Game interaction system combined with limb movement induction

Abstract

The invention relates to a game interaction system combined with limb movement induction, and the system comprises an environment sensing module which collects three-dimensional space data in real time to construct a dynamic environment model; the motion capture module is used for acquiring limb motion data through a multi-mode sensor and storing historical motion modes; the game logic analysis module is used for analyzing a demand action sequence, a target moving path and action demand parameters; the action prediction module is used for generating a predicted action sequence and a space coordinate track; the collision risk assessment module is used for calculating the contact probability and danger level of the predicted action and the obstacle; and the dynamic guidance generation module generates an action correction instruction according to the evaluation result. The system provides safe and smooth interaction experience for a user in a complex dynamic environment through accurate collision detection and intelligent action adjustment, not only meets a game target, but also effectively avoids collision, remarkably enhances immersion and operation safety, and is suitable for various limb action sensing game scenes.

Description

Technical Field

[0001] The present invention relates to the field of somatosensory game interaction, and in particular to a game interaction system combining body motion sensing. Background Art

[0002] In recent years, somatosensory game interaction technology has developed rapidly, providing users with an immersive experience through the integration of motion capture, environmental perception and virtual reality. Existing technologies generally use inertial sensors, depth cameras and other devices to realize body motion recognition, and combine environmental modeling technology to avoid physical obstacles. With the rise of the concepts of metaverse and digital twins, higher requirements are placed on the real-time performance, environmental adaptability and safety protection capabilities of the system. Industry trends focus on multimodal data fusion, dynamic obstacle prediction and personalized action guidance, but existing solutions still have technical bottlenecks in accurate obstacle avoidance and action planning in complex scenarios: Some systems attempt to combine game logic to analyze the sequence of actions that users need to perform, but their ability to integrate the real-time position and movement status of obstacles in a dynamic environment is insufficient, and they do not fully consider the impact of users' historical action patterns on future action predictions.

[0003] In complex dynamic environments, it is impossible to accurately integrate static and dynamic obstacle information in real time to build a comprehensive environmental model; the prediction of the user's future actions does not effectively combine historical action characteristics and game requirement parameters, resulting in poor adaptability between the predicted action sequence and the actual environment; collision risk assessment only stays at a simple spatial position comparison, and a quantitative assessment model based on contact probability has not been established, making it difficult to accurately identify high-risk actions; there is a lack of intelligent action correction mechanism, and it is impossible to dynamically adjust user actions to avoid obstacles while ensuring game goals, which may cause users to have real collisions during the game, affecting the safety of the experience and the smoothness of operation. Summary of the invention

[0004] The present invention aims at the technical problems existing in the prior art and provides a game interaction system combined with body motion sensing.

[0005] The technical solution of the present invention to solve the above technical problems is as follows: a game interaction system combined with body motion sensing, the system comprising: The environmental perception module is used to collect the three-dimensional spatial data of the user's environment in real time, identify the location of obstacles and the movement trajectory of dynamic objects in the environment, and build a dynamic environment model; The motion capture module is used to obtain the user's body motion data in real time through multimodal sensors, analyze the user's current motion characteristics, and store them as the user's historical motion pattern; The game logic analysis module is used to connect to the game content database and analyze the required action sequence, target movement path and game action requirement parameters that the user needs to complete within the subsequent preset time period according to the current game scene; An action prediction module is used to generate a user's future predicted action sequence and a spatial coordinate change trajectory of the corresponding body parts through a time series prediction algorithm based on the historical action patterns stored in the action capture module, the current action features analyzed in real time, and the action requirement parameters output by the game logic analysis module; The collision risk assessment module is used to compare the spatial coordinate trajectory of the predicted action sequence with the dynamic environment model in real time, and calculate the contact probability and collision risk level of each predicted action with the obstacle; The dynamic guidance generation module is used to generate action correction instructions based on the collision risk assessment results, and generate subsequent action guidance that meets the game objectives and avoids obstacles.

[0006] As a further solution of the present invention, the execution steps of the environment perception module include: Acquire environmental point cloud data and identify obstacle information, including static obstacle outlines, and detect the real-time position and movement status of dynamic obstacles; The information of identified static obstacles and dynamic obstacles is integrated to establish a comprehensive dynamic environment model.

