Virtual motion scene real-time adjustment method and system in element universe smart motion

Abstract

The invention provides a real-time adjustment method and system for a virtual motion scene in meta-universe intelligent motion, and relates to the technical field of meta-universe, and the method comprises the steps: obtaining the motion parameter information of a user, calculating a scene matching score, dividing a difficulty level region, and detecting the motion state and physiological data of the user in real time; and calculating a skill grade score and dynamically adjusting scene difficulty distribution and rendering parameters. According to the method, the accurate matching of the virtual scene and the exercise ability of the user is realized, the immersion and individuation degree of interaction experience are improved, meanwhile, the system resource allocation is optimized, and the smooth operation of the meta-universe exercise environment is guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of the metaverse, and in particular to a method and system for real-time adjustment of virtual motion scenes in metaverse intelligent sports. Background Art

[0002] With the rapid development of information technology, the metaverse, as a comprehensive platform integrating various emerging technologies such as virtual reality, augmented reality, and artificial intelligence, is gradually coming into the public eye. In the metaverse environment, intelligent sports, as a new type of interaction mode, enables users to experience various sports in a virtual environment by constructing virtual motion scenes. Such virtual motion scenes can not only break through the time and space limitations of traditional sports but also provide personalized sports experiences for users through technical means. At present, virtual motion scene technology has shown broad application prospects in fields such as fitness, competitive sports training, and rehabilitation medicine.

[0003] However, the existing metaverse virtual motion scene technology has obvious deficiencies in real-time adjustment. Firstly, most virtual motion systems adopt preset fixed scenes and lack the ability to dynamically adjust the scene difficulty according to the user's real-time motion state and skill level, which cannot meet the personalized needs of different users and reduces the user's immersion and exercise effect. Secondly, the linkage between motion parameter monitoring and scene response in the existing system is poor, and it is unable to adjust the exercise challenge in real time according to physiological indicators such as the user's heart rate, etc., which easily leads to inappropriate exercise intensity. Summary of the Invention

[0004] Embodiments of the present invention provide a method and system for real-time adjustment of virtual motion scenes in metaverse intelligent sports, which can solve the problems in the prior art.

[0005] In the first aspect of the embodiments of the present invention, a method for real-time adjustment of virtual motion scenes in metaverse intelligent sports is provided, including: Obtain the motion parameter information of the user in the virtual motion environment, calculate the motion trajectory data of the user in real time, and match the motion trajectory data with the terrain feature data of the preset virtual motion scene to generate a scene matching score; According to the scene matching score, divide the virtual motion scene into multiple difficulty level regions, set different scene parameters and interaction parameters for each difficulty level region, and generate corresponding interaction response instructions according to the scene parameters and the interaction parameters; According to the interaction response instructions, detect the change of the user's motion state in real time, obtain the physiological data information and interaction data of the user in the virtual motion scene, the physiological data information includes the heart rate value, and the interaction data includes the time taken to complete the task; Calculate the user's motion skill level score based on the physiological data information and interaction data, in combination with a preset data mapping table, and dynamically adjust the difficulty level area distribution in the virtual motion scene based on the motion skill level score. At the same time, generate a corresponding set of motion challenge tasks, and set obstacle avoidance requirements and speed control targets for each task in the set of motion challenge tasks; Reset the scene rendering parameters in the virtual motion scene according to the change of the user's motion state. The scene rendering parameters include scene resolution value, refresh frequency value, and rendering accuracy value, so as to realize the dynamic adjustment of the virtual motion environment.

[0006] Match the motion trajectory data with the terrain feature data of the preset virtual motion scene to generate a scene matching score, including: Divide the motion trajectory data into multiple trajectory segments, and calculate the motion feature parameters of each trajectory segment. The motion feature parameters include the length of the trajectory segment, the curvature of the trajectory segment, and the speed change rate of the trajectory segment; Conduct terrain adaptability analysis on each trajectory segment, calculate the matching weight according to the Euclidean distance between the motion feature parameters of the trajectory segment and the terrain feature data of the preset virtual motion scene. The matching weight is the reciprocal of the Euclidean distance, and use the matching weight as the scene matching score of the trajectory segment; Based on the time series weighted average algorithm, comprehensively calculate the scene matching scores of all trajectory segments to generate an overall scene matching score.

[0007] Divide the virtual motion scene into multiple difficulty level areas according to the scene matching score. Set different scene parameters and interaction parameters for each difficulty level area, and generate corresponding interaction response instructions according to the scene parameters and the interaction parameters, including: Set a difficulty level division threshold according to the scene matching score, and divide the virtual motion scene into a primary difficulty area, an intermediate difficulty area, and a high - level difficulty area. The range of each difficulty area is determined by adjacent difficulty level division thresholds; Set scene parameters for the primary difficulty area, intermediate difficulty area, and high - level difficulty area respectively. The scene parameters include terrain slope value, obstacle density value, and environmental resistance value, and set interaction parameters. The interaction parameters include collision detection distance value and response delay time value; Based on the real - time position information of the user in each difficulty area, construct a difficulty transition connection mechanism, and set a difficulty gradient interval between adjacent difficulty areas. The scene parameters and interaction parameters in the difficulty gradient interval change continuously with the change of the user's position; Calculate the interaction probability between the user and the scene elements according to the user's motion state in each difficulty area, and set the interaction trigger conditions for different difficulty areas based on the interaction probability, and generate an interaction response instruction including the trigger timing, trigger intensity, and trigger duration.

[0008] Calculate the user's motion skill level score according to the physiological data information and interaction data, in combination with a preset data mapping table, and dynamically adjust the distribution of difficulty level areas in the virtual motion scene based on the motion skill level score. At the same time, generate a corresponding set of motion challenge tasks including: The data mapping table includes physiological data mapping rules and interaction data mapping rules. Among them, the physiological data mapping rule maps the heart rate value to a physiological state score, and the interaction data mapping rule maps the time taken to complete the task to an interaction performance score; Based on the data mapping table, calculate the user's physiological state score and interaction performance score respectively, and perform weighted calculation on the physiological state score and interaction performance score according to a preset weighting coefficient to generate the user's motion skill level score. The weighting coefficient is dynamically adjusted according to the user's motion duration; Set a scene adjustment coefficient according to the motion skill level score. The scene adjustment coefficient is used to determine the reference area and distribution density of the difficulty level area. Dynamically adjust the spatial distribution and area size of the difficulty level area in the virtual motion scene based on the scene adjustment coefficient and motion trajectory data. At the same time, select motion challenge tasks of corresponding difficulty according to the user's motion skill level score, and combine the selected motion challenge tasks in ascending order of difficulty to form a set of motion challenge tasks.

