Intelligent driving training method and system for green driver and electronic equipment

Abstract

The invention discloses an intelligent driving training method for a green driver, and relates to the field of intelligent driving, and the method comprises the steps: obtaining vehicle driving data, and uploading the data to a data server; obtaining preset driving behavior data, a traffic rule table and a rule keyword table; the data server calculates real-time driving behavior data of the user according to the vehicle driving data, and generates a comparison result of the real-time driving behavior data and the rule keyword table; according to a comparison result, judging whether the real-time driving behavior data of the user has a matching item or not; if yes, a violation record generation mechanism is triggered, and a driving behavior correction prompt is triggered; if not, recording related driving behavior data, and performing driving skill evaluation according to the driving behavior data; the data server generates a single driving report according to the vehicle driving data, and feeds back the single driving report to the intelligent driving coach APP; the target user feeds back an operation demand through the intelligent driving coach APP, and the data server adjusts a training strategy according to the single driving report and the operation demand.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of intelligent driving, in particular to an intelligent driving training method for novice drivers, an intelligent driving training system for novice drivers, an electronic device and a storage medium. BACKGROUND

[0002] With the continuous growth of the number of cars, the challenges faced by novice drivers on the road are also increasing. Insufficient driving skills, weak safety awareness and other problems have become key factors restricting the safe travel of novice drivers. Traditional driving teaching methods rely on on-site guidance by accompanying coaches. Although this method is intuitive, it has many limitations. First, the limited coach resources result in low teaching efficiency, which cannot meet the training needs of a large number of novice drivers. Second, the level and experience of coaches vary, making it difficult to guarantee the teaching effect. Finally, the high cost of coaches is also one of the obstacles for novice drivers to learn to drive.

[0003] For example, Chinese patent, the invention name is: an assisted driving interaction method and device, vehicle and storage medium, application number: CN202410224356.7, specifically discloses that by acquiring the driving behavior data of the novice driver, establishing evaluation criteria, matching and playing corresponding teaching videos, using AI technology to provide virtual accompanying training services, helping the novice driver to quickly consolidate driving skills, correct bad habits, enhance the confidence of independent driving, and avoid the risk of the non-standard accompanying training market.

[0004] Chinese patent, the invention name is: a method, device and electronic equipment for assisting the driving of a novice driver, application number: CN202211255366.4, specifically discloses that in the assisted driving mode, driving information is acquired, including road condition information and navigation information, and according to these information, corresponding driving suggestions are displayed, such as vehicle distance, steering light prompt, lane deviation correction, etc., providing comprehensive driving suggestions without the need to turn on each assisted function one by one. SUMMARY

[0005] Therefore, the purpose of the present application is to provide an intelligent driving training method for novice drivers, an intelligent driving training system for novice drivers, an electronic device and a storage medium, which aims to solve the problems existing in the traditional teaching method, and can also help the novice driver to improve the driving skills and safety awareness through intelligent and personalized teaching methods, reduce the traffic accident rate, and improve the road safety level.

[0006] The present application provides the following solutions:

[0007] According to one aspect of the present application, an intelligent driving training method for novice drivers is provided, comprising the following steps:

[0008] Obtaining vehicle driving data and uploading the data to a data server;

[0009] Obtaining preset driving behavior data, a traffic rule table and a rule keyword table;

[0010] The data server calculates target user real-time driving behavior data according to the vehicle driving data, and generates a comparison result of the real-time driving behavior data and the rule keyword table;

[0011] According to the comparison result, it is judged whether the target user real-time driving behavior data has a matching item;

[0012] If yes, a violation record generation mechanism is triggered, and a driving behavior correction prompt is triggered;

[0013] If not, record the relevant driving behavior data, and evaluate the driving skill according to the driving behavior data;

[0014] The data server generates a single driving report according to the vehicle driving data, and feeds back to the intelligent driving coach APP;

[0015] The target user feeds back the operation demand through the intelligent driving coach APP, and the data server adjusts the training strategy according to the single driving report and the operation demand.

[0016] Further, comprising:

[0017] The vehicle driving data includes vehicle speed, driving trajectory, steering light state and vehicle machine headlight state; the vehicle driving data is received and uploaded through Kafka.

[0018] Further, comprising:

[0019] The data server receives the vehicle driving data through Kafka, and generates an analysis result through a distributed computing processing engine for real-time analysis.

[0020] Further, comprising:

[0021] The analysis result is saved to Mysql through jdbc or directly calls the voice and recommendation service interface.

