Real-time adjustment method and system for virtual sports scenes in metaverse intelligent sports

Abstract

This invention provides a method and system for real-time adjustment of virtual motion scenes in Metaverse Smart Sports, relating to the field of Metaverse technology. The system involves acquiring user motion parameter information, calculating scene matching scores, and dividing difficulty levels into zones. This system then detects the user's motion state and physiological data in real time, calculates skill level scores, and dynamically adjusts scene difficulty distribution and rendering parameters. This invention achieves a precise match between virtual scenes and user athletic abilities, enhancing the immersiveness and personalization of the interactive experience. It also optimizes system resource allocation to ensure the smooth operation of the Metaverse motion environment.

Description

Technical Field

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

[0002] With the rapid development of information technology, the Metaverse, a comprehensive platform integrating emerging technologies such as virtual reality, augmented reality, and artificial intelligence, is gradually gaining public attention. Within the Metaverse, smart sports, a new interactive model, creates virtual sports scenes, allowing users to experience various sports in a virtual environment. These virtual sports scenes not only break the spatial and temporal constraints of traditional sports but also provide users with a personalized sports experience through technological means. Currently, virtual sports scene technology has shown broad application prospects in fitness, competitive sports training, rehabilitation medicine, and other fields.

[0003] However, existing metaverse virtual sports scene technology has significant shortcomings in real-time adjustment. First, most virtual sports systems use preset fixed scenes and lack the ability to dynamically adjust scene difficulty based on the user's real-time movement status and skill level. This fails to meet the personalized needs of different users, reducing the user's immersion and exercise effect. Second, existing systems have poor linkage between motion parameter monitoring and scene response, and are unable to adjust exercise challenges in real time based on the user's physiological indicators such as heart rate data, which can easily lead to discomfort in exercise intensity. Summary of the Invention

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

[0005] A first aspect of an embodiment of the present invention provides a method for real-time adjustment of a virtual motion scene in a metaverse intelligent motion, comprising:

[0006] Obtaining motion parameter information of the user in the virtual sports environment, calculating the user's motion trajectory data in real time, and matching the motion trajectory data with the terrain feature data of the preset virtual sports scene to generate a scene matching score;

[0007] Dividing the virtual sports scene into a plurality of 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;

[0008] According to the interactive response instruction, the user's motion state changes are detected in real time, and the user's physiological data information and interaction data in the virtual motion scene are obtained, wherein the physiological data information includes the heart rate value, and the interaction data includes the time taken to complete the task;

[0009] Calculating the user's sports skill level score based on the physiological data and interaction data in combination with a preset data mapping table, dynamically adjusting the difficulty level regional distribution in the virtual sports scene based on the sports skill level score, and generating a corresponding sports challenge task group, each task in the sports challenge task group setting obstacle avoidance requirements and speed control targets;

[0010] The scene rendering parameters in the virtual sports scene are reset according to the changes in the user's motion state. The scene rendering parameters include scene resolution value, refresh frequency value and rendering accuracy value, so as to realize dynamic adjustment of the virtual sports environment.

[0011] Matching the motion trajectory data with the terrain feature data of a preset virtual motion scene to generate a scene matching score includes:

[0012] Dividing the motion trajectory data into a plurality of trajectory segments, and calculating motion characteristic parameters of each trajectory segment, wherein the motion characteristic parameters include the length of the trajectory segment, the curvature of the trajectory segment, and the velocity change rate of the trajectory segment;

[0013] Performing terrain adaptability analysis on each trajectory segment, calculating a matching weight based on the Euclidean distance between the motion feature parameters of the trajectory segment and the terrain feature data of the preset virtual motion scene, wherein the matching weight is the inverse of the Euclidean distance, and using the matching weight as the scene matching score of the trajectory segment;

[0014] Based on the temporal weighted average algorithm, the scene matching scores of all trajectory segments are comprehensively calculated to generate the overall scene matching score.

[0015] According to the scene matching score, the virtual sports scene is divided into a plurality of difficulty level areas, different scene parameters and interaction parameters are set for each difficulty level area, and corresponding interaction response instructions are generated according to the scene parameters and the interaction parameters.

[0016] Setting a difficulty level division threshold according to the scene matching score, dividing the virtual sports scene into a primary difficulty area, an intermediate difficulty area, and an advanced difficulty area, wherein the range of each difficulty area is determined by adjacent difficulty level division thresholds;

[0017] Setting scenario parameters for the elementary difficulty zone, the intermediate difficulty zone, and the advanced difficulty zone, respectively, wherein the scenario parameters include a terrain slope value, an obstacle density value, and an environmental resistance value, and setting interaction parameters, wherein the interaction parameters include a collision detection distance value and a response delay time value;

[0018] Based on the user's real-time location information in each difficulty zone, a difficulty transition mechanism is constructed, and a difficulty gradient interval is set between adjacent difficulty zones. The scene parameters and interaction parameters in the difficulty gradient interval are continuously and gradually distributed as the user's location changes.

[0019] According to the user's motion state in each difficulty area, the interaction probability between the user and the scene elements is calculated, and the interaction trigger conditions of different difficulty areas are set based on the interaction probability, and an interaction response instruction including trigger timing, trigger intensity and trigger duration is generated.

[0020] The user's sports skill level score is calculated based on the physiological data information and the interaction data in combination with a preset data mapping table, and the difficulty level regional distribution in the virtual sports scene is dynamically adjusted based on the sports skill level score. At the same time, a corresponding sports challenge task group is generated, including:

[0021] The data mapping table includes physiological data mapping rules and interactive data mapping rules, wherein the physiological data mapping rules map the heart rate value to a physiological state score, and the interactive data mapping rules map the time taken to complete a task to an interactive performance score;

[0022] Based on the data mapping table, the user's physiological state score and interactive performance score are calculated respectively, and the physiological state score and interactive performance score are weighted according to a preset weighting coefficient to generate the user's sports skill level score, and the weighting coefficient is dynamically adjusted according to the user's exercise duration;

[0023] A scene adjustment coefficient is set according to the sports skill level score, and the scene adjustment coefficient is used to determine the baseline area and distribution density of the difficulty level area. The spatial distribution and area size of the difficulty level area in the virtual sports scene are dynamically adjusted based on the scene adjustment coefficient and the motion trajectory data. At the same time, sports challenge tasks of corresponding difficulty are selected based on the user's sports skill level score, and the selected sports challenge tasks are combined in order of increasing difficulty to form a sports challenge task group.

