Modular skating training method and system integrating AR and Internet of Things feedback

Abstract

The invention provides a modular skating training method and system integrating AR and Internet of Things feedback, and relates to the technical field of physical training. According to the technical scheme provided by the invention, the initial virtual track in the AR virtual scene is generated by responding to the training mode selected by the user, the ice blade inclination angle and the sliding track data of the user on the ice-surface-imitating track platform of the skating machine are acquired in real time to determine the ice blade steering angle of the user, and then the track deflection angle in the training mode is calculated; and then adjusting to obtain a target virtual track. Generating a customized virtual training scheme according to the expression of the user on the target virtual track, controlling entity training equipment of the skating machine to execute the corresponding training scheme when the user reaches a triggering condition, and analyzing training data after the user completes each virtual training scheme to generate an evaluation report. Targeted training is carried out through virtual-real combined immersive training experience, so that the training effect of the user can be improved.

Description

Technical Field

[0001] This application relates to the field of sports training technology, specifically to a modular skating training method and system integrating AR and IoT feedback. Background Technology

[0002] With the popularization of winter sports, skating machines, as a device for simulating skating training, are widely used in the training and teaching of skating skills. Skating machines mainly consist of an ice-simulating track platform and a supporting frame. Their core component is an ice-simulating track made of high-polymer material, driven by a motor to simulate the low-friction characteristics of real ice. When trainees stand on the moving track platform, they can experience a feeling similar to skating on real ice. This design allows trainees to practice skating skills in ambient temperatures, overcoming the limitations of traditional ice rink training due to venue and weather conditions.

[0003] The training method using ice skating machines in related technologies primarily simulates different skating environments by adjusting the speed and resistance of the tracked platform. Trainees need to repeatedly practice basic skating postures and simple turning movements on the track, gradually increasing the track speed to adapt to higher-intensity training requirements. Some ice skating machines are equipped with simple timers and speed displays to record training duration and running speed. However, the lack of functionality to simulate real competition scenarios results in poor training effectiveness for users. Summary of the Invention

[0004] This application provides a modular skating training method and system that integrates AR and IoT feedback, which can improve the user's training effect.

[0005] In a first aspect, this application provides a modular skating training method integrating AR and IoT feedback, the method comprising: In response to the user's selected training mode, an initial virtual track is generated in the AR virtual scene; The user's blade tilt angle and gliding trajectory on the ice-simulated ice surface track platform of the ice skating machine are obtained, and the user's blade turning angle is determined. The track deflection angle in the training mode is calculated based on the ice skate turning angle, and the initial virtual track is adjusted based on the track deflection angle to obtain the target virtual track. Generate a virtual training plan for the user on the target virtual track, and when the user meets the triggering conditions corresponding to the virtual training plan, control the physical training equipment of the skating machine to execute the virtual training plan; Obtain the training data after the user completes each of the virtual training schemes, and generate a training evaluation report.

[0006] By employing the aforementioned technical solution, the system generates an initial virtual track within an AR virtual scene in response to the user's selected training mode. It also acquires real-time data on the user's skate blade tilt angle and gliding trajectory on the ice-simulated track platform of the skating machine to determine the user's skate blade turning angle. Based on this turning angle, the system calculates the track deflection angle under the training mode and adjusts the initial virtual track accordingly to obtain the target virtual track. Furthermore, the system generates customized virtual training plans based on the user's performance on the target virtual track. When the user meets trigger conditions, the system intelligently controls the physical training equipment of the skating machine to execute the corresponding training plan. Simultaneously, it records and analyzes the training data after the user completes each virtual training plan to generate an evaluation report. This training method, which dynamically combines the user's real-time skating movements with the virtual scene, can adaptively adjust the training difficulty and content according to the user's actual performance. Through an immersive training experience combining virtual and real elements, it provides targeted training, thereby improving the user's training effectiveness.

[0007] Optionally, the step of calculating the track deflection angle in the training mode based on the ice skate turning angle, and adjusting the initial virtual track according to the track deflection angle to obtain the target virtual track, includes: When the training mode is the experience mode, the ice skate turning angle is substituted into the first calculation formula to obtain the track deflection angle in the experience mode; When the training mode is the competition mode, the ice skate turning angle is substituted into the second calculation formula to obtain the track deflection angle in the competition mode; The initial virtual track is adjusted according to the deflection direction and angle of the track deflection angle to obtain the target virtual track.

[0008] By adopting the above technical solution, differentiated calculation methods are used for different training modes. Specifically, in the trial mode, the skate angle is used in the first calculation formula to calculate the track deflection angle; in the competitive mode, the skate angle is used in the second calculation formula. Based on the calculated deflection direction and angle, the initial virtual track is adjusted accordingly to obtain a target virtual track suitable for the user's current training level. This technical solution, based on differentiated calculation and dynamic track adjustment according to training mode, provides training scenarios more tailored to the actual needs of users at different training levels. Beginners can gradually master basic skills in the trial mode, while advanced users can undergo more challenging professional training in the competitive mode. This achieves precise matching and step-by-step improvement in training difficulty, effectively enhancing the adaptability and relevance of training.

[0009] Optionally, generating a virtual training plan for the user on the target virtual track, and controlling the physical training equipment of the skating machine to execute the virtual training plan when the user meets the triggering conditions corresponding to the virtual training plan, includes: Generate a virtual training plan for the user on the target virtual track, and generate a virtual image corresponding to the physical training equipment. The virtual training plan includes triggering conditions and control instructions for the virtual image. When the user meets the triggering conditions corresponding to the virtual training scheme, the control command is sent to the physical training device corresponding to the virtual image, so that the physical training device of the skating machine executes the virtual training scheme.