[0009] Dynamically adjusting the spatial distribution and area size of the difficulty level area in the virtual motion scene based on the scene adjustment coefficient and motion trajectory data includes: Determine the reference area and distribution density of the initial difficulty level area based on the scene adjustment coefficient, divide the difficulty partition of the virtual motion scene according to the reference area, and set the spatial distribution rule of the difficulty level area based on the distribution density. The spatial distribution rule includes the area interval distance, area overlap degree, and area distribution direction; Based on the motion trajectory data, calculate the user's area switching frequency and area residence duration. The area switching frequency represents the number of times the user crosses adjacent difficulty level areas per unit time, and the area residence duration represents the continuous motion time of the user in each difficulty level area; Construct a dynamic transition zone based on the region switching frequency and the region staying duration, dynamically adjust the width of the transition zone according to a preset region switching frequency threshold, expand the width of the transition zone when the region switching frequency exceeds the first frequency threshold, and reduce the width of the transition zone when the region switching frequency is lower than the second frequency threshold; adjust the range of the transition zone according to a preset region staying duration threshold, reduce the width of the transition zone when the region staying duration exceeds the first duration threshold, and increase the width of the transition zone when the region staying duration is lower than the second duration threshold.

[0010] Reset the scene rendering parameters in the virtual motion scene according to the change of the user's motion state. The scene rendering parameters include the scene resolution value, the refresh frequency value, and the rendering accuracy value, including: Adjust the scene resolution value according to the rate of change of speed; adjust the refresh frequency value according to the rate of change of acceleration; adjust the rendering accuracy value according to the rate of change of the attitude angle. Perform constraint processing on the updated scene rendering parameters, and limit the scene rendering parameters within a preset parameter range. The preset parameter range includes the resolution value range, the refresh frequency value range, and the rendering accuracy value range. Apply the scene rendering parameters after constraint processing to the rendering process of the virtual motion scene to achieve real-time optimization of the scene rendering effect.

[0011] In the second aspect of the embodiments of the present invention, a real-time adjustment system for a virtual motion scene in metaverse intelligent motion is provided, including: A first unit for obtaining the motion parameter information of the user in the virtual motion environment, calculating the motion trajectory data of the user in real time, and matching the motion trajectory data with the terrain feature data of the preset virtual motion scene to generate a scene matching score. A second unit for dividing the virtual motion scene into multiple difficulty level regions according to the scene matching score, setting different scene parameters and interaction parameters for each difficulty level region, and generating corresponding interaction response instructions according to the scene parameters and the interaction parameters. A third unit for detecting the change of the user's motion state in real time according to the interaction response instructions, and obtaining the physiological data information and interaction data of the user in the virtual motion scene. The physiological data information includes the heart rate value, and the interaction data includes the time used to complete the task. A fourth unit for calculating the motion skill level score of the user according to the physiological data information and the interaction data in combination with a preset data mapping table, dynamically adjusting the distribution of the difficulty level regions in the virtual motion scene based on the motion skill level score, and simultaneously generating a corresponding motion challenge task group. Each task in the motion challenge task group sets obstacle avoidance requirements and speed control targets. The fifth unit is used to reset the scene rendering parameters in the virtual motion scene according to the changes in the user's motion state. The scene rendering parameters include the scene resolution value, the refresh frequency value, and the rendering accuracy value, so as to realize the dynamic adjustment of the virtual motion environment.

[0012] In the third aspect of the embodiments of the present invention, an electronic device is provided, including: A processor; A memory for storing instructions executable by the processor; Wherein, the processor is configured to call the instructions stored in the memory to execute the method described above.

[0013] In the fourth aspect of the embodiments of the present invention, a computer-readable storage medium is provided, on which computer program instructions are stored. When the computer program instructions are executed by a processor, the method described above is realized.

[0014] The beneficial effects of this application are as follows: By obtaining the user's motion parameters and calculating the motion trajectory data in real time, and matching it with the terrain features of the preset virtual scene, the present invention realizes the intelligent dynamic adjustment of the virtual motion scene, enhancing the user's immersion and participation in the metaverse environment.

[0015] Based on the user's physiological data information and interaction data, the present invention calculates the motion skill level score, dynamically adjusts the difficulty level area distribution, and generates corresponding challenge tasks, making the virtual motion experience more personalized, effectively improving the user's sports enthusiasm and willingness to continue participating.

[0016] According to the changes in the user's motion state, the present invention resets the scene rendering parameters, realizing the real-time optimization of the virtual motion environment, ensuring both the smooth operation of the system and improving the quality of the user's interaction experience, and solving the technical problems such as insufficient scene adaptation and single user experience in traditional virtual motion systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a schematic flowchart of the method for real-time adjustment of the virtual motion scene in the metaverse intelligent motion of the embodiments of the present invention.

[0018] Figure 2 It is a schematic flowchart of dynamically adjusting the spatial distribution and area size of the difficulty level areas in the virtual motion scene. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0020] The technical solutions of the present invention will be described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments.

[0021] Reference Figure 1 and Figure 2 , the real-time adjustment method for virtual motion scenes in the metaverse intelligent motion of the embodiments of the present invention includes: Obtain the motion parameter information of the user in the virtual motion environment, calculate the motion trajectory data of the user in real time, and match the motion trajectory data with the terrain feature data of the preset virtual motion scene to generate a scene matching score; According to the scene matching score, divide the virtual motion scene into multiple difficulty level regions, set different scene parameters and interaction parameters for each difficulty level region, and generate corresponding interaction response instructions according to the scene parameters and the interaction parameters; According to the interaction response instructions, detect the change of the user's motion state in real time, obtain the physiological data information and interaction data of the user in the virtual motion scene, where the physiological data information includes the heart rate value, and the interaction data includes the time used to complete the task; According to the physiological data information and interaction data, calculate the motion skill level score of the user in combination with the preset data mapping table, and dynamically adjust the distribution of the difficulty level regions in the virtual motion scene based on the motion skill level score. At the same time, generate a corresponding motion challenge task group, and set obstacle avoidance requirements and speed control targets for each task in the motion challenge task group; Reset the scene rendering parameters in the virtual motion scene according to the change of the user's motion state. The scene rendering parameters include the scene resolution value, the refresh frequency value, and the rendering accuracy value, so as to realize the dynamic adjustment of the virtual motion environment.

[0022] In an alternative embodiment, matching the motion trajectory data with the terrain feature data of the preset virtual motion scene to generate a scene matching score includes: Divide the motion trajectory data into multiple trajectory segments, and calculate the motion feature parameters of each trajectory segment. The motion feature parameters include the length of the trajectory segment, the curvature of the trajectory segment, and the speed change rate of the trajectory segment; Perform terrain adaptability analysis on each trajectory segment, calculate the matching weight according to the Euclidean distance between the motion characteristic parameters of the trajectory segment and the terrain characteristic data of the preset virtual motion scene, where the matching weight is the reciprocal of the Euclidean distance, and use the matching weight as the scene matching score of the trajectory segment; Based on the time series weighted average algorithm, comprehensively calculate the scene matching scores of all trajectory segments to generate an overall scene matching score.

[0023] The system receives the original motion trajectory data containing position and time information. This data can be a sequence of GPS coordinate points collected by a smartphone, a wearable device, or a professional motion tracking device. Each coordinate point includes longitude, latitude, altitude, and the corresponding timestamp information.

