Riding early warning method and device, electronic equipment and storage medium

Abstract

The embodiment of the invention discloses a riding early warning method and device, electronic equipment and a storage medium. The method comprises the steps that riding map data, real-time road condition data and real-time riding data of a user are acquired; performing riding accident prediction based on the riding map data, the real-time road condition data and the real-time riding data to obtain an accident prediction result; generating corresponding alarm information based on the accident prediction result; and transmitting the alarm information to intelligent riding glasses currently worn by the user, so as to give an alarm prompt to the user based on the alarm information through the intelligent riding glasses. According to the riding early warning scheme provided by the scheme, by monitoring and predicting the riding accident in real time, the riding safety of the user is effectively improved, the occurrence of traffic accidents is reduced, real-time feedback is provided for the user through the intelligent riding glasses, and the riding experience feeling of the user is improved.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a riding warning method, device, electronic device and storage medium. Background Art

[0002] As urban traffic becomes increasingly complex, the safety challenges faced by cyclists are also increasing. In the current traffic environment, cyclists often need to share the road with motor vehicles, pedestrians and other cyclists on busy streets, which increases the risk of accidents. Although many cities have taken measures such as setting up bicycle lanes, traffic lights and traffic signs to improve cycling safety, these measures often cannot completely eliminate safety hazards during cycling.

[0003] In existing technologies, cycling safety mainly depends on the rider's alertness and immediate response to the surrounding environment. However, it is difficult for riders to pay attention to traffic conditions in multiple directions at the same time while riding, especially when their vision is obstructed or their attention is distracted. In addition, traditional traffic signal systems usually only provide information at fixed locations, and their ability to respond to dynamically changing traffic environments and emergencies is limited.

[0004] In addition, existing cycling safety assistance systems often rely on riders to actively check maps and road conditions on their mobile phones or navigation devices, which not only distracts riders but may also fail to provide necessary safety warnings in an emergency. Therefore, a system is needed that can monitor the cycling environment in real time and actively provide safety warnings to assist riders in making quick responses and reduce the possibility of accidents. Summary of the invention

[0005] The embodiments of the present application provide a riding warning method, device, electronic device, and storage medium, which can ensure the safety and experience of users during riding.

[0006] In a first aspect, an embodiment of the present application provides a riding warning method, comprising:

[0007] Obtain cycling map data, real-time traffic data, and users’ real-time cycling data;

[0008] Perform cycling accident prediction based on the cycling map data, the real-time traffic data, and the real-time cycling data to obtain an accident prediction result;

[0009] Generate corresponding warning information based on the accident prediction result;

[0010] The warning information is transmitted to the smart cycling glasses currently worn by the user, so that the smart cycling glasses can provide a warning prompt to the user based on the warning information.

[0011] Optionally, in some embodiments of the present application, the step of obtaining cycling map data, real-time traffic data, and real-time cycling data of the user includes:

[0012] Real-time monitoring of users’ physiological parameters through smart wearable devices;

[0013] The riding speed, riding acceleration and riding posture are monitored in real time through sensors installed on the riding tool;

[0014] Obtain cycling map data and real-time traffic data based on mobile smart terminals.

[0015] Optionally, in some embodiments of the present application, the step of obtaining cycling map data and real-time traffic data based on a mobile smart terminal includes:

[0016] Acquiring cycling map data based on location information of the mobile smart terminal, and;

[0017] Real-time traffic data is obtained based on a database preset by the mobile intelligent terminal.

[0018] Optionally, in some embodiments of the present application, performing cycling accident prediction based on the cycling map data, the real-time traffic data, and the real-time cycling data to obtain an accident prediction result includes:

[0019] fusing the cycling map data, the real-time traffic data and the real-time cycling data to obtain fused data;

[0020] Extracting weather information, road network information and cycling information from the fused data;

[0021] Cycling accident prediction is performed based on the weather information, road network information and cycling information to obtain an accident prediction result.

[0022] Optionally, in some embodiments of the present application, performing cycling accident prediction according to the weather information, road network information, and cycling information to obtain an accident prediction result includes:

[0023] Constructing a feature set according to the weather information, road network information, and riding information;

[0024] Cycling accident prediction is performed based on the feature set and a preset accident prediction model to obtain an accident prediction result.

[0025] Optionally, in some embodiments of the present application, performing cycling accident prediction based on the feature set and a preset accident prediction model to obtain an accident prediction result includes:

[0026] Inputting the feature set into a preset accident prediction model, and outputting the probability of the user encountering each cycling accident while riding;

[0027] An accident prediction result is generated according to the riding accident and the probability corresponding to the riding accident.