[0022] Further, comprising:

[0023] The traffic rule table includes a unique identifier, the name of the rule, the description and the effective start and end time.

[0024] Further, comprising:

[0025] The rule keyword table, according to the traffic rule table, uses the jieba segmentation library to extract the keyword unit, and the keyword unit is uniquely identified by the unique identifier and associated with the name of the rule.

[0026] Further, comprising:

[0027] The trigger violation record generation mechanism comprises generating a violation record table, the violation record table comprising a unique identifier of the violation record, an identifier of the violating vehicle, a traffic rule ID involved, an exact time of the violation occurrence and a specific location of the violation occurrence.

[0028] According to two aspects of the present application, an intelligent driving training system for novice drivers is provided, comprising:

[0029] a data acquisition and uploading module, a rule acquisition module, a calculation and comparison module, a judgment module, a report generation module and a feedback optimization module;

[0030] The data acquisition and uploading module is configured to acquire vehicle driving data and upload the data to a data server.

[0031] The rule acquisition module is configured to acquire preset driving behavior data, a traffic rule table and a rule keyword table.

[0032] The calculation and comparison module is configured to calculate target user real-time driving behavior data according to the vehicle driving data, and generate a comparison result of the real-time driving behavior data and the rule keyword table.

[0033] The judgment module is configured to judge whether the target user real-time driving behavior data has a matching item according to the comparison result.

[0034] If yes, a violation record generation mechanism is triggered and a driving behavior correction prompt is triggered.

[0035] If no, relevant driving behavior data is recorded and driving skill evaluation is performed according to the driving behavior data.

[0036] The report generation module is configured to integrate the vehicle driving data and generate a single driving report, which is fed back to a smart driving coach APP.

[0037] The feedback optimization module is configured to feed back operation requirements through the smart driving coach APP, and adjust training strategies according to the single driving report and the operation requirements.

[0038] According to three aspects of the present application, an electronic device is provided, comprising a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory complete mutual communication through the communication bus.

[0039] The memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of an intelligent driving training method for novice drivers.

[0040] According to the four aspects of the present application, a computer readable storage medium is provided, which stores a computer program executable by an electronic device, when the computer program runs on the electronic device, makes the electronic device execute the steps of an intelligent driving training method for novice drivers.

[0041] Compared with the prior art, the present application has the following advantages:

[0042] The present application realizes real-time monitoring and intelligent analysis of the driving behavior of novice drivers by integrating advanced sensor technology, positioning technology, data analysis technology, etc. At the same time, combined with the personal characteristics and learning needs of novice drivers, the system can provide personalized teaching guidance to help novice drivers quickly master driving skills and improve safety awareness.

[0043] The present application reduces the learning cost of novice drivers: drivers do not need to rely on the on-site guidance of accompanying driving instructors, but only need to interact with the system through the APP to complete the driving skill improvement, thereby reducing the learning cost and time cost.

[0044] The present application can help novice drivers quickly master driving skills and improve safety awareness through real-time teaching guidance and personalized learning suggestions, thereby reducing the incidence of traffic accidents and improving road safety.

[0045] The present application can timely discover and correct the wrong operation of novice drivers by capturing and analyzing the driving behavior of novice drivers in real time. This instant feedback mechanism can help novice drivers master driving skills faster and shorten the learning period.

[0046] The present application can enable novice drivers to clearly understand their learning situation and timely adjust their learning strategy through data analysis and evaluation functions. At the same time, the coach can also provide more accurate guidance for the novice drivers according to the data provided by the system. BRIEF DESCRIPTION OF DRAWINGS

[0047] In order to more clearly illustrate the specific embodiments of the present application or the technical solutions in the prior art, the following will briefly introduce the drawings needed to be used in the specific embodiments or prior art description. Obviously, the drawings in the following description are some embodiments of the present application, and those skilled in the art can obtain other drawings according to these drawings without creative labor.

[0048] Figure 1 is a flowchart of an intelligent driving training method for novice drivers provided by one or more embodiments of the present application.

[0049] Figure 2 is a structural diagram of an intelligent driving training system for novice drivers provided by one or more embodiments of the present application.

[0050] Figure 3 is a flow chart of an intelligent driving training method according to an embodiment of the present application.

[0051] Figure 4 is a functional composition diagram of an intelligent driving training system according to an embodiment of the present application.

[0052] Figure 5 is a data analysis implementation method flow chart of an intelligent driving training method according to an embodiment of the present application.