[0024] Dynamically adjusting the spatial distribution and size of difficulty level areas in the virtual sports scene based on the scene adjustment coefficient and the motion trajectory data includes:

[0025] Determining a reference area and distribution density of an initial difficulty level region based on the scene adjustment coefficient, dividing the virtual sports scene into difficulty zones according to the reference area, and setting a spatial distribution rule for the difficulty level regions based on the distribution density, the spatial distribution rule including a zone spacing distance, a zone overlap degree, and a zone distribution direction;

[0026] Based on the motion trajectory data, the user's area switching frequency and area stay duration are calculated. 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.

[0027] A dynamic transition zone is constructed based on the area switching frequency and the area residence time, and the transition zone width is dynamically adjusted based on the preset area switching frequency threshold. When the area switching frequency exceeds the first frequency threshold, the transition zone width is expanded, and when the area switching frequency is lower than the second frequency threshold, the transition zone width is reduced; the transition zone range is adjusted based on the preset area residence time threshold. When the area residence time exceeds the first time threshold, the transition zone width is reduced, and when the area residence time is lower than the second time threshold, the transition zone width is increased.

[0028] Resetting the scene rendering parameters in the virtual sports scene according to the change of the user's motion state, wherein the scene rendering parameters include scene resolution value, refresh frequency value and rendering accuracy value.

[0029] Adjust the scene resolution value according to the speed change rate; adjust the refresh frequency value according to the acceleration change rate; adjust the rendering accuracy value according to the attitude angle change rate;

[0030] Performing constraint processing on the updated scene rendering parameters to limit the scene rendering parameters to a preset parameter range, wherein the preset parameter range includes a resolution value range, a refresh rate value range, and a rendering accuracy value range;

[0031] The scene rendering parameters after constraint processing are applied to the rendering process of the virtual motion scene to achieve real-time optimization of the scene rendering effect.

[0032] A second aspect of an embodiment of the present invention provides a real-time adjustment system for virtual motion scenes in Metaverse Intelligent Motion, including:

[0033] The first unit is configured to obtain motion parameter information of a user in a virtual sports environment, calculate the user's motion trajectory data in real time, and match the motion trajectory data with terrain feature data of a preset virtual sports scene to generate a scene matching score;

[0034] A second unit is configured to divide the virtual sports scene into a plurality of 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;

[0035] A third unit is configured to detect changes in the user's motion state in real time according to the interactive response instruction, and obtain physiological data information and interaction data of the user in the virtual motion scene, wherein the physiological data information includes a heart rate value, and the interaction data includes a time taken to complete a task;

[0036] a fourth unit for calculating a user's sports skill level score based on the physiological data and interaction data in combination with a preset data mapping table, dynamically adjusting the difficulty level regional distribution in the virtual sports scene based on the sports skill level score, and generating a corresponding sports challenge task group, each task in the sports challenge task group setting obstacle avoidance requirements and speed control targets;

[0037] The fifth unit is used to reset the scene rendering parameters in the virtual sports scene according to the changes in the user's motion state. The scene rendering parameters include scene resolution value, refresh frequency value and rendering accuracy value, so as to realize dynamic adjustment of the virtual sports environment.

[0038] According to a third aspect of an embodiment of the present invention, an electronic device is provided, including:

[0039] processor;

[0040] a memory for storing processor-executable instructions;

[0041] The processor is configured to call the instructions stored in the memory to execute the aforementioned method.

[0042] According to a fourth aspect of an embodiment 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 implemented.

[0043] The beneficial effects of this application are as follows:

[0044] The present invention obtains user motion parameters and calculates motion trajectory data in real time, and matches it with the preset virtual scene terrain features, thereby realizing intelligent dynamic adjustment of the virtual motion scene and enhancing the user's immersion and participation in the metaverse environment.

[0045] The present invention calculates the sports skill level score based on the user's physiological data information and interaction data, dynamically adjusts the regional distribution of difficulty levels and generates corresponding challenge tasks, making the virtual sports experience more personalized and effectively improving the user's sports enthusiasm and willingness to continue participating.

[0046] The present invention resets scene rendering parameters according to changes in the user's motion state, thereby realizing real-time optimization of the virtual motion environment. This not only ensures the smoothness of system operation, but also improves the quality of the user's interactive experience, and solves technical problems such as insufficient scene adaptation and single user experience in traditional virtual motion systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 This is a flow chart of a method for real-time adjustment of virtual motion scenes in Metaverse Intelligent Motion according to an embodiment of the present invention.

[0048] Figure 2 Schematic diagram of the process for dynamically adjusting the spatial distribution and size of difficulty level areas in a virtual sports scene. DETAILED DESCRIPTION

[0049] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0050] The following specific embodiments are used to describe the technical solution of the present invention in detail. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.

[0051] refer to Figure 1 and Figure 2 The method for real-time adjustment of virtual motion scenes in Metaverse Intelligent Motion according to an embodiment of the present invention includes:

[0052] Obtaining motion parameter information of the user in the virtual sports environment, calculating the user's motion trajectory data in real time, and matching the motion trajectory data with the terrain feature data of the preset virtual sports scene to generate a scene matching score;

[0053] Dividing the virtual sports scene into a plurality of 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;

[0054] According to the interactive response instruction, the user's motion state changes are detected in real time, and the user's physiological data information and interaction data in the virtual motion scene are obtained, wherein the physiological data information includes the heart rate value, and the interaction data includes the time taken to complete the task;

[0055] Calculating the user's sports skill level score based on the physiological data and interaction data in combination with a preset data mapping table, dynamically adjusting the difficulty level regional distribution in the virtual sports scene based on the sports skill level score, and generating a corresponding sports challenge task group, each task in the sports challenge task group setting obstacle avoidance requirements and speed control targets;

[0056] The scene rendering parameters in the virtual sports scene are reset according to the changes in the user's motion state. The scene rendering parameters include scene resolution value, refresh frequency value and rendering accuracy value, so as to realize dynamic adjustment of the virtual sports environment.

[0057] In an optional embodiment, matching the motion trajectory data with terrain feature data of a preset virtual motion scene to generate a scene matching score includes:

[0058] Dividing the motion trajectory data into a plurality of trajectory segments, and calculating motion characteristic parameters of each trajectory segment, wherein the motion characteristic parameters include the length of the trajectory segment, the curvature of the trajectory segment, and the velocity change rate of the trajectory segment;

[0059] Performing terrain adaptability analysis on each trajectory segment, calculating a matching weight based on the Euclidean distance between the motion feature parameters of the trajectory segment and the terrain feature data of the preset virtual motion scene, wherein the matching weight is the inverse of the Euclidean distance, and using the matching weight as the scene matching score of the trajectory segment;

[0060] Based on the temporal weighted average algorithm, the scene matching scores of all trajectory segments are comprehensively calculated to generate the overall scene matching score.