[0028] Optionally, in some embodiments of the present application, the generating corresponding warning information based on the accident prediction result includes:

[0029] Get the alarm level corresponding to the preset event;

[0030] Each predicted riding event in the accident prediction result is compared with a preset event, and corresponding warning information is generated according to the comparison result and the warning level corresponding to the preset event.

[0031] In a second aspect, an embodiment of the present application provides a riding warning device, comprising:

[0032] The acquisition module is used to obtain cycling map data, real-time traffic data and real-time cycling data of users;

[0033] A prediction module, used to predict cycling accidents based on the cycling map data, the real-time traffic data and the real-time cycling data, and obtain an accident prediction result;

[0034] A generating module, used to generate corresponding warning information based on the accident prediction result;

[0035] The transmission module is used to transmit the warning information to the smart cycling glasses currently worn by the user, so as to issue a warning prompt to the user based on the warning information through the smart cycling glasses.

[0036] Correspondingly, the present application also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the steps of any of the above methods when executing the program.

[0037] The present application also provides a storage medium, wherein the storage medium stores a computer program, and the computer program implements any of the above methods when the computer program is executed by a processor.

[0038] The present application provides a cycling warning method, device, electronic device and storage medium. First, cycling map data, real-time traffic data and real-time cycling data of the user are obtained; then, cycling accidents are predicted based on the cycling map data, real-time traffic data and real-time cycling data to obtain accident prediction results; then, corresponding warning information is generated based on the accident prediction results; finally, the warning information is transmitted to the smart cycling glasses currently worn by the user, so that the user is warned based on the warning information through the smart cycling glasses. In the cycling warning scheme provided by the present application, during the user's riding process, by real-time monitoring and prediction of cycling accidents, the user's riding safety is effectively improved, the occurrence of traffic accidents is reduced, and real-time feedback is provided to the user through smart cycling glasses to improve the user's riding experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0040] Figure 1 It is a flowchart of a riding warning method provided by an embodiment of the present application;

[0041] Figure 2 It is a structural schematic diagram of a riding warning device provided in an embodiment of the present application;

[0042] Figure 3 It is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0043] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Instead, they are merely examples of systems and methods consistent with the examples detailed in the attached claims, or with some aspects of the present application.

[0044] It should be noted that, in this article, the terms "include", "comprises" or any other variants thereof are intended to cover descriptions such as non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element. In addition, components, features, and elements with the same name in different embodiments of the present application may have the same meaning or different meanings, and their specific meanings need to be determined by their explanation in the specific embodiment or further combined with the context of the specific embodiment.

[0045] It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0046] In the subsequent description, the suffixes such as "module", "component" or "unit" used to represent elements are only used to facilitate the description of the present application, and have no specific meanings. Therefore, "module", "component" or "unit" can be used in a mixed manner.

[0047] Embodiments of the present application provide a riding warning method, device, electronic device, and storage medium.

[0048] Among them, the riding warning device can be deployed in a terminal or a server. The server can include an independently operated server or a distributed server, or a server cluster composed of multiple servers. The terminal can include a mobile phone, a tablet computer or a personal computer (PC).

[0049] It should be noted that the order of description of the following embodiments is not intended to limit the priority order of the embodiments.

[0050] The present application provides a cycling warning method, including: obtaining cycling map data, real-time traffic data, and real-time cycling data of a user; predicting cycling accidents based on the cycling map data, real-time traffic data, and real-time cycling data to obtain accident prediction results; generating corresponding warning information based on the accident prediction results; transmitting the warning information to smart cycling glasses currently worn by the user, so as to warn the user based on the warning information through the smart cycling glasses.

[0051] See also Figure 1 , Figure 1 : is a flow chart of the riding warning method provided by the present application, and the riding warning method specifically includes the following process:

[0052] S1. Obtain cycling map data, real-time traffic data, and real-time cycling data of users;

[0053] Specifically, for step S1, the detailed map data of the user's current location is obtained through the mobile smart terminal using GPS or other positioning technologies, including road conditions, traffic rules, etc. The current road condition information is obtained using the mobile network or a preset database, such as traffic congestion, accident reports, construction areas, etc. The speed, acceleration, posture, and physiological parameters (such as heart rate, breathing rate) of the user during riding are collected through smart wearable devices and sensors installed on the riding tool.

