Bicycle refined intelligent navigation method, system and equipment and storage medium

Abstract

The invention relates to the technical field of navigation, in particular to a bicycle refined intelligent navigation method, system and device and a storage medium, and the method comprises the following steps: obtaining information such as gradient and length of a ridable route; a physical exhaustion result is obtained through a physical exhaustion calculation model in combination with user weight, bicycle type and the like; calculating a time consumption result according to the riding ability of the user and evaluating a route difficulty level; acquiring real-time information of weather and road conditions, and generating detailed prompt information according to the real-time information; and sorting routes are screened according to user preferences, recommended and displayed. According to the method, refinement and intellectualization of bicycle navigation can be improved, physical power and time consumption are accurately calculated by integrating multiple factors, comprehensive and detailed route prompt and personalized recommendation are provided for users, different user requirements are met, and riding experience and efficiency are improved.

Description

Technical Field

[0001] This application relates to the field of navigation technology, and particularly to a refined intelligent navigation method, system, device and storage medium for bicycles. Background Art

[0002] In today's society, bicycles, as an environmentally friendly, healthy and convenient means of transportation and a form of leisure exercise, are loved by more and more people. With the continuous progress of technology, bicycle navigation technology has emerged and achieved certain development. However, the existing bicycle navigation technology still has many deficiencies.

[0003] Most traditional bicycle navigation systems only rely on basic map data and simple GPS positioning technology, and can only provide relatively rough route planning for cyclists. Usually, it simply calculates the distance from the starting point to the end point and recommends the shortest or fastest path based on this. This method ignores many key factors during the cycling process and cannot meet the growing refined needs of cyclists.

[0004] Therefore, based on the above problems, the existing technology needs to be improved. Summary of the Invention

[0005] The purpose of this application is to provide a refined intelligent navigation method, system, device and storage medium for bicycles, aiming to solve the problems of insufficient refinement and intelligence in bicycle navigation.

[0006] One purpose of this application is to provide a refined intelligent navigation method for bicycles, including: Obtain the route information of several rideable routes, where the route information includes slope information and route length information; According to the user's weight information, bicycle type information, and the obtained slope information and route length information, through a preset physical exertion calculation model, calculate the physical exertion required for cycling on each route to obtain a physical exertion result; Combine the user's cycling ability data, slope information and route length information, and calculate the time required for cycling on each route to obtain a time consumption result; According to the calculated physical exertion result and time consumption result, evaluate the difficulty level for each route; Obtain the weather information and real-time road conditions information of the cycling time; According to the weather information, real-time road conditions information, physical exertion result, time consumption result and difficulty level, generate detailed prompt information for each route; Obtain the user-set preference information, and according to the user preference information, screen and sort each route, and preferentially recommend and display the routes and related information that meet the user's preferences.

[0007] By adopting the above technical solutions, in terms of physical exertion calculation, multiple factors such as the user's weight, bicycle type, slope, and route length are comprehensively considered, enabling cyclists to accurately understand the physical exertion on different routes, thereby better planning their rides and avoiding affecting the journey or causing safety problems due to physical exhaustion. In terms of time consumption calculation, combining the user's cycling ability, real-time road conditions information, and the characteristics of different road sections, more accurate time estimates are provided for cyclists, which helps to reasonably arrange travel time. By evaluating the route difficulty level and generating detailed prompt information, multi-dimensional information such as weather, road conditions, physical exertion, time consumption, and difficulty level is integrated to provide comprehensive decision-making basis for cyclists, enabling them to choose the most suitable route according to their own situations. At the same time, route screening and sorting are carried out according to user preferences, meeting the personalized needs of different cyclists, improving the satisfaction and experience of cycling, and truly realizing the refinement and intelligence of bicycle navigation.

[0008] In a possible implementation manner of this application, the method further includes: During the cycling process, continuously monitor the user's actual cycling state and update the road conditions information in real time; According to the user's actual cycling state and real-time road condition changes, dynamically adjust the recommended route and recalculate the physical exertion and time estimate; When it is detected that the user deviates from the recommended route by a certain distance, promptly issue a deviation reminder and re-plan the navigation route according to the current position; After the cycling ends, record the actual data of this ride, including the actual cycling route, actual physical exertion, actual time spent, and actual speed changes, for optimizing the subsequent physical exertion calculation model and time estimate algorithm; Generate a cycling summary report, which includes statistics of various data of this ride, comparative analysis with the recommended route, cycling achievements, and suggestions for improving physical exertion distribution, and provide it for the user to view and share.

[0009] By adopting the above technical solution, the actual riding status of the user and real-time road condition information are continuously monitored during the riding process, ensuring that the user can always obtain the latest navigation guidance, dynamically adjusting the recommended route and recalculating the physical exertion and time estimation, making the navigation more in line with the actual situation, and improving the flexibility and adaptability of riding. When the user deviates from the recommended route, a reminder is sent in a timely manner and the navigation route is replanned, effectively preventing the user from getting lost and ensuring the smooth progress of the ride. After the ride, the actual data is recorded for optimizing subsequent calculation models and algorithms, enabling the system to continuously learn and improve, and providing more and more accurate services for the user. The generated riding summary report not only provides the user with comprehensive data statistics and comparative analysis, enabling the user to understand their riding performance, but also gives suggestions for improving physical exertion distribution, helping the user improve their riding skills, while increasing the user's sense of participation and achievement, and further enhancing the refinement and intelligence level of bicycle navigation.

[0010] In a possible implementation manner of this application, the steps of generating detailed prompt information for each route include: Analyze the geographical features along each rideable route; Assign corresponding scenic score weights for different types of geographical features; According to the scenic score weights and preset scenic spot screening criteria, select scenic spots with ornamental value along the rideable route as scenic sightseeing recommended spots; Calculate the recommended stay time for each scenic sightseeing recommended spot according to the scale of the scenic sightseeing recommended spot, the richness of the tour content, and the statistical data of the average stay time of general tourists; Integrate the best viewing angle, recommended stay time, brief scenic spot introduction and characteristic highlights information of each scenic sightseeing recommended spot to generate sightseeing recommendation content for each rideable route.

[0011] By adopting the above technical solution, through the analysis of the geographical features along the rideable route, a richer dimension of riding experience is provided for the user. Assigning scenic score weights to different geographical features and screening out scenic spots with ornamental value meet the user's pursuit of beautiful scenery during the riding process. Calculating the recommended stay time according to the scenic spot scale, tour content and average stay time, and integrating information such as the best viewing angle, recommended stay time and scenic spot introduction to generate sightseeing recommendation content enables the user to better plan the sightseeing activities during the ride, make full use of the riding time to enjoy the scenery along the way, increase the fun and leisure of riding, and make the bicycle navigation no longer limited to route guidance, but become a comprehensive travel and sightseeing assistance tool.

[0012] In a possible implementation of this application, the steps of calculating the time required for cycling on each route and obtaining the time consumption result by combining the user's cycling ability data, slope information, and route length information include: Obtain the road condition type information and traffic flow data of each section in the rideable routes, and at the same time collect the historical road condition data of different time periods in a specific area to establish a historical road condition database; Compare and analyze the road condition type information and traffic flow data with the historical data to evaluate the congestion degree of the current section and obtain the congestion degree evaluation result; According to the congestion degree evaluation result, determine the average speed reduction ratio of the current congested section from the preset corresponding table and dynamically adjust it according to the real-time traffic flow; According to the preset slope-physical fitness-speed adjustment coefficient model, consider the slope size and the user's physical fitness condition to calculate the speed adjustment coefficient for the uphill section; Calculate the basic cycling time for the normal section according to the route length and the user's average cycling speed on the normal section; For the congested section, calculate the first time change amount according to the calculated average speed reduction ratio and the section length. For the uphill section, calculate the second time change amount according to the speed adjustment coefficient and the slope length. Similarly, calculate the third time change amount for special road condition sections such as downhill and construction; Add the basic time of the normal section to the first time change amount, the second time change amount, and the third time change amount of each special road condition section to obtain the total time consumption estimation result.

