A method and apparatus for outdoor cycling

Abstract

The application relates to an outdoor cycling method and device, the method comprising: obtaining a historical cycling data set of a user, and constructing a sports ability model reflecting the cycling performance of the user under different environmental characteristics based on the historical cycling data set; performing path segmentation on an outdoor route to be cycled, and extracting environmental characteristic parameters corresponding to each path segment; matching corresponding historical cycling performance data from the sports ability model according to the environmental characteristic parameters of each path segment, so as to generate a virtual training target and guide parameters suitable for each path segment; and comparing real-time cycling data of the user with the guide parameters during the cycling process, and dynamically adjusting the guide parameters of the virtual training target according to the comparison result. The application improves the dynamics, individualization and adaptability of the virtual opponent generated in outdoor cycling through personalized matching of the historical cycling data of the user and the virtual opponent.

Description

Technical Field

[0001] This invention belongs to the field of cycling and virtual reality technology, specifically relating to an outdoor cycling method and device. Background Technology

[0002] Most mainstream cycling apps and smart cycling devices nowadays feature route planning and cycling data collection capabilities. For example, the iGPSPORT app allows users to manually plan outdoor cycling routes or choose routes recommended by the platform. Simultaneously, it collects data such as speed, cadence, heart rate, altitude, and calorie consumption during the ride using the phone's sensors or smart cycling devices. After the ride, it generates a data summary for users to review their performance. The iGPSPORT dedicated cycling computer achieves high-precision data collection, and combined with a cloud platform storing past cycling data, it can generate simple cycling ability analysis reports.

[0003] Existing technologies disclose a "method and cycling simulation system for synchronizing real cycling with cycling animation," the core of which is to synchronize real cycling actions with virtual animation, focusing on the virtual recreation of cycling scenarios, but not involving technology for generating virtual guidance targets based on user / instructor data. The core drawback of this type of technology is that it only achieves route planning and data collection, storage, and display, without in-depth analysis of past cycling data. It cannot generate targeted virtual guidance targets on newly planned routes based on the user's / instructor's cycling abilities. Users lack clear, ability-appropriate reference benchmarks while cycling, making it difficult to improve training efficiency.

[0004] Current virtual cycling technologies primarily focus on indoor scenarios. For example, the AI ​​cycling coach system launched by the ROUVY platform can create personalized training plans by analyzing user cycling data and provide virtual coaching functionality. However, this function is only applicable to fixed indoor cycling equipment and cannot adapt to dynamically changing outdoor cycling routes. Other existing technologies generate virtual scooters based on fixed parameters of indoor cycling trainers for users to follow during training. However, this technology is only applicable to indoor scenarios and does not address outdoor route planning and dynamic adaptation. Furthermore, the parameters of the virtual scooters are not deeply linked to the user's past outdoor cycling data, failing to reflect the user's true cycling ability in complex outdoor road conditions. In addition, some outdoor cycling navigation systems have a "speed reminder" function, but they can only set fixed speed thresholds and cannot dynamically adjust based on the gradient and road conditions of newly planned routes, combined with the user's past cycling data, lacking personalization and adaptability. Summary of the Invention

[0005] To improve the dynamism, personalization, and adaptability of virtual adversary generation in outdoor cycling, a first aspect of the present invention provides an outdoor cycling method, comprising: acquiring a user's historical cycling dataset and constructing a motion ability model reflecting the user's cycling performance under different environmental characteristics based on the historical cycling dataset; segmenting the outdoor route to be cycled into paths and extracting environmental feature parameters corresponding to each path segment; matching corresponding historical cycling performance data from the motion ability model according to the environmental feature parameters of each path segment to generate virtual training targets and their guidance parameters adapted to each path segment; and during cycling, comparing the user's real-time cycling data with the guidance parameters and dynamically adjusting the guidance parameters of the virtual training targets based on the comparison results.

[0006] In some embodiments of the present invention, the construction of the athletic ability model includes: performing outlier removal and filtering on the historical cycling dataset; classifying and storing the processed data according to path gradient, road surface type and cycling scenario; and constructing the athletic ability model by mining the user's average athletic performance under different classification dimensions through algorithms, wherein the athletic ability model represents the mapping relationship between environmental feature parameters and athletic ability.

[0007] In some embodiments of the present invention, the path segmentation and extraction of environmental feature parameters include: dividing the outdoor route to be ridden into multiple path units according to a preset length interval; and extracting at least one of the following for each path segment: slope value, road surface type, altitude change, and curve complexity, as environmental feature parameters of that path segment.