[0024] Preprocess the original motion trajectory data, including removing abnormal points, smoothing, and noise reduction. The system uses the sliding window median filtering method to remove abnormal points, and the window size is set to 5 sampling points. Then, the Savitzky-Golay filtering algorithm is used to smooth the trajectory, with a filtering window length of 7 points and a polynomial order of 3. This can effectively reduce signal noise while retaining the main features of the trajectory.

[0025] After the preprocessing is completed, the system divides the motion trajectory data into multiple trajectory segments. The specific implementation method is to divide according to a fixed distance or a fixed time interval. In this embodiment, a fixed distance interval of 15 meters is used for division, that is, every 15 meters of the motion trajectory is used as a trajectory segment. For example, a motion trajectory with a total length of 300 meters will be divided into 20 trajectory segments.

[0026] For each trajectory segment, the system calculates its motion characteristic parameters, including the length, curvature, and speed change rate of the trajectory segment. The length of the trajectory segment is calculated by accumulating the distances between adjacent sampling points within the segment. For example, for a trajectory segment containing 10 sampling points, the system calculates the distances between adjacent points and sums them up to obtain the actual length of the segment as 15.2 meters.

[0027] The curvature of the trajectory segment reflects the degree of bending of the trajectory and is an important indicator for judging the turning situation of the motion path. The system calculates the curvature of the circle formed by every three consecutive points on the trajectory segment and then takes the average value as the overall curvature of the segment. For example, the calculated average curvature of a certain trajectory segment is 0.025, indicating that the bending degree of this segment of the trajectory is small and close to a straight line.

[0028] The rate of change of speed represents the change in speed within a trajectory segment. First, the system calculates the instantaneous speed of each point based on the distance and time difference between adjacent sampling points, and then calculates the ratio of the standard deviation of speed to the average speed as the rate of change of speed. For example, if the average speed of a certain trajectory segment is 3.5 m / s and the standard deviation of speed is 0.7 m / s, then the rate of change of speed is 0.2, indicating that the speed fluctuation of this segment of the trajectory is relatively small.

[0029] After calculating the motion characteristic parameters of the trajectory segment, the system conducts a terrain adaptability analysis for each trajectory segment. This step compares the motion characteristics of the trajectory segment with the terrain characteristics of the preset virtual motion scenario to evaluate its adaptability.

[0030] The terrain characteristic data of the preset virtual motion scenario includes various terrain types, such as flat ground, uphill, downhill, curves, etc., and each terrain type has corresponding characteristic parameters. For example, the characteristic parameters of flat ground are: curvature range 0 - 0.01, rate of change of speed range 0 - 0.15; the characteristic parameters of uphill are: curvature range 0 - 0.02, rate of change of speed range 0.15 - 0.3; the characteristic parameters of curves are: curvature range 0.02 - 0.05, rate of change of speed range 0.1 - 0.25.

[0031] The system evaluates the adaptability by calculating the Euclidean distance between the motion characteristic parameters of the trajectory segment and the terrain characteristic parameters of each type. Suppose the curvature of a certain trajectory segment is 0.025 and the rate of change of speed is 0.18, then the Euclidean distances from it to flat ground, uphill, and curves are calculated as follows: The Euclidean distance to flat ground: By calculating the square of the difference between the curvature 0.025 of this trajectory segment and the mid - point curvature 0.005 of flat ground, plus the square of the difference between the rate of change of speed 0.18 and the mid - point rate of change of speed 0.075 of flat ground, and then taking the square root, the value obtained is 0.108.

[0032] The Euclidean distance to uphill: By the same method, the Euclidean distance to the uphill terrain is calculated as 0.072. The Euclidean distance to curves: By the same method, the Euclidean distance to the curve terrain is calculated as 0.035.

[0033] The system takes the reciprocal of the Euclidean distance as the matching weight, that is, the matching weights of this trajectory segment to each terrain are: flat ground 9.26, uphill 13.89, curves 28.57. Since the matching weight to curves is the largest, the system determines that this trajectory segment is most suitable for the curve terrain and takes 28.57 as the scene matching score of this segment.

[0034] After completing the terrain adaptability analysis for all trajectory segments, the system comprehensively calculates the scene matching scores of all segments based on the time-series weighted average algorithm to generate an overall scene matching score. In the time-series weighted average algorithm, the trajectory segments closer to the current moment have a greater weight, and the weight decays over time.

[0035] In specific implementation, the system assigns a weight coefficient Wi = 0.95^(n - i) to the i-th trajectory segment, where n is the total number of trajectory segments, and i is numbered from 1 to n. For example, for 20 trajectory segments, the weight of the last segment is 0.95^0 = 1, and the weight of the second-to-last segment is 0.95^1 = 0.95, and so on.

[0036] The system multiplies the scene matching score of each trajectory segment by the corresponding weight coefficient, sums them up, and then divides by the sum of the weight coefficients to obtain the overall scene matching score. Suppose the weighted average result of 20 trajectory segments is 22.8, then the overall matching score of this motion trajectory and the virtual motion scene is 22.8.

[0037] This method can accurately evaluate the matching degree between the motion trajectory and the virtual scene by finely analyzing the characteristic parameters of the motion trajectory, combining terrain adaptability evaluation and time-series weighted algorithm, providing a basis for subsequent virtual scene recommendation and motion experience optimization.

[0038] In an optional implementation manner, according to the scene matching score, the virtual motion scene is divided into multiple difficulty level regions, different scene parameters and interaction parameters are set for each difficulty level region, and generating corresponding interaction response instructions according to the scene parameters and the interaction parameters includes: Set the difficulty level division threshold according to the scene matching score, and divide the virtual motion scene into a primary difficulty region, an intermediate difficulty region, and a high difficulty region. The range of each difficulty region is determined by adjacent difficulty level division thresholds; Set scene parameters for the primary difficulty region, intermediate difficulty region, and high difficulty region respectively. The scene parameters include terrain slope value, obstacle density value, and environmental resistance value, and set interaction parameters. The interaction parameters include collision detection distance value and response delay time value; Based on the real-time position information of the user in each difficulty region, construct a difficulty transition connection mechanism, and set a difficulty gradual change interval between adjacent difficulty regions. The scene parameters and interaction parameters in the difficulty gradual change interval are continuously and gradually distributed with the change of the user's position; Calculate the interaction probability between the user and the scene elements according to the motion state of the user in each difficulty region, and set the interaction trigger conditions for different difficulty regions based on the interaction probability, generating interaction response instructions including trigger timing, trigger intensity, and trigger duration.

[0039] Set the difficulty level division threshold according to the scene matching score, and divide the virtual sports scene into a primary difficulty area, an intermediate difficulty area, and an advanced difficulty area. In specific implementation, the system first obtains the scene matching score, and the score range can be set from 0 to 100 points. Then, set two difficulty level division thresholds: the first threshold is set to 40 points, and the second threshold is set to 75 points. According to these two thresholds, the virtual sports scene is divided into three difficulty areas: the area with a scene matching score between 0 and 40 points is the primary difficulty area; the area with a scene matching score between 40 and 75 points is the intermediate difficulty area; the area with a scene matching score between 75 and 100 points is the advanced difficulty area.

