Virtual riding route planning method and system

Abstract

The invention discloses a virtual riding route planning method and system. The method comprises the following steps: acquiring first geographic data and first weather data in a target area; performing a first classification operation on the first geographic data and the first weather data in the target area; establishing a virtual scene corresponding to a first classification result for the first geographic data and the first weather data after the first classification operation in combination with digital twinning; establishing a first riding route division model; real-time user preferences are obtained, and virtual riding route planning is completed according to the first riding route division model. Personalization and accuracy of virtual riding route planning are improved, and customized riding routes can be provided according to actual demands and preferences of users.

Description

Technical Field

[0001] The present invention relates to the technical field of virtual cycling route planning, and in particular to a virtual cycling route planning method and system. Background Art

[0002] With the development of virtual reality (VR) technology and digital twin technology, people have begun to try to move the real-world cycling experience to a virtual environment. Most existing virtual cycling platforms rely on fixed data sets and preset routes, lacking the ability to be personalized and dynamically adjusted. Users can often only choose from a limited number of routes, and the design of these routes usually fails to fully consider the impact of factors such as weather changes and user preferences. Therefore, it is particularly important to develop a system that can dynamically plan cycling routes based on users' real-time preferences and environmental data.

[0003] In addition, existing solutions are often not detailed enough in terms of road classification and scenery evaluation, resulting in the routes recommended to users not fully meeting their expectations. For example, for cycling enthusiasts who like challenges, a road with beautiful scenery but too flat may not meet their needs; and for users who seek leisure and relaxation, a route with average scenery but full of challenges may not be suitable. Therefore, a more sophisticated classification method is needed to distinguish different types of cycling roads and provide personalized cycling route planning services based on this. Summary of the invention

[0004] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0005] In view of the above existing problems, the present invention is proposed.

[0006] Therefore, the present invention provides a virtual cycling route planning method and system, which can solve the problems mentioned in the background technology.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0008] In a first aspect, the present invention provides a virtual cycling route planning method, comprising:

[0009] Acquire first geographic data and first weather data in a target area;

[0010] Performing a first classification operation on the first geographic data and the first weather data in the target area;

[0011] Establishing a virtual scene corresponding to the first classification result by combining the first geographic data and the first weather data after the first classification operation by the digital twin;

[0012] Establish the first cycling route division model;

[0013] Real-time user preferences are obtained, and virtual cycling route planning is completed according to the first cycling route division model.

[0014] As a preferred solution of the virtual cycling route planning method of the present invention, the first classification operation includes:

[0015] The first classification operation includes a first reclassification and a second reclassification;

[0016] The first reclassification is a road difficulty classification, which includes at least four types of road difficulty; the second reclassification is a road scenery classification, which includes at least three types of road scenery; the second reclassification is a classification based on the result of the first reclassification.

[0017] As a preferred solution of the virtual cycling route planning method of the present invention, the first cycling route division model includes:

[0018] The output of the first cycling route division model is a cycling route, which is a combination of virtual scenes corresponding to the first classification result. The input of the first cycling route division model is user preferences, which include target cycling route distance, difficulty level, and scenery level.

[0019] As a preferred solution of the virtual cycling route planning method of the present invention, the establishing of the first cycling route division model further comprises:

[0020] Acquire the virtual scene corresponding to the first classification result, perform a second classification on the virtual scene to obtain a plurality of virtual scenes, and use a combination of the corresponding virtual scenes as an output of the first cycling route division model;

[0021] The input of the first cycling route classification model is user preference, and the user preference includes the distance, difficulty level and scenery level of the target cycling route, and the difficulty level and scenery level are the classification level results after the first classification operation;

[0022] The distance of the target cycling route is a distance parameter set by the user.

[0023] As a preferred solution of the virtual cycling route planning method of the present invention, the establishing of the first cycling route division model further comprises:

[0024] The acquisition of the training sample of the first cycling route division model includes setting a judgment threshold for the target cycling route distance, wherein the judgment threshold includes a first threshold and a second threshold;

[0025] When the distance parameter set by the user is less than the first threshold, the output of the first cycling route division model is a single virtual scene in the first classification result;

[0026] The first threshold is the route length within the target area under the difficulty classification corresponding to the difficulty level selected by the user;

[0027] When the distance parameter set by the user is greater than the first threshold and less than the second threshold, the output of the first cycling route division model is a combination of several virtual scenes in the first classification result. If the difficulty level selected by the user is level three or level four, the output of the first cycling route division model is a combination of three virtual scenes in the first classification result, and one of the three virtual scenes must be a virtual scene corresponding to level one difficulty at the same scenery level selected by the user;