[0008] In some embodiments of the present invention, generating virtual training targets and their guidance parameters includes: for the current path segment, retrieving historical cycling records with the same or similar environmental characteristic parameters in the exercise ability model; combining the user's training target for this ride, performing gain or loss calculations on the speed, cadence, and power parameters in the retrieved historical cycling records, and determining the calculation results as the guidance parameters.

[0009] In some embodiments of the present invention, the dynamic adjustment includes: real-time monitoring of the deviation between the user's cycling data and the guidance parameters; determining whether the deviation magnitude and duration reach a preset adjustment threshold; if the real-time cycling data is consistently higher than the guidance parameters and meets the adjustment threshold, then increasing the guidance parameters within a preset ratio range; if the real-time cycling data is consistently lower than the guidance parameters and meets the adjustment threshold, then decreasing the guidance parameters within a preset ratio range.

[0010] In some embodiments of the present invention, the method further includes: rendering the digital image of the virtual training target in real time in the navigation interface, and simultaneously displaying the relative distance between the virtual training target and the user; and outputting voice or text feedback information in real time to guide the user to adjust the riding state based on the comparison results.

[0011] A second aspect of the present invention provides an outdoor cycling device, comprising: an acquisition module for acquiring a user's historical cycling dataset and constructing a motor ability model reflecting the user's cycling performance under different environmental characteristics based on the historical cycling dataset; an extraction module for segmenting an outdoor route to be cycled and extracting environmental feature parameters corresponding to each path segment; a generation module for matching corresponding historical cycling performance data from the motor ability model according to the environmental feature parameters of each path segment to generate virtual training targets and their guidance parameters adapted to each path segment; and an adjustment module for comparing the user's real-time cycling data with the guidance parameters during cycling and dynamically adjusting the guidance parameters of the virtual training target based on the comparison results.

[0012] A third aspect of the present invention provides an electronic device comprising: one or more processors; and a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the outdoor cycling method provided in the first aspect of the present invention.

[0013] A fourth aspect of the present invention provides a computer-readable medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the outdoor cycling method provided in the first aspect of the present invention.

[0014] The beneficial effects of this invention are: This invention establishes a user's cycling ability model by using the user's past cycling data (including cycling parameters, physical indicators, and route parameters), and combines it with the segmented parameters of the newly planned outdoor cycling route (slope, road opponent type, etc.) to generate a virtual pair that is adapted to the user's own cycling ability, thereby realizing personalized virtual guidance for outdoor cycling. The parameters of the virtual opponent can be dynamically adjusted based on the user's real-time data during the ride (speed, cadence, heart rate, etc.) and the actual road conditions of the newly planned route, ensuring that the virtual opponent always adapts to the user's riding status and complex outdoor scenarios. Through a closed-loop process of data collection, preprocessing, and model updating, the system enables in-depth mining and recycling of users' past cycling data, allowing virtual opponent parameters to be gradually optimized as users' cycling abilities improve, thereby continuously enhancing cycling training efficiency. Attached Figure Description

[0015] Figure 1 This is a basic flowchart of an outdoor cycling method in some embodiments of the present invention; Figure 2 This is a schematic diagram of the specific process of the outdoor cycling method in some embodiments of the present invention; Figure 3 This is a schematic diagram of the structure of an outdoor cycling device in some embodiments of the present invention; Figure 4 This is a schematic diagram of the structure of an electronic device in some embodiments of the present invention. Detailed Implementation

[0016] The principles and features of the present invention are described below with reference to the accompanying drawings. The examples given are only for explaining the present invention and are not intended to limit the scope of the present invention.

[0017] Example 1 refer to Figure 1 and Figure 2 In a first aspect of the present invention, an outdoor cycling method is provided, comprising: S100. acquiring a user's historical cycling dataset and constructing a motor ability model reflecting the user's cycling performance under different environmental characteristics based on the historical cycling dataset; S200. segmenting the outdoor route to be cycled and extracting environmental feature parameters corresponding to each path segment; S300. matching corresponding historical cycling performance data from the motor ability model according to the environmental feature parameters of each path segment to generate virtual training targets and their guidance parameters adapted to each path segment; S400. comparing the user's real-time cycling data with the guidance parameters during cycling and dynamically adjusting the guidance parameters of the virtual training target according to the comparison results.