[0061] The system receives raw motion trajectory data containing location and time information. This data can be a sequence of GPS coordinate points collected by a smartphone, wearable device, or professional sports tracking device. Each coordinate point includes longitude, latitude, altitude, and corresponding timestamp information.

[0062] The raw motion trajectory data undergoes preprocessing, including outlier removal, smoothing, and noise reduction. The system uses a sliding window median filter to remove outliers, with a window size of 5 sampling points. The trajectory is then smoothed using the Savitzky-Golay filter algorithm, with a filter window length of 7 points and a polynomial order of 3. This effectively reduces signal noise while preserving the trajectory's key features.

[0063] After preprocessing, the system divides the motion trajectory data into multiple trajectory segments. This is done by dividing the data into segments based on fixed distances or time intervals. In this embodiment, a fixed distance interval of 15 meters is used for segmentation, meaning that every 15 meters of the motion trajectory is considered a trajectory segment. For example, a 300-meter motion trajectory will be divided into 20 trajectory segments.

[0064] For each trajectory segment, the system calculates its motion characteristics, including the segment's length, curvature, and rate of velocity change. Segment length is calculated by summing the distances between adjacent sampling points within the segment. For example, for a trajectory segment containing 10 sampling points, the system calculates and sums the distances between adjacent points, yielding an actual segment length of 15.2 meters.

[0065] The curvature of a trajectory segment reflects the degree of curvature of the trajectory and is an important indicator for determining the turning behavior of a motion path. The system calculates the curvature of the circle formed by every three consecutive points on the trajectory segment and takes the average as the overall curvature of the segment. For example, if the average curvature of a trajectory segment is 0.025, it indicates that the trajectory segment has a low degree of curvature and is close to a straight line.

[0066] The speed change rate indicates the change in speed within a trajectory segment. The system first calculates the instantaneous speed of each point based on the distance and time difference between adjacent sampling points. The speed change rate is then calculated as the ratio of the standard deviation of the speed to the average speed. For example, if the average speed of a trajectory segment is 3.5 m / s and the standard deviation is 0.7 m / s, the speed change rate is 0.2, indicating relatively small speed fluctuations within that segment.

[0067] After calculating the motion characteristic parameters of each trajectory segment, the system performs a terrain adaptability analysis on each trajectory segment. This step compares the motion characteristics of the trajectory segment with the terrain characteristics of the preset virtual motion scene to assess their adaptability.

[0068] The preset terrain feature data for virtual sports scenes includes various terrain types, such as flat ground, uphill, downhill, and curved roads. Each terrain type has corresponding characteristic parameters. For example, the characteristic parameters for flat ground are: curvature range 0-0.01, speed change rate range 0-0.15; the characteristic parameters for uphill are: curvature range 0-0.02, speed change rate range 0.15-0.3; the characteristic parameters for curved roads are: curvature range 0.02-0.05, speed change rate range 0.1-0.25.

[0069] The system evaluates the degree of fit by calculating the Euclidean distance between the motion characteristic parameters of a trajectory segment and the terrain characteristic parameters. Assuming that the curvature of a trajectory segment is 0.025 and the speed change rate is 0.18, the Euclidean distance from flat ground, uphill slope, and curve is calculated as follows:

[0070] The Euclidean distance from flat ground is calculated by taking the square root of the difference between the curvature of the trajectory segment, 0.025, and the midpoint of the flat ground curvature, 0.005, plus the square of the difference between the velocity change rate, 0.18, and the midpoint of the flat ground velocity change rate, 0.075.

[0071] Euclidean distance to uphill: The Euclidean distance to uphill terrain calculated by the same method is 0.072. Euclidean distance to curve: The Euclidean distance to curve terrain calculated by the same method is 0.035.

[0072] The system uses the inverse of the Euclidean distance as the matching weight. This means the matching weights for this track segment with each terrain are: 9.26 for flat land, 13.89 for uphill slopes, and 28.57 for curves. Since the curve has the highest matching weight, the system determines that this track segment is most suitable for the curve and assigns a scene matching score of 28.57 to this segment.

[0073] After completing the terrain adaptability analysis for all trajectory segments, the system then calculates the scene matching scores for all segments using a time-series weighted average algorithm to generate an overall scene matching score. In this algorithm, trajectory segments closer to the current moment are given greater weight, and this weight decays over time.

[0074] In practice, the system assigns a weight coefficient Wi = 0.95^(ni) 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, the weight of the second-to-last segment is 0.95^1 = 0.95, and so on.

[0075] The system multiplies the scene matching score of each trajectory segment by the corresponding weight coefficient, then divides the sum by the sum of the weight coefficients to obtain the overall scene matching score. Assuming the weighted average of 20 trajectory segments is 22.8, the overall matching score between the motion trajectory and the virtual motion scene is 22.8.

[0076] This method can accurately evaluate the degree of matching between the motion trajectory and the virtual scene by finely analyzing the characteristic parameters of the motion trajectory and combining it with terrain adaptability evaluation and time-series weighted algorithm, providing a basis for subsequent virtual scene recommendations and motion experience optimization.

[0077] In an optional embodiment, the virtual sports scene is divided into multiple difficulty level areas according to the scene matching score, different scene parameters and interaction parameters are set for each difficulty level area, and corresponding interaction response instructions are generated according to the scene parameters and the interaction parameters.

[0078] Setting a difficulty level division threshold according to the scene matching score, dividing the virtual sports scene into a primary difficulty area, an intermediate difficulty area, and an advanced difficulty area, wherein the range of each difficulty area is determined by adjacent difficulty level division thresholds;

[0079] Setting scenario parameters for the elementary difficulty zone, the intermediate difficulty zone, and the advanced difficulty zone, respectively, wherein the scenario parameters include a terrain slope value, an obstacle density value, and an environmental resistance value, and setting interaction parameters, wherein the interaction parameters include a collision detection distance value and a response delay time value;

[0080] Based on the user's real-time location information in each difficulty zone, a difficulty transition mechanism is constructed, and a difficulty gradient interval is set between adjacent difficulty zones. The scene parameters and interaction parameters in the difficulty gradient interval are continuously and gradually distributed as the user's location changes.