[0028] If the difficulty level selected by the user is level one or level two, the output of the first cycling route division model is a combination of two virtual scenes in the first classification result, and at least one of the two virtual scenes is a virtual scene corresponding to level two difficulty at the same scenery level selected by the user;

[0029] The second threshold is half of the sum of the route lengths within the target area under the difficulty classification corresponding to all difficulty levels;

[0030] When the distance parameter set by the user is greater than the second threshold, the output of the first cycling route division model is a combination of several virtual scenes in the first classification result. If the difficulty level selected by the user is level three or level four, the output of the first cycling route division model is a combination of at least five virtual scenes in the first classification result, and there must be one virtual scene among the virtual scenes that corresponds to level four difficulty at the same scenery level selected by the user;

[0031] If the difficulty level selected by the user is level one or level two, the output of the first cycling route division model is a combination of four virtual scenes in the first classification result, and at least two of the four virtual scenes are virtual scenes corresponding to the level two difficulty at the same scenery level selected by the user.

[0032] As a preferred solution of the virtual cycling route planning method of the present invention, it also includes:

[0033] The difficulty levels include level one, level two, level three and level four, which correspond to the first road difficulty classification, the second road difficulty classification, the third road difficulty classification and the fourth road difficulty classification in the difficulty classification respectively;

[0034] The scenery levels include first-level scenery, second-level scenery and third-level scenery, which respectively correspond to the first road scenery classification, the second road scenery classification and the third road scenery classification in the scenery classification.

[0035] As a preferred solution of the virtual cycling route planning method of the present invention, the training samples of the first cycling route division model further include:

[0036] If the user preference only includes the target cycling route distance, a combination of several virtual scenes is randomly generated as the cycling route.

[0037] In a second aspect, the present invention provides a virtual cycling route planning system, comprising:

[0038] A data acquisition module, used to acquire first geographic data and first weather data in a target area;

[0039] A data processing module, configured to perform a first classification operation on the first geographic data and the first weather data in the target area;

[0040] A scene establishment module, used to establish a virtual scene corresponding to the first classification result in combination with the first geographic data and the first weather data after the digital twin performs the first classification operation;

[0041] A model building module, used to build a first cycling route division model;

[0042] The planning module is used to obtain real-time user preferences and complete virtual cycling route planning according to the first cycling route division model.

[0043] In a third aspect, the present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above-mentioned method when executing the computer program.

[0044] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps of the method described above when executed by a processor.

[0045] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention proposes a method and system for virtual cycling route planning, which obtains first geographic data and first weather data in a target area; performs a first classification operation on the first geographic data and the first weather data in the target area; establishes a virtual scene corresponding to the first classification result by combining the digital twin with the first geographic data and the first weather data after the first classification operation; establishes a first cycling route division model; obtains real-time user preferences, and completes virtual cycling route planning according to the first cycling route division model. The personalization and accuracy of virtual cycling route planning are improved, and customized cycling routes can be provided according to the actual needs and preferences of users. By introducing the classification operation of geographic data and weather data, the realism and immersion of the virtual scene are enhanced, so that users can get a closer to reality experience in virtual cycling. The generation algorithm of the virtual cycling route is optimized, so that the route planning is more scientific and reasonable, and meets the needs of different users for cycling distance, difficulty and scenery level. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0047] Figure 1 A method flow chart of a virtual cycling route planning method and system provided by one embodiment of the present invention;

[0048] Figure 2 An internal structural diagram of a computer device of a virtual cycling route planning method and system provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0049] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0050] Example 1

[0051] Reference Figure 1-Figure 2 , which is the first embodiment of the present invention, and provides a virtual cycling route planning method and system, including:

[0052] Before describing the embodiments of the present application in detail, some related concepts are first explained for the sake of clarity.

[0053] Virtual riding: Virtual riding refers to the process of simulating the real riding experience with the help of virtual reality (VR) technology. Users can enjoy the fun of riding in a virtual environment by wearing VR helmets or other display devices, and using spinning bikes or similar devices. This method is not restricted by weather, time, or location, and can be exercised anytime, anywhere, while also providing a rich visual experience and interactivity.

[0054] Virtual Cycling Route: A virtual cycling route is a pre-designed cycling path in virtual cycling. This path can be displayed in a virtual environment, including different scenery, terrain changes, etc. Virtual cycling routes can be customized according to user needs, such as choosing different difficulty levels, scenery types, etc. The purpose of this route is to provide users with a virtual experience that is as close to the real cycling feeling as possible.

[0055] Virtual Cycling Route Planning: Virtual Cycling Route Planning refers to the process of automatically or semi-automatically designing a virtual cycling route based on the user's requirements and preferences through algorithms and technical means. The planning process will take into account a variety of factors, such as cycling distance, road difficulty, and scenery level, to generate the most suitable virtual cycling path for the user.