[0010] By adopting the above technical solution, a virtual training plan is generated on the target virtual track, and a corresponding virtual avatar is generated for the physical training equipment. Trigger conditions and control commands for the virtual avatar are set in the virtual training plan. When the user meets the trigger conditions, the system sends the control commands to the corresponding physical training equipment, thereby achieving linkage between the virtual scene and the physical training equipment. This virtual-real integrated training mechanism allows the user's training behavior in the virtual scene to trigger a real-time response from the physical training equipment. This not only enhances the realism and immersion of the training but also helps users understand and complete training actions more intuitively through the visual display of the virtual avatar. Simultaneously, the synchronous response of the physical training equipment provides users with realistic physical feedback, thus offering a complete training experience in both visual and tactile dimensions, effectively improving the interactivity and effectiveness of the training.

[0011] Optionally, the physical training equipment includes a ball-handling board, a digital target, and a passer. Generating the user's virtual training plan on the target virtual track and generating a virtual avatar corresponding to the physical training equipment further includes: The virtual image of the opposing player corresponding to the ball-handling board, the virtual shooting position area corresponding to the digital target, and the virtual image of the teammate player corresponding to the passer are generated. Retrieve the standard training movements of the virtual avatar from the database, and generate standard control commands for the opposing player virtual avatar and the teammate player virtual avatar based on the standard training movements; The standard control commands are combined to generate a virtual training plan for the user on the target virtual track.

[0012] By adopting the above technical solution, this approach generates corresponding virtual avatars of opposing players, teammates, and shooting positions for physical training equipment such as paddleboards, digital targets, and passers. It then retrieves standard training movements from a database for these virtual avatars, generating the opposing player and teammate avatars along with their standard control commands. Finally, the combination of these standard control commands forms a complete virtual training program for the user on the target virtual track. This technical solution, which integrates multiple physical training devices with a virtual environment, can simulate realistic ice hockey training scenarios. Data support for standard training movements ensures the professionalism and standardization of the virtual training. Simultaneously, the coordinated operation of multiple training devices can simulate diverse training situations, allowing users to receive comprehensive ice hockey skill training while skating. This organically combines basic skating training with professional ice hockey skill training, effectively enhancing the comprehensiveness and practicality of the training.

[0013] Optionally, after sending the control command to the physical training device corresponding to the virtual avatar when the user meets the triggering condition corresponding to the virtual training scheme, the method further includes: Real-time acquisition of feedback data from the sensors of the physical training device; Training interaction information is generated based on the feedback data, and the training interaction information is projected into the visible area of ​​the AR virtual scene.

[0014] By adopting the above technical solution, after the user meets the trigger conditions of the virtual training program and activates the physical training device, the system collects feedback data from the training device's sensors in real time. This data is then transformed into training interaction information and projected into the visible area of ​​the AR virtual scene, thus constructing a real-time, two-way feedback mechanism. This technical solution, which visualizes the sensor data of the physical device in the AR scene, allows users to intuitively understand the interaction effect between themselves and the training device, and adjust their training actions and strategies in a timely manner. Simultaneously, by displaying training interaction information in the visible area, the system provides users with immediate feedback on their training performance. This enhances the real-time interactivity of the training process and provides an intuitive display of training results, helping users better grasp the training rhythm and master the key points, thereby improving the accuracy and efficiency of training.

[0015] Optionally, the method further includes: The system acquires the user's actual body image in real time and performs posture analysis on the actual body image to obtain the user's real-time body posture. When the real-time body posture meets the preset dangerous training conditions, the power supply to the skating machine is cut off to brake it.

[0016] By adopting the above technical solutions, the user's movement status can be continuously tracked during training. Pre-set dangerous training conditions can be used to identify and warn of potential dangerous actions in a timely manner. When necessary, the emergency braking mechanism of the skating machine can be automatically triggered, thereby effectively preventing training accidents and providing users with comprehensive safety protection. This allows users to focus on training under the premise of full safety protection, thereby improving the safety and reliability of training.

[0017] Secondly, this application provides a modular skating training system integrating AR and IoT feedback, the system comprising: The initialization module is used to generate an initial virtual track in the AR virtual scene in response to the user's selected training mode; The data acquisition module is used to acquire the user's blade tilt angle and skating trajectory on the ice-simulated track platform of the skating machine, and to determine the user's blade turning angle. The processing module is used to calculate the track deflection angle in the training mode based on the ice skate turning angle, and adjust the initial virtual track based on the track deflection angle to obtain the target virtual track. The execution module is used to generate a virtual training plan for the user on the target virtual track, and control the physical training equipment of the skating machine to execute the virtual training plan when the user meets the triggering conditions corresponding to the virtual training plan; the output module is used to obtain the training data after the user completes each of the virtual training plans and generate a training evaluation report.

[0018] Thirdly, this application provides a computer storage medium storing a plurality of instructions adapted for loading by a processor and executing any of the methods described above.

[0019] Fourthly, this application provides an electronic device including a processor, a memory, and a transceiver, wherein the memory is used to store instructions, the transceiver is used to communicate with other devices, and the processor is used to execute the instructions stored in the memory to cause the electronic device to perform any of the methods described above.

[0020] In summary, the beneficial effects of the technical solution of this application include: By employing the aforementioned technical solution, the system generates an initial virtual track within an AR virtual scene in response to the user's selected training mode. It also acquires real-time data on the user's skate blade tilt angle and gliding trajectory on the ice-simulated track platform of the skating machine to determine the user's skate blade turning angle. Based on this turning angle, the system calculates the track deflection angle under the training mode and adjusts the initial virtual track accordingly to obtain the target virtual track. Furthermore, the system generates customized virtual training plans based on the user's performance on the target virtual track. When the user meets trigger conditions, the system intelligently controls the physical training equipment of the skating machine to execute the corresponding training plan. Simultaneously, it records and analyzes the training data after the user completes each virtual training plan to generate an evaluation report. This training method, which dynamically combines the user's real-time skating movements with the virtual scene, can adaptively adjust the training difficulty and content according to the user's actual performance. Through an immersive training experience combining virtual and real elements, it provides targeted training, thereby improving the user's training effectiveness. Attached Figure Description

[0021] Figure 1 This is a flowchart illustrating a modular skating training method integrating AR and IoT feedback according to an embodiment of this application. Figure 2 This is a schematic diagram of the structure of a modular skating training system integrating AR and IoT feedback according to an embodiment of this application; Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0022] Explanation of reference numerals in the attached drawings: 300, electronic device; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. Detailed Implementation

[0023] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0024] In the description of the embodiments of this application, words such as "illustrative," "for example," or "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "illustrative," "for example," or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design solutions. Rather, the use of words such as "illustrative," "for example," or "for example" is intended to present the relevant concepts in a specific manner.