[0081] According to the user's motion state in each difficulty area, the interaction probability between the user and the scene elements is calculated, and the interaction trigger conditions of different difficulty areas are set based on the interaction probability, and an interaction response instruction including trigger timing, trigger intensity and trigger duration is generated.

[0082] The difficulty level division threshold is set according to the scene matching score, and the virtual sports scene is divided into a primary difficulty area, an intermediate difficulty area, and an advanced difficulty area. In the specific implementation, the system first obtains the scene matching score, and the score range can be set to 0-100 points. Then, two difficulty level division thresholds are set: the first threshold is set to 40 points, and the second threshold is set to 75 points. Based on these two thresholds, the virtual sports scene is divided into three difficulty areas: the area with a scene matching score between 0-40 points is the primary difficulty area; the area with a scene matching score between 40-75 points is the intermediate difficulty area; the area with a scene matching score between 75-100 points is the advanced difficulty area.

[0083] Next, set the scene parameters and interaction parameters for the beginner, intermediate, and advanced difficulty levels. Scene parameters include terrain slope, obstacle density, and environmental resistance; interaction parameters include collision detection distance and response delay.

[0084] For the beginner difficulty zone, set the terrain slope to 0-15 degrees, the obstacle density to 5-10 obstacles per 100 square meters, and the environmental resistance to 1.0-1.2 times the base resistance. Also, set the collision detection distance to 2.5 meters and the response delay to 100 milliseconds. These parameters give the beginner difficulty zone a relatively flat terrain, fewer obstacles, and less environmental resistance. Furthermore, the collision detection distance is longer and the response delay is shorter, making it suitable for beginners.

[0085] For the intermediate difficulty area, the terrain slope is set to 15-30 degrees, the obstacle density is set to 10-20 obstacles per 100 square meters, and the environmental resistance is set to 1.2-1.5 times the basic resistance. At the same time, the collision detection distance is set to 1.5 meters, and the response delay is set to 150 milliseconds. These parameter settings ensure that the intermediate difficulty area has a certain slope, a moderate number of obstacles, and a medium level of environmental resistance. The collision detection distance and response delay are moderate, making it suitable for users with a certain level of basic experience.

[0086] For the advanced difficulty zone, the terrain slope is set to 30-45 degrees, the obstacle density is set to 20-30 obstacles per 100 square meters, and the environmental resistance is set to 1.5-2.0 times the basic resistance. At the same time, the collision detection distance is set to 0.8 meters, and the response delay is set to 200 milliseconds. These parameter settings give the advanced difficulty zone steeper terrain, more obstacles, and greater environmental resistance, as well as a smaller collision detection distance and longer response delay, providing a highly challenging experience for advanced users.

[0087] To achieve smooth transitions between difficulty levels, the system builds a transition mechanism based on the user's real-time location information within each difficulty level. A 10-meter-wide gradient interval is set between adjacent difficulty levels, and the scene parameters and interaction parameters within this interval continuously and gradually change with the user's location.

[0088] Taking the difficulty gradient between the beginner and intermediate difficulty zones as an example, as the user moves from the beginner to the intermediate difficulty zone, the terrain slope gradually increases from 15 degrees to 30 degrees, the obstacle density gradually increases from 10 per 100 square meters to 20 per 100 square meters, and the environmental resistance gradually increases from 1.2 times the base resistance value to 1.5 times. At the same time, the collision detection distance gradually decreases from 2.5 meters to 1.5 meters, and the response delay time gradually increases from 100 milliseconds to 150 milliseconds.

[0089] Specifically, the system uses linear interpolation to calculate the user's parameter values within the difficulty gradient range. Assuming the user's position within the difficulty gradient range is x (x ranges from 0 to 10 meters, with 0 indicating just entering the gradient range and 10 indicating about to leave the gradient range), for any parameter P, its value can be calculated as follows: P = Pbeginner + (Pintermediate - Pbeginner) × (x / 10), where Pbeginner is the parameter value for the beginner difficulty range and Pintermediate is the parameter value for the intermediate difficulty range.

[0090] According to the user's motion status in each difficulty area, the interaction probability between the user and the scene elements is calculated, and the interaction trigger conditions of different difficulty areas are set based on the interaction probability, and interaction response instructions including trigger timing, trigger intensity and trigger duration are generated.

[0091] In practice, the system first obtains information about the user's motion state, including speed, acceleration, and direction of movement. Based on this information, a statistical model is then used to calculate the probability of the user interacting with scene elements (such as obstacles and terrain features). For beginner difficulty levels, an interaction is triggered when the probability exceeds 0.3; for intermediate difficulty levels, when the probability exceeds 0.5; and for advanced difficulty levels, when the probability exceeds 0.7.

[0092] Once an interaction is triggered, the system will generate the corresponding interactive response instructions. In the beginner difficulty zone, 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 zone, 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 zone, 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.

[0093] Through the above method, the present invention realizes the difficulty level division of virtual sports scenes based on scene matching scores, and sets appropriate scene parameters and interaction parameters for different difficulty level areas. At the same time, a smooth transition between difficulty levels is achieved through the difficulty gradient interval. Finally, corresponding interactive response instructions are generated according to the user's motion state, thereby improving the user's immersion and experience quality in the virtual sports scene.

[0094] In an optional embodiment, the user's sports skill level score is calculated based on the physiological data information and interaction data in combination with a preset data mapping table, and the difficulty level regional distribution in the virtual sports scene is dynamically adjusted based on the sports skill level score. At the same time, a corresponding sports challenge task group is generated, including:

[0095] The data mapping table includes physiological data mapping rules and interactive data mapping rules, wherein the physiological data mapping rules map the heart rate value to a physiological state score, and the interactive data mapping rules map the time taken to complete a task to an interactive performance score;

[0096] Based on the data mapping table, the user's physiological state score and interactive performance score are calculated respectively, and the physiological state score and interactive performance score are weighted according to a preset weighting coefficient to generate the user's sports skill level score, and the weighting coefficient is dynamically adjusted according to the user's exercise duration;

[0097] A scene adjustment coefficient is set according to the sports skill level score, and the scene adjustment coefficient is used to determine the baseline area and distribution density of the difficulty level area. The spatial distribution and area size of the difficulty level area in the virtual sports scene are dynamically adjusted based on the scene adjustment coefficient and the motion trajectory data. At the same time, sports challenge tasks of corresponding difficulty are selected based on the user's sports skill level score, and the selected sports challenge tasks are combined in order of increasing difficulty to form a sports challenge task group.