[0056] Digital twin: A digital twin is a digital mapping of a physical entity or system. It uses data collected by sensors to create a virtual copy of a physical object to simulate its behavior and performance. In the application of virtual cycling route planning, digital twins can be used to simulate the real cycling environment, including details such as weather and road conditions, making the virtual cycling experience more realistic. Digital twin technology helps to improve the realism and user experience of virtual cycling routes.

[0057] There are some technical defects in the existing related technologies. For example, the virtual cycling routes are not personalized and accurate enough, which cannot fully meet the diverse needs of users for cycling experience.

[0058] This application provides a method that can effectively solve the above-mentioned problems. Next, how to implement the virtual cycling route planning method will be described in detail in combination with multiple embodiments;

[0059] Figure 1 A method flow chart of a virtual cycling route planning method and system is shown, including:

[0060] S101, acquiring first geographic data and first weather data within a target area;

[0061] In an optional embodiment, the target area is a historical area obtained using GIS (Geographic Information System), or may be a randomly selected regional location, or may be a target area selected by a user, etc.

[0062] In the embodiment of the present application, no matter which method is used to select the target area, the size of the final target area is limited to a fixed range. If it is larger than the fixed range, the time or resources required for the overall cycling route planning process or the training process of the first cycling route division model will become uneconomical. The fixed range can be a circular area range, a square area range, or any irregular area range, but the area of ​​the fixed range should be limited, for example, within 5 square kilometers.

[0063] In an optional embodiment, the first geographic data and the first weather data may include geographic information such as terrain features, road types, traffic conditions, vegetation coverage, etc., and weather information such as temperature, humidity, wind speed, precipitation, etc. These data may be derived from a public geographic information system database, weather station records, satellite remote sensing data, etc.

[0064] It should be noted that the benefit of step S101 is that it can provide detailed geographical and weather background information for virtual cycling route planning. By obtaining such data, it can be ensured that the planned route is not only visually attractive, but also simulates the cycling conditions in the real world as much as possible in terms of cycling experience.

[0065] S102, performing a first classification operation on first geographic data and first weather data within the target area;

[0066] In the embodiment of the present application, the first classification operation includes:

[0067] The first classification operation includes a first reclassification and a second reclassification;

[0068] The first classification is the road difficulty classification, which includes at least four types of road difficulty; the second classification is the road scenery classification, which includes at least three types of road scenery; the second classification is based on the results of the first classification.

[0069] In the embodiment of the present application, four types of road difficulty and three types of road scenery are selected, specifically including a first road difficulty classification, a second road difficulty classification, a third road difficulty classification and a fourth road difficulty classification, a first road scenery classification, a second road scenery classification and a third road scenery classification.

[0070] In an embodiment of the present application, the first road difficulty classification is a straight road or a curved road under a flat road surface, the second road difficulty classification is a downhill road, the third road difficulty classification is an uphill road, the fourth road difficulty classification is a rugged road or a narrow road, the first road scenery classification is a sunny day, the second road scenery classification is a rainy day, and the third road scenery classification is a snowy day.

[0071] In an optional embodiment, different technicians can select different road surfaces as different grading standards, and can also use more than four classifications. They can also perform more detailed scenery classifications, such as scenery classifications of green vegetation, urban appearance, desert appearance or mountainous appearance. More types of scenery classifications can also be selected. This application is not limited to three scenery classification methods.

[0072] It should be noted that structuring the classified first geographic data and first weather data in the target area helps improve the efficiency and accuracy of virtual cycling route planning. By classifying the data, it is easier to identify and select routes that are suitable for different cycling needs. For example, for cyclists who seek challenges, they can choose routes that include uphill and rugged roads; while for cyclists who want to enjoy the scenery, they can choose scenic sunny or rainy routes. In addition, the classification operation can also provide clear input parameters for the subsequent route planning algorithm, thereby generating a more personalized and diverse cycling experience.

[0073] S103, establishing a virtual scene corresponding to the first classification result by combining the first geographic data and the first weather data after the first classification operation by the digital twin;

[0074] It should be noted that, combined with digital twin technology, a virtual scene corresponding to the target area in the real world can be created. This virtual scene not only includes geographical information such as terrain, roads, vegetation, etc., but also simulates weather conditions such as temperature, humidity, wind speed and precipitation. Through digital twin technology, the virtual scene can reflect the real-time status of the target area in real time, providing users with a dynamically changing riding environment.