[0053] Figure 6 is a structural block diagram of an electronic device for an intelligent driving training method for novice drivers according to one or more embodiments of the present application. DETAILED DESCRIPTION

[0054] The technical solutions of the present application will be described clearly and completely below in conjunction with the drawings. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor fall within the scope of protection of the present application.

[0055] Figure 1 is a flow chart of an intelligent driving training method according to one or more embodiments of the present application.

[0056] As shown in Figure 1 , the following steps are included:

[0057] Step S1, acquiring vehicle driving data and uploading the data to a data server;

[0058] Step S2, acquiring preset driving behavior data, a traffic rule table, and a rule keyword table;

[0059] Step S3, the data server calculates target user real-time driving behavior data according to the vehicle driving data, generates a comparison result of real-time driving behavior data and the rule keyword table;

[0060] Step S4, according to the comparison result, judging whether there is a matching item in the target user real-time driving behavior data;

[0061] If yes, triggering a violation record generation mechanism and triggering a driving behavior correction prompt;

[0062] If no, recording relevant driving behavior data and performing driving skill evaluation according to the driving behavior data;

[0063] Step S5, the data server generates a single driving report according to the vehicle driving data and feeds back to a smart driving coach APP;

[0064] Step S6, the target user feeds back the operation demand through the smart driving coach APP, and the data server adjusts the training strategy according to the single driving report and the operation demand.

[0065] Specifically, by integrating advanced sensor technology, positioning technology, data analysis technology, etc., real-time monitoring and intelligent analysis of the driving behavior of novice drivers are realized. At the same time, combined with the personal characteristics and learning needs of novice drivers, the system can provide personalized teaching guidance to help novice drivers quickly master driving skills and improve safety awareness.

[0066] Improve the learning efficiency of novice drivers: through intelligent and personalized teaching methods, the system can accurately teach according to the personal characteristics and learning needs of novice drivers, improving teaching efficiency and learning effect.

[0067] Reduce the learning cost of novice drivers: drivers do not need to rely on the on-site guidance of accompanying coaches, but only need to interact with the system through the APP to complete the vehicle skill improvement, reducing the learning cost and time cost.

[0068] Improve the level of road safety: through real-time teaching guidance and personalized learning suggestions, the system can help novice drivers quickly master driving skills and improve safety awareness, thereby reducing the incidence of traffic accidents and improving the level of road safety.

[0069] Further, comprising:

[0070] The vehicle driving data includes: vehicle speed, driving trajectory, steering light state and vehicle headlight state; the vehicle driving data is received by Kafka to upload data.

[0071] Further, comprising:

[0072] The data server receives the vehicle driving data through Kafka, performs real-time analysis through a distributed computing processing engine, and generates analysis results.

[0073] Further, comprising:

[0074] The analysis results are saved to Mysql through jdbc or directly call voice and recommendation service interface.

[0075] Further, comprising:

[0076] The traffic rule table includes: unique identifier, rule name, description, and effective start and end time.

[0077] Further, comprising:

[0078] A rule keyword table, according to the traffic rule table, uses the jieba segmentation library to extract keyword units, which are uniquely identified by unique identifiers and associated with the names of the rules.

[0079] Further comprising:

[0080] The trigger violation record generation mechanism includes generating a violation record table, which includes the unique identifier of the violation record, the identifier of the violating vehicle, the involved traffic rule ID, the exact time of the violation, and the specific location of the violation.

[0081] Specifically, by designing a traffic rule table (TrafficRules), which is one of the core tables, it carries detailed information of each traffic rule. The table not only contains the unique identifier of the rule (Rule ID) for uniquely identifying each rule in the system, but also records the name of the rule (RuleName) and detailed description (RuleDescription), which are crucial for users to understand the rule content. In addition, each rule is explicitly marked with its effective start date (EffectiveStart) and end date (EffectiveEndDate), which is crucial for determining the applicability of the rule at a specific time point.