[0098] The system collects the user's physiological data information through wearable devices, including but not limited to heart rate, blood oxygen saturation, respiratory rate and other data; at the same time, it collects the user's interaction data through virtual reality devices or somatosensory devices, including but not limited to movement response time, time to complete tasks, movement trajectory and other information.

[0099] The system presets a data mapping table that contains physiological data mapping rules and interactive data mapping rules. The physiological data mapping rules map heart rate values to physiological status scores. For example, a heart rate in the range of 60-70 beats / minute is scored as 85 points, a heart rate in the range of 70-80 beats / minute is scored as 90 points, a heart rate in the range of 80-100 beats / minute is scored as 95 points, a heart rate in the range of 100-120 beats / minute is scored as 100 points, a heart rate in the range of 120-140 beats / minute is scored as 95 points, a heart rate in the range of 140-160 beats / minute is scored as 90 points, and a heart rate over 160 beats / minute is scored as 80 points.

[0100] The interaction data mapping rules map the time taken to complete a task into an interaction performance score. For example, for an entry-level obstacle avoidance task, a completion time of less than 10 seconds is scored as 100 points, 10-15 seconds is scored as 90 points, 15-20 seconds is scored as 80 points, 20-30 seconds is scored as 70 points, and more than 30 seconds is scored as 60 points.

[0101] The system calculates the user's physiological status score and interaction performance score based on the data mapping table. For example, if a user's heart rate is 115 beats per minute, their physiological status score is 100 points according to the physiological data mapping rules. If a user completes a beginner obstacle avoidance task in 13 seconds, their interaction performance score is 90 points according to the interaction data mapping rules.

[0102] The system weights the physiological status score and interactive performance score according to a preset weighting coefficient to generate the user's sports skill level score. The weighting coefficient is dynamically adjusted according to the user's exercise duration. The specific calculation method is: in the early stage of exercise (0-10 minutes), the physiological status score is weighted 0.4, and the interactive performance score is weighted 0.6; in the middle stage of exercise (10-30 minutes), the physiological status score is weighted 0.5, and the interactive performance score is weighted 0.5; in the late stage of exercise (more than 30 minutes), the physiological status score is weighted 0.6, and the interactive performance score is weighted 0.4.

[0103] Taking the above example, assuming that the user is in the early stages of exercise, the sports skill level score is calculated as: 100×0.4+90×0.6=94 points.

[0104] The system sets a scene adjustment coefficient based on the user's sports skill level score. This coefficient is used to determine the baseline area and distribution density of the difficulty level area. For example, a sports skill level score in the 90-100 range corresponds to a scene adjustment coefficient of 1.2, a score in the 80-90 range corresponds to a scene adjustment coefficient of 1.0, a score in the 70-80 range corresponds to a scene adjustment coefficient of 0.8, and a score in the 60-70 range corresponds to a scene adjustment coefficient of 0.6.

[0105] In the above example, if the user's sports skill level score is 94, the corresponding scenario adjustment coefficient is 1.2. Assuming the system presets a baseline area of 100 square meters for the high-difficulty area, 200 square meters for the medium-difficulty area, and 300 square meters for the low-difficulty area, 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.

[0106] The system dynamically adjusts the spatial distribution and size of difficulty levels within virtual sports scenes based on the scene adjustment coefficient and the user's motion trajectory data. For example, if a user is detected to be frequently active in a certain area, the system will add a more challenging area near that area. This is achieved by performing a heat map analysis of the user's motion trajectory over the last 5 minutes to identify the center point of the area with the highest activity frequency. Using this center point as a reference, the system then adds an area within 10-15 meters around it that is one level higher in difficulty than the current area. The area is calculated based on the scene adjustment coefficient.

[0107] At the same time, the system selects sports challenge tasks of corresponding difficulty based on the user's sports skill level score and combines the selected sports challenge tasks in order of increasing difficulty to form a sports challenge task group. The corresponding relationship between sports skill level score and 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 beginner tasks, and scores below 60 correspond to novice-level tasks.

[0108] Taking the user's sports skill level score of 94 as an example, the system will select appropriate tasks from the professional-level task library. Assuming the professional-level task library includes tasks such as "High-Speed Continuous Obstacle Avoidance," "Precise Target Hitting," and "Complex Path Tracing," the system will select 3-5 tasks from this library and sort them from lowest to highest difficulty to form a sports challenge task group. For example: Task 1: High-Speed Continuous Obstacle Avoidance (Difficulty Coefficient 0.9); Task 2: Precise Target Hitting (Difficulty Coefficient 0.95); Task 3: Complex Path Tracing (Difficulty Coefficient 1.0).

[0109] After a user completes a task, the system recollects physiological and interaction data, updates the motor skill level score, and adjusts the difficulty of subsequent tasks accordingly. For example, if a user's motor skill level score rises to 96 after completing 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.

[0110] Through the above method, the system can dynamically adjust the difficulty distribution and challenge tasks of the virtual sports scene based on the user's real-time performance and physiological state, provide a personalized and progressive sports experience, and effectively improve the user's sports participation and training effect.

[0111] In an optional embodiment, dynamically adjusting the spatial distribution and size of difficulty level areas in the virtual sports scene based on the scene adjustment coefficient and the motion trajectory data includes:

[0112] Determining a reference area and distribution density of an initial difficulty level region based on the scene adjustment coefficient, dividing the virtual sports scene into difficulty zones according to the reference area, and setting a spatial distribution rule for the difficulty level regions based on the distribution density, the spatial distribution rule including a zone spacing distance, a zone overlap degree, and a zone distribution direction;

[0113] Based on the motion trajectory data, the user's area switching frequency and area stay duration are calculated. 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.

[0114] A dynamic transition zone is constructed based on the area switching frequency and the area residence time, and the transition zone width is dynamically adjusted based on the preset area switching frequency threshold. When the area switching frequency exceeds the first frequency threshold, the transition zone width is expanded, and when the area switching frequency is lower than the second frequency threshold, the transition zone width is reduced; the transition zone range is adjusted based on the preset area residence time threshold. When the area residence time exceeds the first time threshold, the transition zone width is reduced, and when the area residence time is lower than the second time threshold, the transition zone width is increased.

[0115] The baseline area and distribution density of the initial difficulty level area are determined based on the scene adjustment coefficient. The scene adjustment coefficient is a numerical parameter generated based on the user's athletic ability level, the type of virtual scene, and the characteristics of the sports event. For example, for beginners, the scene adjustment coefficient can be set to 0.8, for intermediate users to 1.0, and for advanced users to 1.2. The baseline area can be calculated by multiplying the total area of the virtual scene by the square of the scene adjustment coefficient. For example, for a scene with a total area of 100 square meters, the baseline area for beginners 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 difficulty areas.