[0040] Next, set the scene parameters and interaction parameters for the primary difficulty area, intermediate difficulty area, and advanced difficulty area respectively. The scene parameters include the terrain slope value, the obstacle density value, and the environmental resistance value; the interaction parameters include the collision detection distance value and the response delay time value.

[0041] For the primary difficulty area, set the terrain slope value to 0 - 15 degrees, the obstacle density value to 5 - 10 obstacles per 100 square meters area, and the environmental resistance value to 1.0 - 1.2 times the basic resistance value. At the same time, set the collision detection distance value to 2.5 meters and the response delay time value to 100 milliseconds. These parameter settings make the primary difficulty area have a relatively gentle terrain, fewer obstacles, and less environmental resistance, and a larger collision detection distance and a shorter response delay time, which is suitable for beginners to practice.

[0042] For the intermediate difficulty area, set the terrain slope value to 15 - 30 degrees, the obstacle density value to 10 - 20 obstacles per 100 square meters area, and the environmental resistance value to 1.2 - 1.5 times the basic resistance value. At the same time, set the collision detection distance value to 1.5 meters and the response delay time value to 150 milliseconds. These parameter settings make the intermediate difficulty area have a terrain with a certain slope, a moderate number of obstacles, and a medium level of environmental resistance, and the collision detection distance and response delay time are moderate, which is suitable for users with a certain foundation to challenge.

[0043] For the advanced difficulty area, set the terrain slope value to 30 - 45 degrees, the obstacle density value to 20 - 30 obstacles per 100 square meters area, and the environmental resistance value to 1.5 - 2.0 times the basic resistance value. At the same time, set the collision detection distance value to 0.8 meters and the response delay time value to 200 milliseconds. These parameter settings make the advanced difficulty area have a relatively steep terrain, more obstacles, and greater environmental resistance, and a smaller collision detection distance and a longer response delay time, providing a highly challenging experience for high-level users.

[0044] To achieve a smooth transition between different difficulty regions, the system constructs a difficulty transition and connection mechanism based on the real-time position information of the user in each difficulty region. A difficulty gradient interval with a width of 10 meters is set between adjacent difficulty regions, and the scene parameters and interaction parameters within this interval are continuously and gradually distributed with the change of the user's position.

[0045] Taking the difficulty gradient interval between the beginner difficulty region and the intermediate difficulty region as an example, when the user moves from the beginner difficulty region to the intermediate difficulty region, the terrain slope value will gradually increase from 15 degrees to 30 degrees, the obstacle density value will gradually increase from 10 per 100 square meters to 20 per 100 square meters, and the environmental resistance value will gradually increase from 1.2 times the basic resistance value to 1.5 times. At the same time, the collision detection distance value will gradually decrease from 2.5 meters to 1.5 meters, and the response delay time value will gradually increase from 100 milliseconds to 150 milliseconds.

[0046] Specifically, the system uses linear interpolation to calculate the parameter values of the user in the difficulty gradient interval. Assuming the user's position in the difficulty gradient interval is x (x ranges from 0 to 10 meters, 0 means just entering the gradient interval, and 10 means about to leave the gradient interval), then for any parameter P, its value can be calculated as follows: P = Pbeginner + (Pintermediate - Pbeginner) × (x / 10), where Pbeginner is the parameter value of the beginner difficulty region and Pintermediate is the parameter value of the intermediate difficulty region.

[0047] According to the user's motion state in each difficulty region, calculate the interaction probability between the user and the scene elements, and set the interaction trigger conditions for different difficulty regions based on the interaction probability, generating interaction response instructions including trigger timing, trigger intensity, and trigger duration.

[0048] In practical applications, the system first obtains the user's motion state information, including speed, acceleration, motion direction, etc. Then, based on this information, a statistical model is used to calculate the probability of the user interacting with scene elements (such as obstacles, terrain features, etc.). For the beginner difficulty region, the interaction is triggered when the interaction probability exceeds 0.3; for the intermediate difficulty region, the interaction is triggered when the interaction probability exceeds 0.5; for the advanced difficulty region, the interaction is triggered when the interaction probability exceeds 0.7.

[0049] Once the interaction is triggered, the system will generate corresponding interaction response instructions. In the beginner difficulty region, the trigger timing is 1 second before the predicted collision, the trigger intensity is 30% of the maximum intensity, and the trigger duration is 500 milliseconds; in the intermediate difficulty region, the trigger timing is 0.6 seconds before the predicted collision, the trigger intensity is 60% of the maximum intensity, and the trigger duration is 300 milliseconds; in the advanced difficulty region, the trigger timing is 0.3 seconds before the predicted collision, the trigger intensity is 90% of the maximum intensity, and the trigger duration is 200 milliseconds.

[0050] Through the above method, the present invention realizes the division of the difficulty level of the virtual motion scene based on the scene matching score, sets appropriate scene parameters and interaction parameters for different difficulty level regions, and realizes the smooth transition between difficulty levels through the difficulty gradient interval. Finally, corresponding interaction response instructions are generated according to the user's motion state, improving the immersion and experience quality of the user in the virtual motion scene.

[0051] In an optional implementation manner, according to the physiological data information and interaction data, the user's motion skill level score is calculated in combination with a preset data mapping table, and the spatial distribution of the difficulty level regions in the virtual motion scene is dynamically adjusted based on the motion skill level score. At the same time, a corresponding set of motion challenge tasks is generated, including: The data mapping table includes a physiological data mapping rule and an interaction data mapping rule. The physiological data mapping rule maps the heart rate value to a physiological state score, and the interaction data mapping rule maps the time taken to complete a task to an interaction performance score. Based on the data mapping table, the user's physiological state score and interaction performance score are calculated respectively, and the physiological state score and interaction performance score are weighted and calculated according to a preset weighting coefficient to generate the user's motion skill level score. The weighting coefficient is dynamically adjusted according to the user's motion duration. A scene adjustment coefficient is set according to the motion skill level score. The scene adjustment coefficient is used to determine the reference area and distribution density of the difficulty level regions. Based on the scene adjustment coefficient and the motion trajectory data, the spatial distribution and region size of the difficulty level regions in the virtual motion scene are dynamically adjusted. At the same time, motion challenge tasks of corresponding difficulty are selected based on the user's motion skill level score, and the selected motion challenge tasks are combined in ascending order of difficulty to form a set of motion challenge tasks.

[0052] The system collects the user's physiological data information through a wearable device, including but not limited to data such as heart rate, blood oxygen saturation, and breathing frequency; at the same time, the user's interaction data is collected through a virtual reality device or a somatosensory device, including but not limited to information such as motion response time, time taken to complete a task, and motion trajectory.

