0076]

[0077] Among them, f*g is the convolution operation, f and g are two functions, and n is the position of the convolution kernel.

[0078] (2) Store the actual maintenance items and results in the database. The database will store the image data of the maintenance items and the maintenance results for query and analysis.

[0079] S104: Generate a vehicle maintenance record form, match the actual maintenance items with the maintenance standard items in the database, generate a vehicle maintenance record form, and review and confirm the generated vehicle maintenance record form.

[0080] This step uses natural language generation (NLG) technology to generate a maintenance record form based on the actual maintenance items and standard items, including the following steps:

[0081] (1) Use NLG technology to retrieve the maintenance record form template from the database according to the actual maintenance items and standard items.

[0082] f LSTM (x, h prev ) = tanh(W x x + W h h prev + b)

[0083] Among them, f LSTM is the function of the LSTM cell, which calculates the output of the current input and the hidden state at the previous moment.

[0084] (2) Automatically generate a maintenance record form. NLG technology matches the actual maintenance items and standard items with the template in the database to generate a complete maintenance record form.

[0085] S105: Store and provide query and printing functions. The generated vehicle maintenance record form is stored in the database, and functions such as user query and printing are provided, and the owner is notified of the generation and storage of the vehicle maintenance record form via email or text message.

[0086] This step uses big data storage technology to store and query the maintenance record form, including the following steps:

[0087] (1) Use the key-value pair or document model of the MongDB database (NoSQL database) to store unstructured data such as maintenance record sheets;

[0088] (2) Provide fast data retrieval and printing functions to improve the user experience.

[0089] S106: Big data analysis of vehicle maintenance. Conduct big data mining and analysis on vehicle maintenance records to provide vehicle owners with vehicle maintenance suggestions and predict future maintenance needs, enhancing the personalization and foresight of services.

[0090] (1) Use the data mining algorithm K-means clustering analysis method to analyze and predict maintenance records to discover patterns and associations in the data;

[0091]

[0092] where J is the within-cluster sum of squares, S i is the i-th cluster, and μ i is the center point of the i-th cluster.

[0093] (2) Generate vehicle maintenance suggestions for vehicle owners. Through the analysis and prediction of maintenance records, provide personalized maintenance suggestions to improve service quality.

[0094] S107: Real-time push of reminders and update information. Through the vehicle owner's mobile application, the system real-time pushes vehicle maintenance reminders and record sheet update information to improve the vehicle owner's maintenance awareness, service transparency, and user experience. The specific methods are as follows.

[0095] (1) By using mobile push technologies such as HTTP / 2 and WebSocket, achieve efficient and real-time information push, and use the vehicle owner's mobile application to real-time push maintenance reminders and record sheet update information.

[0096] (2) Monitor and calculate the transmission delay of information from the server to the mobile application to ensure that the information can be real-time pushed to the vehicle owner's mobile application.

[0097] As Figure 2 shown, the structure diagram of the intelligent vehicle maintenance record sheet generation system of the present invention includes the following modules:

[0098] Data information collection module: Responsible for receiving the basic vehicle information input by the vehicle owner, such as license plate number, vehicle model, purchase time, etc. Through natural language processing (NLP) technology, extract key information from the vehicle owner's voice or text input and convert it into a processable data format.

[0099] Maintenance standard query module: According to the basic vehicle information, use machine learning algorithms to predict maintenance requirements and query the corresponding maintenance standard items and cycles from the database.

[0100] Maintenance service input module: Receives the actual vehicle maintenance items and maintenance results input by vehicle maintenance service personnel. Using convolutional neural network (CNN) image recognition technology, it recognizes and classifies the actual maintenance items and results, and stores them in the database.

[0101] Record form generation module: Adopts natural language generation (NLG) technology. According to the actual maintenance items and standard items, it retrieves the maintenance record form template from the database and generates the maintenance record form.

[0102] Data storage and query module: Stores the generated vehicle maintenance record form in the database and provides functions such as query and printing. Specifically, using big data storage technology, it constructs a NoSQL database (MongoDB) to store and retrieve the maintenance record form to support fast query and printing.