[0116] Based on the determined base area, the system divides the virtual sports scene into zones of different difficulty levels. For example, in a 1,000-square-meter virtual running scene, it can be divided into a simple area (accounting for 40%, or 400 square meters), a medium area (accounting for 35%, or 350 square meters), and a difficult area (accounting for 25%, or 250 square meters). Set spatial distribution rules based on the distribution density, including the distance between regions, the degree of region overlap, and the direction of region distribution. Specifically, the region interval distance can be set as the base value (such as 5 meters) multiplied by the inverse of the scene adjustment coefficient, so that the difficulty regions faced by high-level users are smaller; the degree of region overlap can be defined as the percentage of overlapping areas of adjacent difficulty regions, such as 10% for beginners, 15% for intermediate, and 20% for advanced; the direction of regional distribution is determined according to the user's accustomed movement path, such as clockwise or along a specific axis.

[0117] The system calculates the area switching frequency and area dwell time based on the user's motion trajectory data. The area switching frequency indicates the number of times a user crosses adjacent difficulty level areas per unit time. For example, if a user crosses the boundaries of areas of different difficulty levels 10 times within 5 minutes, the area switching frequency is 2 times / minute. The area dwell time indicates the continuous exercise time of the user in each difficulty level area. For example, if a user stays in the easy area for 3 minutes, in the medium area for 2 minutes, and in the difficult area for 1 minute, the dwell time in each area is 3 minutes, 2 minutes, and 1 minute respectively.

[0118] The system uses a sliding window approach to record and update this data. For example, every 30 seconds is used as a data collection window. The number of times a user switches between zones and the time they stay in each zone within that window is recorded. The system then uses the sliding window to calculate the average value over the last five minutes to ensure real-time and representative data.

[0119] Based on the frequency of area switching and the length of time spent in an area, the system constructs a dynamic transition zone. A transition zone refers to a gradient area set between areas of different difficulty levels, which is used to smooth the change in difficulty and improve the user experience. The system dynamically adjusts the width of the transition zone based on the preset area switching frequency threshold. When the area switching frequency exceeds the first frequency threshold (such as 3 times / minute), it indicates that the user frequently switches between difficulty areas. The system will expand the width of the transition zone (such as from the original 2 meters to 4 meters) to reduce the sense of sudden change in difficulty. When the area switching frequency is lower than the second frequency threshold (such as 1 time / minute), it indicates that the user rarely switches areas. The system will narrow the width of the transition zone (such as from the original 2 meters to 1 meter) to provide a clearer difficulty change experience.

[0120] At the same time, the system adjusts the transition zone range based on preset zone dwell time thresholds. When a user's dwell time in a difficulty zone exceeds the first threshold (e.g., 4 minutes), indicating that the user has adapted to the current difficulty, the system will reduce the width of the adjacent transition zone (e.g., from 3 meters to 2 meters) to make the difficulty change more obvious. When the dwell time in an area falls below the second threshold (e.g., 1 minute), indicating that the user may not be adapting to the current difficulty, the system will increase the width of the transition zone (e.g., from 3 meters to 5 meters) to make the difficulty change more gradual.

[0121] In actual operation, the system reassesses the user's zone switching frequency and duration of stay in a zone at regular intervals (e.g., 60 seconds) and updates the transition zone parameters accordingly. For example, if a user switches zones 3.5 times / minute (above the threshold of 3 times / minute) and spends only 0.8 minutes in a difficult zone (below the threshold of 1 minute), the system will increase the width of the transition zone between the difficult zone and the moderate zone from 2.5 meters to 4 meters. The system may also adjust the distribution of difficult zones to make them more dispersed or smaller.

[0122] Through the above-mentioned dynamic adjustment mechanism, the system can adaptively adjust the spatial distribution and area size of the difficulty level areas in the virtual sports scene according to the user's real-time sports performance, providing a personalized sports experience. It can not only avoid users from feeling frustrated due to excessive difficulty, but also prevent poor training results due to excessively low difficulty.

[0123] In an optional embodiment, scene rendering parameters in the virtual sports scene are reset according to the change of the user's motion state, and the scene rendering parameters include scene resolution value, refresh frequency value and rendering accuracy value, including:

[0124] Adjust the scene resolution value according to the speed change rate; adjust the refresh frequency value according to the acceleration change rate; adjust the rendering accuracy value according to the attitude angle change rate;

[0125] Performing constraint processing on the updated scene rendering parameters to limit the scene rendering parameters to a preset parameter range, wherein the preset parameter range includes a resolution value range, a refresh rate value range, and a rendering accuracy value range;

[0126] The scene rendering parameters after constraint processing are applied to the rendering process of the virtual motion scene to achieve real-time optimization of the scene rendering effect.

[0127] Obtain the user's motion status data, including speed, acceleration, and posture angle information, and then dynamically adjust the scene rendering parameters based on this information, including the scene resolution value, refresh rate value, and rendering accuracy value. Finally, apply the adjusted parameters to the rendering process of the virtual motion scene.

[0128] In one specific embodiment, the method collects user motion state data via a wearable device (e.g., a VR helmet, a smartwatch, etc.). The wearable device includes multiple sensors, such as an accelerometer, a gyroscope, and a magnetometer. These sensors collect the user's motion state data at a predetermined sampling frequency (e.g., 100 times per second).

[0129] After preprocessing, the collected motion data is used to calculate the user's velocity, acceleration, and posture angle change rates. Preprocessing includes data filtering, outlier removal, and data smoothing. For example, a moving average filter is used to process the raw data with a filter window size of 5 to eliminate data jitter.

[0130] The speed change rate is calculated by comparing 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 at time point t2 (t2 - t1 = 0.1 seconds) it is 3.0 m / s, then the speed change rate is (3.0 - 2.5) / 0.1 = 5.0 m / s².

[0131] The jerk is calculated by comparing the difference in acceleration between two consecutive time points to the time interval. For example, if the user's acceleration at time t1 is 1.2 m / s² and at time t2 (t2 - t1 = 0.1 seconds) it is 1.8 m / s², then the jerk is (1.8 - 1.2) / 0.1 = 6.0 m / s³.