[0075] In the virtual scene, users can experience visual and sensory effects similar to real riding. For example, when the simulated weather is rainy, the roads in the virtual scene will appear wet and slippery, the vegetation will appear greener, and users can also feel the raindrops falling on the helmet and clothes. This highly simulated experience makes virtual riding not just a simple sports simulation, but a full range of sensory enjoyment.

[0076] In addition, digital twin technology can also be used to test and optimize cycling routes. In the planning stage, digital twin scenarios can be used to simulate different cycling conditions and evaluate the safety and comfort of the route. In this way, potential problems can be discovered and corrected in advance, ensuring that the final cycling route is both safe and challenging to meet the needs of different cyclists.

[0077] In the embodiments of the present application,

[0078] Data preparation: Obtain classified geographic data G and weather data W from the classification operation. The geographic data G includes information such as road type and terrain features, while the weather data W includes information such as temperature and humidity.

[0079] Digital twin mapping: Use digital twin technology to map the classified geographic data G and weather data W into the virtual environment. This process involves converting the geographical features of the real world into parameters in the virtual environment. For example, a road classified as "uphill" can be represented as a slope with a specific slope θ in the virtual environment; for "rainy" weather, it can be represented as the precipitation rate R and humidity H in the virtual environment.

[0080] Virtual scene construction: Based on the mapping results, a specific virtual scene S is constructed. This step includes setting specific parameters in the virtual environment. For example, if the classification result indicates an uphill road, the slope parameter θ is set in the virtual scene, and weather parameters such as rainfall rate R and humidity H are set according to the weather classification. Specifically, the virtual scene S can be defined as a set of parameter vectors S = (θ, R, H), where θ represents the inclination angle of the road, R represents the rainfall rate, and H represents the humidity.

[0081] Scene combination: According to user preferences, multiple virtual scenes S are combined into a complete virtual cycling route. For example, if the user preferences include difficulty level N and scenery level F, multiple virtual scenes with different θ, R, and H parameters can be combined to form a diverse cycling experience.

[0082] Personalized Adjustment: Finally, fine-tuning is performed based on the user’s real-time preferences. For example, if the user’s preferences change, such as from rainy to sunny, the virtual scene needs to readjust its parameters S to accommodate the new preferences.

[0083] To sum up, the process of building a virtual scene can be summarized into the following steps:

[0084] S=T(G,W)

[0085] Among them, T is a mapping function that converts the classified geographic data G and weather data W into a virtual scene S. In specific implementation, each virtual scene can be represented as a vector containing specific geographic and weather parameters, and then these vectors are combined to form a complete virtual cycling route.

[0086] In an embodiment of the present application, the virtual scene is twelve different virtual scenes of different road scenery under different road difficulties. The twelve different virtual scenes of the present application include straight or curved roads under sunny scenery, straight or curved roads under rainy scenery, straight or curved roads under snowy scenery, downhill roads under sunny scenery, downhill roads under rainy scenery, downhill roads under snowy scenery, uphill roads under sunny scenery, uphill roads under rainy scenery, uphill roads under snowy scenery, rugged roads or narrow roads under sunny scenery, rugged roads or narrow roads under rainy scenery, and rugged roads or narrow roads under snowy scenery. These virtual scenes not only provide riders with rich visual experience, but also can simulate the riding feeling under different weather conditions.

[0087] In an optional embodiment, the virtual scene that has been generated by digital twin can also be sliced, and the sliced ​​virtual scene can be used as a new virtual scene to be planned.

[0088] Exemplarily, when the sliced ​​virtual scene is selected as the output of the first cycling route division model, the flexibility and adaptability of the virtual cycling route planning can be further improved. Through slicing technology, a complex virtual scene can be broken down into multiple smaller, more manageable segments. Each segment contains specific geographical and weather information and can be planned and adjusted independently. For example, if a cyclist wants to experience a rugged mountain road on a rainy day, the system can select virtual scene slices related to rainy days and rugged roads, and then combine these slices into a complete cycling route. This slicing method not only makes route planning more accurate, but also allows for quick adjustments based on real-time feedback from cyclists, thereby providing a more personalized and dynamic cycling experience.

[0089] S104, establishing a first cycling route division model;

[0090] In the embodiment of the present application, the first cycling route division model includes:

[0091] The output of the first cycling route classification model is a cycling route, which is a combination of virtual scenes corresponding to the first classification result. The input of the first cycling route classification model is user preferences, which include target cycling route distance, difficulty level, and scenery level.