[0103] Information push module: Uses mobile push technology to push vehicle maintenance reminders and record form update information to the vehicle owner's mobile application in real time, improving the vehicle owner's maintenance awareness, service transparency, and user experience.

[0104] Big data analysis module: Applies data mining algorithms to analyze and predict the vehicle maintenance record form, providing vehicle owners with vehicle maintenance suggestions and predicting future maintenance needs, enhancing the personalization and foresight of the service.

[0105] Cloud database module: Utilizes the distributed storage and computing capabilities of the cloud database to facilitate vehicle owners and maintenance service providers to access and update maintenance records anytime and anywhere.

[0106] Data security and privacy protection module: Implements measures such as data encryption, access control, and logging to protect data security and user privacy in accordance with national data security laws and regulations.

[0107] Main control module: Monitors and manages the entire system by receiving data and feedback from each module. Coordinates data interaction and task allocation between each module to ensure the stable and efficient operation of the system.

[0108] Through the vehicle maintenance record form intelligent generation method and system provided by the present invention, it can effectively improve the production efficiency, accuracy, and intelligent push service of vehicle maintenance record forms, provide a better maintenance experience for vehicle owners, and also provide more business opportunities for service providers.

[0109] The above are only the preferred embodiments of the present invention. For those of ordinary skill in the art, many changes and modifications can be made based on the above content without departing from the spirit or essence of the invention, and these should all fall within the protection scope of the present invention.

Claims

1. A method for intelligently generating an automobile maintenance record form, characterized in that, It includes the following steps: S101: Collect basic vehicle information and maintenance information, receive and process the basic vehicle information and maintenance information input by the vehicle owner, including license plate number, vehicle model, purchase time, and receive the vehicle maintenance history record input by the vehicle owner; S102: Query maintenance standard items and cycles, according to the collected basic vehicle information, query the corresponding vehicle maintenance standard items and cycles from the database, and automatically recommend maintenance items according to the vehicle mileage or time; S103: Receive actual maintenance items and maintenance results: receive the actual maintenance items and maintenance results of the vehicle input by the vehicle maintenance service personnel; S104: Generate a vehicle maintenance record sheet, match the actual maintenance items with the maintenance standard items in the database, generate a vehicle maintenance record sheet, and review and confirm the generated vehicle maintenance record sheet; S105: Store and provide query and printing functions, the generated vehicle maintenance record sheet is stored in the database, and functions including user query and printing are provided, and the vehicle owner is notified of the generation and storage of the vehicle maintenance record sheet by email or text message; S106: Big data analysis of vehicle maintenance, conduct big data mining and analysis on vehicle maintenance records to provide vehicle owners with vehicle maintenance suggestions and predict future maintenance needs, and improve the personalization and foresight of services; S107: Real-time push of reminders and updated information, through the vehicle owner's mobile application, the system real-time pushes vehicle maintenance reminders and record sheet updated information, improving the vehicle owner's maintenance awareness, service transparency and user experience.

2. The intelligent generation method of the vehicle maintenance record sheet according to claim 1, wherein In step S101, natural language processing (NLP) technology is used to collect the input information of the vehicle owner through speech recognition or text input, including the following steps: S201: The NLP technology identifies and parses the input of the vehicle owner, and extracts key information, including license plate number, vehicle model, purchase time; S202: Store the identified and parsed result information in the database; Among them, is the predicted basic information and maintenance information of the vehicle, w i is the weight, x i is the input feature, b is the bias term, and σ is the activation function.

3. The intelligent generation method of the vehicle maintenance record sheet according to claim 1, wherein, In step S102, machine learning algorithms are used to predict maintenance needs according to the basic vehicle information, including the following steps: S301: According to the collected basic vehicle information, predict maintenance needs through the random forest algorithm; Among them, IG(feature)IG(feature) is the information gain of the feature, which is used to select the best splitting feature; S302: Query the corresponding maintenance standard items and cycles from the database.