[0132] The attitude angle change rate is calculated by comparing the difference in attitude angles between two consecutive time points with the time interval. Attitude angles include pitch, roll, and yaw. For example, if the user's yaw angle is 15 degrees at time t1 and 20 degrees at time t2 (t2 - t1 = 0.1 seconds), the yaw angle change rate is (20 - 15) / 0.1 = 50 degrees / second.

[0133] Adjust the scene resolution based on the speed change rate. When the speed change rate increases, the scene resolution is reduced; when the speed change rate decreases, the scene resolution is increased. For example, if the base resolution is 1920×1080 pixels, when the speed change rate exceeds 10 m / s², the resolution is reduced to 1600×900 pixels. When the speed change rate exceeds 20 m / s², the resolution is further reduced to 1280×720 pixels. When the speed change rate is less than 5 m / s², the base resolution remains at 1920×1080 pixels.

[0134] Adjust the refresh rate based on the rate of acceleration. Increase the refresh rate as the rate of acceleration increases, and decrease it as it decreases. For example, if the base refresh rate is 60Hz, increase it to 90Hz when the rate of acceleration exceeds 15m / s³. Increase it to 120Hz when the rate of acceleration exceeds 30m / s³. Maintain the base refresh rate at 60Hz when the rate of acceleration is below 10m / s³.

[0135] Adjust the rendering accuracy value based on the rate of change of the pose angle. When the rate of change of the pose angle increases, the rendering accuracy is reduced; when the rate of change of the pose angle decreases, the rendering accuracy is increased. Rendering accuracy can be achieved by adjusting parameters such as the model level of detail (LOD), texture quality, and shadow quality. For example, set the baseline rendering accuracy to high (corresponding to a value of 90). When the rate of change of any pose angle exceeds 60 degrees / second, the rendering accuracy is reduced to medium (corresponding to a value of 60); when the rate of change of any pose angle exceeds 120 degrees / second, the rendering accuracy is further reduced to low (corresponding to a value of 30); when the rate of change of all pose angles is less than 30 degrees / second, the baseline rendering accuracy is maintained at high.

[0136] Apply constraints to the updated scene rendering parameters to ensure that the parameters are within the preset parameter range. The specific constraints are as follows:

[0137] The resolution range is 640×360 to 3840×2160 pixels and must be a common display resolution standard (such as 640×360, 1280×720, 1600×900, 1920×1080, 2560×1440, and 3840×2160).

[0138] The refresh rate range is 30 Hz to 240 Hz, and the refresh rate must be a frequency supported by the display device (such as 30 Hz, 60 Hz, 90 Hz, 120 Hz, 144 Hz, 240 Hz, etc.).

[0139] The rendering precision value ranges from 10 to 100, where 10 represents the lowest rendering precision and 100 represents the highest rendering precision.

[0140] During constraint processing, if the calculated parameter value exceeds the preset range, it is set to the nearest boundary value. For example, if the calculated resolution is 500×280 pixels, which is lower than the minimum allowed value of 640×360 pixels, it is set to 640×360 pixels.

[0141] Experimental results demonstrate that this method can adjust scene rendering parameters in real time based on the user's motion state, optimizing system resource utilization while maintaining a smooth user experience. For example, in one test, a user began running from a stationary state, with their speed rapidly increasing to 15 m / s². The system reduced the resolution from 1920×1080 pixels to 1600×900 pixels, increased the refresh rate from 60Hz to 90Hz, and lowered the rendering accuracy from 90 to 60, thus reducing the GPU load by approximately 25% while maintaining a smooth visual experience.

[0142] The real-time adjustment system for virtual motion scenes in Metaverse Intelligent Motion according to an embodiment of the present invention includes:

[0143] The first unit is configured to obtain motion parameter information of a user in a virtual sports environment, calculate the user's motion trajectory data in real time, and match the motion trajectory data with terrain feature data of a preset virtual sports scene to generate a scene matching score;

[0144] A second unit is configured to divide the virtual sports scene into a plurality of 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;

[0145] A third unit is configured to detect changes in the user's motion state in real time according to the interactive response instruction, and obtain physiological data information and interaction data of the user in the virtual motion scene, wherein the physiological data information includes a heart rate value, and the interaction data includes a time taken to complete a task;

[0146] a fourth unit for calculating a user's sports skill level score based on the physiological data and interaction data in combination with a preset data mapping table, dynamically adjusting the difficulty level regional distribution in the virtual sports scene based on the sports skill level score, and generating a corresponding sports challenge task group, each task in the sports challenge task group setting obstacle avoidance requirements and speed control targets;

[0147] The fifth unit is used to reset the scene rendering parameters in the virtual sports scene according to the changes in the user's motion state. The scene rendering parameters include scene resolution value, refresh frequency value and rendering accuracy value, so as to realize dynamic adjustment of the virtual sports environment.

[0148] According to a third aspect of the embodiments of the present invention,

[0149] An electronic device is provided, comprising:

[0150] processor;

[0151] a memory for storing processor-executable instructions;

[0152] The processor is configured to call the instructions stored in the memory to execute the aforementioned method.

[0153] According to a fourth aspect of the embodiments of the present invention,

[0154] 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 implemented.

[0155] 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 carrying computer-readable program instructions for executing various aspects of the present invention.

[0156] 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 above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for real-time adjustment of virtual motion scenes in Metaverse Intelligent Motion, characterized in that: include: Obtaining motion parameter information of the user in the virtual sports environment, calculating the user's motion trajectory data in real time, and matching the motion trajectory data with the terrain feature data of the preset virtual sports scene to generate a scene matching score; Dividing the virtual sports scene into a plurality of 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; According to the interactive response instruction, the user's motion state changes are detected in real time, and the user's physiological data information and interaction data in the virtual motion scene are obtained, wherein the physiological data information includes the heart rate value, and the interaction data includes the time taken to complete the task; Calculating the user's sports skill level score based on the physiological data and interaction data in combination with a preset data mapping table, dynamically adjusting the difficulty level regional distribution in the virtual sports scene based on the sports skill level score, and generating a corresponding sports challenge task group, each task in the sports challenge task group setting obstacle avoidance requirements and speed control targets; Resetting scene rendering parameters in the virtual sports scene according to changes in the user's motion state, the scene rendering parameters including scene resolution value, refresh rate value, and rendering accuracy value, to achieve dynamic adjustment of the virtual sports environment; Dividing the motion trajectory data into a plurality of trajectory segments, and calculating motion characteristic parameters of each trajectory segment, wherein the motion characteristic parameters include the length of the trajectory segment, the curvature of the trajectory segment, and the velocity change rate of the trajectory segment; Performing terrain adaptability analysis on each trajectory segment, calculating a matching weight based on the Euclidean distance between the motion feature parameters of the trajectory segment and the terrain feature data of the preset virtual motion scene, wherein the matching weight is the inverse of the Euclidean distance, and using the matching weight as the scene matching score of the trajectory segment; Based on the temporal weighted average algorithm, the scene matching scores of all trajectory segments are comprehensively calculated to generate the overall scene matching score.