[0013] By adopting the above technical solution, by collecting road condition type information, traffic flow data, and historical road condition data of different time periods to establish a database, it is possible to comprehensively and accurately evaluate the congestion degree of the section and various special road conditions. Determine the average speed reduction ratio according to the congestion degree and dynamically adjust it, and calculate the speed adjustment coefficient according to the slope and physical fitness condition, so that the time consumption calculation can more accurately reflect the actual situation. Calculate the basic cycling time of the normal section and the time change amounts of different special road condition sections respectively, and finally obtain the total time consumption estimation result, providing a very reliable time planning basis for users. Such a solution enables users to fully understand the cycling time of different routes under various road conditions before traveling, reasonably arrange the itinerary, avoid the inconvenience caused by inaccurate time estimation, greatly improve the practicality and intelligent level of bicycle navigation, and realize the refined calculation and management of cycling time.

[0014] In a possible implementation of this application, the method includes: When multiple people are cycling, obtain the user information of each member in the team, and jointly plan the optimal cycling route suitable for the entire team according to the cycling ability information, preference information, and real-time road condition information of each member; Share and display the locations of all members in real time; Dynamically adjust the navigation prompts corresponding to each member according to the average riding speed of team members and real-time road conditions information to ensure the consistency of the overall speed and location of the team; Based on the riding progress of the team, the physical fitness of members, and the surrounding environment, intelligently recommend locations suitable for the team to take a collective rest; When a certain member encounters an emergency, generate and send emergency occurrence information to the client of each team member.

[0015] By adopting the above technical solutions, in the scenario of multi-person cycling, according to the riding abilities, preferences of team members and real-time road conditions information, collaboratively plan the optimal route, which meets the overall needs of the team, improves the efficiency and satisfaction of cycling. Sharing and displaying members' locations in real time enhances the teamwork and safety of the team, enabling members to know each other's locations at any time. Dynamically adjusting navigation prompts ensures the consistency of the overall speed and location of the team, avoiding too large a distance between members and enhancing the coordination of team cycling. Intelligently recommending collective rest locations takes into account the riding progress of the team, the physical fitness of members and the surrounding environment, enabling the team to rest at the right time and place to recover physical strength. When a member encounters an emergency, generating and sending information to the client of each member in a timely manner enables prompt response measures to be taken, providing strong safety guarantee for team cycling, and fully reflecting the refinement and intelligence of bicycle navigation in the scenario of multi-person cycling.

[0016] In a possible implementation manner of this application, the steps of dynamically adjusting the navigation prompts corresponding to each member according to the average riding speed of team members and real-time road conditions information to ensure the consistency of the overall speed and location of the team include: During multi-person cycling, automatically assign different roles according to the riding abilities and experience of members; Provide corresponding task prompts and collaboration guidance for each role; According to the role task requirements, display exclusive prompt information for each member on the member client.

[0017] By adopting the above technical solutions, in multi-person cycling, automatically assigning different roles according to members' riding abilities and experience makes the team division of labor clearer and gives full play to the advantages of each member. Providing task prompts and collaboration guidance for different roles enhances the teamwork and cooperation degree among team members, improves the efficiency and safety of cycling. Displaying exclusive prompt information for each member according to role task requirements enables members to quickly and accurately understand their tasks and responsibilities and better achieve the team cycling goal. This way makes multi-person cycling more orderly and efficient, fully reflecting the refined management and intelligent service of bicycle navigation in the scenario of multi-person cycling, and bringing great convenience and good experience to team cycling.

[0018] In a possible implementation manner of the present application, the method further includes: Establish a bicycle equipment information database; After obtaining the user's bicycle type information, match the corresponding parameters from the database, and optimize and adjust the physical exertion calculation model and the riding performance prediction according to these parameters; Combine the real-time road condition information, the performance characteristics of the user's bicycle equipment, and the optimized riding performance prediction to plan a personalized riding route for the user.

[0019] By adopting the above technical solution, establishing a bicycle equipment information database provides a data basis for accurate navigation. By matching the user's bicycle type information and optimizing the physical exertion calculation model and the riding performance prediction according to the database parameters, the calculation results can more accurately reflect the user's actual riding situation. Combining the real-time road condition information, the performance characteristics of the user's bicycle equipment, and the optimized riding performance prediction to plan a personalized riding route can better meet the specific needs of the user and improve the efficiency and comfort of riding. This solution fully considers the impact of bicycle equipment on riding, realizes the personalization and refinement of bicycle navigation, and provides a better riding experience for users.

[0020] The second object of the present application is to provide a refined intelligent bicycle navigation system, which includes: Route information acquisition module: Acquire the route information of several rideable routes, and the route information includes slope information and route length information; Physical exertion result calculation module: According to the user's weight information, bicycle type information, and the acquired slope information and route length information, calculate the physical exertion required for riding on each route through a preset physical exertion calculation model to obtain a physical exertion result; Time consumption result calculation module: Combine the user's riding ability data, slope information, and route length information to calculate the time required for riding on each route to obtain a time consumption result; Route difficulty level evaluation module: Evaluate the difficulty level of each route according to the calculated physical exertion result and time consumption result; Real-time road condition information acquisition module: Acquire the weather information and real-time road condition information during the riding time; Detailed prompt information generation module: Generate detailed prompt information for each route according to the weather information, real-time road condition information, physical exertion result, time consumption result, and difficulty level; User-preferred route recommendation display module: Acquire the preference information set by the user; According to the user preference information, screen and sort each route, and preferentially recommend and display the routes and related information that meet the user's preferences.

[0021] By adopting the above technical solutions, in terms of physical exertion calculation, multiple factors such as the user's weight, bicycle type, slope, and route length are comprehensively considered, enabling cyclists to accurately understand the physical exertion on different routes, thereby better planning their rides and avoiding affecting the journey or causing safety problems due to physical exhaustion. In terms of time consumption calculation, by combining the user's cycling ability, real-time road conditions information, and the characteristics of different road sections, a more accurate time estimate is provided for cyclists, which helps to reasonably arrange the travel time. By evaluating the route difficulty level and generating detailed prompt information, multi-dimensional information such as weather, road conditions, physical exertion, time consumption, and difficulty level is integrated to provide comprehensive decision-making basis for cyclists, enabling them to choose the most suitable route according to their own situations. At the same time, route screening and sorting are carried out according to user preferences, meeting the personalized needs of different cyclists, improving the satisfaction and experience of cycling, and truly realizing the refinement and intelligence of bicycle navigation.

[0022] The third object of this application is to provide a refined and intelligent bicycle navigation device, which includes: A memory and a processor, and a computer program capable of being loaded and executed by the processor for the above-mentioned refined and intelligent bicycle navigation method is stored on the memory.

[0023] The fourth object of this application is to provide a storage medium.

[0024] The above fourth object of this application is achieved through the following technical solutions: A storage medium, in which a computer program capable of being loaded and executed by the processor for the above-mentioned refined and intelligent bicycle navigation method is stored.

[0025] In summary, this application includes at least one of the following beneficial technical effects: 1. The refinement of bicycle navigation is realized. By comprehensively considering multiple factors such as the user's weight, bicycle type, road conditions, and weather, the physical exertion and time estimate are accurately calculated, and detailed route prompt information and personalized route recommendations are provided for users, meeting the refined needs of users for cycling.

[0026] 2. The intelligent level of bicycle navigation is improved. During the cycling process, the user's status and road condition changes are continuously monitored, the route and navigation prompts are dynamically adjusted, and after the cycling ends, the calculation model and algorithm can be optimized according to the actual data, providing a more intelligent navigation service for users.

[0027] 3. The cycling experience is enriched. By analyzing the geographical features along the route, scenic sightseeing recommendation points are screened out and the recommended stay time and sightseeing suggestion content are provided, enabling users to not only reach the destination during cycling but also enjoy the scenery along the way, increasing the fun and leisure of cycling.