[0082] Furthermore, a rule keyword table (RuleKeywords) is introduced. First, the detailed description of traffic regulations is obtained from the traffic rule table. Then, using the jieba word segmentation library, keywords are extracted in precise mode (precise splitting). Specific implementation details: For example, splitting rule_text = "No parking in the yellow line area". First, the jieba word segmentation library is introduced. Jieba word segmentation comes with a dictionary called dict.txt, where each line contains the entry, the frequency of the entry, and the part of speech. Custom dictionaries are also supported. If new words appear in the traffic regulations, they can be added using the jieba.load_userdict("filepath") method. We create a file containing the new word's name, frequency, and part of speech, call the dictionary loading function, and the new entry will be appended to the existing dictionary. Then, the seg=jieba.cut(rule_text) method is called, and then seg is output using print(" / ".join(seg)), the output content being: No / parking / in / yellow / line / area. Finally, the output terms can also be adjusted by changing the dictionary frequency. For example, to make "on the yellow line" a separate term, the frequency of the term "on the yellow line" in the dictionary can be adjusted, resulting in the output being "prohibited / parking / on the yellow line / within the area." This design breaks down complex traffic rules into more basic and easily searchable keyword units. By assigning multiple keywords to each rule, we have built an efficient indexing system that allows for rapid location of potentially relevant traffic rules when faced with large amounts of vehicle data. In the RuleKeywords table, each keyword is uniquely identified by KeywordID and associated with RuleID, ensuring a close connection between the keyword and the original rule.

[0083] When the vehicle's infotainment system collects keyword data during the vehicle's operation in real time, this data is used as input and efficiently compared with the data in the RuleKeywords table. Once a match is found, the system automatically triggers the violation record generation mechanism, recording the relevant information in the ViolationRecords table. This table records detailed information about each violation, including the unique identifier of the violation record (RecordID), the identifier of the violating vehicle (VehicleID), the traffic rule ID involved (RuleID), the exact time the violation occurred (ViolationTime), and the specific location of the violation (ViolationLocation).

[0084] Figure 2 This is a structural diagram of an intelligent driving training system for novice drivers provided by one or more embodiments of the present invention.

[0085] likeFigure 2 As shown, comprising:

[0086] Data acquisition and uploading module, rule acquisition module, calculation and comparison module, judgment module, report generation module and feedback optimization module;

[0087] The data acquisition and uploading module is configured to acquire vehicle driving data and upload the data to a data server;

[0088] The rule acquisition module is configured to acquire preset driving behavior data, a traffic rule table and a rule keyword table;

[0089] The calculation and comparison module is configured to calculate user real-time driving behavior data according to the vehicle driving data, and generate a comparison result of the real-time driving behavior data and the rule keyword table;

[0090] The judgment module is configured to judge whether the user real-time driving behavior data has a matching item according to the comparison result;

[0091] If yes, a rule violation record generation mechanism is triggered, and a driving behavior correction prompt is triggered;

[0092] If no, relevant driving behavior data is recorded, and driving skill evaluation is performed according to the driving behavior data;

[0093] The report generation module is configured to integrate the vehicle driving data, generate a single driving report, and feed back to a smart driving coach APP;

[0094] The feedback optimization module is configured to feed back operation requirements through the smart driving coach APP, adjust training strategies according to the single driving report and the operation requirements.

[0095] Specifically, vehicle driving data such as vehicle speed, trajectory, turn signal state, brake frequency, etc. is collected in real time through vehicle-mounted sensor radars and cameras, and uploaded to a data server through Kafka.

[0096] Preset rules are acquired from authoritative traffic databases such as national standard traffic rules. Rules and keywords are associated through MySQL tables to support second-level retrieval, and Kafka data (such as rapid acceleration frequency and number of lane changes without turning on the light) is analyzed in real time based on a Flink stream computing engine and matched with rule keywords.

[0097] The rule violation record generation mechanism is triggered, such as recording time, location, RuleID, calling TTS voice broadcast, such as “Please reduce speed to 30km / h in the school area ahead”.

[0098] Single driving data (mileage, number of rule violations, skill score) is integrated to generate a visual report (bar chart, heat map) and synchronized to a smart driving coach APP.

[0099] At the same time, personalized analysis is conducted: compared with historical data (such as "the number of emergency brakings this week decreased by 20% year-on-year"), and the progress points and areas for improvement are marked.

[0100] Violation records are automatically linked to instructional videos (such as "failure to signal when changing lanes" - recommended video: "5 Steps to Safe Lane Changing").

[0101] It is worth noting that although only some basic functional modules are disclosed in this embodiment, it does not mean that the composition of this system is limited to the above-mentioned basic functional modules. On the contrary, what this embodiment intends to express is that, based on the above-mentioned basic functional modules, those skilled in the art can arbitrarily add one or more functional modules in combination with existing technology to form an infinite number of embodiments or technical solutions. That is to say, this system is open rather than closed. The fact that this embodiment only discloses a few basic functional modules does not mean that the scope of protection of the claims of this invention is limited to the disclosed basic functional modules. At the same time, for the convenience of description, the above device is described separately according to its functions as various units and modules. Of course, in implementing this invention, the functions of each unit and module can be implemented in one or more software and / or hardware.