[0025] In the description of the embodiments of this application, the term "multiple" means two or more. For example, multiple systems means two or more systems, and multiple screen terminals means two or more screen terminals. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the indicated technical features. Thus, a feature defined with "first" or "second" may explicitly or implicitly include one or more of that feature. The terms "comprising," "including," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0026] Please see Figure 1 This is a flowchart illustrating a modular skating training method integrating AR and IoT feedback, provided in an embodiment of this application. This method can be implemented using a computer program, a microcontroller, or run on a modular skating training system based on the von Neumann architecture that integrates AR and IoT feedback. The computer program can be integrated into the application or run as a standalone utility application. The specific steps of the modular skating training method integrating AR and IoT feedback are described in detail below.

[0027] S101: Responds to the user-selected training mode and generates an initial virtual track in the AR virtual scene; The training modes refer to the types of skating training programs preset by the system for different training goals and user skill levels, mainly including experience mode and competitive mode. The experience mode is suitable for beginners or recreational trainees. The system will use a first calculation formula to generate a relatively relaxed track deflection angle based on the user's skate turning angle, making the adjustment of the virtual track more closely match the user's actual skating trajectory and reducing the training difficulty. The competitive mode is aimed at professional trainees or competitive athletes. It generates a more challenging track deflection angle through a second calculation formula, requiring users to maintain more precise steering control and more standardized skating posture.

[0028] The initial virtual track refers to a standardized skating route pre-generated by the system in an AR virtual scene based on the user's selected training mode. This track includes basic skating route elements such as straightaways and curves, and exhibits specific geometric features in space, such as width, slope, and turning radius. The initial virtual track serves as a baseline route for training, providing users with a basic skating reference trajectory.

[0029] In specific implementation, according to the specific embodiments of this application, when a user selects a training mode through the human-computer interaction interface, the system responds to the selection signal and generates a corresponding initial virtual track in the AR virtual scene. The generation process of the initial virtual track involves the coordinated cooperation of multiple technical aspects. First, the system retrieves basic track parameters from a preset track template library. These parameters include geometric parameters such as the track's three-dimensional spatial coordinates, track width, curve curvature, and slope changes, as well as physical property parameters of the track surface, such as the coefficient of friction and elasticity. The setting of these parameters directly affects the realism of the virtual track and the accuracy of the training.

[0030] After acquiring the basic parameters, the system constructs a virtual track using a specialized track generation algorithm. This algorithm first determines the baseline route of the track based on spatial coordinates, and then extends it laterally based on the track width parameter to form a track with the width required for actual training. To accurately overlay the virtual track onto the real training environment, the system employs precise spatial positioning technology. Multiple spatial positioning sensors work collaboratively to acquire the user's location information and head orientation data in real time. The system combines this data with the site's location information to calculate the precise projection position of the virtual track in real space.

[0031] S102: Obtain the user's blade tilt angle and skating trajectory on the ice-simulated ice surface track platform of the ice skating machine, and determine the user's blade turning angle; The blade tilt angle refers to the angle at which the skater's blade is tilted relative to the vertical plane when skating on the simulated ice surface tracked platform of the skating machine. This angle is measured by capturing the real-time posture of the user's blade using binocular cameras mounted on both sides of the skating machine. Specifically, the binocular cameras collect the projection angle of the blade in the vertical direction, and the angle between the blade and the simulated ice surface tracked platform is calculated using image processing algorithms.

[0032] The gliding trajectory refers to the spatial displacement path formed when the user moves on the ice-simulated track platform of the skating machine. This trajectory obtains the contact pressure distribution data between the user's skates and the platform by an array of pressure sensors deployed on the surface of the track platform. At the same time, combined with the real-time speed data collected by the speed sensor, the system performs time-series correlation analysis on these two sets of data to calculate complete trajectory information including spatial position, movement speed and pressure distribution.

[0033] The blade steering angle refers to the real-time steering parameter calculated by the system based on the user's blade tilt angle and skating trajectory. This parameter reflects the magnitude of the user's directional changes during skating. Specifically, the system uses blade tilt angle data collected by a binocular camera, combined with trajectory data obtained from pressure and speed sensors, to calculate the change in blade deflection angle on the horizontal plane. This ensures that the training scenario can respond to the user's steering intentions in real time.

[0034] In practice, to accurately acquire the user's skating motion parameters, a dedicated camera system is installed at the front of the skating machine to capture the tilt angle and skating trajectory of the skates in real time. This camera system employs a deep learning object detection algorithm, using a pre-trained neural network model to identify and locate the skates in the video stream in real time. The system focuses on the user's forward-extended main control skate, as the skating direction is primarily determined by the forward-extended foot movement during skating.

[0035] During the data acquisition process, the system first identifies the position information of the ice skates in the video frames using a target detection algorithm, obtaining their pixel coordinates in image space. To convert these two-dimensional image coordinates into position information in actual physical space, the system uses the perspective transformation method provided by OpenCV to establish a mapping relationship between image space and the simulated ice track platform. This transformation takes into account the camera's installation angle and distortion parameters to ensure accurate reconstruction of the ice skate's true position on the platform. Simultaneously, the system analyzes the ice skate's posture changes in consecutive video frames to calculate the tilt angle of the ice skate relative to the vertical plane, i.e., the ice skate tilt angle.