[0028] 4. Applicable to the scenario of group cycling. It can collaboratively plan the group cycling route, share the positions of members in real time, dynamically adjust navigation prompts, intelligently recommend rest locations and handle emergencies, improving the efficiency and safety of group cycling.

[0029] 5. Considering bicycle equipment factors. Establish a bicycle equipment information database, optimize navigation calculations and plan personalized routes according to equipment parameters, and provide users with more practical cycling services. Description of the Drawings

[0030] Figure 1 is a flowchart of a refined intelligent navigation method for a bicycle provided by an embodiment of the present application; Figure 2 is a virtual structure diagram of a refined intelligent navigation system for a bicycle provided by an embodiment of the present application. Detailed Embodiments

[0031] To make the objectives, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Apparently, the described embodiments are some, but not all, of the embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts shall fall within the protection scope of the present application.

[0032] In addition, the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this article generally represents an "or" relationship between the associated objects before and after unless otherwise specified.

[0033] The following further describes the embodiments of the present application in detail with reference to the accompanying drawings of the specification.

[0034] The embodiment of the present application provides a refined intelligent navigation method for a bicycle. Referring to Figure 1 , the main processes of the method are described as follows: S1: Obtain the route information of several rideable routes, where the route information includes slope information and route length information; Among them, detailed information on bike-ridable routes within a specific area is obtained from various data sources. Among them, the slope information is crucial for evaluating the riding difficulty and physical exertion. A larger slope will increase the riding difficulty and physical exertion; the route length information directly affects the riding time and overall workload. By collaborating with map service providers and using their map data interfaces, bike-ridable routes within a specific area are obtained. These route information includes but is not limited to road names, start and end coordinates, slope information (the slope of different sections can be calculated through the elevation data on the map), and route length information (obtained by calculating the distance between the start and end points plus the length of the curved parts on the route). It is also possible to collaborate with urban traffic management departments to obtain the latest road construction, temporary closures, etc. information to more accurately plan bike-ridable routes. At the same time, using the crowdsourcing method, users are allowed to upload new bike-ridable routes and road condition information they discover to continuously enrich the route database.

[0035] S2: According to the user's weight information, bike type information, and the obtained slope information and route length information, through a preset physical exertion calculation model, calculate the physical exertion required for riding on each route to obtain the physical exertion result; Among them, multiple factors affecting physical exertion are considered, and the physical exertion required for cycling on a specific route is accurately calculated through a preset calculation model. Different user weights result in different amounts of work done against gravity during cycling. Generally, the greater the weight, the greater the physical exertion. Differences in bicycle types lead to different cycling resistances, weights, etc., thereby affecting physical exertion. The slope information and route length information directly determine the total amount of work done against gravity during cycling and the total distance traveled. A physical exertion calculation model is established, which takes into account the influence of multiple factors on physical exertion. For example, for users with different weights, the greater the weight, the more energy is required to overcome gravity during cycling, and the corresponding physical exertion increases. For different types of bicycles, road bikes have less cycling resistance on flat roads due to their lightweight design and narrow tires, while mountain bikes consume more physical exertion under the same conditions because of their heavier bodies and wide tires. Combining slope information, more physical exertion is required when climbing slopes, and physical exertion can be saved by relying on gravity when going downhill. Based on these factors, the physical exertion for each route is calculated. Suppose a user weighs 70 kilograms and rides a road bike on a 10-kilometer route with some small slope sections. The system calculates the physical exertion coefficient for the flat section according to the model, and then increases the physical exertion for the climbing section based on the slope information, and finally obtains the physical exertion result on this route as approximately 500 calories. The physical exertion calculation model can also be further optimized by combining the user's health data, such as heart rate, blood pressure, etc. At the same time, the influence of different seasons on physical exertion can be considered. For example, in hot summer, the human body needs to consume more energy to dissipate heat, and the physical exertion may increase.

[0036] S3: Combine the user's cycling ability data, slope information, and route length information to calculate the time required for cycling on each route, and obtain the time consumption result; Among them, the time required to ride on each route is calculated by comprehensively considering factors such as the user's personal riding ability, the slope and length of the route. The user's riding ability includes average riding speed, climbing ability, etc., which reflect the user's riding efficiency under different road conditions. Slope information will affect the riding speed. The speed will usually decrease when climbing and may increase when going downhill. The length of the route directly determines the total distance of the ride. Collect the user's previous riding data and determine that the user's average riding speed is 18 kilometers per hour and the climbing speed is 10 kilometers per hour. Ride on a route with a length of 12 kilometers and a 3-kilometer climbing section with a slope of 6%. The system first calculates the time required for the flat section based on the user's normal speed, and then calculates the time required for the climbing section based on the slope and the user's climbing ability. Finally, the two parts of time are added together to obtain the total time consumption required to ride on this route, which is about 55 minutes. Real-time traffic data can also be introduced to consider the interaction with other means of transportation. For example, if there are many pedestrians or other bicycles on the route, it may affect the riding speed. At the same time, machine learning algorithms can be used to continuously optimize the time calculation model and improve the accuracy of the prediction based on the user's riding habits and historical data.

[0037] S4: Assess the difficulty level of each route based on the calculated physical exertion and time consumption results; Among them, a difficulty level is determined for each route by comprehensively analyzing the two key indicators of physical energy consumption and time consumption. This helps users choose a suitable cycling route according to their abilities and needs. Generally speaking, the greater the physical energy consumption and the longer the time, the higher the difficulty of the route. Example: Set different ranges of physical energy consumption and time consumption to correspond to different difficulty levels. For example, a route with a physical energy consumption of 0-300 calories and a time consumption of less than 30 minutes is rated as low difficulty; a route with a physical energy consumption of 301-600 calories and a time consumption of 30-60 minutes is rated as medium difficulty; a route with a physical energy consumption of more than 600 calories and a time consumption of more than 60 minutes is rated as high difficulty. If the physical energy consumption result of a route is 400 calories and the time consumption result is 40 minutes, then this route can be rated as medium difficulty. In addition to physical energy consumption and time consumption, other factors can also be considered to assess the difficulty of the route, such as the flatness of the road, traffic flow, weather conditions, etc. For example, on rainy or windy days, the difficulty of riding may increase, and the difficulty level can be adjusted accordingly.

[0038] S5: Obtain weather information and real-time road condition information during riding time; Among them, obtain the weather conditions and road conditions information during cycling to provide users with more comprehensive cycling suggestions. The weather information includes temperature, humidity, wind direction, wind speed, whether there is rain, etc. These factors will directly affect the comfort and safety of cycling. The real-time road conditions information includes road congestion, construction sections, traffic accidents, etc. These information can help users choose a smoother route. By cooperating with meteorological data providers, obtain the weather information at a specific cycling time, such as the temperature is 28 degrees Celsius, the humidity is 60%, gentle breeze, no rain. At the same time, use the traffic data interface to obtain the real-time road conditions information and find that there is a section of road under construction on a certain route, which may affect the cycling speed. The weather forecast data can be combined to provide users with cycling suggestions for the next few days. For example, if heavy rain is expected in the next few days, the system can remind users to avoid choosing routes that are prone to waterlogging during these time periods. At the same time, cooperation with the traffic management department can be carried out to obtain more accurate road conditions information, such as the specific time and scope of road closures, etc.

[0039] S6: Generate detailed prompt information for each route according to the weather information, real-time road conditions information, physical exertion results, time consumption results, and difficulty level; Among them, comprehensively consider various factors to generate detailed prompt information for each route to help users better understand the characteristics and potential risks of the route. These prompt information include the impact of weather on cycling, road conditions changes, physical exertion and time estimation, scenic spots along the way, etc. For a medium-difficulty route, the weather is clear but the temperature is relatively high, and there is a section of road under construction. The prompt information can include: "This route has a moderate physical exertion, and the estimated cycling time is [specific time]. The current weather is good, but the temperature is relatively high. Pay attention to replenishing water. There is a section of road under construction on the route, which may affect the cycling speed. Please make preparations in advance. There are scenic spots such as [specific scenic spot name] along the way, and you can stop appropriately to enjoy." Virtual reality (VR) or augmented reality (AR) technology can be used to provide users with a more intuitive route preview. For example, users can view information such as scenic spots and construction sections on the route through a mobile application, and can obtain real-time navigation and prompt information during actual cycling through AR technology.