[0018] In step S100 of some embodiments of the present invention, a user's historical cycling dataset is obtained, and a motor ability model reflecting the user's cycling performance under different environmental characteristics is constructed based on the historical cycling dataset; wherein, constructing the motor ability model includes: S101. Perform outlier removal and filtering on the historical cycling dataset; Specifically, real-time cycling data collection includes: during the user's outdoor cycling session, real-time data such as current speed, cadence, heart rate, power, and current location are collected, along with real-time road conditions of the planned route (e.g., route gradient, number of curves, road surface type, and lane type). After the ride, the data is transmitted to the cloud for storage via Bluetooth or other means. Data verification involves removing outliers from the collected data (e.g., heart rate exceeding the normal range, sudden speed changes, extreme gradient jumps), and using software filtering algorithms to eliminate false triggers caused by vibration, ensuring data accuracy.

[0019] S102. Classify and store the processed data according to the path gradient, road surface type, and cycling scenario; Specifically, a dual mode of local storage + cloud storage is adopted. Local storage is used to cache real-time data of the current ride, while cloud storage is used to store the user's past ride data for a long time, supporting data synchronization and avoiding data loss. Data classification: Past ride data is classified and organized according to dimensions such as ride route type (flat road, mountain, hill), ride scenario (commuting, training, leisure), and ride duration. S103. By mining the user's average exercise index under different classification dimensions through algorithms, the exercise ability model is constructed, which represents the mapping relationship between environmental feature parameters and exercise ability.

[0020] Specifically, algorithms are used to mine past data, calculating user average speed, average cadence, average heart rate, and other characteristic parameters under different inclines and road surface types to build a user cycling ability model. Multi-stage compression technology is employed to extract core feature parameters from complex user cycling data, reducing subsequent computational burden. User's past cycling data is updated in real time; after each ride, the data is automatically categorized and stored in the corresponding database to update the user cycling ability model. A cyclic regression architecture is used to ensure the continuity of model updates and avoid data breakpoints. Route planning: Users can manually input the start and end points or select preset routes (such as commuting routes or training routes) using A / B algorithm. It combines Dijkstra's hybrid optimization algorithm with real-time road conditions (road surface, total lift, lane type, etc.) to generate the optimal cycling route, while also supporting user-defined route preferences (such as prioritizing flat roads or scenic routes). In step S200 of some embodiments of the present invention, the outdoor route to be cycled is segmented, and environmental feature parameters corresponding to each route segment are extracted; wherein, the route segmentation and extraction of environmental feature parameters include: S201. Divide the outdoor route to be cycled into multiple path units according to a preset length interval; S202. Extract at least one of the following for each path segment: slope value, road surface type, elevation change, and curve complexity, as environmental feature parameters of that path segment.

[0021] Specifically, the newly planned route is segmented (each segment is 100-500 meters long, and the segment length is adjusted according to the complexity of the route). Key parameters of each segment are extracted, including gradient (uphill / downhill / flat road, uphill gradient 0-30°, downhill gradient 0-20°), road surface type (asphalt road, cement road, gravel road), number of curves, elevation changes, and estimated riding time. The planned route and segment parameters are synchronized to the virtual rabbit parameter generation module to ensure accurate matching between the virtual rabbit and the route.

[0022] In step S300 of some embodiments of the present invention, based on the environmental feature parameters of each path segment, corresponding historical cycling performance data is matched from the exercise ability model to generate virtual training targets and their guidance parameters adapted to each path segment; wherein, generating virtual training targets and their guidance parameters includes: S301. For the current path segment, retrieve historical cycling records with the same or similar environmental characteristic parameters from the motion ability model; S302. Based on the user's training goal for this ride, perform gain or loss calculations on the speed, cadence, and power parameters in the retrieved historical cycling records, and determine the calculation results as the guidance parameters.

[0023] Specifically, based on the segmentation parameters (slope, road surface type, lane type, etc.) of the newly planned route, the same or similar route types and slope ranges are matched in the user's past cycling data, and the average speed, cadence, heart rate and other feature parameters in this type of data are extracted as the basic parameters of the virtual rabbit. In step S400 of some embodiments of the present invention, during cycling, the user's real-time cycling data is compared with the guidance parameters, and the guidance parameters of the virtual training target are dynamically adjusted based on the comparison result. The dynamic adjustment includes: S401. Monitor the deviation between the user's cycling data and the guidance parameters in real time; S402. Determine whether the deviation range and duration of the deviation value reach a preset adjustment threshold; if the real-time cycling data is continuously higher than the guidance parameter and meets the adjustment threshold, then increase the guidance parameter within a preset ratio range; if the real-time cycling data is continuously lower than the guidance parameter and meets the adjustment threshold, then decrease the guidance parameter within a preset ratio range.