[0092] It should be noted that when users perform virtual cycling operations, by inputting preference information, the system can automatically adjust the difficulty and scenery of the cycling route based on this information. Users can set the distance of the target cycling route, select the difficulty level and the scenery level through the preset display screen to achieve a personalized cycling experience. For example, if the user wants to experience a challenging uphill section, the system will select the corresponding uphill virtual scene according to the difficulty level set by the user, and combine the scenery level to plan a cycling route for the user that is both visually attractive and meets the user's physical requirements.

[0093] In the embodiment of the present application, establishing the first cycling route division model further includes:

[0094] Acquire a virtual scene corresponding to the first classification result, perform a second classification on the virtual scene to obtain a plurality of virtual scenes, and use a combination of the corresponding virtual scenes as an output of the first cycling route division model;

[0095] The input of the first cycling route classification model is the user preference, which includes the distance, difficulty level and scenery level of the target cycling route, and the difficulty level and scenery level are the classification level results after the first classification operation;

[0096] The distance of the target cycling route is a distance parameter set by the user.

[0097] In the embodiment of the present application, the virtual scenes are subjected to a second classification to obtain a plurality of virtual scenes, namely the twelve different virtual scenes mentioned above.

[0098] In an optional embodiment, the virtual scenes are subjected to the second classification to obtain a plurality of virtual scenes, which may also be sliced ​​virtual scenes.

[0099] In the embodiment of the present application, establishing the first cycling route division model further includes:

[0100] Acquiring the training sample of the first cycling route division model includes setting a judgment threshold about the target cycling route distance, where the judgment threshold includes a first threshold and a second threshold;

[0101] When the distance parameter set by the user is less than the first threshold, the output of the first cycling route division model is a single virtual scene in the first classification result;

[0102] The first threshold is the route length within the target area under the difficulty classification corresponding to the difficulty level selected by the user;

[0103] It should be noted that the input in the training sample of the first cycling route division model is the target cycling route distance, difficulty level and scenery level with a route length of 0-a first threshold, and the output is a single virtual scene in the first classification result.

[0104] When the distance parameter set by the user is greater than the first threshold and less than the second threshold, the output of the first cycling route division model is a combination of several virtual scenes in the first classification result. If the difficulty level selected by the user is level three or level four, the output of the first cycling route division model is a combination of three virtual scenes in the first classification result, and one of the three virtual scenes must be a virtual scene corresponding to level one difficulty at the same scenery level selected by the user;

[0105] If the difficulty level selected by the user is level one or level two, the output of the first cycling route division model is a combination of two virtual scenes in the first classification result, and at least one of the two virtual scenes is a virtual scene corresponding to level two difficulty at the same scenery level selected by the user;

[0106] The second threshold is half of the sum of the route lengths within the target area under the difficulty classification corresponding to all difficulty levels;

[0107] When the distance parameter set by the user is greater than the second threshold, the output of the first cycling route division model is a combination of several virtual scenes in the first classification result. If the difficulty level selected by the user is level three or level four, the output of the first cycling route division model is a combination of at least five virtual scenes in the first classification result, and there must be one virtual scene among the virtual scenes that corresponds to level four difficulty at the same scenery level selected by the user;

[0108] If the difficulty level selected by the user is level one or level two, the output of the first cycling route division model is a combination of four virtual scenes in the first classification result, and at least two of the four virtual scenes are virtual scenes corresponding to the level two difficulty at the same scenery level selected by the user.

[0109] In the embodiment of the present application, the first cycling route division model is obtained by training through a machine learning algorithm. The model can automatically select and combine virtual scenes according to the target cycling route distance, difficulty level and scenery level set by the user to generate a cycling route that meets the user's preferences. During the training process, the model will learn how to predict the most appropriate virtual scene combination based on different input parameters to provide the best cycling experience.

[0110] In an optional embodiment, the first cycling route division model can also be modeled in other ways, for example, through an expert system or a rule-based approach. The expert system can use the knowledge and experience of experts in the cycling field to build a model, and use a series of rules to decide how to select and combine virtual scenes based on user preferences. For example, the system may include a set of rules that recommend specific virtual scene combinations based on the difficulty and scenery level selected by the user, as well as the current weather conditions. The rule-based approach may involve simpler logical judgments. If the user prefers sunny days and selects an uphill section, the system will select one from the sunny uphill virtual scenes to build a cycling route. Although these methods may not be as flexible and accurate as machine learning models, they can provide fast and intuitive solutions in some cases.

[0111] In the embodiment of the present application, the specific steps of training the first cycling route division model by a machine learning algorithm are:

[0112] First, according to the set judgment threshold, the number of virtual scenes corresponding to different target cycling route distances is determined;

[0113] Secondly, a database or mapping diagram of the distance, difficulty level and scenery level of the target cycling route and the final selected virtual scene or virtual scene combination is established;

[0114] Thirdly, establish a machine learning algorithm model network architecture for this application, and perform model training according to the database or mapping relationship in the above steps;

[0115] Finally, the first cycling route division model after training is verified until the first cycling route division model meets the design requirements and is output.