[0036] S103: Calculate the track deflection angle in training mode based on the ice skate turning angle, and adjust the initial virtual track according to the track deflection angle to obtain the target virtual track; The track deflection angle refers to a parameter used by the system to adjust the virtual scene's perspective based on the user's skate blade turning angle, calculated using formulas applicable to different training modes. This parameter controls the real-time deflection of the user's viewpoint within the AR virtual scene. When the user makes a turning motion, the system substitutes the detected skate blade turning angle into the corresponding formula to calculate the required deflection angle for the virtual scene. This allows the virtual scene displayed on the AR device to change in real-time following the user's turning motion. The track deflection angle is designed to enhance the realism of the virtual training scene. Through dynamic adjustment of the perspective, it provides users with a visual experience consistent with real ice skating during turns, enhancing immersion during training.

[0037] The target virtual track refers to the real-time training scene obtained by the system after dynamically adjusting the perspective of the initial virtual track based on the track deflection angle. Specifically, when the system detects the user's turning action and calculates the corresponding track deflection angle, it will deflect the perspective in the AR virtual scene in real time according to that angle, so that the virtual track scene seen by the user in the AR display device can change synchronously with the user's turning action.

[0038] In practice, the system first selects the appropriate angle conversion formula based on the currently set training mode, substitutes the detected skate steering angle into the formula for calculation, and obtains the track deflection angle. During the calculation process, the system sets different conversion coefficients according to the characteristics of different training modes to ensure that the adjustment range of the viewing angle accurately reflects the user's steering intention without causing visual discomfort.

[0039] After obtaining the track deflection angle, the system immediately adjusts the perspective of the initial virtual track presented on the AR display device. Specifically, the adjustment process involves the AR scene rendering engine real-time deflecting the virtual scene's perspective according to the calculated track deflection angle. The system employs a smooth transition algorithm to achieve a gradual change in perspective, avoiding visual jitter caused by abrupt changes and ensuring a smooth visual experience for the user.

[0040] Based on the above embodiments, as an optional implementation method, the way to obtain the target virtual track can be adjusted, which can be achieved through the following steps S201-S203.

[0041] S201: When the training mode is the experience mode, the blade turning angle is substituted into the first calculation formula to obtain the track deflection angle in the experience mode; For beginners, children, and other users unfamiliar with ice skating, the system features a specially designed experience mode. In this mode, the system uses a primary calculation formula to determine the track deflection angle. This formula, by setting a small angle conversion coefficient and strict amplitude limiting parameters, ensures that the visual perspective changes in the virtual scene remain within a safe and controllable range. Specifically, the system substitutes the detected skate turning angle into the primary calculation formula, which conservatively scales the turning angle. Even if the user makes a large turning motion, the calculated track deflection angle will be controlled within a relatively gentle range, thus avoiding discomfort or dizziness that may be caused by excessive changes in visual perspective.

[0042] Specifically, the first calculation formula is: θ virtual =k0·θ real +Δθ offset ; Where, θ virtual The θ represents the track deflection angle, k0 represents the scene scaling factor in the experience mode, and θ represents the track deflection angle. real Indicates the blade turning angle, Δθ offset Indicates calibration deviation; Specifically, the track yaw angle in the formula refers to the change in the virtual scene's perspective ultimately presented to the user by the system. The experience mode scaling factor is a preset parameter less than 1, used to reduce the impact of actual steering actions on the virtual scene, ensuring a smoother change in perspective. The blade steering angle is the user's steering action parameter detected by the system in real time. The standard deviation term is used to compensate for system detection errors and environmental interference, ensuring the stability of perspective changes.

[0043] In a specific embodiment of this application, the first calculation formula maps real-world movements to a virtual environment through a linear transformation. In this formula, the final output virtual track deflection angle is calculated from the turning angle of the real ice skates (captured by a binocular camera). For example, when a 10-degree leftward deflection of the ice skates is detected, the system records the real turning angle as +10 degrees.

[0044] The scene scaling factor controls the sensitivity of virtual scene deflection and can be adjusted according to the user's proficiency, ranging from 0.5 to 3.0. For novice users, the system sets this factor to a value less than 1 to reduce sensitivity and prevent dizziness. For experienced users, the system sets this factor to a value greater than 1 to enhance scene response speed. For example, when the factor is set to 2, a real 10-degree deflection will be mapped to a 20-degree deflection in the virtual environment, thereby enhancing the user's immersion.

[0045] The standard deviation is primarily used to compensate for hardware installation errors or for user customization. The system can automatically calculate this value through a power-on calibration process (e.g., detecting blade angle deviation when the user is stationary). This deviation value is limited to ±5 degrees. For example, when the camera is installed at an angle, calibration can bring this deviation value to zero, ensuring accurate system response.

[0046] S202: When the training mode is the competition mode, the blade turning angle is substituted into the second calculation formula to obtain the track deflection angle in the competition mode; In practice, the system substitutes the detected ice skate turning angle into the second calculation formula. This formula responds more actively to the turning angle, and the calculated track deflection angle can more realistically reflect the user's turning action, thus presenting a more sensitive and accurate perspective change in the virtual scene.

[0047] Specifically, the second calculation formula is: θ virtual =k1·θ real +k2·θ virtual 3 +Δθ offset ; Where, θ virtualThe value represents the track deflection angle, k1 represents the baseline sensitivity coefficient in competitive mode, k2 represents the nonlinear weight in competitive mode, and Δθ represents the nonlinear weight. offset This indicates the calibration deviation.

[0048] Specifically, the formula comprises three main parts: a linear response term, a nonlinear enhancement term, and standard deviation compensation. The linear response term maps the actual steering angle to the baseline sensitivity coefficient; the nonlinear enhancement term introduces a cubic term of the track yaw angle, controlling its influence through a nonlinear weighting coefficient; and the standard deviation term compensates for system errors.