[0054] S2. Predict cycling accidents based on cycling map data, real-time traffic data, and real-time cycling data to obtain accident prediction results;

[0055] Specifically, for step S2, the acquired cycling map data, real-time road condition data and user real-time cycling data are first fused to form a comprehensive cycling environment model. Key information such as weather conditions, road types, traffic flow, etc., as well as the user's cycling habits and current cycling status are extracted from the fused data. Using machine learning algorithms, combined with the extracted feature information, an accident prediction model is constructed to predict possible accidents during cycling.

[0056] S3. Generate corresponding warning information based on the accident prediction results;

[0057] Specifically, for step S3, the prediction result is matched with the preset riding events (such as sharp turns, slippery roads, traffic jams, etc.). According to the matching result and the severity of the event, the corresponding warning level is determined. According to the warning level and event type, specific warning information is generated, such as visual prompts, sound alarms or vibration reminders.

[0058] S4. Transmitting the warning information to the smart cycling glasses currently worn by the user, so as to warn the user based on the warning information through the smart cycling glasses;

[0059] Specifically, for step S4, the generated warning information is transmitted to the smart cycling glasses currently worn by the user through wireless technology. The smart cycling glasses issue safety prompts to the user in real time through display, sound or tactile feedback according to the received warning information.

[0060] It can be seen that the cycling warning method provided in this embodiment can significantly reduce the incidence of cycling accidents and ensure the safety of cyclists through real-time prediction and warning; the use of smart cycling glasses enhances the user's sense of technology and convenience, making the cycling experience more comfortable and enjoyable; the system can suggest users to adjust their cycling routes and avoid high-risk areas based on real-time road conditions and accident prediction results; through big data analysis, it can provide users with more accurate accident predictions and safety suggestions to help users make safer and more efficient cycling decisions.

[0061] Optionally, in some embodiments, step S1 of "obtaining cycling map data, real-time traffic data, and real-time cycling data of the user" may specifically include:

[0062] S11. Real-time monitoring of the user's physiological parameters through smart wearable devices;

[0063] S12. Real-time monitoring of riding speed, riding acceleration and riding posture through sensors installed on the riding tool;

[0064] S13. Obtain cycling map data and real-time traffic data based on a mobile smart terminal.

[0065] Specifically, the data acquisition of step S1 includes: real-time monitoring of the user's physiological parameters, such as heart rate, body temperature, blood pressure, etc., through wearable devices such as smart watches and heart rate belts. These data help to assess the user's health and riding status. Install sensors on riding tools (such as bicycles) to monitor riding speed, acceleration and riding posture in real time. These data are essential for understanding the user's riding behavior and predicting potential risks. Utilize the GPS function and map service API of the mobile smart terminal to obtain detailed map data of the user's current location, including road type, traffic rules, terrain and other information. Connect to the Internet through the mobile smart terminal to obtain real-time road conditions information, such as traffic congestion, accident reports, weather conditions, etc. This information can be obtained through the transportation department's API, social media updates or user reports.

[0066] In addition, this embodiment also needs to ensure real-time synchronization of data between smart wearable devices, cycling tool sensors and mobile smart terminals in order to make accurate accident predictions. The collected data is cleaned, denoised and formatted to improve the quality and availability of the data.

[0067] In a specific embodiment, this embodiment also provides a method for identifying accident-prone areas during the user's riding process, which mainly includes two identification methods. The first identification method is to actively or passively query map data based on GPS coordinates to confirm whether this place is an "accident-prone area", wherein the active query method includes the following steps: when the user starts riding or enters a new area, the system actively triggers the query. The system updates the GPS coordinates regularly or in real time according to changes in the user's moving speed and direction. Using the API of the map service provider, such as Google Maps API or OpenStreetMap API, query the map data according to the user's real-time GPS coordinates. The map data contains geographic tag information, such as traffic signals, accident history records, etc. The system analyzes these data to determine whether the current location is an accident-prone area. The passive query method includes the following steps: when the system detects a specific event (such as sudden braking, sudden turning), the query is passively triggered. The user can enter the destination through smart riding glasses or a mobile smart terminal, and the system queries the accident-prone areas along the way according to the input destination. The system analyzes historical accident data to determine whether accidents often occur in a specific area. The second identification method is to query "accident-prone areas" based on the "risk database" established by itself. The specific process is as follows: First, the risk database is constructed, and historical accident data, traffic flow data, weather conditions, etc. are collected to build a risk database. Analyze the data to identify the patterns and characteristics of accident-prone areas. Develop a risk assessment model to assess the risk level of a specific area based on the collected data. The query process obtains the user's real-time GPS coordinates. The user's GPS coordinates are matched with the accident-prone area data in the risk database. If the user's current location or the area to be entered is marked as an accident-prone area in the risk database, the system will generate a risk warning. By identifying accident-prone areas, this embodiment can warn users in advance and prompt them to take preventive measures. Users can make safer and more informed cycling route choices based on the information provided by the system. Using a data-driven approach to predict and prevent accidents improves the accuracy and reliability of early warnings. Users can rely on the risk information provided by the system to get a safer and more comfortable cycling experience.