[0116] In the embodiment of the present application, the network architecture of the machine learning algorithm model for the present application is:

[0117] Input layer: The input dimension is 3 (target cycling route distance, difficulty level, and scenery level).

[0118] Batch Normalization: Adding a batch normalization layer after the input layer helps stabilize the training process and speed up convergence.

[0119] Hidden layer 1: contains 64 nodes, uses ReLU activation function, and introduces nonlinearity.

[0120] Dropout layer: Add a dropout layer to prevent overfitting. The drop ratio can be set to 0.5.

[0121] Hidden layer 2: contains 32 nodes and continues to use the ReLU activation function.

[0122] Residual Connection: After hidden layer 2, the output of hidden layer 1 is added to the output of hidden layer 2 to form a residual connection, which helps in training deep networks.

[0123] Hidden layer 3: contains 16 nodes and continues to use the ReLU activation function.

[0124] Output layer: The number of nodes in the output layer is equal to the number of all possible virtual scenes or scene combinations. Assuming there are 12 different virtual scene combinations, the output layer should have 12 nodes. Use the Softmax activation function to output the probability distribution of each scene combination.

[0125] In the embodiment of the present application, the loss function used is:

[0126] L=λ·L CE +(1-λ)·L Rank

[0127] Among them, L CE is the cross entropy loss, which measures the difference between the predicted probability distribution and the actual label. Rank is the ranking loss, which measures the difference between the predicted order and the actual preference. λ is a balancing factor that controls the weight between classification loss and ranking loss.

[0128] It should be noted that in the present application, the loss function of the machine learning algorithm model needs to be specially designed to fit the specific needs of virtual cycling route planning. Taking into account that the task of the model in this application is to generate a suitable virtual scene combination based on user preferences (such as target cycling route distance, difficulty level, and scenery level), the loss function needs to be able to effectively measure the difference between the virtual scene combination predicted by the model and the user's actual preferences. A loss function that fits the content of this application can be a composite loss function that combines classification loss and sorting loss. Classification loss is used to ensure that the model correctly classifies virtual scene combinations that suit user preferences, while sorting loss is used to ensure that among multiple possible combinations, the model can correctly arrange the priority of the combinations according to the user's preference level.

[0129] In the embodiment of the present application, the difficulty level includes level 1 difficulty, level 2 difficulty, level 3 difficulty and level 4 difficulty, which respectively correspond to the first road difficulty classification, the second road difficulty classification, the third road difficulty classification and the fourth road difficulty classification in the difficulty classification;

[0130] The scenery levels include first-level scenery, second-level scenery and third-level scenery, which respectively correspond to the first road scenery classification, the second road scenery classification and the third road scenery classification in the scenery classification.

[0131] In the embodiment of the present application, the training samples of the first cycling route division model further include:

[0132] If the user preference only includes the target cycling route distance, a combination of several virtual scenes is randomly generated as the cycling route.

[0133] In an optional embodiment, in the process of generating training samples, preference factors that may not be explicitly specified by the user, such as weather conditions, time periods, etc., can also be considered to ensure that the model can adapt to various riding environments. In addition, the training samples will also contain user feedback data on specific virtual scenes, which will be used to adjust the prediction accuracy of the model. For example, if the user gives negative feedback after selecting a virtual scene, the system will record this information and reduce the probability of the scene being recommended in subsequent training. After the model training is completed, a series of tests will be conducted to verify the performance of the model. The tests include but are not limited to: the recommendation accuracy of the model under different user preferences, the adaptability of the model to new user preferences, and the performance of the model under different combinations of difficulty and scenery levels. Through these tests, it can be ensured that the model can provide high-quality cycling route planning services in practical applications. Finally, the first cycling route division model will be integrated into the virtual cycling route planning system, working in conjunction with the user interface and other system components to provide users with a personalized cycling experience. The system will continuously optimize the recommendation algorithm based on the user's real-time feedback and historical cycling data in order to achieve the best user experience.

[0134] S105: Acquire real-time user preferences and complete virtual cycling route planning according to the first cycling route division model.

[0135] In an optional embodiment, the planned virtual cycling route can also be displayed to the user through a user interface, and interactive functions can be provided to allow the user to adjust it according to personal preferences. Real-time feedback from users during the virtual cycling process can also be collected, including evaluations of route difficulty, scenery satisfaction, etc. The parameters of the first cycling route division model can also be dynamically adjusted based on the collected user feedback data to optimize future route recommendations. The system can also be regularly maintained and upgraded to ensure that the virtual cycling route planning system can adapt to changes in user needs and provide a richer cycling experience. It is also possible to continuously learn and improve by analyzing user cycling data and feedback in order to achieve the highest standard of personalized service, so that every virtual ride of the user is full of fun and challenges.