[0049] The calculation principle of this formula is to achieve more sensitive and precise viewpoint adjustments by combining linear and nonlinear responses. The linear term ensures the basic response characteristics, while the nonlinear term provides additional response enhancement at larger turning angles, making viewpoint changes closer to real-world racing conditions. At the same time, the introduction of a standard deviation term ensures the accuracy of the system's response.

[0050] In the implementation of the competitive mode, the system adjusts the cubic term coefficient k2 to achieve different training effects. When k2 is positive, the system amplifies the response to large-angle turns. For example, when the ice skate's turning angle exceeds 30 degrees, the cubic term significantly increases the deflection speed of the virtual scene, thus simulating the acceleration effect of a "sharp turn" in real ice skating. This design is particularly suitable for professional training modes and gamified interactive scenarios that require enhanced immersion. When k2 is negative, the system suppresses the response to large-angle turns, partially offsetting the contribution of the linear term through the cubic term, preventing excessive deflection of the virtual scene from causing user dizziness. This design is suitable for novice protection mechanisms.

[0051] The system limits the value of k2 to between -0.2 and +0.2, and can dynamically optimize and adjust it through machine learning algorithms. k2 works in conjunction with the linear term coefficient k1, where k1 dominates the baseline sensitivity for small-angle turns, while k2 controls the enhancement or suppression effect for large-angle turns. The system strictly adheres to three principles when dynamically adjusting k2: the principle of physical matching, ensuring that the virtual deflection response closely approximates the physical laws of real ice skating; the principle of user experience, ensuring that the system response matches the user's skill level; and the principle of system stability, preventing oscillations or loss of control in the virtual scene.

[0052] This design has several advantages: First, different user modes can be flexibly selected through the mobile app. For example, when users are not yet proficient, k1 = 0.8 and k2 = -0.1 can be set to suit the operating habits of different users. Second, the negative value of k2 can effectively suppress the loss of control of the virtual scene caused by accidental operation such as violent shaking. Finally, the introduction of the cubic term can better simulate the nonlinear frictional force changes between the ice skate and the ice surface in real ice skating.

[0053] S203: Adjust the initial virtual track according to the deflection direction and deflection angle of the track deflection angle to obtain the target virtual track.

[0054] The specific adjustment process begins with determining the direction. The system identifies the positive or negative value of the track's deflection angle to determine the direction of the viewpoint change. When the deflection angle is positive, it indicates that the user is turning right, and the system deflects the virtual scene to the right; when the deflection angle is negative, it indicates that the user is turning left, and the system deflects the virtual scene to the left. After determining the direction, the system uses a 3D graphics transformation algorithm to rotate the virtual scene based on the absolute value of the deflection angle.

[0055] In the specific implementation of scene adjustments, the system employs a perspective interpolation algorithm to ensure smooth scene transitions. When a new track deflection angle is detected, the system does not directly jump the scene to the target angle. Instead, it gradually transitions to the target perspective over a short period (usually a few milliseconds to tens of milliseconds) through interpolation calculations. This smooth transition mechanism effectively avoids abrupt visual changes and reduces user discomfort. Simultaneously, the system also synchronously adjusts all visual elements in the scene, including the track, background, timers, and indicators, ensuring the consistency of the entire virtual environment.

[0056] To enhance the realism of scene adjustments, the system considers physical motion characteristics when changing the viewpoint. For example, during sharp turns, the system appropriately simulates the tilting effect of centrifugal force; during rapid turns, it adds a moderate dynamic blur effect to enhance the sense of motion. These details make the changes in the virtual scene closer to the visual experience of real ice skating.

[0057] S104: Generate a virtual training plan for the user on the target virtual track, and when the user meets the triggering conditions corresponding to the virtual training plan, control the physical training equipment of the skating machine to execute the virtual training plan. The virtual training solution refers to a systematic and digital training plan and implementation scheme designed for figure skating training. This solution combines real-world skating movements with virtual scene interaction to provide users with an immersive training experience. In this embodiment, the virtual training solution can be understood as a complete training system encompassing multiple stages, including training mode selection, movement parameter acquisition, scene rendering adjustment, and training data feedback. This system captures the user's skate blade turning angle in real time, combines it with angle conversion algorithms under different training modes, maps the user's actual movements onto a virtual scene, and provides corresponding visual feedback based on the user's training level and needs.

[0058] In a specific implementation of this application, the system generates a targeted virtual training plan based on the user's training performance on the target virtual track, and sets corresponding triggering conditions and system response mechanisms. Taking a real training scenario as an example, when the system detects that the user can control the ball three or more times consecutively (this number can be adjusted according to the training difficulty), the system immediately activates the belt speed boost mechanism, increasing the speed by 15%-30% within 2-3 seconds. This sudden speed change aims to cultivate the user's ability to make immediate acceleration decisions at high speeds, helping the user improve their reaction speed to unexpected situations.

[0059] Another typical scenario is training design targeting technical errors. When the system detects a ball control error by the user during acceleration, it immediately triggers a response mechanism that causes the speed to drop sharply to the base value. The purpose of this design is to help users recognize their technical errors through the punitive speed adjustment, thereby paying more attention to the accuracy of their movements in subsequent training. Conversely, when a user successfully completes a shot or pass during acceleration, the system activates an AR target point reward effect, using positive reinforcement to enhance the accuracy of the user's high-speed movements.

[0060] Based on the above embodiments, as an optional implementation method, the generation and execution of a specific virtual training scheme can be achieved through the following steps S301-S302.

[0061] S301: Generate a virtual training plan for the user on the target virtual track and generate a virtual image corresponding to the physical training equipment. The virtual training plan includes triggering conditions and control instructions for the virtual image. In the specific implementation of the virtual training solution, the system first establishes a connection between physical training equipment such as ball-handling boards, passing devices, and digital targets and the virtual scene via the Zigbee protocol, and generates a corresponding virtual avatar for each device. These virtual avatars not only reflect the real-time status changes of the physical equipment, but also demonstrate standard movement techniques through preset animation effects. The system sets a series of triggering conditions and control commands according to the training objectives to coordinate the interaction between the physical equipment and the virtual avatars.