[0102] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0103] Figure 3 This is a flowchart of an intelligent driving training method according to a specific embodiment of the present invention.

[0104] like Figure 3 As shown,

[0105] The driving behavior data collection switch is configured to ensure user privacy and user experience. The switch has an on / off state, with the default being on. When on, user behavior data is not recorded; when on, user behavior data is recorded.

[0106] The system collects vehicle driving data. Integrated sensors (such as radar and cameras) monitor the vehicle's driving status in real time, including key information such as vehicle speed, driving trajectory, turn signal status, and headlight status. It receives and uploads signal data via Kafka.

[0107] Data collection and analysis, Flink (a distributed computing engine) real-time consumption of kafka data, real-time calculation of user driving behavior data, comparison of calculation results with preset data, and saving of calculation results to Mysql or directly calling voice and recommendation service interface, calculation results as input, triggering voice and recommendation business functions.

[0108] Among them, there are data analysis implementation methods, such as Figure 5 as shown.

[0109] Specifically, the data service first accurately obtains the latest traffic rule information from widely recognized and authoritative third-party public databases. This process not only ensures the authority and timeliness of the data, but also lays a solid foundation for subsequent compliance checks. The obtained traffic rule data needs to be carefully designed and planned and stored in a structured form in the database for efficient query and flexible application.

[0110] To achieve this goal, a traffic rules table (TrafficRules) is designed, which serves as one of the core tables and carries detailed information for each traffic rule. The table not only contains the unique identifier of the rule (Rule ID) for uniquely identifying each rule in the system, but also records the name of the rule (RuleName) and detailed description (RuleDescription), which are crucial for users to understand the rule content. In addition, each rule is explicitly marked with its effective start date (EffectiveStart) and end date (EffectiveEndDate), which is crucial for determining the applicability of the rule at a specific time point.

[0111] To further improve the flexibility and search efficiency of the rules, a rule keyword table (RuleKeywords) is introduced. First, a detailed description of the traffic rules is obtained from the traffic rule table. Then, using the jieba word segmentation library, keywords are extracted in precise mode (precise splitting). Specific implementation details: For example, splitting rule_text = "No parking in the yellow line area". First, the jieba word segmentation library is introduced. Jieba word segmentation comes with a dictionary called dict.txt, where each line contains the entry, the frequency of the entry, and the part of speech. Custom dictionaries are also supported. If new words appear in the traffic rules, they can be added using the jieba.load_userdict("filepath") method. We create a file containing the new word's name, frequency, and part of speech, call the dictionary loading function, and the new entry will be appended to the existing dictionary. Then, the seg=jieba.cut(rule_text) method is called, and then seg is output using print(" / ".join(seg)), the output content being: No / parking / in / yellow / line / area. Finally, the output terms can also be adjusted by changing the dictionary frequency. For example, to make "on the yellow line" a separate term, the frequency of the term "on the yellow line" in the dictionary can be adjusted, resulting in the output being "prohibited / parking / on the yellow line / within the area." This design breaks down complex traffic rules into more basic and easily searchable keyword units. By assigning multiple keywords to each rule, we have built an efficient indexing system that allows for rapid location of potentially relevant traffic rules when faced with large amounts of vehicle data. In the RuleKeywords table, each keyword is uniquely identified by KeywordID and associated with RuleID, ensuring a close connection between the keyword and the original rule.

[0112] When the vehicle's infotainment system collects keyword data during the vehicle's operation in real time, this data is used as input and efficiently compared with the data in the RuleKeywords table. Once a match is found, the system automatically triggers the violation record generation mechanism, recording the relevant information in the ViolationRecords table. This table records detailed information about each violation, including the unique identifier of the violation record (RecordID), the identifier of the violating vehicle (VehicleID), the traffic rule ID involved (RuleID), the exact time the violation occurred (ViolationTime), and the specific location of the violation (ViolationLocation).

[0113] For the voice service, the system first sets up an efficient and stable background service that is responsible for regularly polling the violation record table. Whenever a new violation record is detected and added to the table, the system immediately triggers the voice broadcast process.

[0114] To implement this process, the system needs to integrate with a voice service interface. The voice service here typically refers to a Text-To-Speech (TTS) conversion service that can convert text information stored in the database (such as violation record details) into audible voice output. To achieve high configurability and flexibility, the system should support multiple TTS engines and allow administrators to select the appropriate engine for configuration based on actual needs.