[0136] For example, suppose the user wants to experience a challenging uphill section through virtual riding, and hopes to see beautiful scenery. The total riding distance set by the user is 20 kilometers, and the desired difficulty level is level 3 (uphill road), and the scenery level is level 1 (sunny day).

[0137] Furthermore, users input their preferences through the virtual cycling system's interface, including a 20-kilometer riding distance, three levels of difficulty (uphill roads), and one level of scenery (sunny days).

[0138] Furthermore, the system obtains geographic data and weather data from the target area and classifies them. For example, the system classifies an uphill road as level three difficulty and a sunny day as level one scenery.

[0139] Furthermore, combined with digital twin technology, the system builds a virtual scene based on the user's preferences. In this scene, the system simulates an uphill road under clear weather conditions, and the user can see the scenery under the sun and feel the warm sunshine and fresh air.

[0140] Furthermore, the system obtains output from the pre-trained first cycling route division model based on the user's preferences and the set distance parameters. Since the distance set by the user is 20 kilometers, which exceeds the first threshold (15 kilometers) but does not reach the second threshold (30 kilometers), the system outputs a combination of three virtual scenes. These scenes include a gentle starting section (straight road, level 1 difficulty, sunny day), followed by an uphill section (level 3 difficulty, sunny day), and finally ending with a downhill section (level 2 difficulty, sunny day).

[0141] Furthermore, during the ride, if the user's preferences change, such as wanting to add some scenery changes, the system can immediately adjust the virtual scene. For example, the user may decide to change part of the road to rainy days, and the system will adjust the weather parameters accordingly to show the visual effects and sensory experience of a rainy day.

[0142] Going a step further, the user begins riding in the simulated environment, experiencing the challenges of uphill climbs and the beautiful scenery of sunny days. As the ride progresses, the system continuously adjusts the virtual environment based on the user's real-time feedback and changing preferences, ensuring that the riding experience is both challenging and enjoyable.

[0143] In summary, the present invention proposes a method and system for planning a virtual cycling route, which obtains first geographic data and first weather data in a target area; performs a first classification operation on the first geographic data and the first weather data in the target area; establishes a virtual scene corresponding to the first classification result by combining the digital twin with the first geographic data and the first weather data after the first classification operation; establishes a first cycling route division model; obtains real-time user preferences, and completes virtual cycling route planning according to the first cycling route division model. The personalization and accuracy of virtual cycling route planning are improved, and customized cycling routes can be provided according to the actual needs and preferences of users. By introducing the classification operation of geographic data and weather data, the realism and immersion of the virtual scene are enhanced, so that users can get a closer to reality experience in virtual cycling. The generation algorithm of the virtual cycling route is optimized, so that the route planning is more scientific and reasonable, and meets the needs of different users for cycling distance, difficulty and scenery level.

[0144] This embodiment also provides a virtual cycling route planning system, including:

[0145] A data acquisition module, used to acquire first geographic data and first weather data in a target area;

[0146] A data processing module, used for performing a first classification operation on the first geographic data and the first weather data in the target area;

[0147] A scene establishment module, used to establish a virtual scene corresponding to the first classification result in combination with the first geographic data and the first weather data after the digital twin performs the first classification operation;

[0148] A model building module, used to build a first cycling route division model;

[0149] The planning module is used to obtain real-time user preferences and complete virtual cycling route planning according to the first cycling route division model.

[0150] The above-mentioned unit modules may be embedded in or independent of a processor in a computer device in the form of hardware, or may be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to the above-mentioned modules.

[0151] This embodiment also provides a computer device, which may be a terminal, and its internal structure diagram may be as shown in FIG. Figure 2As shown. The computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a virtual riding route planning method is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device housing, or an external keyboard, touchpad or mouse, etc.

[0152] This embodiment further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0153] Acquire first geographic data and first weather data in a target area;

[0154] Performing a first classification operation on the first geographic data and the first weather data in the target area;

[0155] Establishing a virtual scene corresponding to the first classification result by combining the first geographic data and the first weather data after the first classification operation by the digital twin;

[0156] Establish the first cycling route division model;

[0157] Real-time user preferences are obtained, and virtual cycling route planning is completed according to the first cycling route division model.

[0158] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

[0159] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code. The scheme in the embodiments of the present application can be implemented in various computer languages, for example, object-oriented programming language Java and literal scripting language JavaScript, etc.