[0062] For example, when a defensive player attempts to steal the puck in a virtual scenario, the system simultaneously illuminates a blue indicator light on the physical puck board, providing the user with real-time location information. If the user's puck touches the blue light area, it indicates a steal by the defensive player, and the system records this mistake and displays the corresponding failure effect through the virtual avatar. Conversely, when the user successfully maintains continuous puck control, the system not only triggers a brief acceleration of the tracked platform to simulate a "sprint" effect, but the virtual avatar also displays an animation of breaking through the defense, enhancing the user's experience of success.

[0063] S302: When the user meets the triggering conditions corresponding to the virtual training scheme, the control command is sent to the physical training device corresponding to the virtual image so that the physical training device of the skating machine executes the virtual training scheme.

[0064] In its implementation, the system monitors the user's training status in real time through an IoT training space established via the Zigbee protocol. When the user meets the trigger conditions, such as successfully controlling the ball three times consecutively, the system immediately generates a series of control commands and sends them to the corresponding physical training equipment. These commands precisely control the speed changes of the tracked platform (increasing by 15%-30% within 2-3 seconds), while coordinating the indicator lights on the ball-handling board and the position adjustment of the passer. For example, when a defender appears in the virtual scene, the system controls the lotus light on the ball-handling board to illuminate, providing the user with an intuitive positional cue; when the user needs to practice shooting, the system adjusts the position and response sensitivity of the digital target.

[0065] This command transmission mechanism is designed to cater to the needs of users at different training levels. For novice users, the system sends control commands that keep the physical device in a relatively stable operating state, such as maintaining a constant platform speed and a fixed target position. For professional users, the control commands allow the physical device to exhibit more variations, such as rapid speed adjustments and random target movements. The system also dynamically adjusts the parameters of the control commands based on the user's real-time performance. For example, when it detects that the user can maintain stable ball control at high speeds, it will appropriately increase the difficulty level of subsequent commands.

[0066] In one alternative implementation, a virtual image of the opposing player corresponding to the ball-handling board, a virtual shooting area corresponding to the digital target, and a virtual image of the teammate player corresponding to the passer are generated. Retrieve standard training movements for virtual avatars from the database, and generate virtual avatars of opposing players and standard control commands for those virtual avatars based on these standard training movements; Standard control commands are combined to generate a virtual training plan for the user on the target virtual track.

[0067] In practical implementation, regarding the movement control of the virtual avatar, the system retrieves standard training movement data from a professional training movement database. This data includes the standard movement essentials of professional athletes in different competition scenarios, such as the tackling actions of defensive players, the running routes of passing teammates, and the selection of shooting timing. Based on this standard movement data, the system generates the movement trajectory and behavior patterns of the virtual player avatar and formulates corresponding standard control commands. For example, when the system detects that the user is practicing ball control, it will have the virtual defensive player exhibit the standard tackling actions of a professional defensive player, and the blue indicator light on the ball-handling board will light up accordingly, providing the user with real-time position feedback.

[0068] When generating the final virtual training program, the system intelligently combines these standard control commands based on the training objectives and the user's skill level. For example, a sequence of actions such as "defensive interception - passing combination - shooting opportunity" can be combined into a complete training scenario. When the user successfully evades the virtual defender's interception (i.e., avoids the blue area on the ball-handling board), the system immediately triggers the virtual teammate corresponding to the passer to begin a standard positioning action, while simultaneously displaying the optimal passing timing and route in the virtual scene. If the user successfully completes the pass, a scoring opportunity will appear in the shooting area corresponding to the digital target, guiding the user to complete the final shooting action.

[0069] In one optional implementation, feedback data from the sensors of the physical training device is acquired in real time; training interaction information is generated based on the feedback data, and the training interaction information is projected onto the visible area of ​​the AR virtual scene.

[0070] In its implementation, the system collects user training data in real time using sensors deployed on physical training equipment such as puck boards, passers, and digital targets. These sensors, including speed sensors, pressure sensors, and angle sensors, accurately record user motion parameters, such as the puck's contact point, the force and angle of a pass, and the accuracy of a shot. During training, these sensors transmit the data in real time to the system's data processing module via the Zigbee protocol.

[0071] The system then transforms this raw data into easily understandable training interaction information. For example, when the sensor detects that the user has completed a successful ball control action, the system displays real-time ball control success rate, current speed, and other data in the visible area of ​​the AR virtual scene; when the user is practicing passing, the system displays a predicted passing trajectory line and a prompt for the best passing opportunity in the virtual scene based on the force and angle data fed back by the sensor; in shooting training, the system displays shooting accuracy and scoring zone prompts in real time based on the sensor data of the digital target.

[0072] These training interactions are overlaid in augmented reality (AR) and displayed in a way that is eye-catching but does not interfere with training. For example, the system uses different colored light effects to represent the accuracy of movements (green for perfect, yellow for average, and red for needing improvement), uses dynamic arrows to indicate the optimal movement direction, or displays the number of consecutive successes via a digital counter. When the user reaches a preset training goal, the system also triggers special visual effects to reinforce positive feedback.

[0073] Optionally, the following safety measures are adopted throughout the training process: real-time acquisition of the user's actual body image and posture analysis of the actual body image to obtain the user's real-time body posture; when the real-time body posture meets the preset dangerous training conditions, the power supply of the skating machine is cut off to brake it.

[0074] In its implementation, the system uses a high-speed camera to capture real-time images of the user's body, focusing on tracking the relative position of the user's feet to the training platform. After processing by the posture analysis module, the system can accurately calculate the real-time height of the user's feet from the training platform. When the system detects that both feet simultaneously leave the training platform and the height exceeds a preset 10-centimeter warning line, it starts a timer to monitor the duration. If this off-ground state lasts for 2 seconds, the system immediately determines that the current state meets the dangerous training conditions, thus avoiding misjudgments caused by normal jumping movements during training.