[0068] It can be seen that this embodiment improves the accuracy and reliability of the cycling warning system through the integration of multi-source data, making accident prediction more accurate; real-time monitoring of the user's physiological state and cycling behavior helps to promptly discover potential health problems and cycling risks, thereby issuing early warnings and reducing accidents; through the seamless integration of smart wearable devices and mobile smart terminals, users can more conveniently obtain cycling-related information and improve their cycling experience; through real-time data analysis, the system can provide users with more personalized and accurate cycling suggestions to help users make safer and more efficient cycling decisions.

[0069] Optionally, in some embodiments, step S13 “obtaining cycling map data and real-time traffic data based on a mobile smart terminal” may specifically include:

[0070] Acquiring cycling map data based on location information of the mobile smart terminal, and;

[0071] Obtain real-time traffic data based on the database preset by the mobile smart terminal.

[0072] Specifically, for step S13, the GPS module or other positioning technology built into the mobile smart terminal is used to accurately obtain the user's current geographic location. By calling the API of the map service provider, such as Google Maps or Baidu Maps, detailed map data of the user's current location is obtained, including road information, traffic signs, terrain, etc. A database containing various road condition information is constructed, which can be historical data, real-time updated data, or user-reported data. The road condition information in the database is updated regularly or in real time from sources such as transportation departments, social media, and other users.

[0073] In addition, it is necessary to match the location information obtained through GPS with the map data returned by the map service API to ensure the accuracy and real-time nature of the data. Integrate real-time traffic data with map data to form a comprehensive cycling environment model to provide support for cycling accident prediction.

[0074] This embodiment uses high-precision GPS positioning technology to ensure the accuracy of user location information and provide a reliable basis for obtaining cycling map data; through the real-time updated road condition database, users can obtain the latest road condition information, including traffic congestion, accidents, construction, etc., so as to make more reasonable cycling decisions; through accurate map data and real-time road condition information, the cycling warning system can provide more accurate and practical warning information to improve the user's cycling safety; users can rely on the detailed maps and road condition information provided by the system to plan safer and more efficient cycling routes and improve the cycling experience.

[0075] Optionally, in some embodiments, step S2 of “predicting cycling accidents based on cycling map data, real-time traffic data, and real-time cycling data to obtain accident prediction results” may specifically include:

[0076] S21. Fusing the cycling map data, real-time traffic data and real-time cycling data to obtain fused data;

[0077] S22. extracting weather information, road network information and cycling information from the fused data;

[0078] S23. Perform cycling accident prediction based on weather information, road network information, and cycling information to obtain an accident prediction result.

[0079] Specifically, for step S2, the cycling map data, real-time road condition data and real-time cycling data of the user obtained from the smart wearable device, cycling tool sensor and mobile smart terminal are integrated. Ensure that the information of all data sources is synchronized so as to make accurate accident prediction. Extract weather conditions such as temperature, humidity, wind speed, rainfall, etc. from the fused data, all of which may affect cycling safety. Extract road network information, including road type, traffic signs, traffic signals, road conditions, etc. Extract the user's cycling information, including speed, acceleration, cycling posture, physiological parameters, etc. Based on the extracted weather information, road network information and cycling information, construct a feature set for the accident prediction model. Input the feature set into the preset cycling accident prediction model, which can be based on machine learning or deep learning algorithms. The probability of the user encountering various cycling accidents during the cycling process is calculated by the cycling accident prediction model. Generate accident prediction results based on the calculated probability, which will be used for subsequent alarm information generation.

[0080] This embodiment improves the accuracy of cycling accident prediction by comprehensively considering weather, road network and cycling information; accurate accident prediction results enable the system to issue early warnings to users in a timely manner, thereby avoiding potential cycling accidents; the system can provide personalized safety suggestions based on users' real-time cycling data to enhance users' cycling experience; riders can make safer and more reasonable cycling decisions based on the prediction results provided by the system.