[0024] Specifically, the basic parameters of the virtual rabbit are adjusted according to the user's goals for this ride (such as fat loss, speed improvement, or leisurely riding). For example, if the goal is to lose fat, the speed of the virtual rabbit is adjusted so that the user's heart rate is maintained at 60%-70% of the maximum heart rate; if the goal is to improve speed, the average speed of the virtual rabbit is appropriately increased.

[0025] In some embodiments of the present invention, the method further includes: S501. Rendering the digital image of the virtual training target in real time in the navigation interface, and simultaneously displaying the relative distance between the virtual training target and the user; Specifically, a virtual image of a virtual rabbit (which can be customized, such as a cartoon rabbit icon) is generated, and calculated parameters such as speed and cadence are bound to the virtual rabbit, enabling the virtual rabbit to "ride" on the newly planned route according to these parameters. The virtual rabbit's position is synchronized with the route in real time. An end-to-end streaming re-rendering architecture is used to ensure the real-time rendering of the virtual rabbit, with latency reduced to the millisecond level. The various parameters of the virtual rabbit are stored on the local terminal for easy dynamic adjustment and retrieval later.

[0026] Specifically, the generated virtual rabbit is rendered and displayed in real time on the user's terminal to ensure that the user can clearly see the virtual rabbit's position and status, thus achieving visual guidance.

[0027] Specifically, real-time rendering: a lightweight rendering algorithm is used to render the image of the virtual rabbit on the navigation interface of the user terminal in real time. The movement speed of the virtual rabbit is consistent with the bound parameters and synchronized with the user's real-time location and route. Display method: Navigation map mode, the virtual rabbit is displayed on the map, synchronized with the user's current location and route trajectory, and the distance between the virtual rabbit and the user is clearly displayed; Customizable settings: Users can customize the virtual rabbit's appearance, size, color, and display position (such as top or center of the screen) to enhance the user experience.

[0028] S502. Based on the comparison results, output voice or text feedback information in real time to guide the user to adjust the riding status.

[0029] Based on real-time data comparison results and the adjustments made by the virtual rabbit, real-time feedback prompts are output to the user to guide the user to adjust their riding status and improve training effectiveness.

[0030] Specific functions: Prompt method: Supports both voice and text prompts. Voice prompts are output through the user terminal's speaker, while text prompts are displayed on the terminal screen; Prompt content: Outputs corresponding prompts based on the comparison results, such as "Current speed is better than the virtual rabbit, keep it up," "Current speed is lower than the virtual rabbit, you can speed up appropriately," "Uphill ahead, the virtual rabbit has adjusted its speed"; Feedback frequency: Dynamically adjusts the feedback frequency according to the riding status. When the user's parameters match those of the virtual rabbit, the feedback frequency is once every 5 minutes; when the deviation is large, the feedback frequency is once every 30 seconds to avoid frequent prompts affecting the user's riding.

[0031] In one specific embodiment of the present invention, the following steps are included: Step 1: Data collection and preprocessing. Users collect, store, and classify past cycling data through user terminals and external data collection devices to establish a user cycling ability model. The cloud server synchronously backs up the data and optimizes the model. Step 2: Route planning. Users input the starting point and destination of their ride or select a preset route through their user terminal. The route planning module generates a new outdoor cycling navigation route and extracts route segment parameters (slope, road surface type, etc.), which are then synchronized to the relevant modules. Step 3: Virtual Rabbit Generation. The virtual rabbit parameter generation module uses the user's past cycling data to match the cycling characteristics corresponding to the new route segment parameters. Combined with the user's current cycling goals, it calculates the virtual rabbit's core parameters such as speed and cadence, generates the virtual rabbit image, and binds the parameters. Step 4: Virtual Rabbit Display. The virtual rabbit rendering and display module renders the virtual rabbit in real time on the user terminal navigation interface, and synchronously displays the virtual rabbit's position, distance and related parameters from the user. Step 5: Real-time cycling and data comparison. The user starts outdoor cycling, the data acquisition module collects the user's cycling data in real time, and the real-time data comparison module compares the user's real-time data with the virtual rabbit parameters and outputs the comparison results. Step 6: Dynamic adjustment and feedback. The dynamic adjustment module adjusts the virtual rabbit parameters in real time based on the comparison results and actual changes in the route; the feedback module outputs voice or text prompts to the user based on the comparison results and adjustment status. Step 7: After the ride ends, the data acquisition module records the complete data of this ride, and the data storage and preprocessing module classifies and stores the data into the database, updates the user's riding ability model, and provides data support for the next virtual rabbit generation.