[0115] In terms of broadcast content, the system formats violation record information according to pre-set templates. These templates can be customized according to different violation types, violation locations, violation times, etc., to ensure that the broadcast content is both comprehensive and specific. For example, for speeding violations, the template may include vehicle type, license plate number, speeding location, speeding time, and specific speeding value; while for illegal parking, the template may emphasize vehicle location, parking time, and whether parking is allowed in that area, etc.

[0116] To improve user experience, the system can also support multi-language voice broadcast. This means that both Chinese users and users of other languages can choose the appropriate language for broadcast according to their needs.

[0117] Smart driving coach app involves multiple key services and technical modules, including driving behavior data display, learning video recommendation, sharing, comments, collection, traffic video data display, etc.

[0118] Driving behavior data display obtains user driving behavior data and violation data from data services for display. These data include but are not limited to driving speed, acceleration, brake frequency, violation type, time, location, etc. Graphs, dashboards, and other intuitive data visualization tools are used to convert complex driving behavior data into easy-to-understand graphical interfaces. Users can visually see their driving performance in different time periods and under different road conditions, as well as the distribution of violation behavior through the graphs.

[0119] Learning video recommendation, high-quality driving teaching video resources are obtained through third parties. Video content is classified according to different themes and difficulties, such as basic driving skills, advanced driving skills, and safe driving knowledge. Through personalized recommendation algorithms, content filtering (Content-Based Filtering) and collaborative filtering (Collaborative Filtering), suitable learning videos are recommended to users according to their driving behavior data, learning progress, and interest preferences, as well as violation situations. Specific implementation methods:

[0120] Analyze user driving behavior data. When the user's driving behavior data contains behaviors such as sudden acceleration, sudden deceleration, sudden turning, speeding, FCW triggering, and not wearing a seat belt, record the number of behaviors, cumulative number, and according to the number, the higher the number, the higher the priority for recommending related learning videos.

[0121] Recommend learning videos based on user preferences. Count the number of videos with the longest learning duration in the last 7 days and the number of videos that users have liked and collected. Through collaborative filtering and content filtering algorithms, similar learning videos are recommended to users.

[0122] Recommend videos based on user violation situations. When a user has a violation, get the violation record from the violation record table, match the keywords in the violation record with the video title, description, or tags in the video library, get related videos, and recommend them to the user.

[0123] Social functions, integrate mature social function frameworks such as comment systems, like mechanisms, favorites, and sharing functions. These functions should be designed to be simple and easy to use, allowing users to quickly get started and effectively interact and communicate. To improve user engagement and interactive experience, optimize the layout and interaction logic of the comment input box, support multimedia comment methods such as pictures and videos. At the same time, set up like animations and prompt sound effects to enhance user operation feedback. Establish a strict content review mechanism to review user comments and shared content in real time to ensure the legality and health of the content. At the same time, provide a reporting function to encourage users to work together to maintain a good community atmosphere. Build a user relationship network to support users following other users, joining interest groups or communities. Through the user relationship network, users can more conveniently find and communicate with like-minded driving learning partners.

[0124] New driver driving behavior data display and sharing, the result data stored in Mysql is displayed in the intelligent driving coach app on the car machine or other mobile devices, users can also share, comment, etc. The system will also regularly send safety traffic rules knowledge and videos.

[0125] Figure 4 is a functional composition diagram of the intelligent driving training system of one specific embodiment of the present application.

[0126] As shown in Figure 4 The system comprises:

[0127] The intelligent coach terminal, data server and smart driving coach APP are three parts. The intelligent coach terminal is installed in the vehicle, and the driving data of the vehicle such as speed, direction, acceleration, etc. are obtained in real time through sensors and positioning systems (most cars have this function at present); the data server is responsible for storing and processing these data, and provides personalized learning suggestions and feedback for novice drivers through big data analysis technology; the APP is installed in the vehicle terminal, and can also be installed in mobile devices (mobile phones, personal computers, etc.), which is used for novice drivers to interact with the system, view real-time teaching guidance, training progress and driving report information, etc.

[0128] Personalized teaching plan, when using the system for the first time, the novice driver needs to register through the APP and input personal information, including age, gender, driving experience, learning needs, etc. The system formulates a personalized teaching plan for the novice driver according to these information, combined with the built-in driving teaching model and big data analysis technology. The teaching plan includes learning objectives, learning progress, learning content, etc., aiming to help the novice driver master the driving skills in the shortest time and improve the safety awareness.