[0040] S7: Obtain the preference information set by the user, and according to the user preference information, screen and sort each route, and preferentially recommend and display the routes and related information that meet the user's preferences.

[0041] Among them, users are allowed to set preference information according to their own preferences and needs. The system filters and sorts the routes based on these preferences, and provides route recommendations that best meet their needs. Users' preferences may include beautiful scenery, flat roads, short cycling time, low difficulty, etc. The user sets preferences in the system for routes with beautiful scenery, low difficulty, and a cycling time not exceeding 1 hour. The system filters the obtained routes according to these preferences, sorts the eligible routes according to certain rules, and preferentially recommends routes with high scenic beauty, low difficulty, and a cycling time close to the user's requirements, and displays the detailed information of these routes on the user's navigation device, including route maps, physical exertion, time estimates, introductions to scenic spots, etc. Social elements can be introduced to allow users to share their favorite routes and cycling experiences. At the same time, based on the user's historical cycling data and feedback, the recommendation algorithm can be continuously optimized to improve the accuracy and personalization of the recommendation.

[0042] Specifically, in some possible embodiments, the method further includes: During the cycling process, continuously monitor the user's actual cycling status, and at the same time, update the road conditions information in real time; According to the user's actual cycling status and real-time road condition changes, dynamically adjust the recommended route, and recalculate the physical exertion and time estimate; When it is detected that the user deviates from the recommended route by a certain distance, send a deviation reminder in a timely manner, and re-plan the navigation route according to the current location; After the cycling ends, record the actual data of this cycling, including the actual cycling route, actual physical exertion, actual time spent, actual speed changes, for optimizing the subsequent physical exertion calculation model and time estimate algorithm; Generate a cycling summary report, which includes various data statistics of this cycling, comparative analysis with the recommended route, cycling achievements, and improvement suggestions for physical exertion distribution, and provide it for the user to view and share.

[0043] Among them, through the user's mobile phone or a dedicated bicycle intelligent device, use the built-in GPS positioning system, acceleration sensor, etc. to continuously monitor the user's position, speed, acceleration and other information, so as to judge the user's cycling status. At the same time, connect to the traffic data platform in real time to obtain real-time updates of road congestion conditions, construction information, traffic accidents and other road conditions information. For example, during the cycling process in Shenzhen, the device updates the user's position information every 5 seconds, and obtains the surrounding road conditions information from the traffic data platform every 1 minute. If it is found that there is a traffic accident in front of the road where the user is cycling, resulting in congestion, the system will immediately receive this information. It can be more deeply integrated with the sensors of the intelligent bicycle to obtain more data about the bicycle status, such as wheel speed, chain tension, etc., to more comprehensively understand the user's cycling status.

[0044] If it is detected that the user's cycling speed suddenly drops and it is found that the road ahead is severely congested based on the road condition information, the system will re-plan a smoother route according to the user's current location and destination. At the same time, based on factors such as the slope information, length of the new route, the user's weight, and the type of bicycle, the physical exertion and time estimate are recalculated. For example, when the user was originally cycling on Shennan Boulevard and there was a traffic accident causing congestion in the front section, the system re-planned a route on a nearby secondary road according to the user's current location and calculated that the physical exertion of this new route was approximately 400 calories and the time estimate was 40 minutes. Machine learning algorithms can be used to further optimize the accuracy of route adjustment and calculation according to the user's cycling habits and reactions under different road conditions.

[0045] Set a deviation distance threshold, such as 500 meters. When the distance between the user's location and the recommended route exceeds this threshold, the device will emit a sound and vibration to remind the user that they have deviated from the route. At the same time, the system re-plans a route to the destination according to the user's current location. For example, when the user deviates from the recommended route while enjoying the scenery during cycling and the distance exceeds 500 meters, the device gives a reminder and the system immediately re-plans a route on the nearby road according to the user's current location. Options for the reason of deviation can be provided for the user to choose, such as "deliberately deviated" and "lost", so that the system can better understand the user's needs and perform more personalized route planning.

[0046] After the cycling ends, the device uploads all the data of this cycling to the server. The server analyzes these data, compares them with the previous calculation results, finds the differences and adjusts the parameters of the calculation model and algorithm. For example, if the actual physical exertion is much higher than the estimated value, the system will analyze the reasons, which may be that the road condition is worse than expected or the user's cycling state is affected by other factors. Then, according to the analysis results, some parameters in the physical exertion calculation model are adjusted, such as the influence coefficient of slope on physical exertion. Users can be invited to evaluate and give feedback on this cycling, and the calculation model and algorithm can be further optimized in combination with the user's feedback.

[0047] After the ride, the system generates a detailed ride summary report. The report includes statistics such as the total distance of this ride, the actual time spent, the average speed, and the maximum speed; the comparison with the recommended route, such as the differences between the actual route and the recommended route, and the comparison between the actual physical exertion and the estimated one; the ride achievements could be that the distance of this ride has broken a personal record or a difficult route has been completed, etc.; the suggestions for improving physical exertion distribution are based on the situation of this ride and provide suggestions for users on how to better distribute their physical strength on different sections. For example, the report may point out that the user consumes too much physical strength on the uphill section and suggest that the user adjust the gear in advance during the next ride to reduce physical exertion. The report is presented in a graphically rich form on the user's mobile application, and the user can share it on social platforms to share their ride experience with friends. It can be deeply integrated with social platforms so that users can interact with other cyclists while sharing the report, learning from and borrowing each other's ride experiences.

[0048] Specifically, in some possible embodiments, the steps of generating detailed prompt information for each route include: Analyze the geographical features along each rideable route. Assign corresponding scenic score weights to different types of geographical features. According to the scenic score weights and the preset scenic spot screening criteria, select the scenic spots with ornamental value along the rideable route as scenic sightseeing recommendation points. Calculate the recommended stay time for each scenic sightseeing recommendation point based on the scale, richness of the tour content, and the statistical data of the average stay time of general tourists. Integrate the best viewing angle, recommended stay time, brief scenic spot introduction, and special highlights information of each scenic sightseeing recommendation point to generate the sightseeing recommendation content for each rideable route.

[0049] Among them, the geographical information system (GIS) technology and satellite image data are used to analyze the geographical features around the rideable route. For example, for a cycling route through the mountains, analyze whether there are natural landscapes such as mountains, canyons, and streams, and whether there are cultural landscapes such as ancient villages and relics in the area it passes through. At the same time, consider factors such as the terrain undulation and altitude change of the route. Through these analyses, determine the main geographical features along the route. The user's cycling historical data can be combined to understand the user's preferences for different geographical features, so as to conduct more targeted analyses.

[0050] According to factors such as the aesthetic degree, rarity, and historical and cultural value of geographical features, scenic score weights are assigned to different types of geographical features. For example, a beautiful canyon can be assigned a relatively high weight, such as 0.8; an ancient village can be assigned a weight of 0.7; an ordinary field can be assigned a weight of 0.5. In this way, in subsequent scenic spot screening and scoring, these weights can be used to determine which geographical features are more valuable for viewing. Professional tourism experts or photographers can be invited to participate in the setting of weights to ensure the rationality and accuracy of the weights.

[0051] Set some scenic spot screening criteria, such as the distance of the scenic spot from the cycling route should not exceed a certain range (for example, 500 meters), and the area of the scenic spot should not be less than a certain value, etc. Then, according to the scenic score weights and these screening criteria, scenic spots with viewing value are screened out from along the cycling route as scenic sightseeing recommended spots. For example, on a cycling route, if there is a small canyon 300 meters away from the route, according to its scenic score weight and screening criteria, it can be determined as a scenic sightseeing recommended spot. The user's evaluations and feedback can be used to continuously adjust the scenic spot screening criteria and weights to improve the accuracy and satisfaction of the recommendations.