[0053] The system presets a data mapping table, which contains physiological data mapping rules and interaction data mapping rules. The physiological data mapping rules map the heart rate value to a physiological state score. For example, when the heart rate is in the range of 60 - 70 beats per minute, the score is 85 points; when the heart rate is in the range of 70 - 80 beats per minute, the score is 90 points; when the heart rate is in the range of 80 - 100 beats per minute, the score is 95 points; when the heart rate is in the range of 100 - 120 beats per minute, the score is 100 points; when the heart rate is in the range of 120 - 140 beats per minute, the score is 95 points; when the heart rate is in the range of 140 - 160 beats per minute, the score is 90 points; when the heart rate exceeds 160 beats per minute, the score is 80 points.

[0054] The interaction data mapping rules map the time taken to complete a task to an interaction performance score. For example, for an obstacle avoidance task of primary difficulty, if the completion time is less than 10 seconds, the score is 100 points; if it is 10 - 15 seconds, the score is 90 points; if it is 15 - 20 seconds, the score is 80 points; if it is 20 - 30 seconds, the score is 70 points; if it exceeds 30 seconds, the score is 60 points.

[0055] The system calculates the user's physiological state score and interaction performance score based on the data mapping table. For example, if the user's heart rate is 115 beats per minute, according to the physiological data mapping rules, the physiological state score is 100 points; if the time taken by the user to complete the primary difficulty obstacle avoidance task is 13 seconds, according to the interaction data mapping rules, the interaction performance score is 90 points.

[0056] The system performs a weighted calculation on the physiological state score and the interaction performance score according to the preset weighting coefficients to generate the user's motor skill level score. The weighting coefficients are dynamically adjusted according to the user's exercise duration. The specific calculation method is as follows: in the initial stage of exercise (0 - 10 minutes), the weight of the physiological state score is 0.4, and the weight of the interaction performance score is 0.6; in the middle stage of exercise (10 - 30 minutes), the weight of the physiological state score is 0.5, and the weight of the interaction performance score is 0.5; in the later stage of exercise (more than 30 minutes), the weight of the physiological state score is 0.6, and the weight of the interaction performance score is 0.4.

[0057] Taking the above example, assuming the user is in the initial stage of exercise, the motor skill level score is calculated as: 100×0.4 + 90×0.6 = 94 points.

[0058] The system sets a scenario adjustment coefficient according to the user's motor skill level score, and this coefficient is used to determine the reference area and distribution density of the difficulty level area. For example, when the motor skill level score is in the range of 90 - 100 points, the corresponding scenario adjustment coefficient is 1.2; when it is in the range of 80 - 90 points, the corresponding scenario adjustment coefficient is 1.0; when it is in the range of 70 - 80 points, the corresponding scenario adjustment coefficient is 0.8; when it is in the range of 60 - 70 points, the corresponding scenario adjustment coefficient is 0.6.

[0059] Taking the user's sports skill level score of 94 in the above example, the corresponding scenario adjustment coefficient is 1.2. Assuming that the benchmark area of the high-difficulty area preset by the system is 100 square meters, the benchmark area of the medium-difficulty area is 200 square meters, and the benchmark area of the low-difficulty area is 300 square meters, after applying the scenario adjustment coefficient, the actual area of the high-difficulty area is 120 square meters, the actual area of the medium-difficulty area is 240 square meters, and the actual area of the low-difficulty area is 360 square meters.

[0060] The system dynamically adjusts the spatial distribution and area of the difficulty level areas in the virtual sports scenario based on the scenario adjustment coefficient and the user's motion trajectory data. For example, when it detects that the user frequently moves in a certain area, the system will add a higher-difficulty challenge area near that area. The specific implementation method is as follows: conduct a heat map analysis on the user's motion trajectory in the last 5 minutes, find the center point of the area with the highest activity frequency, and then, based on this center point, add an area with a difficulty level one level higher than the current area within a range of 10 - 15 meters around it, and the area is calculated according to the scenario adjustment coefficient.

[0061] At the same time, the system selects corresponding-difficulty sports challenge tasks based on the user's sports skill level score, and combines the selected sports challenge tasks in ascending order of difficulty to form a sports challenge task group. The corresponding relationship between the sports skill level score and the task difficulty is as follows: 90 - 100 points correspond to professional-level tasks, 80 - 90 points correspond to advanced tasks, 70 - 80 points correspond to intermediate tasks, 60 - 70 points correspond to primary tasks, and below 60 points correspond to novice-level tasks.

[0062] Taking the user's sports skill level score of 94 as an example above, the system will select appropriate tasks from the professional-level task library. Assuming that the professional-level task library contains tasks such as "High-speed continuous obstacle dodging", "Precise target hitting", and "Complex path tracking", the system will select 3 - 5 tasks, sort them from low to high in terms of difficulty, and form a sports challenge task group. For example: Task 1: High-speed continuous obstacle dodging (difficulty coefficient 0.9); Task 2: Precise target hitting (difficulty coefficient 0.95); Task 3: Complex path tracking (difficulty coefficient 1.0).

[0063] After the user completes a task, the system re-collects physiological data and interaction data, updates the sports skill level score, and accordingly adjusts the difficulty of subsequent tasks. For example, if the sports skill level score rises to 96 points after the user completes the first task, the system may adjust the difficulty coefficient of the third task from 1.0 to 1.05 to provide a more challenging experience.

[0064] Through the above method, the system can dynamically adjust the difficulty distribution and challenge tasks of the virtual motion scene based on the user's real-time performance and physiological state, provide a personalized and progressive motion experience, and effectively improve the user's motion participation and training effect.

[0065] In an optional implementation manner, dynamically adjusting the spatial distribution and area size of the difficulty level areas in the virtual motion scene based on the scene adjustment coefficient and the motion trajectory data includes: Determine the reference area and distribution density of the initial difficulty level areas based on the scene adjustment coefficient, divide the difficulty zones of the virtual motion scene according to the reference area, and set the spatial distribution rules of the difficulty level areas based on the distribution density. The spatial distribution rules include the area interval distance, the area overlap degree, and the area distribution direction; Based on the motion trajectory data, calculate the area switching frequency and area staying duration of the user. The area switching frequency represents the number of times the user crosses adjacent difficulty level areas per unit time, and the area staying duration represents the continuous motion time of the user in each difficulty level area; According to the area switching frequency and area staying duration, construct a dynamic transition zone, and dynamically adjust the width of the transition zone according to a preset area switching frequency threshold. When the area switching frequency exceeds the first frequency threshold, expand the width of the transition zone. When the area switching frequency is lower than the second frequency threshold, narrow the width of the transition zone; adjust the range of the transition zone according to a preset area staying duration threshold. When the area staying duration exceeds the first duration threshold, reduce the width of the transition zone. When the area staying duration is lower than the second duration threshold, increase the width of the transition zone.