[0129] Data analysis and feedback, the data server will store and analyze the driving data of the novice driver, and generate the driving skill report and evaluation results of the novice driver. These reports and results include the driving trajectory, speed change, braking distance and other detailed data of the novice driver, as well as the problems and improvement suggestions in the driving process of the novice driver. The novice driver can check these reports and results at any time through the smart driving coach APP of the vehicle terminal or other mobile device platform (mobile phone, personal computer, etc.), and understand the driving skills and progress of the novice driver. At the same time, the system will provide personalized learning suggestions and feedback for the novice driver according to the learning situation and evaluation results of the novice driver. For example, when the system finds that the novice driver often makes a certain error operation in the driving process, it will recommend relevant exercises or teaching videos to help the novice driver improve the driving skills.

[0130] Real-time guidance: During the driving process, the intelligent coach terminal will obtain the driving data of the vehicle in real time through sensors and positioning systems, such as speed, direction, acceleration, etc. At the same time, combined with the driving behavior of novice drivers (such as steering, acceleration, braking, etc.), the system will evaluate the driving skills of novice drivers in real time. According to the evaluation results, the system will provide personalized teaching guidance, including voice prompts, text instructions, animation demonstrations, etc. For example, when there are dangerous behaviors such as speeding or changing lanes without turning on the turn signal during driving, the system will promptly issue voice prompts or text warnings to help novice drivers correct their mistakes. In addition, the system will also provide targeted learning suggestions and feedback according to the driving habits and learning progress of novice drivers, helping them better master driving skills.

[0131] Interactive learning and community sharing: The smart driving coach APP also provides interactive learning and community sharing functions. Novice drivers can exchange learning experiences and share experiences on the APP, encouraging and supporting each other. In addition, the system will regularly publish articles and videos on driving skills and safety knowledge for novice drivers to learn from. Through interactive learning and community sharing, novice drivers can better master driving skills and safety knowledge, and improve their driving confidence and safety.

[0132] As mentioned above, the novice driver smart driving coach system can bring many beneficial effects:

[0133] 1. Personalized learning experience: The system tailors personalized teaching plans based on the individual characteristics, learning needs, and driving experience of each novice driver. This personalized teaching method ensures that the teaching content is closely related to the actual situation of the novice driver, making the learning process more efficient and interesting. Through the smart driving coach APP, novice drivers can check their learning progress at any time, receive real-time feedback, and personalized teaching guidance. This interactive learning method can stimulate the learning enthusiasm of novice drivers and improve learning effectiveness.

[0134] 2. Improve learning efficiency: The smart driving coach system can quickly identify and correct the mistakes of novice drivers by capturing and analyzing their driving behavior in real time. This immediate feedback mechanism can help novice drivers master driving skills more quickly and shorten the learning period.

[0135] The data analysis and evaluation functions provided by the system enable novice drivers to clearly understand their learning situation and adjust their learning strategies in a timely manner. At the same time, the coach can provide more accurate guidance to novice drivers based on the data provided by the system.

[0136] 3. Reducing learning cost: The intelligent driving coach system replaces the traditional accompanying driving coach mode, reducing the learning cost. Novice drivers do not need to pay high accompanying driving fees, but can interact with the system through the novice driver APP and complete the learning process. The flexibility and convenience of the system also reduce the time cost. Novice drivers can arrange their own learning time without being limited by traditional teaching time.

[0137] 4. Improving safety awareness: The system not only teaches driving skills, but also focuses on cultivating the safety awareness of novice drivers. Through real-time teaching guidance and safety prompts, novice drivers can develop good driving habits and reduce the risk of traffic accidents. The safety knowledge learning and case analysis function provided by the system can help novice drivers better understand the importance of traffic safety and improve their self-protection ability.

[0138] 5. Promoting road safety: Through the training of the intelligent driving coach system, novice drivers can quickly master driving skills and improve safety awareness. This helps to reduce traffic accidents caused by improper operation of novice drivers and improve road safety.

[0139] In summary, the novice driver intelligent driving coach system provides a high-efficiency, convenient and safe driving learning solution for novice drivers through personalized learning experience, improved learning efficiency, reduced learning cost, improved safety awareness and promoted road safety.

[0140] Figure 6 An electronic device structure block diagram of a method for intelligent driving training for novice drivers provided by one or more embodiments of the present application.