[0052] For each scenic sightseeing recommended spot, collect statistical data on its scale (such as area, length, etc.), the richness of the tour content (such as facilities and activities within the scenic spot), and the average stay time of general tourists. Then, based on these data, establish a mathematical model to calculate the recommended stay time for each scenic spot. For example, for a relatively large canyon with rich tour content, according to the statistical data and model calculation, the recommended stay time can be 60 minutes; while for a smaller village, the recommended stay time may be 30 minutes. The influence of seasonal factors on the stay time can be considered. For example, in summer, the stay time by the stream may increase, while in winter, the stay time at some mountain scenic spots may decrease.

[0053] For each scenic spot recommendation point, determine its best viewing angle. For example, in a canyon, a best viewing position can be determined; in front of an ancient village, a best photo-taking angle can be determined. Then, integrate the information of the best viewing angle, recommended stay time, and a brief introduction to the scenic spot (such as the historical background and cultural connotations of the scenic spot) and characteristic highlights (such as unique natural landscapes and ancient architectural styles) to generate the sightseeing recommendation content for each rideable route. For example, for a rideable route, the sightseeing recommendation content can include: "There is a spectacular canyon at [specific location] on the route. The best viewing angle is [specific angle], and the recommended stay time is 60 minutes. This canyon has steep cliffs and clear streams, making it an excellent place to appreciate the beauty of nature." VR or AR technology can be used to provide users with more intuitive sightseeing recommendation content. For example, during the user's ride, the 3D model and virtual tour experience of the scenic spot can be shown through a mobile application.

[0054] Specifically, in some possible embodiments, the steps of calculating the time required to ride on each route based on the user's riding ability data, slope information, and route length information to obtain the time consumption result include: Obtain the road condition type information and traffic flow data of each section in the rideable route, and at the same time collect the historical road condition data of different time periods in a specific area to establish a historical road condition database; Compare and analyze the road condition type information and traffic flow data with the historical data to evaluate the congestion degree of the current section and obtain the congestion degree evaluation result; According to the congestion degree evaluation result, determine the average speed reduction ratio of the current congested section from the preset corresponding table and dynamically adjust it according to the real-time traffic flow; Based on the preset slope-physical fitness-speed adjustment coefficient model, calculate the speed adjustment coefficient for the uphill section considering the slope size and the user's physical fitness condition; Calculate the basic riding time for the normal section according to the route length and the user's average riding speed on the normal section; For the congested section, calculate the first time change amount based on the calculated average speed reduction ratio and the section length. For the uphill section, calculate the second time change amount according to the speed adjustment coefficient and the slope length. Similarly, calculate the third time change amount for special road condition sections such as downhill and construction; Add the basic time of the normal section to the first time change amount, the second time change amount, and the third time change amount of each special road condition section to obtain the estimated total time consumption result.

[0055] Among them, by means of the data interface with the traffic management department or by using the real-time traffic condition function of map software, the traffic condition type information of each section of the rideable route is obtained, such as whether the road is a flat asphalt road, whether there are potholes, whether there is construction, etc., as well as the traffic flow data, including the number of vehicles and pedestrians. At the same time, the historical traffic condition data of a specific area at different time periods is collected, such as the congestion situation and construction situation of each section at different time periods in the past week, month or even year. These data are stored in a database for subsequent analysis and use. For example, a relational database or a non-relational database can be used to store these data, and each record includes information such as section identification, timestamp, traffic condition type, traffic flow, etc. A sensor network can be utilized, such as traffic flow sensors and traffic condition monitoring sensors installed on the road, to obtain more accurate real-time traffic condition information. At the same time, data mining and analysis can be carried out on the historical traffic condition data to find out the laws and trends of traffic condition changes in order to better predict future traffic conditions.

[0056] Compare and analyze the currently obtained traffic condition type information and traffic flow data with the data in the historical traffic condition database. Data analysis algorithms, such as regression analysis and clustering analysis, can be adopted to evaluate the congestion degree of the current section. For example, the congestion degree level of the current section, such as smooth, slightly congested, moderately congested, severely congested, etc., can be determined according to factors such as the ratio of traffic flow to historical average flow and the difference between traffic condition type and historical common traffic conditions. For example, if the traffic flow of the current section is 1.5 times the historical average flow and the traffic condition type is construction, then this section can be evaluated as moderately congested. Artificial intelligence algorithms, such as neural networks and deep learning, can be combined to more accurately evaluate and predict traffic conditions. At the same time, the variation law of traffic flow at different time periods can be considered, such as the congestion situation during the morning and evening rush hours on weekdays is usually more serious than other time periods, in order to more accurately evaluate the congestion degree.

[0057] Pre - establish a correspondence table that correlates the congestion level with the reduction ratio of the average speed. For example, the reduction ratio of the average speed in a smooth traffic state is 0, 0.2 for mild congestion, 0.4 for moderate congestion, and 0.6 for severe congestion. Based on the congestion level assessment result obtained previously, determine the reduction ratio of the average speed of the current congested section from the correspondence table. At the same time, as the real - time traffic flow changes, continuously monitor the change of the road conditions. If the congestion level increases or decreases, dynamically adjust the reduction ratio of the average speed accordingly. For example, if the initially evaluated section is mildly congested with an average speed reduction ratio of 0.2, but as time goes by, the traffic flow increases and the section becomes moderately congested, then the average speed reduction ratio is adjusted to 0.4. More refined adjustments to the average speed reduction ratio can be made according to the characteristics of different types of vehicles and cyclists' behaviors. For example, bicycles may be more likely to pass through congested sections than cars, so the speed reduction ratio of bicycles in congested sections can be appropriately reduced. At the same time, real - time traffic data and machine - learning algorithms can be used to continuously optimize the ratio values in the correspondence table to improve the accuracy of speed adjustment.

[0058] Establish a slope - physical fitness - speed adjustment coefficient model that takes into account the influence of slope size and user physical fitness on the speed in climbing sections. For example, the physical fitness level of the user can be determined based on factors such as the user's age, health status, cycling experience, etc., and then the speed adjustment coefficient can be determined in combination with the slope size. Assuming the user has good physical fitness, when facing a small slope (such as less than 5%), the speed adjustment coefficient can be 0.8, that is, the climbing speed is 80% of the normal speed; when facing a large slope (such as more than 5%), the speed adjustment coefficient can be 0.6. If the user's physical fitness is average, the corresponding speed adjustment coefficient can be appropriately reduced. The real - time physiological data of the user, such as heart rate, breathing rate, etc., can be combined to more accurately evaluate the user's physical fitness, thereby adjusting the speed adjustment coefficient. At the same time, the speed adjustment coefficient can be further refined according to the influence of different types of bicycles on climbing performance.

[0059] If there are no special road conditions (such as congestion, climbing, downhill, construction, etc.) in the route, the user's cycling speed can be regarded as the average cycling speed on normal sections. Based on the route length and this speed, calculate the basic cycling time for normal sections. For example, if the route length is 10 kilometers and the user's average cycling speed on normal sections is 20 kilometers per hour, then the basic cycling time for normal sections is 0.5 hours (10 kilometers ÷ 20 kilometers per hour). The influence of different terrains on the average cycling speed on normal sections can be considered. For example, the normal speeds in plain areas and mountainous areas may be different. At the same time, the average cycling speed on normal sections can be adjusted personalized according to the user's cycling habits and historical data.