[0032] In one scenario, a comprehensive cycling training exercise is conducted in hilly terrain. The prerequisites for implementation are: User type: Intermediate cyclist (with 1 year of cycling experience and a cumulative cycling distance of 2000-3000km), the goal of this ride is to improve overall ability; Hardware equipment: User terminal: Smart cycling computer, 5m positioning accuracy, Bluetooth 5.2, supports external sensors; Data acquisition equipment: External heart rate monitor, cadence meter, power meter; the computer has a built-in inertial measurement unit and barometer; Positioning module, communication module: Bluetooth 5.2 + Wi-Fi 6, data transmission latency 50ms; Cloud server: Alibaba Cloud server, response latency 250ms; Past cycling data collection and preprocessing: Collect data from the user's past 10+ cycling trips, covering flat roads, light uphill (slope 2-6°), and light downhill (slope -1 to -5°). After data verification, classification and storage, preprocessing is used to generate a cycling ability model. Key characteristic parameters (point values): average speed on flat roads 23.2km / h, average speed on uphill (4°) 16.8km / h, average cadence 85 rpm, average power 150W, average heart rate 145 beats / minute (maximum heart rate 185 beats / minute). Route planning: A 15km hilly cycling route is planned, divided into segments of 200 meters each (the midpoint of the segment length defined in the claim, which is between 100-500 meters). Segment parameters (point values) are extracted as follows: flat section (slope 0°), uphill section (slope 4°, the midpoint of the uphill slope defined in the claim, which is between 0-30°), and downhill section (slope -3°, the midpoint of the downhill slope defined in the claim, which is between 0-20°). The road surface type is cement road, with 8 curves and an elevation change of 50m. The estimated cycling time is 37 minutes.

[0033] Implementation steps Step 1. Data Collection and Preprocessing: Collect, verify, classify and store data from the past 10 rides, back it up synchronously in the cloud, use AI algorithms to mine riding characteristics under different slopes, establish a riding ability model, and use multi-stage compression technology to extract core parameters; Step 2. Route Planning: The route planning module combines real-time traffic conditions and uses A... A 15km hilly route was generated using the Dijkstra hybrid optimization algorithm, and parameters such as slope and road surface type were extracted in segments and synchronized to the virtual rabbit parameter generation module and the positioning module. Step 3. Virtual Rabbit Generation: The virtual rabbit parameter generation module matches similar past data of the user according to the route segments, and combines it with the comprehensive ability improvement goal to generate segmented virtual rabbit parameters (point values): flat road segment speed 24km / h (the midpoint of the virtual rabbit speed range of 18-28km / h), cadence 85 rpm, power 160W; uphill segment (4°) speed 17km / h, cadence 90 rpm, power 180W; downhill segment speed 32km / h, cadence 70 rpm. A custom cycling icon virtual image is generated, the parameters are bound and stored, and the rendering delay is 30ms. Step 4. Virtual Rabbit Display: A simplified display mode is adopted, showing the virtual rabbit image, current speed, and distance to the user in the center of the speedometer screen to avoid obstructing navigation information. The virtual rabbit's movement is synchronized with the route and the user's location in real time. Step 5. Real-time cycling and data comparison: The data acquisition module collects user speed, cadence, power, and heart rate data in real time at a frequency of 1 time / second. The real-time data comparison module compares user data with the segmented parameters of the virtual rabbit in real time, calculates the difference, and classifies it. Step 6. Dynamic Adjustment and Feedback: Dynamic adjustment conditions: deviation > 5% and lasts for 20 seconds (an intermediate adaptation value for a duration ≥ 30 seconds as specified in the claims, balancing guidance effect and user experience), adjustment range ± 8% (an intermediate value for an adjustment range ≤ 10% as specified in the claims), and real-time adjustment of virtual rabbit parameters based on changes in route segments (such as uphill turning into flat road); the feedback module dynamically adjusts the feedback frequency based on the deviation, once every 3 minutes when the deviation is small, and once every 20 seconds when the deviation is large; Step 7. End of Cycling and Model Update: After the ride ends, the data from this ride is automatically categorized and stored locally and in the cloud, the user's cycling ability model is updated, and the parameter matching logic under different inclines is optimized to provide more accurate virtual rabbit parameters for subsequent rides.