4. The intelligent generation method of the vehicle maintenance record sheet according to claim 1, wherein In step S103, image recognition technology is used to identify and classify the actual maintenance items and results, including the following steps: S401: Use convolutional neural network (CNN) technology to identify and classify image data; Among them, f*g is the convolution operation, f and g are two functions, and n is the position of the convolution kernel; S402: Store the actual maintenance items and results in the database.

5. The intelligent generation method of the vehicle maintenance record sheet according to claim 1, wherein, In step S104, natural language generation (NLG) technology is also included to generate a maintenance record sheet according to the actual maintenance items and standard items, including the following steps: S501: Use NLG technology to retrieve the maintenance record sheet template from the database according to the actual maintenance items and standard items; f LSTM (x, h prev ) = tanh(W x + W h h prev + b) where, f LSTM is the function of the LSTM cell, calculating the output of the current input and the hidden state at the previous moment; S502: Generate a maintenance record sheet.

6. The intelligent generation method of the automobile maintenance record sheet according to claim 1, characterized in that, In step S105, big data storage technology is used to store and query maintenance record sheets, including the following steps: S601: Use the key-value pair or document model of the NoSQL database to store maintenance record sheets; S602: Provide functions of quick query and printing.

7. The intelligent generation method of the vehicle maintenance record sheet according to claim 1, wherein In step S106, data mining algorithms are used to analyze and predict maintenance records, including the following steps: S701: Use the data mining algorithm K-means clustering analysis method to analyze and predict maintenance records; Among them, J is the within-cluster sum of squares, S i is the i-th cluster, μ i is the center point of the i-th cluster; S702: Generate maintenance suggestions for car owners.

8. The intelligent generation method of the automobile maintenance record sheet according to claim 1, characterized in that, In step S107, mobile push technology is also used to push maintenance reminders and record sheet update information in real time through the car owner's mobile application, including the following steps: S701: Push maintenance reminders and record sheet update information in real time through the car owner's mobile application; S702: Monitor and calculate the transmission delay of information from the server to the mobile application to ensure that the information can be pushed to the car owner's mobile application in real time.

9. An intelligent generation system for vehicle maintenance record sheets, which is used to implement the intelligent generation method for vehicle maintenance record sheets described in any one of claims 1-8, and is characterized in that, Including the following modules: Data information collection module: Responsible for receiving the basic information of the car input by the car owner, including license plate number, vehicle model, and purchase time; Through natural language processing technology, extract key information from the voice or text input of the car owner and convert it into a data format that can be processed; Maintenance standard query module: According to the basic information of the car, use machine learning algorithms to predict maintenance requirements, and query the corresponding maintenance standard items and cycles from the database; Maintenance service input module: Receive the actual maintenance items and maintenance results of the car input by the car maintenance service personnel, use convolutional neural network image recognition technology to identify and classify the actual maintenance items and results, and store them in the database; Record sheet generation module: Adopt natural language generation technology to retrieve the maintenance record sheet template from the database according to the actual maintenance items and standard items, and generate a maintenance record sheet; Data storage and query module: Store the generated car maintenance record sheet in the database, and provide functions including query and printing. Use big data storage technology to build a NoSQL database to store and retrieve maintenance record sheets to support quick query and printing; Information push module: Use mobile push technology to push car maintenance reminders and record sheet update information in real time through the car owner's mobile application, and push it to the car owner's mobile application in real time to improve the car owner's maintenance awareness, service transparency and user experience; Big data analysis module: Apply data mining algorithms to analyze and predict car maintenance record sheets to provide car maintenance suggestions for car owners and predict future maintenance requirements, and enhance the personalization and forward-looking of services; Cloud database module: Utilize the distributed storage and computing capabilities of the cloud database to facilitate car owners and maintenance service providers to access and update maintenance records anytime and anywhere; Data security and privacy protection module: Implement measures including data encryption, access control and logging to protect data security and user privacy in accordance with national data security laws and regulations; Main control module: Monitor and manage the entire system by receiving data and feedback from each module; Coordinate data interaction and task allocation between each module to ensure the stable and efficient operation of the system.

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