[0060] For a congested section, given the average speed reduction ratio and the section length, calculate the first time change. For example, if the length of the congested section is 2 kilometers, the average speed reduction ratio is 0.4, and the average cycling speed on a normal section is 20 kilometers per hour, then the travel time on the congested section is 2 km ÷ (20 km / h × (1 - 0.4)). Assuming the normal travel time is 2 km ÷ 20 km / h = 0.1 hour, then the first time change is the travel time on the congested section minus the normal travel time. For a climbing section, given the speed adjustment coefficient and the slope length, calculate the second time change. For example, if the length of the climbing section is 1 kilometer, the speed adjustment coefficient is 0.8, and the average cycling speed on a normal section is 20 kilometers per hour, then the travel time on the climbing section is 1 km ÷ (20 km / h × 0.8). Assuming the normal travel time is 1 km ÷ 20 km / h, then the second time change is the travel time on the climbing section minus the normal travel time. Similarly, for special road condition sections such as downhill and construction, calculate the third time change according to the corresponding calculation methods. The mutual influence between different types of special road conditions can be considered. For example, for a section that is immediately followed by a downhill after climbing, the speed adjustment may be more complex. At the same time, according to the changes in real-time road conditions, the calculation method for the time change of special road condition sections can be dynamically adjusted.

[0061] Add the basic cycling time on the normal section obtained from the previous calculation to the time changes of each special road condition section to obtain the estimated result of the total time consumption. For example, if the basic cycling time on the normal section is 0.5 hour, the first time change on the congested section is 0.1 hour, the second time change on the climbing section is 0.2 hour, and the third time change on the downhill section is -0.05 hour (time saved on the downhill), then the estimated result of the total time consumption is 0.5 + 0.1 + 0.2 - 0.05 = 0.75 hour. The influence of uncertain factors on the estimated result of the total time consumption, such as weather changes and emergencies, can be considered, and a certain time margin can be reserved. At the same time, according to the feedback from users and actual cycling data, the calculation method for estimating the total time consumption can be continuously adjusted to improve the accuracy.

[0062] Specifically, in some possible embodiments, the method includes: When multiple people are cycling, obtain the user information of each member in the team, and based on the cycling ability information, preference information, and real-time road condition information of each member, collaboratively plan the optimal cycling route suitable for the entire team; Real-time share and display the positions of all members; According to the average cycling speed of the team members and the real-time road condition information, dynamically adjust the navigation prompts corresponding to each member to ensure the consistency of the overall speed and position of the team; Based on the cycling progress of the team, the physical condition of the members, and the surrounding environment, intelligently recommend locations suitable for the team to take a collective rest; When an emergency occurs to a certain member, generate and send an emergency occurrence message to each team member's client.

[0063] Among them, before the start of a multi-person cycling, collect the user information of each member through the cycling navigation application or device used by the team members, including cycling ability information such as age, gender, cycling experience, and usual average cycling speed, as well as preference information for route scenery, difficulty, etc. At the same time, use real-time traffic data to obtain the current traffic conditions, such as road congestion, construction sections, etc. Then, comprehensively consider these factors and adopt an optimization algorithm (such as simulated annealing algorithm, genetic algorithm, etc.) to plan an optimal cycling route suitable for the entire team. For example, a cycling team consisting of five people has different cycling abilities among the members. Some members are good at climbing slopes, while some members have a faster speed but limited endurance. At the same time, some members hope for a scenic route, and some members hope for a route with moderate difficulty. Combining the real-time traffic information, the system plans a route with certain scenic viewing points, a difficulty that can meet the needs of most members, and avoids congested and construction sections. It is possible to allow team members to interact and negotiate during the planning process, such as putting forward their own opinions and suggestions through the in-app chat function to jointly determine the final cycling route. At the same time, the route planning can be dynamically adjusted according to the real-time feedback and actual cycling situation of the team members.

[0064] Utilize the positioning function of the intelligent devices (such as smart phones, smart watches, etc.) carried by the team members to upload the location information of each member to the server in real time through the wireless network. The server then distributes this location information to other members in the team, enabling each member to see the locations of other members on their own devices. For example, using a dedicated cycling navigation application, display the icons of all members in the team on the map interface, and members can view the locations and moving directions of other members at any time. This can facilitate the team members to take care of each other and avoid members getting separated. Different location display modes can be set, such as only showing the locations of nearby members, showing the locations of specific members, etc., to meet different needs. At the same time, combined with virtual reality (VR) or augmented reality (AR) technology, provide a more intuitive location display effect for members, such as seeing the location marks of other members in the actual environment through AR glasses.

[0065] During the cycling process, the system continuously monitors the cycling speeds of team members and calculates the average cycling speed of the team. Based on this average speed and real-time road condition information, it dynamically adjusts the navigation prompts for each member. For example, if some members are cycling too fast while others are slower, the system can prompt the faster members to slow down appropriately to maintain the overall speed consistency of the team. At the same time, if there are changes in road conditions, such as road congestion, construction, etc., the system can promptly adjust the navigation prompts and guide the members to choose a suitable route. For example, when it detects road congestion ahead, the system will prompt the members to detour to other roads and adjust the navigation route according to the members' positions and speeds to ensure that the team members can gather together again as soon as possible. Personalized navigation prompts can be provided for different members according to their cycling abilities and role assignments. For example, designating a member as the team leader and providing more detailed route planning and navigation prompts for the leader to lead the team forward; providing more frequent rest suggestions for members with weaker physical strength, etc.

[0066] During the cycling process, the system continuously monitors the cycling progress of the team, including the distance traveled, time elapsed, etc. At the same time, it obtains information about the physical condition of the members, such as heart rate, fatigue level, etc., through the members' smart devices. Combining the information of the surrounding environment, such as the locations and evaluations of places like parks, cafes, restaurants, etc., it intelligently recommends suitable locations for the team to take a collective rest. For example, after the team has cycled for a period of time, the system recommends a location near a park and a cafe as a rest point based on the physical condition of the members and the surrounding environment. This location can not only allow the members to relax and rest but also provide food and supplies. Personalized recommendations for rest locations can be made according to the preferences and needs of the team. For example, if there are members in the team with special requirements for food, a rest location near a specialty restaurant can be recommended. At the same time, it can cooperate with the merchants at the rest locations to provide preferential and convenient services for the team.

[0067] Team members' smart devices can set emergency buttons or trigger emergency notifications through specific operations (such as long pressing the power button, etc.). When a member encounters an emergency (such as falling, injury, bicycle failure, etc.), the device will immediately send emergency information to the server, including the member's location, the type of emergency, etc. After receiving the information, the server will immediately send the emergency information to each team member's user end, such as an emergency notification pop-up on the smartphone application to remind other members to go to the rescue. For example, member A fell and was injured during the ride. He pressed the emergency button on the smart watch, and the system immediately sent the emergency information to other members. Other members can quickly reach member A's location for rescue according to the navigation prompts. You can cooperate with emergency rescue agencies. When an emergency occurs, you can automatically send a distress message to nearby rescue agencies so that professional rescue can be obtained in time. At the same time, you can set emergency contact methods between members, such as a one-click call function, to facilitate members to quickly contact in an emergency.

[0068] Specifically, in some possible embodiments, the steps of dynamically adjusting the navigation prompts corresponding to each member according to the average riding speed of the team members and the real-time information of the road conditions to ensure the consistency of the overall speed and position of the team include: During multi-person riding, different roles are automatically assigned based on the members’ riding abilities and experience; Provide corresponding task prompts and collaboration guidance for each role; Based on role-task requirements, exclusive prompt information is displayed for each member on the member user terminal.

[0069] Among them, before the multi-person ride begins, the members' riding ability and experience level are understood by collecting members' riding history data, self-assessment information, etc. For example, the riding ability can be evaluated based on the average speed, longest riding distance, climbing ability and other indicators of the members' previous rides. Based on this information, the system automatically assigns different roles to members. Common roles may include leader, cruiser, and finisher. The leader is usually a member with strong riding ability and rich experience, responsible for leading the team forward and determining the riding route and speed. The cruiser is a member with medium riding ability, mainly maintaining a stable speed to follow the leader. The finisher is responsible for taking care of the slower or difficult members in the team. For example, in a cycling team of eight people, based on the members' abilities and experience, the system automatically assigns member A as the leader, members B, C, and D as cruisers, and members E, F, G, and H as finishers. Members can choose or adjust their roles within a certain range to better adapt to the needs of the team and personal preferences. At the same time, the role assignment can be dynamically adjusted according to the actual situation during the ride. For example, if the leader encounters an unexpected situation and cannot continue to lead the team, the system can automatically reallocate the leader role.