[0066] Determine the reference area and distribution density of the initial difficulty level areas based on the scene adjustment coefficient. The scene adjustment coefficient is a numerical parameter comprehensively generated according to the user's motion ability level, the type of virtual scene, and the characteristics of the sports event. For example, for novice users, the scene adjustment coefficient can be set to 0.8, for intermediate users to 1.0, and for advanced users to 1.2. The calculation of the reference area can be obtained by multiplying the total area of the virtual scene by the square of the scene adjustment coefficient. For a scene with a total area of 100 square meters, the reference area for novice users is approximately 64 square meters. The distribution density is directly affected by the scene adjustment coefficient. The larger the coefficient, the denser the distribution of the difficulty areas.

[0067] Based on the determined reference area, the system divides the virtual motion scene into different difficulty zones. For example, in a 1000-square-meter virtual running scene, it can be divided into an easy zone (accounting for 40%, i.e., 400 square meters), a medium zone (accounting for 35%, i.e., 350 square meters), and a difficult zone (accounting for 25%, i.e., 250 square meters). Spatial distribution rules are set according to the distribution density, including the distance between zones, the degree of zone overlap, and the direction of zone distribution. Specifically, the distance between zones can be set as the reference value (such as 5 meters) multiplied by the reciprocal of the scene adjustment coefficient, so that the interval of difficulty zones faced by high-level users is smaller; the degree of zone overlap can be defined as the percentage of the overlapping area of adjacent difficulty zones. For example, it is set to 10% for beginners, 15% for intermediate levels, and 20% for advanced levels; the direction of zone distribution is determined according to the user's habitual motion path, such as clockwise or arranged along a specific axis.

[0068] Based on the user's motion trajectory data, the system calculates the zone switching frequency and the zone residence duration. The zone switching frequency represents the number of times the user crosses the boundary between adjacent difficulty levels per unit time. For example, if the user crosses the boundaries of 10 different difficulty zones within 5 minutes, the zone switching frequency is 2 times per minute. The zone residence duration represents the continuous motion time of the user in each difficulty level zone. For instance, if the user stays in the easy zone for 3 minutes, in the medium zone for 2 minutes, and in the difficult zone for 1 minute, the residence durations in each zone are 3 minutes, 2 minutes, and 1 minute respectively.

[0069] The system uses the sliding window method to record and update this data. For example, every 30 seconds is used as a data acquisition window to record the number of zone switches and the residence time in each zone of the user within the window, and then the average value within the most recent 5 minutes is calculated by moving the window to ensure the real-time and representativeness of the data.

[0070] Based on the zone switching frequency and the zone residence duration, the system constructs a dynamic transition zone. The transition zone refers to a gradually changing zone set between different difficulty level zones, which is used to smooth the difficulty change and enhance the user experience. The system dynamically adjusts the width of the transition zone according to a preset zone switching frequency threshold. When the zone switching frequency exceeds the first frequency threshold (such as 3 times per minute), it indicates that the user frequently switches between difficulty zones, and the system will widen the width of the transition zone (such as increasing from the original 2 meters to 4 meters) to reduce the sense of sudden difficulty change; when the zone switching frequency is lower than the second frequency threshold (such as 1 time per minute), it indicates that the user rarely switches zones, and the system will narrow the width of the transition zone (such as reducing from the original 2 meters to 1 meter) to provide a more distinct difficulty change experience.

[0071] Meanwhile, the system adjusts the transition zone range according to a preset regional residence duration threshold. When the user's residence duration in a certain difficulty area exceeds the first duration threshold (e.g., 4 minutes), it indicates that the user has adapted to the current difficulty, and the system will reduce the width of the adjacent transition zone (e.g., from the original 3 meters to 2 meters) to make the difficulty change more obvious; when the regional residence duration is lower than the second duration threshold (e.g., 1 minute), it indicates that the user may not be adapted to the current difficulty, and the system will increase the width of the transition zone (e.g., from the original 3 meters to 5 meters) to make the difficulty change more gentle.

[0072] In actual operation, the system re-evaluates the user's area switching frequency and area residence duration at regular intervals (e.g., every 60 seconds) and updates the transition zone parameters accordingly. For example, if it is detected that the user's area switching frequency is 3.5 times per minute (higher than the threshold of 3 times per minute), and the residence duration in the difficult area is only 0.8 minutes (lower than the threshold of 1 minute), the system will expand the width of the transition zone between the difficult area and the medium area from the original 2.5 meters to 4 meters and may adjust the distribution range of the difficult area to make it more dispersed or reduce the area.

[0073] Through the above dynamic adjustment mechanism, the system can adaptively adjust the spatial distribution and area size of the difficulty level areas in the virtual motion scene according to the user's real-time motion performance, providing a personalized motion experience, which can not only avoid the user's frustration due to excessive difficulty but also prevent the poor training effect caused by too low difficulty.

[0074] In an optional implementation manner, the scene rendering parameters in the virtual motion scene are reset according to the change of the user's motion state. The scene rendering parameters include scene resolution value, refresh frequency value, and rendering accuracy value, including: Adjust the scene resolution value according to the rate of change of speed; adjust the refresh frequency value according to the rate of change of acceleration; adjust the rendering accuracy value according to the rate of change of attitude angle; Perform constraint processing on the updated scene rendering parameters to limit the scene rendering parameters within a preset parameter range. The preset parameter range includes resolution value range, refresh frequency value range, and rendering accuracy value range; Apply the constraint-processed scene rendering parameters to the rendering process of the virtual motion scene to achieve real-time optimization of the scene rendering effect.

[0075] Obtain the user's motion state data, including speed, acceleration, and attitude angle information, then dynamically adjust the scene rendering parameters according to this information, including scene resolution value, refresh frequency value, and rendering accuracy value, and finally apply the adjusted parameters to the rendering process of the virtual motion scene.

[0076] In a specific embodiment, the method collects user motion state data through wearable devices (such as VR helmets, smart watches, etc.). The wearable device includes multiple sensors, such as an acceleration sensor, a gyroscope sensor, and a magnetometer sensor, etc. These sensors collect the user's motion state data at a predetermined sampling frequency (such as 100 times per second).

[0077] After the collected motion state data is preprocessed, the user's speed change rate, acceleration change rate, and attitude angle change rate are calculated. The preprocessing includes steps such as data filtering, outlier removal, and data smoothing. For example, the original data is processed using a moving average filter with a filter window size of 5 to eliminate data jitter.

[0078] The calculation of the speed change rate is by comparing the ratio of the speed difference between two consecutive time points to the time interval. For example, if the user's speed at time point t1 is 2.5 m / s and the speed at time point t2 (t2 - t1 = 0.1 s) is 3.0 m / s, then the speed change rate is (3.0 - 2.5) / 0.1 = 5.0 m / s².

[0079] The acceleration change rate is by comparing the ratio of the acceleration difference between two consecutive time points to the time interval. For example, if the user's acceleration at time point t1 is 1.2 m / s² and the acceleration at time point t2 (t2 - t1 = 0.1 s) is 1.8 m / s², then the acceleration change rate is (1.8 - 1.2) / 0.1 = 6.0 m / s³.