[0141] As shown in Figure 6 The present application provides an electronic device, which includes a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other through the communication bus.

[0142] The memory stores a computer program, which, when executed by the processor, causes the processor to execute the steps of a method for intelligent driving training for novice drivers.

[0143] The present application also provides a computer readable storage medium storing a computer program executable by an electronic device, which, when executed on the electronic device, causes the electronic device to execute the steps of a method for intelligent driving training for novice drivers.

[0144] For the method embodiments, the description is made in a series of action combinations for simplicity and clarity, but those skilled in the art should know that the present application is not limited to the action sequence described, because according to the present application, certain steps can be performed in other sequences or at the same time. In addition, those skilled in the art should know that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily essential to the present application.

[0145] From the above description of the embodiments, those skilled in the art can clearly understand that the present application can be implemented by means of software and the necessary general hardware platform. Based on such understanding, the technical solutions of the present application can be embodied in the form of a software product, which can be stored in a storage medium, such as a ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments of the present application.

[0146] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for intelligent driving training for novice drivers, characterized in that, The method comprises the following steps: acquiring vehicle driving data and uploading the data to a data server; acquiring preset driving behavior data, a traffic rule table, and a rule keyword table; the data server calculates target user real-time driving behavior data according to the vehicle driving data, generates a comparison result of the real-time driving behavior data and the rule keyword table; judging whether there is a matching item in the target user real-time driving behavior data according to the comparison result; if yes, triggering a violation record generation mechanism and a driving behavior correction prompt; if no, recording relevant driving behavior data and performing driving skill evaluation according to the driving behavior data; the data server generates a single driving report according to the vehicle driving data and feeds back to a mobile terminal; a target user feeds back operation requirements through the mobile terminal, and the data server adjusts training strategies according to the single driving report and the operation requirements. 2.The intelligent driving training method for new drivers according to claim 1, wherein The vehicle driving data includes vehicle speed, driving trajectory, steering light state, and vehicle headlight state, and the vehicle driving data is received by Kafka to upload data. 3.The intelligent driving training method for new drivers according to claim 1, wherein, The data server receives the vehicle driving data by Kafka, performs real-time analysis by a distributed computing processing engine, and generates an analysis result. 4.The intelligent driving training method for new drivers according to claim 3, wherein, The analysis result is saved to Mysql by jdbc or directly calls a voice and recommendation service interface. 5.The intelligent driving training method for new drivers according to claim 1, wherein, The traffic rule table includes a unique identifier, the name of a rule, a description, and the effective start and end time. 6.The intelligent driving training method for new drivers according to claim 5, wherein, The rule keyword table extracts keyword units from the traffic rule table using a jieba segmentation library, the keyword units are uniquely identified by unique identifiers, and are associated with the name of the rule. 7.The intelligent driving training method for new drivers according to claim 1, wherein, The triggering of the violation record generation mechanism includes generating a violation record table, and the violation record table includes a unique identifier of a violation record, an identifier of a violating vehicle, a traffic rule ID involved, an exact time of violation occurrence, and a specific location of violation occurrence.

8. An intelligent driving training system for a novice driver, characterized by, It comprises: a data acquisition and uploading module, a rule acquisition module, a calculation and comparison module, a judgment module, a report generation module, and a feedback optimization module; the data acquisition and uploading module is used to acquire vehicle driving data and upload the data to a data server; the rule acquisition module is used to acquire preset driving behavior data, a traffic rule table, and a rule keyword table; the calculation and comparison module is used to calculate target user real-time driving behavior data according to the vehicle driving data, and generate a comparison result of the real-time driving behavior data and the rule keyword table; the judgment module is used to judge whether there is a matching item in the target user real-time driving behavior data according to the comparison result; if yes, triggering a violation record generation mechanism and a driving behavior correction prompt; if no, recording relevant driving behavior data and performing driving skill evaluation according to the driving behavior data; the report generation module is used to integrate the vehicle driving data, generate a single driving report, and feed back to a mobile terminal; the feedback optimization module is used to feed back operation requirements through the mobile terminal, adjust training strategies according to the single driving report and the operation requirements.

9. An electronic device, comprising: It comprises: The processor, the communication interface, the memory and the communication bus are in communication with each other through the communication bus; The memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the intelligent driving training method for novice drivers according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The memory stores a computer program executable by the electronic device, and when the computer program runs on the electronic device, the electronic device executes the steps of the intelligent driving training method for novice drivers according to any one of claims 1-7.

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