[0070] For different roles, the system provides corresponding task prompts and collaboration guidance to ensure the smooth progress of the team's cycling. The task prompts for the team leader can include route planning, speed control, paying attention to road condition changes, etc., and provide collaboration guidance such as waiting for other members at intersections and promptly conveying road condition information. The task prompts for the cruiser can be maintaining a stable speed, paying attention to the distance from the members in front and behind, assisting the team leader in observing road conditions, etc. The task prompts for the sweeper can be paying attention to the situation of the members at the end of the team, providing necessary assistance, and promptly reporting problems to the team leader. For example, during the cycling, the team leader receives a system prompt "There is construction ahead at the intersection, please slow down and notify other members", and at the same time, the system also provides corresponding prompts for the cruiser and the sweeper, such as "The team leader notifies that there is construction ahead at the intersection, please slow down and follow". Task prompts and collaboration guidance can be ensured to be received by members in a timely manner through voice prompts, vibration reminders, etc. At the same time, a real-time communication channel can be established among members to facilitate communication and collaboration during cycling.

[0071] According to the role task requirements of the members, exclusive prompt information is displayed on the cycling navigation application or device they use. The user terminal of the team leader can display information such as the overall position of the team, the distribution of members, route navigation, etc., and prominently display the road conditions and task prompts that need attention. The user terminal of the cruiser can display information such as their position in the team, the distance from the members in front and behind, the instructions of the team leader, etc. The user terminal of the sweeper can display information such as the position of the members at the end of the team, the information of the members in need of help, the contact channels with the team leader, etc. For example, on the mobile application of the team leader, a map will be displayed, which marks the positions of each member in the team and the road conditions ahead, and there is also a task list to remind the team leader to make rest arrangements or route adjustments at specific locations. The device of the cruiser will display the distance from the members in front and behind and the current recommended cycling speed. The device of the sweeper will display the position and status information of the members that need special attention. The display method of the prompt information can be personalized according to the personal preferences and device characteristics of the members. For example, some members may prefer voice prompts, while some members are more inclined to graphical displays. At the same time, augmented reality (AR) technology can be used to directly overlay the prompt information in the field of vision of the members to improve the readability and practicality of the information.

[0072] Specifically, in some possible embodiments, the method further includes: Establish a bicycle device information database; After obtaining the bicycle type information of the user, match the corresponding parameters from the database, and optimize and adjust the physical exertion calculation model and cycling performance prediction according to these parameters; Combine the real-time road condition information, the performance characteristics of the user's bicycle device, and the optimized cycling performance prediction to plan a personalized cycling route for the user.

[0073] Among them, detailed information of various types of bicycles is collected, including parameters such as the brand, model, frame material, tire type, and gear shifting system of the bicycle, as well as information such as the corresponding performance characteristics and applicable scenarios. These information are sorted out and stored in a database. A relational database management system can be used to ensure the structured storage and efficient query of data. For example, the database may contain information about a mountain bike, such as the brand is "XX", the model is "YY", the frame material is aluminum alloy, the tires are wide and have good grip, and the gear shifting system has 21 gears. At the same time, record the performance of the bicycle under different road conditions, such as climbing ability, flat ground speed, shock absorption effect, etc. The database can be continuously updated, and relevant information can be added in a timely manner as new bicycle models are launched. Bicycle experts and enthusiasts can also be invited to review and supplement the information in the database to improve the accuracy and integrity of the data.

[0074] When the user uses the cycling navigation service, the system obtains the user's bicycle type information. For example, the user selects their bicycle as a "road bike", the brand is "ZZ", and the model is "WW" in the settings. The system queries in the bicycle device information database based on this information and finds the corresponding parameters. Then, adjust the physical exertion calculation model and cycling performance prediction according to these parameters. If it is a road bike, it is usually lighter and has narrow tires, with less resistance on flat roads and relatively lower physical exertion. The system can adjust the coefficients in the physical exertion calculation model according to these characteristics to make the calculation result more in line with the actual situation of the road bike. At the same time, optimize the cycling performance prediction according to parameters such as the gear shifting system and braking performance of the bicycle. For example, consider the gear range and efficiency of the gear shifting system when calculating the climbing ability. It can be adjusted in combination with the user's personalized modification information of the bicycle. For example, if the user replaces the wheels with lighter ones or a more efficient gear shifting system, the system can further optimize the calculation model and prediction results according to these changes.

[0075] Taking into comprehensive consideration the real-time road conditions information (such as road congestion, construction sections, slopes, etc.), the performance characteristics of the user's bicycle equipment (such as suitable road conditions, climbing ability, speed range, etc.), and the optimized estimated cycling performance, a personalized cycling route is planned for the user. For example, if the user's bicycle is a mountain bike with good shock absorption and climbing ability but relatively slow speed on flat roads. When planning the route, the system can preferentially select routes with some gentle climbs and rough sections while avoiding urban roads with traffic congestion. If the real-time road conditions information shows that there is construction on a certain route, but the user's bicycle can detour through some unpaved roads, the system can recommend this detour route. During the planning process, according to the performance characteristics of the user's bicycle and the estimated cycling performance, information such as the estimated cycling time and physical exertion can also be provided to the user to help the user make decisions. The user can further adjust the planned route according to their own preferences and needs. For example, the user can choose a more challenging route or a route that pays more attention to the scenery. At the same time, by combining the user's historical cycling data and feedback, the route planning algorithm can be continuously optimized to improve the degree of personalization and user satisfaction.

[0076] Another embodiment of the present application provides a refined intelligent navigation system for bicycles. Among them, referring to Figure 2 , a refined intelligent navigation system for bicycles includes: Route information acquisition module 100: Acquire the route information of several rideable routes, and the route information includes slope information and route length information; Physical exertion result calculation module 200: According to the user's weight information, bicycle type information, and the acquired slope information and route length information, calculate the physical exertion required for cycling on each route through a preset physical exertion calculation model to obtain the physical exertion result; Time consumption result calculation module 300: Combine the user's cycling ability data, slope information, and route length information to calculate the time required for cycling on each route to obtain the time consumption result; Route difficulty level assessment module 400: Assess the difficulty level of each route according to the calculated physical exertion result and time consumption result; Real-time road conditions information acquisition module 500: Acquire the weather information and real-time road conditions information during the cycling time; Detailed prompt information generation module 600: Generate detailed prompt information for each route according to the weather information, real-time road conditions information, physical exertion result, time consumption result, and difficulty level; User-preferred route recommendation display module 700: Acquire the preference information set by the user; according to the user preference information, screen and sort each route, and preferentially recommend and display the routes and related information that meet the user's preferences.

[0077] A refined intelligent navigation system for bicycles provided in this embodiment can achieve the steps of the foregoing embodiment due to the functions of its respective modules and their logical connections to each other, and thus can achieve the same technical effects as the foregoing embodiment. For the principle analysis, reference can be made to the relevant descriptions of the steps of the foregoing refined intelligent navigation method for bicycles, which will not be repeated here.

[0078] An embodiment of the present application also provides a refined intelligent navigation device for bicycles, including a memory and a processor, and a computer program capable of being loaded and executed by the processor for the foregoing refined intelligent navigation method for bicycles is stored on the memory.

[0079] An embodiment of the present application also provides a storage medium, in which a computer program capable of being loaded and executed by the processor for the foregoing refined intelligent navigation method for bicycles is stored.

[0080] Since the computer program in the storage medium provided in this embodiment, after being loaded and run on the processor, will implement the steps of the foregoing embodiment, it can achieve the same technical effects as the foregoing embodiment. For the principle analysis, reference can be made to the relevant descriptions of the foregoing method steps, which will not be repeated here.