[0081] Optionally, in some embodiments, step S23 of "predicting cycling accidents according to weather information, road network information, and cycling information to obtain accident prediction results" may specifically include:

[0082] S231. Construct a feature set based on weather information, road network information, and cycling information;

[0083] S232. Perform cycling accident prediction based on the feature set and a preset accident prediction model to obtain an accident prediction result.

[0084] Specifically, for step S23, the data obtained from smart wearable devices, cycling tool sensors, and mobile smart terminals are integrated to form a comprehensive cycling environment model. Key features such as weather conditions, road types, traffic flow, cycling speed, cycling posture, etc. are selected from the integrated data. These features are crucial for predicting cycling accidents, and a feature set is constructed. Select or develop suitable machine learning or deep learning models, such as random forests, support vector machines, neural networks, etc., to process feature sets and predict accidents. Train the model using historical cycling data and accident records to ensure that the model can accurately identify accident risks. Input the constructed feature set into the preset accident prediction model. The model analyzes the feature set and calculates the probability of the user encountering various cycling accidents during riding. According to the probability output by the model, analyze the possibility of the user encountering an accident under specific conditions. Convert the analysis results into specific accident prediction results, which will be used to generate warning information.

[0085] This embodiment significantly improves the accuracy of cycling accident prediction by using advanced machine learning models and precise feature sets. It can evaluate cycling risks in real time and provide users with timely safety warnings. Through timely accident warnings, it effectively reduces the occurrence of cycling accidents and protects the lives of users. Users can adjust their cycling plans or behaviors based on the system's warning information to obtain a safer and more comfortable cycling experience.

[0086] Optionally, in some embodiments, step S232 of “predicting a cycling accident based on the feature set and a preset accident prediction model to obtain an accident prediction result” may specifically include:

[0087] Input the feature set into the preset accident prediction model and output the probability of the user encountering each cycling accident while riding;

[0088] Generate accident prediction results based on cycling accidents and their corresponding probabilities.

[0089] Specifically, for step S232, the feature set extracted from the cycling map data, real-time traffic data and real-time cycling data of the user is input into the preset accident prediction model. The accident prediction model analyzes the input feature set and uses algorithms (such as decision trees, neural networks, etc.) to identify potential cycling accident risks. The model calculates the probability of users encountering various types of cycling accidents during riding, which may include collisions, falls, traffic violations, etc. According to the output of the model, the risk level of users encountering accidents under specific conditions is evaluated. The probability output by the model is combined with the specific type of cycling accident to generate a detailed accident prediction result. In addition, the prediction results can also be post-processed, such as adjusting the probability threshold, applying a weighted algorithm, etc., to improve the accuracy and practicality of the prediction.

[0090] Based on the accident prediction results, the system generates corresponding warning information, which will guide users to take preventive measures. Through smart cycling glasses or other user interfaces, the warning information is fed back to users in real time to help them make timely responses.

[0091] This embodiment improves the accuracy of cycling accident prediction through precise feature sets and advanced prediction models, thereby preventing accidents more effectively; it can monitor the riding environment and user status in real time, provide risk warnings in a timely manner, and help users avoid potential dangers; through real-time accident predictions and warnings, it enhances users' safety awareness and encourages them to adopt safer riding behaviors; by reducing the occurrence of accidents, it improves users' riding experience and makes riding more enjoyable and secure.

[0092] Optionally, in some embodiments, step S3 “generating corresponding warning information based on the accident prediction result” may specifically include:

[0093] S31. Get the alarm level corresponding to the preset event;

[0094] S32. Compare each predicted riding event in the accident prediction result with the preset event, and generate corresponding warning information according to the comparison result and the warning level corresponding to the preset event.

[0095] Specifically, for step S3, the system predefines the warning levels of different types of riding events, which are based on the severity and urgency of the events. Classify the possible riding events and assign an warning level to each type of event, such as minor, moderate, severe, etc. Match the predicted events in the accident prediction results with the preset riding events to determine which predicted events need to generate warnings. Evaluate the risk level of the predicted event and compare it with the warning level of the preset event to determine whether an alarm needs to be generated. Customize the specific alarm information content, including the alarm type, recommended action measures, etc., based on the comparison results and the alarm level of the preset event. Determine the format of the alarm information, such as text, sound, graphics, etc., to ensure that the user can clearly receive the alarm information. Transmit the alarm information to the smart riding glasses worn by the user through wireless communication technology, such as Bluetooth or Wi-Fi. After receiving the alarm information, the smart riding glasses display it in real time or issue a sound prompt to ensure that the user can notice the alarm in time.