[0075] Once a dangerous training condition is triggered, the system will immediately implement a series of safety protection measures: First, the system will immediately cut off the power supply to the skating machine and activate the emergency braking device to ensure the training platform stops safely in the shortest possible time; simultaneously, the system will issue a prominent warning message to the user through the AR display device, reminding the user to pay attention to training safety and avoid jumping or other potentially dangerous actions. This rapid-response safety protection mechanism can effectively prevent falls or other accidents that may be caused by jumping.

[0076] S105: Obtain training data after the user completes each virtual training scheme and generate a training evaluation report.

[0077] During data collection, the system comprehensively records various metrics of the user during training. These data sources include sensor data from physical training equipment (such as ball control success rate on the ball-handling board, passing accuracy recorded by the passer, and shooting percentage from the digital target), user body posture data (such as changes in center of gravity and balance indicators), and completion data from the virtual training program (such as completion time for each training task, number of errors, and consecutive successes). Simultaneously, the system also records the user's performance differences under different training difficulties, as well as key indicators such as the stability of technical movements at high speeds.

[0078] The system performs multi-dimensional analysis on this raw data, generating a comprehensive evaluation report that includes technical assessment, ability improvement, and training suggestions. In the technical assessment section, the system analyzes in detail the user's performance in basic technical movements such as ball control, passing, and shooting, including the accuracy, stability, and fluidity of the movements. In the ability improvement section, the system generates progress curves for each skill by comparing historical training data, clearly demonstrating the training results. In the training suggestions section, the system provides targeted improvement suggestions and follow-up training plans based on the user's performance characteristics and professional training experience.

[0079] The following are system embodiments of this application, which can be used to execute the method embodiments of this application. For details not disclosed in the system embodiments of this application, please refer to the method embodiments of the application.

[0080] Please see Figure 2 This illustration shows a schematic diagram of a modular skating training system integrating AR and IoT feedback, provided in an exemplary embodiment of this application. The system can be implemented entirely or partially through software, hardware, or a combination of both. The modular skating training system integrating AR and IoT feedback includes: The initialization module is used to generate an initial virtual track in the AR virtual scene in response to the user's selected training mode; The data acquisition module is used to obtain the user's blade tilt angle and skating trajectory on the ice-simulated ice surface track platform of the skating machine, and to determine the user's blade turning angle. The processing module is used to calculate the track deflection angle in training mode based on the ice skate turning angle, and adjust the initial virtual track according to the track deflection angle to obtain the target virtual track. The execution module is used to generate a virtual training plan for the user on the target virtual track, and control the physical training equipment of the skating machine to execute the virtual training plan when the user meets the triggering conditions corresponding to the virtual training plan. The output module is used to acquire training data after the user completes each virtual training scheme and generate a training evaluation report.

[0081] Based on the above embodiments, as an optional embodiment, the processing module is further configured to, when the training mode is the experience mode, input the blade turning angle into the first calculation formula to obtain the track deflection angle in the experience mode; when the training mode is the competition mode, input the blade turning angle into the second calculation formula to obtain the track deflection angle in the experience mode; and adjust the initial virtual track according to the deflection direction and deflection angle of the track deflection angle to obtain the target virtual track.

[0082] Based on the above embodiments, as an optional embodiment, the execution module is further configured to generate a virtual training plan for the user on the target virtual track and generate a virtual image corresponding to the physical training equipment. The virtual training plan includes triggering conditions and control instructions for the virtual image. When the user meets the triggering conditions corresponding to the virtual training plan, the control instructions are sent to the physical training equipment corresponding to the virtual image so that the physical training equipment of the skating machine executes the virtual training plan.

[0083] Based on the above embodiments, as an optional embodiment, the execution module is also used to generate virtual images of opposing players corresponding to the ball-handling board, virtual shooting positions corresponding to the digital targets, and virtual images of opposing players corresponding to the passers; obtain standard training actions of the virtual images from the database, and generate virtual images of opposing players and standard control instructions for the virtual images of opposing players based on the standard training actions; combine the standard control instructions to generate a virtual training plan for the user on the target virtual track.

[0084] Based on the above embodiments, as an optional embodiment, the execution module is also used to acquire feedback data from the sensors of the physical training device in real time; generate training interaction information based on the feedback data; and project the training interaction information into the visible area of ​​the AR virtual scene.

[0085] Based on the above embodiments, as an optional embodiment, the output module is also used to acquire the user's actual body image in real time, and perform posture analysis on the actual body image to obtain the user's real-time body posture; when the real-time body posture meets the preset dangerous training conditions, the power supply of the skating machine is cut off to brake it.

[0086] This application also provides a computer storage medium that can store multiple instructions. The instructions are adapted to be loaded and executed by a processor as described above for the modular skating training method integrating AR and IoT feedback. For details of the execution process, please refer to the specific description of the embodiments, which will not be repeated here.

[0087] Please see Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 3 As shown, the electronic device 300 may include: at least one processor 301, at least one network interface 304, user interface 303, memory 305, and at least one communication bus 302.

[0088] The communication bus 302 is used to enable communication between these components.

[0089] The user interface 303 may include a display screen and a camera.

[0090] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0091] The processor 301 may include one or more processing cores. The processor 301 connects to various parts of the server using various interfaces and lines, and performs various server functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in the memory 305, and by calling data stored in the memory 305. Optionally, the processor 301 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 301 may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content required for display; and the modem handles wireless communication. It is understood that the modem may also not be integrated into the processor 301 and may be implemented as a separate chip.

[0092] The memory 305 may include random access memory (RAM) or read-only memory. Optionally, the memory 305 may include a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory 305 may also be at least one storage device located remotely from the aforementioned processor 301. Figure 3 As shown, the memory 305, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and an application program that integrates AR and IoT feedback into a modular skating training method.