[0080] The attitude angle change rate is by comparing the ratio of the attitude angle difference between two consecutive time points to the time interval. The attitude angle includes pitch angle, roll angle, and yaw angle. For example, if the user's yaw angle at time point t1 is 15 degrees and the yaw angle at time point t2 (t2 - t1 = 0.1 s) is 20 degrees, then the yaw angle change rate is (20 - 15) / 0.1 = 50 degrees / s.

[0081] Adjust the scene resolution value according to the speed change rate. When the speed change rate increases, reduce the scene resolution; when the speed change rate decreases, increase the scene resolution. For example, set the reference resolution to 1920×1080 pixels. When the speed change rate exceeds 10 m / s², reduce the resolution to 1600×900 pixels; when the speed change rate exceeds 20 m / s², further reduce the resolution to 1280×720 pixels; when the speed change rate is below 5 m / s², maintain the reference resolution of 1920×1080 pixels.

[0082] Adjust the refresh frequency value according to the acceleration change rate. When the acceleration change rate increases, increase the refresh frequency; when the acceleration change rate decreases, decrease the refresh frequency. For example, set the reference refresh frequency to 60Hz. When the acceleration change rate exceeds 15 m / s³, increase the refresh frequency to 90Hz; when the acceleration change rate exceeds 30 m / s³, further increase the refresh frequency to 120Hz; when the acceleration change rate is lower than 10 m / s³, maintain the reference refresh frequency of 60Hz.

[0083] Adjust the rendering precision value according to the attitude angle change rate. When the attitude angle change rate increases, decrease the rendering precision; when the attitude angle change rate decreases, increase the rendering precision. The rendering precision can be achieved by adjusting parameters such as the model detail level (LOD), texture quality, and shadow quality. For example, set the reference rendering precision to high (corresponding value 90). When any attitude angle change rate exceeds 60 degrees / second, reduce the rendering precision to medium (corresponding value 60); when any attitude angle change rate exceeds 120 degrees / second, further reduce the rendering precision to low (corresponding value 30); when all attitude angle change rates are lower than 30 degrees / second, maintain the reference rendering precision of high.

[0084] Perform constraint processing on the updated scene rendering parameters to ensure that the parameters are within the preset parameter range. The specific constraints are as follows: The resolution value range is from 640×360 pixels to 3840×2160 pixels, and the resolution must be a common display resolution standard (such as 640×360, 1280×720, 1600×900, 1920×1080, 2560×1440, 3840×2160, etc.).

[0085] The refresh frequency value range is from 30Hz to 240Hz, and the refresh frequency must be a frequency supported by the display device (such as 30Hz, 60Hz, 90Hz, 120Hz, 144Hz, 240Hz, etc.).

[0086] The rendering precision value range is from 10 to 100, where 10 represents the lowest rendering precision and 100 represents the highest rendering precision.

[0087] When performing constraint processing, if the calculated parameter value exceeds the preset range, set it to the closest boundary value. For example, if the calculated resolution is 500×280 pixels, which is lower than the minimum allowable value of 640×360 pixels, set it to 640×360 pixels.

[0088] The experimental results show that this method can adjust the scene rendering parameters in real time according to the changes in the user's motion state, optimizing the system resource utilization while ensuring the user experience. For example, in a test, when the user starts running from a stationary state and the speed change rate rapidly increases to 15 m / s², the system reduces the resolution from 1920×1080 pixels to 1600×900 pixels, increases the refresh rate from 60 Hz to 90 Hz, and reduces the rendering accuracy from 90 to 60, thereby reducing the GPU load by approximately 25% while maintaining a smooth visual experience.

[0089] The real-time adjustment system for virtual motion scenes in the metaverse intelligent motion of the embodiments of the present invention includes: The first unit is used to obtain the motion parameter information of the user in the virtual motion environment, calculate the motion trajectory data of the user in real time, and match the motion trajectory data with the terrain feature data of the preset virtual motion scene to generate a scene matching score; The second unit is used to divide the virtual motion scene into multiple difficulty level regions according to the scene matching score, set different scene parameters and interaction parameters for each difficulty level region, and generate corresponding interaction response instructions according to the scene parameters and the interaction parameters; The third unit is used to detect the changes in the user's motion state in real time according to the interaction response instructions, obtain the physiological data information and interaction data of the user in the virtual motion scene, where the physiological data information includes the heart rate value, and the interaction data includes the time taken to complete the task; The fourth unit is used to calculate the user's motion skill level score according to the physiological data information and interaction data in combination with the preset data mapping table, dynamically adjust the distribution of difficulty level regions in the virtual motion scene based on the motion skill level score, and generate a corresponding set of motion challenge tasks at the same time. Each task in the set of motion challenge tasks sets obstacle avoidance requirements and speed control targets; The fifth unit is used to reset the scene rendering parameters in the virtual motion scene according to the changes in the user's motion state. The scene rendering parameters include scene resolution values, refresh rate values, and rendering accuracy values, so as to realize the dynamic adjustment of the virtual motion environment.

[0090] In the third aspect of the embodiments of the present invention, A kind of electronic device is provided, including: A processor; A memory for storing instructions executable by the processor; Wherein, the processor is configured to call the instructions stored in the memory to execute the method described above.

[0091] In the fourth aspect of the embodiments of the present invention, Provided is a computer-readable storage medium having stored thereon computer program instructions, which when executed by a processor implement the method described above.

[0092] The present invention may be a method, an apparatus, a system, and / or a computer program product. The computer program product may include a computer-readable storage medium having thereon computer-readable program instructions for performing various aspects of the present invention.

[0093] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. Real-time adjustment method for virtual sports scenes in the metaverse intelligent sports, characterized in that, it includes: Obtain the motion parameter information of the user in the virtual sports environment, calculate the motion trajectory data of the user in real time, and match the motion trajectory data with the terrain feature data of the preset virtual sports scene to generate a scene matching score; According to the scene matching score, divide the virtual sports scene into multiple difficulty level areas, set different scene parameters and interaction parameters for each difficulty level area, and generate corresponding interaction response instructions according to the scene parameters and the interaction parameters; According to the interaction response instructions, detect the change of the user's motion state in real time, obtain the physiological data information and interaction data of the user in the virtual sports scene, the physiological data information includes the heart rate value, and the interaction data includes the time used to complete the task; According to the physiological data information and interaction data, calculate the user's motion skill level score in combination with the preset data mapping table, and dynamically adjust the distribution of the difficulty level areas in the virtual sports scene based on the motion skill level score. At the same time, generate a corresponding set of motion challenge tasks, and set obstacle avoidance requirements and speed control targets for each task in the set of motion challenge tasks; Reset the scene rendering parameters in the virtual sports scene according to the change of the user's motion state. The scene rendering parameters include the scene resolution value, the refresh frequency value and the rendering accuracy value, so as to realize the dynamic adjustment of the virtual sports environment.