[0096] This embodiment improves the user's response speed to potential dangers by generating and transmitting warning information in real time, thereby reducing the possibility of accidents; accurate warning information can help users take risk avoidance measures in a timely manner, significantly improving the safety of riding; the real-time warning function of the smart cycling glasses enhances the user's riding experience, allowing users to enjoy the fun of riding while also feeling the safety brought by technology.

[0097] In summary, the embodiment of the present application provides a cycling warning method, first, obtaining cycling map data, real-time traffic data and real-time cycling data of the user; then, based on the cycling map data, real-time traffic data and real-time cycling data, cycling accident prediction is performed to obtain accident prediction results; then, corresponding warning information is generated based on the accident prediction results; finally, the warning information is transmitted to the smart cycling glasses currently worn by the user, so that the smart cycling glasses can be used to warn the user based on the warning information. In the cycling warning scheme provided in the embodiment of the present application, during the user's riding process, by real-time monitoring and prediction of cycling accidents, the user's riding safety is effectively improved, the occurrence of traffic accidents is reduced, and real-time feedback is provided to the user through the smart cycling glasses to improve the user's riding experience.

[0098] In order to better implement the riding warning method of the embodiment of the present application, the embodiment of the present application also provides a riding warning device. The meanings of the terms are the same as those in the above riding warning method, and the specific implementation details can refer to the description in the method embodiment.

[0099] See also Figure 2 , Figure 2 This is a schematic diagram of the structure of a riding warning device provided in an embodiment of the present application, wherein the riding warning device may specifically include an acquisition module 201, a prediction module 202, a generation module 203 and a transmission module 203, which may be specifically as follows:

[0100] An acquisition module 201 is used to acquire cycling map data, real-time traffic data, and real-time cycling data of the user;

[0101] A prediction module 202 is used to predict cycling accidents based on cycling map data, real-time traffic data and real-time cycling data to obtain accident prediction results;

[0102] A generating module 203, used to generate corresponding warning information based on the accident prediction result;

[0103] The transmission module 204 is used to transmit the warning information to the smart cycling glasses currently worn by the user, so that the smart cycling glasses can provide a warning prompt to the user based on the warning information.

[0104] Optionally, in some embodiments, the acquisition module 201 may specifically include:

[0105] A first acquisition unit is used to monitor the user's physiological parameters in real time through a smart wearable device;

[0106] A second acquisition unit is used to monitor the riding speed, riding acceleration and riding posture in real time through sensors installed on the riding tool;

[0107] The third acquisition unit is used to acquire cycling map data and real-time traffic data based on the mobile smart terminal.

[0108] Optionally, in some embodiments, the third acquiring unit is specifically configured to:

[0109] Acquiring cycling map data based on location information of the mobile smart terminal, and;

[0110] Obtain real-time traffic data based on the database preset by the mobile smart terminal.

[0111] Optionally, in some embodiments, the prediction module 202 may specifically include:

[0112] A fusion unit, used for fusing the cycling map data, the real-time traffic data and the real-time cycling data to obtain fused data;

[0113] An extraction unit, used to extract weather information, road network information and cycling information from the fused data;

[0114] The prediction unit is used to predict cycling accidents based on weather information, road network information and cycling information to obtain accident prediction results.

[0115] Optionally, in some embodiments, the prediction unit is specifically used for:

[0116] Construct a feature set based on weather information, road network information, and cycling information;

[0117] Cycling accident prediction is performed based on the feature set and the preset accident prediction model to obtain the accident prediction result.

[0118] Optionally, in some embodiments, the prediction unit is specifically used for:

[0119] Input the feature set into the preset accident prediction model and output the probability of the user encountering each cycling accident while riding;

[0120] Generate accident prediction results based on cycling accidents and their corresponding probabilities.

[0121] Optionally, in some embodiments, the generating module 203 may be specifically used for:

[0122] Get the alarm level corresponding to the preset event;

[0123] Each predicted riding event in the accident prediction result is compared with the preset event, and the corresponding alarm information is generated according to the comparison result and the alarm level corresponding to the preset event.