[0093] exist Figure 3 In the electronic device 300 shown, the user interface 303 is mainly used to provide an input interface for the user and to obtain the user input data; while the processor 301 can be used to call an application stored in the memory 305 that integrates AR and IoT feedback into a modular skating training method. When executed by one or more processors, the electronic device executes one or more methods as described in the above embodiments.

[0094] An electronic device readable storage medium stores instructions that, when executed by one or more processors, cause the electronic device to perform one or more methods as described in the above embodiments.

[0095] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0096] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0097] In the several embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the shown or discussed mutual couplings or direct couplings or communication connections may be through some service interfaces; indirect couplings or communication connections between apparatuses or units may be electrical or other forms.

[0098] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0099] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0100] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, portable hard drives, magnetic disks, or optical disks.

[0101] The above are merely exemplary embodiments of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. Those skilled in the art will readily conceive of other embodiments of this disclosure upon considering the specification and the disclosure of practical truths. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described in this disclosure.

Claims

1. A modular skating training method integrating AR and IoT feedback, characterized in that, The method includes: In response to the user's selected training mode, an initial virtual track is generated in the AR virtual scene; The user's skate blade tilt angle and gliding trajectory on the simulated ice surface track platform of the skating machine are obtained to determine the user's skate blade turning angle; The track deflection angle in the training mode is calculated based on the ice skate turning angle, and the initial virtual track is adjusted based on the track deflection angle to obtain the target virtual track. Generate a virtual training plan for the user on the target virtual track, and when the user meets the triggering conditions corresponding to the virtual training plan, control the physical training equipment of the skating machine to execute the virtual training plan; Obtain the training data after the user completes each of the virtual training schemes, and generate a training evaluation report.

2. The method according to claim 1, characterized in that, The step of calculating the track deflection angle in the training mode based on the ice skate turning angle, and adjusting the initial virtual track according to the track deflection angle to obtain the target virtual track, includes: When the training mode is the experience mode, the ice skate turning angle is substituted into the first calculation formula to obtain the track deflection angle in the experience mode; When the training mode is the competition mode, the ice skate turning angle is substituted into the second calculation formula to obtain the track deflection angle in the competition mode; The initial virtual track is adjusted according to the deflection direction and angle of the track deflection angle to obtain the target virtual track.

3. The method according to claim 2, characterized in that, The first calculation formula is: i virtual =k0·θ real +Δθ offset ; Where, θ virtual The track deflection angle is represented by k0, and the scene scaling factor in the experience mode is represented by θ. real The angle Δθ represents the turning angle of the ice skate. offset Indicates calibration deviation; The second calculation formula is: i virtual =k1·θ real +k2·θ virtual 3 +Δθ offset ; Where, θ virtual The track deflection angle is represented by k1, the baseline sensitivity coefficient in the racing mode is represented by k2, and the nonlinear weight in the racing mode is represented by Δθ. offset This indicates the calibration deviation.

4. The method according to claim 1, characterized in that, The process of generating a virtual training plan for the user on the target virtual track, and controlling the physical training equipment of the skating machine to execute the virtual training plan when the user meets the triggering conditions corresponding to the virtual training plan, includes: Generate a virtual training plan for the user on the target virtual track, and generate a virtual image corresponding to the physical training equipment. The virtual training plan includes triggering conditions and control instructions for the virtual image. When the user meets the triggering conditions corresponding to the virtual training scheme, the control command is sent to the physical training device corresponding to the virtual image, so that the physical training device of the skating machine executes the virtual training scheme.

5. The method according to claim 4, characterized in that, The physical training equipment includes a ball-handling board, a digital target, and a passer. Generating the user's virtual training plan on the target virtual track and generating a virtual avatar corresponding to the physical training equipment also includes: The virtual image of the opposing player corresponding to the ball-handling board, the virtual shooting position area corresponding to the digital target, and the virtual image of the teammate player corresponding to the passer are generated. Retrieve the standard training movements of the virtual avatar from the database, and generate standard control commands for the opposing player virtual avatar and the teammate player virtual avatar based on the standard training movements; The standard control commands are combined to generate a virtual training plan for the user on the target virtual track.

6. The method according to claim 4, characterized in that, After the user fulfills the triggering condition corresponding to the virtual training scheme and sends the control command to the physical training device corresponding to the virtual image, the method further includes: Real-time acquisition of feedback data from the sensors of the physical training device; Training interaction information is generated based on the feedback data, and the training interaction information is projected into the visible area of ​​the AR virtual scene.

7. The method according to claim 1, characterized in that, The method further includes: The system acquires the user's actual body image in real time and performs posture analysis on the actual body image to obtain the user's real-time body posture. When the real-time body posture meets the preset dangerous training conditions, the power supply to the skating machine is cut off to brake it.

8. A modular skating training system integrating AR and IoT feedback, characterized in that, The system includes: The initialization module is used to generate an initial virtual track in the AR virtual scene in response to the user's selected training mode; The data acquisition module is used to acquire the user's blade tilt angle and skating trajectory on the ice-simulated track platform of the skating machine, and to determine the user's blade turning angle. The processing module is used to calculate the track deflection angle in the training mode based on the ice skate turning angle, and adjust the initial virtual track based on the track deflection angle to obtain the target virtual track. The execution module is used to generate a virtual training plan for the user on the target virtual track, and control the physical training equipment of the skating machine to execute the virtual training plan when the user meets the triggering conditions corresponding to the virtual training plan; the output module is used to obtain the training data after the user completes each of the virtual training plans and generate a training evaluation report.

9. A computer storage medium, characterized in that, The computer storage medium stores a plurality of instructions adapted for loading by a processor and executing the method as described in any one of claims 1 to 7.

10. An electronic device, characterized in that, The device includes a processor, a memory, and a transceiver, wherein the memory is used to store instructions, the transceiver is used to communicate with other devices, and the processor is used to execute the instructions stored in the memory to cause the electronic device to perform the method as described in any one of claims 1 to 7.

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