[0034] Implementation Results: System performance: Positioning accuracy 5m, data transmission latency 50ms, virtual rabbit rendering latency 30ms, dynamic adjustment response time ≤0.8 seconds, no abnormalities throughout, meeting the performance requirements of the claims; Cycling data matching accuracy: 93% of the time was within ±5% of the user's and virtual rabbit's parameter deviation, and the segment parameter matching accuracy was 100% (segmented adaptation for flat roads, uphill, and downhill), achieving dynamic adaptation to complex hilly road conditions; Cycling effect data (point values): Average speed throughout the journey was 23.7km / h, average speed on the uphill section (4°) was 16.9km / h, average power was 168W, average heart rate was 148 beats / minute, virtual rabbit parameters were adjusted 6 times, all of which were reasonable adjustments to fit the road conditions and user status. Compared with similar routes in the past, the average speed increased by 2.1%, and the effective training time accounted for 92% of the total time. Data loop effect: The cycling data successfully updated the model, improving the parameter matching accuracy of the model for hilly road sections by 8%. The next time a virtual rabbit is generated, it can more accurately adapt to the user's hilly cycling ability. User experience: The virtual rabbit dynamically adjusts to fit the road conditions, and the feedback prompts are accurate. Users rated the dynamic adaptability and training guidance effect at ≥9.0 points, which solves the problems of lack of specificity and inability to adapt to changes in road conditions in outdoor hilly cycling virtual guidance.

[0035] Conclusion: Under the intermediate parameter values ​​specified in the claims, this embodiment fully realizes all technical features, the system operates stably, the virtual rabbit can accurately adapt to complex hilly road conditions and the comprehensive training needs of users, effectively improving cycling training efficiency, fully supporting the claims, and proving that the present invention can be stably implemented within the intermediate parameter range.

[0036] Example 2 refer to Figure 3In a second aspect, the present invention provides an outdoor cycling device 1, comprising: an acquisition module 11, configured to acquire a user's historical cycling dataset and construct a motion ability model reflecting the user's cycling performance under different environmental characteristics based on the historical cycling dataset; an extraction module 12, configured to segment the outdoor route to be cycled into paths and extract environmental feature parameters corresponding to each path segment; a generation module 13, configured to match corresponding historical cycling performance data from the motion ability model according to the environmental feature parameters of each path segment to generate virtual training targets and their guidance parameters adapted to each path segment; and an adjustment module 14, configured to compare the user's real-time cycling data with the guidance parameters during cycling and dynamically adjust the guidance parameters of the virtual training target according to the comparison results.

[0037] Furthermore, the acquisition module includes: a filtering unit for outlier removal and filtering of the historical cycling dataset; a storage unit for classifying and storing the processed data according to path gradient, road surface type, and cycling scenario; and a construction unit for mining the user's average exercise index under different classification dimensions through algorithms to construct the exercise ability model, wherein the exercise ability model represents the mapping relationship between environmental feature parameters and exercise ability.

[0038] Example 3 refer to Figure 4 A third aspect of the present invention provides an electronic device comprising: one or more processors; and a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the outdoor cycling method of the first aspect of the present invention.

[0039] Electronic device 500 may include a processing unit (e.g., a central processing unit, a graphics processing unit, etc.) 501, which can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) 502 or a program loaded from storage device 508 into random access memory (RAM) 503. The RAM 503 also stores various programs and data required for the operation of electronic device 500. The processing unit 501, ROM 502, and RAM 503 are interconnected via bus 504. An input / output (I / O) interface 505 is also connected to bus 504.

[0040] Typically, the following devices can be connected to I / O interface 505: input devices 506 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 507 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 508 including, for example, hard disks; and communication devices 509. Communication device 509 allows electronic device 500 to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 4 An electronic device 500 with various devices is shown; however, it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed alternatively. Figure 4 Each box shown can represent a device or multiple devices as needed.