2. The method according to claim 1, characterized in that, matching the motion trajectory data with the terrain feature data of the preset virtual sports scene to generate a scene matching score includes: Divide the motion trajectory data into multiple trajectory segments, and calculate the motion feature parameters of each trajectory segment. The motion feature parameters include the length of the trajectory segment, the curvature of the trajectory segment and the speed change rate of the trajectory segment; Conduct terrain adaptability analysis on each trajectory segment, calculate the matching weight according to the Euclidean distance between the motion feature parameters of the trajectory segment and the terrain feature data of the preset virtual sports scene. The matching weight is the reciprocal of the Euclidean distance, and use the matching weight as the scene matching score of the trajectory segment; Based on the time series weighted average algorithm, comprehensively calculate the scene matching scores of all trajectory segments to generate an overall scene matching score.

3. The method according to claim 1, characterized in that, dividing the virtual sports scene into multiple difficulty level areas according to the scene matching score, setting different scene parameters and interaction parameters for each difficulty level area, and generating corresponding interaction response instructions according to the scene parameters and the interaction parameters includes: Set the difficulty level division threshold according to the scene matching score, divide the virtual sports scene into a primary difficulty area, an intermediate difficulty area and a high difficulty area, and the range of each difficulty area is determined by adjacent difficulty level division thresholds; Scene parameters are set for the beginner difficulty area, intermediate difficulty area, and advanced difficulty area respectively. The scene parameters include terrain slope value, obstacle density value, and environmental resistance value, and interaction parameters are set. The interaction parameters include collision detection distance value and response delay time value; Based on the real-time position information of the user in each difficulty area, a difficulty transition connection mechanism is constructed, and a difficulty gradual change interval is set between adjacent difficulty areas. The scene parameters and interaction parameters in the difficulty gradual change interval are continuously and gradually distributed with the change of the user's position; According to the motion state of the user in each difficulty area, calculate the interaction probability between the user and the scene elements, and set the interaction trigger conditions for different difficulty areas based on the interaction probability, and generate interaction response instructions including trigger timing, trigger intensity, and trigger duration.

4. The method according to claim 1, wherein, According to the physiological data information and interaction data, calculate the user's motor skill level score in combination with a preset data mapping table, and dynamically adjust the distribution of difficulty level areas in the virtual sports scene based on the motor skill level score. At the same time, generate corresponding sports challenge task groups including: The data mapping table includes physiological data mapping rules and interaction data mapping rules. The physiological data mapping rules map the heart rate value to a physiological state score, and the interaction data mapping rules map the time used to complete the task to an interaction performance score; Based on the data mapping table, calculate the user's physiological state score and interaction performance score respectively, and perform weighted calculation on the physiological state score and interaction performance score according to a preset weighting coefficient to generate the user's motor skill level score. The weighting coefficient is dynamically adjusted with the user's exercise duration; Set a scene adjustment coefficient according to the motor skill level score. The scene adjustment coefficient is used to determine the reference area and distribution density of the difficulty level area. Dynamically adjust the spatial distribution and area size of the difficulty level area in the virtual sports scene based on the scene adjustment coefficient and motion trajectory data. At the same time, select sports challenge tasks of corresponding difficulty according to the user's motor skill level score, and combine the selected sports challenge tasks in ascending order of difficulty to form a sports challenge task group.

5. The method according to claim 4, wherein, Dynamically adjusting the spatial distribution and area size of the difficulty level area in the virtual sports scene based on the scene adjustment coefficient and motion trajectory data includes: Determine the reference area and distribution density of the initial difficulty level area based on the scene adjustment coefficient, divide the difficulty partition of the virtual sports scene according to the reference area, and set the spatial distribution rules of the difficulty level area based on the distribution density. The spatial distribution rules include area interval distance, area overlap degree, and area distribution direction; Based on the motion trajectory data, calculate the user's area switching frequency and area stay duration. The area switching frequency represents the number of times the user crosses adjacent difficulty level areas per unit time, and the area stay duration represents the continuous exercise time of the user in each difficulty level area; Construct a dynamic transition zone according to the region switching frequency and the region staying duration, dynamically adjust the width of the transition zone according to a preset region switching frequency threshold, expand the width of the transition zone when the region switching frequency exceeds the first frequency threshold, and shrink the width of the transition zone when the region switching frequency is lower than the second frequency threshold; adjust the range of the transition zone according to a preset region staying duration threshold, reduce the width of the transition zone when the region staying duration exceeds the first duration threshold, and increase the width of the transition zone when the region staying duration is lower than the second duration threshold.

6. The method according to claim 1, wherein, reset the scene rendering parameters in the virtual motion scene according to the change of the user's motion state, and the scene rendering parameters include the scene resolution value, the refresh frequency value and the rendering accuracy value, including: adjust the scene resolution value according to the rate of change of speed; adjust the refresh frequency value according to the rate of change of acceleration; adjust the rendering accuracy value according to the rate of change of the attitude angle; perform constraint processing on the updated scene rendering parameters, and limit the scene rendering parameters within a preset parameter range, and the preset parameter range includes a resolution value range, a refresh frequency value range and a rendering accuracy value range; apply the scene rendering parameters after the constraint processing to the rendering process of the virtual motion scene to realize real-time optimization of the scene rendering effect.

7. A real-time adjustment system for a virtual motion scene in a metaverse intelligent motion, which is used to implement the method according to any one of claims 1-6, wherein, comprising: a first unit, configured to obtain the motion parameter information of the user in the virtual motion environment, calculate the motion trajectory data of the user in real time, and match the motion trajectory data with the terrain feature data of the preset virtual motion scene to generate a scene matching score; a second unit, configured to divide the virtual motion scene into multiple difficulty level regions according to the scene matching score, set different scene parameters and interaction parameters for each difficulty level region, and generate corresponding interaction response instructions according to the scene parameters and the interaction parameters; a third unit, configured to detect the change of the user's motion state in real time according to the interaction response instruction, and obtain the physiological data information and interaction data of the user in the virtual motion scene, where the physiological data information includes a heart rate value, and the interaction data includes the time taken to complete the task; a fourth unit, configured to calculate the user's motion skill level score according to the physiological data information and the interaction data in combination with a preset data mapping table, dynamically adjust the distribution of the difficulty level regions in the virtual motion scene based on the motion skill level score, and simultaneously generate a corresponding set of motion challenge tasks, and each task in the set of motion challenge tasks sets obstacle avoidance requirements and speed control targets; a fifth unit, configured to reset the scene rendering parameters in the virtual motion scene according to the change of the user's motion state, where the scene rendering parameters include the scene resolution value, the refresh frequency value and the rendering accuracy value, so as to realize dynamic adjustment of the virtual motion environment.

8. An electronic device, wherein, comprising: a processor; a memory for storing instructions executable by the processor; Wherein, the processor is configured to call the instructions stored in the memory to execute the method according to any one of claims 1 to 6.

9. A computer-readable storage medium, on which computer program instructions are stored, wherein, when the computer program instructions are executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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