[0124] The embodiment of the present application provides a riding warning device. First, the acquisition module 201 acquires riding map data, real-time road condition data, and real-time riding data of the user; then, the prediction module 202 predicts riding accidents based on the riding map data, real-time road condition data, and real-time riding data to obtain accident prediction results; then, the generation module 203 generates corresponding warning information based on the accident prediction results; finally, the transmission module 204 transmits the warning information to the smart riding glasses currently worn by the user, so as to warn the user based on the warning information through the smart riding glasses. In the riding warning scheme provided in the embodiment of the present application, during the user's riding process, by real-time monitoring and prediction of riding accidents, the user's riding safety is effectively improved, the occurrence of traffic accidents is reduced, and real-time feedback is provided to the user through the smart riding glasses to improve the user's riding experience.

[0125] In addition, the present application also provides an electronic device, such as Figure 3 As shown, it shows a schematic diagram of the structure of the electronic device involved in the embodiment of the present application, specifically:

[0126] The electronic device may include components such as a processor 301 with one or more processing cores, a memory 302 with one or more computer-readable storage media, a power supply 303, and an input unit 304. Those skilled in the art will appreciate that Figure 3 The electronic device structure shown in the figure does not constitute a limitation on the electronic device, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0127] The processor 301 is the control center of the electronic device. It uses various interfaces and lines to connect various parts of the entire electronic device. By running or executing software programs and / or modules stored in the memory 302, and calling data stored in the memory 302, it executes various functions of the electronic device and processes data, thereby monitoring the electronic device as a whole. Optionally, the processor 301 may include one or more processing cores; preferably, the processor 301 may integrate an application processor and a modem processor, wherein the application processor mainly processes the operating system, user interface, and application programs, and the modem processor mainly processes wireless communications. It is understandable that the above-mentioned modem processor may not be integrated into the processor 301.

[0128] The memory 302 can be used to store software programs and modules. The processor 301 executes various functional applications and riding warning methods by running the software programs and modules stored in the memory 302. The memory 302 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area may store data created according to the use of the electronic device, etc. In addition, the memory 302 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other volatile solid-state storage devices. Accordingly, the memory 302 may also include a memory controller to provide the processor 301 with access to the memory 302.

[0129] The electronic device also includes a power supply 303 for supplying power to various components. Preferably, the power supply 303 can be logically connected to the processor 301 through a power management system, so as to manage charging, discharging, power consumption and other functions through the power management system. The power supply 303 can also include one or more DC or AC power supplies, recharging systems, power failure detection circuits, power converters or inverters, power status indicators and other arbitrary components.

[0130] The electronic device may further include an input unit 304, which may be used to receive input digital or character information and generate keyboard, mouse, joystick, optical or trackball signal input related to user settings and function control.

[0131] Although not shown, the electronic device may further include a display unit, etc., which will not be described in detail herein. Specifically in this embodiment, the processor 301 in the electronic device will load the executable files corresponding to the processes of one or more application programs into the memory 302 according to the following instructions, and the processor 301 will run the application programs stored in the memory 302, thereby realizing various functions, as follows:

[0132] Acquire cycling map data, real-time traffic data and real-time cycling data of the user; predict cycling accidents based on the cycling map data, real-time traffic data and real-time cycling data to obtain accident prediction results; generate corresponding alarm information based on the accident prediction results; transmit the alarm information to the smart cycling glasses currently worn by the user, so as to issue an alarm prompt to the user based on the alarm information through the smart cycling glasses.

[0133] The specific implementation of the above operations can be found in the previous embodiments, which will not be described in detail here.

[0134] The embodiment of the present application first obtains cycling map data, real-time traffic data, and the user's real-time cycling data; then, based on the cycling map data, real-time traffic data, and real-time cycling data, cycling accident prediction is performed to obtain accident prediction results; then, corresponding warning information is generated based on the accident prediction results; finally, the warning information is transmitted to the smart cycling glasses currently worn by the user, so that the user is warned based on the warning information through the smart cycling glasses. In the cycling warning scheme provided in the embodiment of the present application, during the user's riding process, by real-time monitoring and prediction of cycling accidents, the user's riding safety is effectively improved, the occurrence of traffic accidents is reduced, and real-time feedback is provided to the user through the smart cycling glasses to improve the user's riding experience.

[0135] A person of ordinary skill in the art will appreciate that all or part of the steps in the various methods of the above embodiments may be completed by instructions, or by controlling related hardware through instructions. The instructions may be stored in a computer-readable storage medium and loaded and executed by a processor.