[0041] Specifically, according to embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 509, or installed from a storage device 508, or installed from a ROM 502. When the computer program is executed by a processing device 501, it performs the functions defined in the methods of embodiments of this disclosure. It should be noted that the computer-readable medium described in embodiments of this disclosure can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In embodiments of this disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus, or device. In embodiments of this disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. Program code contained on a computer-readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0042] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device. The aforementioned computer-readable medium carries one or more computer programs, which, when executed by the electronic device, cause the electronic device to: Computer program code for performing the operations of embodiments of this disclosure can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages—such as Java, Smalltalk, C++, and Python—and conventional procedural programming languages—such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0043] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0044] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An outdoor cycling method, characterized in that, include: Obtain the user's historical cycling dataset, and construct a sports ability model that reflects the user's cycling performance under different environmental characteristics based on the historical cycling dataset; The outdoor cycling route is segmented, and the environmental feature parameters corresponding to each segment are extracted. Based on the environmental feature parameters of each path segment, the corresponding historical cycling performance data is matched from the sports ability model to generate virtual training targets and their guidance parameters that are adapted to each path segment. During the ride, the user's real-time riding data is compared with the guidance parameters, and the guidance parameters of the virtual training target are dynamically adjusted based on the comparison results.

2. The outdoor cycling method according to claim 1, characterized in that, The construction of the motion capability model includes: The historical cycling dataset was subjected to outlier removal and filtering. The processed data is categorized and stored according to route gradient, road surface type, and cycling scenario. The average exercise index of users under different classification dimensions is mined by the algorithm to construct the exercise ability model, which represents the mapping relationship between environmental feature parameters and exercise ability.

3. The outdoor cycling method according to claim 1, characterized in that, The path segmentation and extraction of environmental feature parameters include: The outdoor cycling route is divided into multiple path units according to a preset length interval; Extract at least one of the following parameters for each path segment: slope value, road surface type, elevation change, and curve complexity, as the environmental feature parameter for that path segment.

4. The outdoor cycling method according to claim 1, characterized in that, The generated virtual coaching target and its guidance parameters include: For the current path segment, retrieve historical cycling records with the same or similar environmental feature parameters from the motion ability model; Based on the user's training goals for this ride, the speed, cadence, and power parameters retrieved from historical riding records are calculated for gain or loss, and the calculation results are determined as the guiding parameters.

5. The outdoor cycling method according to claim 1, characterized in that, The dynamic adjustment includes: Real-time monitoring of the deviation between the user's cycling data and the guidance parameters; Determine whether the deviation magnitude and duration of the deviation value reach a preset adjustment threshold; If the real-time cycling data is consistently higher than the guidance parameter and meets the adjustment threshold, the guidance parameter is increased within a preset ratio range; if the real-time cycling data is consistently lower than the guidance parameter and meets the adjustment threshold, the guidance parameter is decreased within a preset ratio range.

6. The outdoor cycling method according to claim 1, characterized in that, The method further includes: The navigation interface renders the digital image of the virtual training target in real time and simultaneously displays the relative distance between the virtual training target and the user. Based on the comparison results, voice or text feedback information is output in real time to guide the user to adjust their riding status.

7. An outdoor cycling device, characterized in that, include: The acquisition module is used to acquire the user's historical cycling dataset and construct a sports ability model that reflects the user's cycling performance under different environmental characteristics based on the historical cycling dataset. The extraction module is used to segment the outdoor route to be cycled and extract the environmental feature parameters corresponding to each route segment. The generation module is used to match the corresponding historical cycling performance data from the sports ability model according to the environmental feature parameters of each path segment, so as to generate virtual training targets and their guidance parameters that are adapted to each path segment. The adjustment module is used to compare the user's real-time cycling data with the guidance parameters during the cycling process, and dynamically adjust the guidance parameters of the virtual coaching target based on the comparison results.

8. The outdoor cycling device according to claim 7, characterized in that, The acquisition module includes: The filtering unit is used to remove outliers and filter the historical cycling dataset. The storage unit is used to classify and store the processed data according to the path gradient, road surface type, and cycling scenario; The construction unit is used to mine the user's average motion index under different classification dimensions through algorithms and construct the motion ability model, which represents the mapping relationship between environmental feature parameters and motion ability.

9. An electronic device, comprising: One or more processors; A storage device for storing one or more programs, which, when executed by one or more processors, cause the one or more processors to implement the outdoor cycling method as described in any one of claims 1 to 6.

10. A computer-readable medium having a computer program stored thereon, wherein, When the computer program is executed by a processor, it implements the outdoor cycling method as described in any one of claims 1 to 6.

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