[0081] The storage medium includes, for example: various media that can store program codes such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks, or optical discs.

[0082] The steps of the method or algorithm described in combination with the embodiments disclosed in this document can be directly implemented by hardware, software modules executed by a processor, or a combination of both. The software modules can be placed in a random access memory (RAM), internal memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium well-known in the technical field.

[0083] In the description of this specification, the descriptions referring to terms such as "one embodiment", "some embodiments", "examples", "specific examples", or "some examples", etc., mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0084] In addition, features defined by terms "first" and "second" may explicitly or implicitly include at least one of such features. In the description of the present invention, the meaning of "a plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined. It is only for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the quantity of the indicated technical features.

[0085] Thus, any process or method description shown in the flowchart or described in other ways herein can be understood as representing a module, segment, or part of code including one or more executable instructions for implementing a customized logic function or process. And the scope of the preferred embodiments of the present invention includes additional implementations, where the functions can be executed in a substantially simultaneous manner or in the reverse order according to the functions involved, rather than in the order shown or discussed. This should be understood by those skilled in the art to which the embodiments of the present invention pertain.

[0086] The embodiments of this specific implementation manner are all preferred embodiments of this application, and do not limit the protection scope of this application accordingly. Therefore, all equivalent changes made according to the structure, shape, and principle of this application should be covered within the protection scope of this application.

Claims

1. A refined intelligent navigation method for bicycles, characterized in that, Including: Obtain route information of several rideable routes, where the route information includes slope information and route length information; According to the user's weight information, bicycle type information, and the obtained slope information and route length information, through a preset physical exertion calculation model, calculate the physical exertion required for riding on each route to obtain a physical exertion result; Combined with the user's riding ability data, slope information, and route length information, calculate the time required for riding on each route to obtain a time consumption result; According to the calculated physical exertion result and time consumption result, evaluate the difficulty level for each route; Obtain the weather information and real-time road condition information during the riding time; According to the weather information, real-time road condition information, physical exertion result, time consumption result, and difficulty level, generate detailed prompt information for each route; Obtain the user-set preference information, and according to the user preference information, screen and sort each route, and preferentially recommend and display the routes and related information that meet the user's preferences.

2. The refined intelligent navigation method for a bicycle according to claim 1, wherein The method further includes: During the riding process, continuously monitor the user's actual riding state, and at the same time, update the road condition information in real time; According to the user's actual riding state and real-time road condition changes, dynamically adjust the recommended route, and recalculate the physical exertion and time estimation; When it is detected that the user deviates from the recommended route by a certain distance, issue a deviation reminder in time, and re-plan the navigation route according to the current location; After the ride is over, record the actual data of this ride, including the actual riding route, actual physical exertion, actual time spent, and actual speed changes, for optimizing the subsequent physical exertion calculation model and time estimation algorithm; Generate a riding summary report, which includes various data statistics of this ride, comparative analysis with the recommended route, riding achievements, and improvement suggestions for physical exertion distribution, and provide it for the user to view and share.

3. A refined intelligent navigation method for a bicycle according to claim 1, characterized in that, The steps of generating detailed prompt information for each route include: Analyze the geographical features along each rideable route; For different types of geographical features, assign corresponding scenic score weights; According to the scenic score weights and preset scenic spot screening criteria, screen out scenic spots with ornamental value along the rideable route as scenic sightseeing recommended points; According to the scale of the scenic sightseeing recommended point, the richness of the tour content, and the statistical data of the average stay time of general tourists, calculate the recommended stay time for each scenic sightseeing recommended point; Integrate the best viewing angle, recommended stay time, brief scenic spot introduction, and characteristic highlights information of each scenic sightseeing recommended point to generate sightseeing suggestion content for each rideable route.

4. A refined intelligent navigation method for a bicycle according to claim 1, characterized in that, The steps of calculating the time required for riding on each route by combining the user's riding ability data, slope information, and route length information to obtain a time consumption result include: Obtain the road condition type information and traffic flow data of each section in the rideable route, and at the same time collect historical road condition data in different time periods in a specific area to establish a historical road condition database; Compare and analyze the road condition type information and traffic flow data with the historical data to evaluate the congestion degree of the current section and obtain a congestion degree evaluation result; According to the evaluation result of the congestion degree, determine the average speed reduction ratio of the current congested section from the preset corresponding table, and dynamically adjust it with the change of real-time traffic flow; According to the preset slope-physical fitness-speed adjustment coefficient model, calculate the speed adjustment coefficient for the climbing section considering the slope size and the user's physical fitness condition; Calculate the basic riding time for the normal section based on the route length and the user's average riding speed on the normal section; For the congested section, calculate the first time change amount based on the calculated average speed reduction ratio and the section length. For the climbing section, calculate the second time change amount according to the speed adjustment coefficient and the slope length. Similarly, calculate the third time change amount for special road conditions such as downhill and construction sections; Add the basic time of the normal section to the first time change amount, the second time change amount, and the third time change amount of each special road condition section to obtain the estimated result of the total time consumption.

5. A refined intelligent navigation method for a bicycle according to claim 1, characterized in that, The method includes: When multiple people are riding, obtain the user information of each member in the team, and collaboratively plan the optimal riding route suitable for the entire team according to the riding ability information, preference information, and real-time road condition information of each member; Share and display the positions of all members in real time; According to the average riding speed of the team members and the real-time road condition information, dynamically adjust the corresponding navigation prompts for each member to ensure the consistency of the overall speed and position of the team; Based on the riding progress of the team, the physical fitness condition of the members, and the surrounding environment, intelligently recommend locations suitable for the team to take a collective rest; When a member encounters an emergency, generate and send emergency occurrence information to the user terminals of each team member.

6. The refined intelligent navigation method for a bicycle according to claim 5, wherein The steps of dynamically adjusting the corresponding navigation prompts for each member according to the average riding speed of the team members and the real-time road condition information to ensure the consistency of the overall speed and position of the team include: During the process of multiple people riding, automatically assign different roles according to the riding ability and experience of the members; Provide corresponding task prompts and collaboration guidance for each role; According to the role task requirements, display exclusive prompt information for each member on the member user terminal.

7. A refined intelligent navigation method for a bicycle according to claim 1, characterized in that, The method further includes: Establish a bicycle equipment information database; After obtaining the user's bicycle type information, match the corresponding parameters from the database, and optimize and adjust the physical exertion calculation model and riding performance prediction according to these parameters; Combine the real-time road condition information, the performance characteristics of the user's bicycle equipment, and the optimized riding performance prediction to plan a personalized riding route for the user.

8. A refined intelligent navigation system for bicycles, characterized in that, It includes: Route information acquisition module: Acquire the route information of several rideable routes, and the route information includes slope information and route length information; Physical exertion result calculation module: According to the user's weight information, bicycle type information, and the acquired slope information and route length information, calculate the physical exertion required for riding on each route through a preset physical exertion calculation model to obtain the physical exertion result; Time consumption result calculation module: Combine the user's riding ability data, slope information, and route length information to calculate the time required for riding on each route to obtain the time consumption result; Route difficulty level evaluation module: According to the calculated physical exertion result and time consumption result, evaluate the difficulty level of each route; Real-time road condition information acquisition module: Acquire the weather information and real-time road condition information of the riding time; Detailed prompt information generation module: Generate detailed prompt information for each route according to the weather information, real-time road condition information, physical exertion result, time consumption result and difficulty level; User-preferred route recommendation and display module: Acquire the preference information set by the user; According to the user preference information, screen and sort each route, and preferentially recommend and display the routes and related information that meet the user preferences.

9. A refined intelligent navigation device for a bicycle, characterized in that, Including: A memory and a processor, and a computer program capable of being loaded and executed by the processor for any one of the above-mentioned claims 1-7 of the refined intelligent navigation method for bicycles is stored on the memory.

10. A storage medium, characterized in that, Stored with a computer program capable of being loaded and executed by the processor for any one of the above-mentioned claims 1-7 of the refined intelligent navigation method for bicycles.

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