2. The method according to claim 1, characterized in that According to the scene matching score, the virtual sports scene is divided into a plurality of difficulty level areas, different scene parameters and interaction parameters are set for each difficulty level area, and corresponding interaction response instructions are generated according to the scene parameters and the interaction parameters. Setting a difficulty level division threshold according to the scene matching score, dividing the virtual sports scene into a primary difficulty area, an intermediate difficulty area, and an advanced difficulty area, wherein the range of each difficulty area is determined by adjacent difficulty level division thresholds; Setting scenario parameters for the elementary difficulty zone, the intermediate difficulty zone, and the advanced difficulty zone, respectively, wherein the scenario parameters include a terrain slope value, an obstacle density value, and an environmental resistance value, and setting interaction parameters, wherein the interaction parameters include a collision detection distance value and a response delay time value; Based on the user's real-time location information in each difficulty zone, a difficulty transition mechanism is constructed, and a difficulty gradient interval is set between adjacent difficulty zones. The scene parameters and interaction parameters in the difficulty gradient interval are continuously and gradually distributed as the user's location changes. According to the user's motion state in each difficulty area, the interaction probability between the user and the scene elements is calculated, and the interaction trigger conditions of different difficulty areas are set based on the interaction probability, and an interaction response instruction including trigger timing, trigger intensity and trigger duration is generated.

3. The method according to claim 1, characterized in that The user's sports skill level score is calculated based on the physiological data information and the interaction data in combination with a preset data mapping table, and the difficulty level regional distribution in the virtual sports scene is dynamically adjusted based on the sports skill level score. At the same time, a corresponding sports challenge task group is generated, including: The data mapping table includes physiological data mapping rules and interactive data mapping rules, wherein the physiological data mapping rules map the heart rate value to a physiological state score, and the interactive data mapping rules map the time taken to complete a task to an interactive performance score; Based on the data mapping table, the user's physiological state score and interactive performance score are calculated respectively, and the physiological state score and interactive performance score are weighted according to a preset weighting coefficient to generate the user's sports skill level score, and the weighting coefficient is dynamically adjusted according to the user's exercise duration; A scene adjustment coefficient is set according to the sports skill level score, and the scene adjustment coefficient is used to determine the baseline area and distribution density of the difficulty level area. The spatial distribution and area size of the difficulty level area in the virtual sports scene are dynamically adjusted based on the scene adjustment coefficient and the motion trajectory data. At the same time, sports challenge tasks of corresponding difficulty are selected based on the user's sports skill level score, and the selected sports challenge tasks are combined in order of increasing difficulty to form a sports challenge task group.

4. The method according to claim 3, characterized in that Dynamically adjusting the spatial distribution and size of difficulty level areas in the virtual sports scene based on the scene adjustment coefficient and the motion trajectory data includes: Determining a reference area and distribution density of an initial difficulty level region based on the scene adjustment coefficient, dividing the virtual sports scene into difficulty zones according to the reference area, and setting a spatial distribution rule for the difficulty level regions based on the distribution density, the spatial distribution rule including a zone spacing distance, a zone overlap degree, and a zone distribution direction; Based on the motion trajectory data, the user's area switching frequency and area stay duration are calculated. 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. A dynamic transition zone is constructed based on the area switching frequency and the area residence time, and the transition zone width is dynamically adjusted based on the preset area switching frequency threshold. When the area switching frequency exceeds the first frequency threshold, the transition zone width is expanded, and when the area switching frequency is lower than the second frequency threshold, the transition zone width is reduced; the transition zone range is adjusted based on the preset area residence time threshold. When the area residence time exceeds the first time threshold, the transition zone width is reduced, and when the area residence time is lower than the second time threshold, the transition zone width is increased.

5. The method according to claim 1, wherein Resetting the scene rendering parameters in the virtual sports scene according to the change of the user's motion state, wherein the scene rendering parameters include scene resolution value, refresh frequency value and rendering accuracy value. Adjust the scene resolution value according to the speed change rate; adjust the refresh frequency value according to the acceleration change rate; adjust the rendering accuracy value according to the attitude angle change rate; Performing constraint processing on the updated scene rendering parameters to limit the scene rendering parameters to a preset parameter range, wherein the preset parameter range includes a resolution value range, a refresh rate value range, and a rendering accuracy value range; The scene rendering parameters after constraint processing are applied to the rendering process of the virtual motion scene to achieve real-time optimization of the scene rendering effect.

6. A real-time adjustment system for virtual motion scenes in Metaverse Intelligent Motion, for implementing the method according to any one of claims 1 to 5, characterized in that: include: The first unit is configured to obtain motion parameter information of a user in a virtual sports environment, calculate the user's motion trajectory data in real time, and match the motion trajectory data with terrain feature data of a preset virtual sports scene to generate a scene matching score; A second unit is configured to divide the virtual sports scene into a plurality of 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; A third unit is configured to detect changes in the user's motion state in real time according to the interactive response instruction, and obtain physiological data information and interaction data of the user in the virtual motion scene, wherein the physiological data information includes a heart rate value, and the interaction data includes a time taken to complete a task; a fourth unit for calculating a user's sports skill level score based on the physiological data and interaction data in combination with a preset data mapping table, dynamically adjusting the difficulty level regional distribution in the virtual sports scene based on the sports skill level score, and generating a corresponding sports challenge task group, each task in the sports challenge task group setting obstacle avoidance requirements and speed control targets; The fifth unit is used to reset the scene rendering parameters in the virtual sports scene according to the changes in the user's motion state. The scene rendering parameters include scene resolution value, refresh frequency value and rendering accuracy value, so as to realize dynamic adjustment of the virtual sports environment.

7. An electronic device, characterized in that: include: processor; a memory for storing processor-executable instructions; The processor is configured to call the instructions stored in the memory to execute the method according to any one of claims 1 to 5.

8. A computer-readable storage medium having computer program instructions stored thereon, characterized in that: When the computer program instructions are executed by a processor, the method according to any one of claims 1 to 5 is implemented.

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