[0160] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0161] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0162] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0163] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0164] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A virtual cycling route planning method, characterized in that: include: Acquire first geographic data and first weather data in a target area; Performing a first classification operation on the first geographic data and the first weather data in the target area; Establishing a virtual scene corresponding to the first classification result by combining the first geographic data and the first weather data after the first classification operation by the digital twin; Establish the first cycling route division model; Real-time user preferences are obtained, and virtual cycling route planning is completed according to the first cycling route division model.

2. The virtual cycling route planning method according to claim 1, characterized in that: The first classification operation includes: The first classification operation includes a first reclassification and a second reclassification; The first reclassification is a road difficulty classification, which includes at least four types of road difficulty; the second reclassification is a road scenery classification, which includes at least three types of road scenery; the second reclassification is a classification based on the result of the first reclassification.

3. The virtual cycling route planning method according to claim 2, characterized in that: The first cycling route division model includes: The output of the first cycling route division model is a cycling route, which is a combination of virtual scenes corresponding to the first classification result. The input of the first cycling route division model is user preferences, which include target cycling route distance, difficulty level, and scenery level.

4. The virtual cycling route planning method according to claim 3, characterized in that: The establishing of the first cycling route division model further includes: Acquire the virtual scene corresponding to the first classification result, perform a second classification on the virtual scene to obtain a plurality of virtual scenes, and use a combination of the corresponding virtual scenes as an output of the first cycling route division model; The input of the first cycling route classification model is user preference, and the user preference includes the distance, difficulty level and scenery level of the target cycling route, and the difficulty level and scenery level are the classification level results after the first classification operation; The distance of the target cycling route is a distance parameter set by the user.

5. The virtual cycling route planning method according to claim 4, characterized in that: The establishing of the first cycling route division model further includes: The acquisition of the training sample of the first cycling route division model includes setting a judgment threshold for the target cycling route distance, wherein the judgment threshold includes a first threshold and a second threshold; When the distance parameter set by the user is less than the first threshold, the output of the first cycling route division model is a single virtual scene in the first classification result; The first threshold is the route length within the target area under the difficulty classification corresponding to the difficulty level selected by the user; When the distance parameter set by the user is greater than the first threshold and less than the second threshold, the output of the first cycling route division model is a combination of several virtual scenes in the first classification result. If the difficulty level selected by the user is level three or level four, the output of the first cycling route division model is a combination of three virtual scenes in the first classification result, and one of the three virtual scenes must be a virtual scene corresponding to level one difficulty at the same scenery level selected by the user; If the difficulty level selected by the user is level one or level two, the output of the first cycling route division model is a combination of two virtual scenes in the first classification result, and at least one of the two virtual scenes is a virtual scene corresponding to level two difficulty at the same scenery level selected by the user; The second threshold is half of the sum of the route lengths within the target area under the difficulty classification corresponding to all difficulty levels; When the distance parameter set by the user is greater than the second threshold, the output of the first cycling route division model is a combination of several virtual scenes in the first classification result. If the difficulty level selected by the user is level three or level four, the output of the first cycling route division model is a combination of at least five virtual scenes in the first classification result, and there must be one virtual scene among the virtual scenes that corresponds to level four difficulty at the same scenery level selected by the user; If the difficulty level selected by the user is level one or level two, the output of the first cycling route division model is a combination of four virtual scenes in the first classification result, and at least two of the four virtual scenes are virtual scenes corresponding to the level two difficulty at the same scenery level selected by the user.

6. The virtual cycling route planning method according to claim 5, characterized in that: Also includes: The difficulty levels include level one, level two, level three and level four, which correspond to the first road difficulty classification, the second road difficulty classification, the third road difficulty classification and the fourth road difficulty classification in the difficulty classification respectively; The scenery levels include first-level scenery, second-level scenery and third-level scenery, which respectively correspond to the first road scenery classification, the second road scenery classification and the third road scenery classification in the scenery classification.

7. The virtual cycling route planning method according to claim 6, characterized in that: The training samples of the first cycling route division model also include: If the user preference only includes the target cycling route distance, a combination of several virtual scenes is randomly generated as the cycling route.

8. A virtual cycling route planning system, characterized in that: include: A data acquisition module, used to acquire first geographic data and first weather data in a target area; A data processing module, used for performing a first classification operation on the first geographic data and the first weather data in the target area; A scene establishment module, used to establish a virtual scene corresponding to the first classification result in combination with the first geographic data and the first weather data after the digital twin performs the first classification operation; A model building module, used to build a first cycling route division model; The planning module is used to obtain real-time user preferences and complete virtual cycling route planning according to the first cycling route division model.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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