[0136] To this end, an embodiment of the present application provides a storage medium in which a plurality of instructions are stored. The instructions can be loaded by a processor to execute the steps in any of the riding warning methods provided in the embodiments of the present application. For example, the instructions can execute the following steps:

[0137] Acquire cycling map data, real-time traffic data and real-time cycling data of the user; predict cycling accidents based on the cycling map data, real-time traffic data and real-time cycling data to obtain accident prediction results; generate corresponding alarm information based on the accident prediction results; transmit the alarm information to the smart cycling glasses currently worn by the user, so as to issue an alarm prompt to the user based on the alarm information through the smart cycling glasses.

[0138] The specific implementation of the above operations can be found in the previous embodiments, which will not be described in detail here.

[0139] The storage medium may include: a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0140] Since the instructions stored in the storage medium can execute the steps in any riding warning method provided in the embodiments of the present application, the beneficial effects that can be achieved by any riding warning method provided in the embodiments of the present application can be achieved. Please refer to the previous embodiments for details and will not be repeated here.

[0141] The above is a detailed introduction to a riding warning method, device, electronic device and storage medium provided in the embodiments of the present application. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea. At the same time, for technical personnel in this field, according to the idea of ​​the present application, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A riding warning method, characterized in that: include: Obtain cycling map data, real-time traffic data, and users’ real-time cycling data; Perform cycling accident prediction based on the cycling map data, the real-time traffic data, and the real-time cycling data to obtain an accident prediction result; Generate corresponding warning information based on the accident prediction result; The warning information is transmitted to the smart cycling glasses currently worn by the user, so that the smart cycling glasses can provide a warning prompt to the user based on the warning information.

2. The riding warning method according to claim 1, characterized in that: The obtaining of cycling map data, real-time traffic data and real-time cycling data of the user includes: Real-time monitoring of users’ physiological parameters through smart wearable devices; The riding speed, riding acceleration and riding posture are monitored in real time through sensors installed on the riding tool; Obtain cycling map data and real-time traffic data based on mobile smart terminals.

3. The riding warning method according to claim 2, characterized in that: The method of obtaining cycling map data and real-time traffic data based on a mobile smart terminal includes: Acquiring cycling map data based on location information of the mobile smart terminal, and; Real-time traffic data is obtained based on a database preset by the mobile intelligent terminal.

4. The riding warning method according to claim 1, characterized in that: The performing cycling accident prediction based on the cycling map data, the real-time traffic data and the real-time cycling data to obtain an accident prediction result includes: fusing the cycling map data, the real-time traffic data and the real-time cycling data to obtain fused data; Extracting weather information, road network information and cycling information from the fused data; Cycling accident prediction is performed based on the weather information, road network information and cycling information to obtain an accident prediction result.

5. The riding warning method according to claim 4, characterized in that: The performing cycling accident prediction according to the weather information, the road network information and the cycling information to obtain the accident prediction result includes: Constructing a feature set according to the weather information, road network information, and riding information; Cycling accident prediction is performed based on the feature set and a preset accident prediction model to obtain an accident prediction result.

6. The riding warning method according to claim 5, characterized in that: The step of performing cycling accident prediction based on the feature set and a preset accident prediction model to obtain an accident prediction result includes: Inputting the feature set into a preset accident prediction model, and outputting the probability of the user encountering each cycling accident while riding; An accident prediction result is generated according to the riding accident and the probability corresponding to the riding accident.

7. The riding warning method according to any one of claims 1 to 6, characterized in that: The generating corresponding warning information based on the accident prediction result includes: Get the alarm level corresponding to the preset event; Each predicted riding event in the accident prediction result is compared with a preset event, and corresponding warning information is generated according to the comparison result and the warning level corresponding to the preset event.

8. A riding warning device, characterized in that: include: The acquisition module is used to obtain cycling map data, real-time traffic data and real-time cycling data of users; A prediction module, used to predict cycling accidents based on the cycling map data, the real-time traffic data and the real-time cycling data, and obtain an accident prediction result; A generating module, used to generate corresponding warning information based on the accident prediction result; The transmission module is used to transmit the warning information to the smart cycling glasses currently worn by the user, so as to issue a warning prompt to the user based on the warning information through the smart cycling glasses.

9. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program according to the steps of the riding warning method as described in any one of claims 1 to 7.

10. A storage medium, characterized in that: A computer program is stored which can be loaded by a processor and executes the riding warning method according to any one of claims 1 to 7.

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