[0007] As a further solution of the present invention, the execution steps of the game logic analysis module include: Connect to the game content database, obtain the status information of the current game scene, identify the dynamic elements in the current game scene, analyze the game logic, and determine the target tasks that the user needs to complete in the current scene and the corresponding action requirement parameters; Based on the target tasks to be completed in the current scene and the corresponding action requirement parameters, combined with the tasks currently completed by the user, identify the action sequence that the game requires the user to perform within a preset time period in the future, which is defined as the required action sequence; Based on the dynamic environment model and the required action sequence, the required actions of the game are analyzed and combined to form the target movement path; The game content database is read, and the required actions of the game are used as labels to identify the required parameters of each required action, including strength, speed and movement mode, and combine them with the target movement path.

[0008] As a further solution of the present invention, the execution steps of the action prediction module include: Extract the user's historical action pattern, combine it with the dynamic environment model, and identify the user's current position coordinates in the dynamic environment model; Read the action demand parameters, combine the current demand action sequence and the position coordinates of the user in the dynamic environment model at the previous moment, establish a time series prediction algorithm, identify the user predicted action corresponding to each demand action in the demand action sequence, and combine them into a predicted action sequence; According to the predicted action sequence, combined with the dynamic environment model and the current position coordinates of the user in the dynamic environment model, the user's limb movements are simulated in the dynamic environment model and combined into a predicted motion trajectory, and the spatial coordinates corresponding to each user's predicted action in the predicted action sequence are identified.

[0009] As a further solution of the present invention, the execution steps of the collision risk assessment module include: Obtain the spatial coordinates and body part motion trajectory corresponding to each user's predicted action in the predicted action sequence, and at the same time obtain the static obstacle contour coordinates, dynamic obstacle real-time position and movement status data in the dynamic environment model, and establish the three-dimensional obstacle bounding boxes of static obstacles and dynamic obstacles; The motion trajectory of the body parts corresponding to the user's predicted action is spatially mapped in the dynamic environment model. For each user's predicted action, a three-dimensional bounding box of the human body containing the motion range of the human body parts is established based on the spatial coordinates, and the spatial position is matched with the three-dimensional bounding box of the obstacle in the dynamic environment model. According to the contour coordinates of static obstacles and the real-time position and movement status of dynamic obstacles, the spatial overlap between the spatial coordinates of the user's predicted action at the corresponding time point and the three-dimensional bounding box of the obstacle is calculated. Based on the spatial overlap, a contact probability calculation model is constructed to calculate the contact probability between each user's predicted action and the obstacle. A collision risk level threshold interval is preset, and according to the contact probability calculation result, the contact probability of each user's predicted action with the obstacle is compared and matched with the collision risk level threshold interval to determine the corresponding collision risk level; The contact probability and collision risk level corresponding to each user's predicted action are integrated to form a collision risk assessment result corresponding to the predicted action sequence.

[0010] As a further solution of the present invention, the execution steps of the dynamic guidance generation module include: Analyze the collision risk assessment results, identify the predicted actions with high collision risk levels, and identify the action nodes and corresponding body parts that will come into contact with obstacles; Combined with the game goal and the status information of the current game scene, analyze the role and necessity of each user's predicted action above the collision risk level threshold interval in the game logic, and determine whether the action can be adjusted without affecting the game goal; For user predicted actions that are above the collision risk level threshold range and are adjustable and do not affect the game objectives, the motion planning algorithm is used to replan the spatial trajectory and motion parameters of the action while ensuring obstacle avoidance, and generate a revised action plan based on the dynamic environment model and the position and state information of the obstacles. The revised action plan is integrated with the original required action sequence, and the execution order, action direction and action range of the required action sequence are adjusted according to the timing requirements and continuity of the game.

[0011] As a further solution of the present invention, the time series prediction algorithm is established to identify the user prediction action corresponding to each demand action in the demand action sequence and combine them into a prediction action sequence, specifically: Prediction Spatial coordinates of time It is expressed as: ; in, express The user's position coordinates in the dynamic environment model at the moment, Represents the instantaneous velocity vector corresponding to the current demand action, Represents the acceleration vector corresponding to the current demand action, is a preset time interval; Then for the first Action, its predicted position recursively calculates the cumulative motion of all previous actions: .

[0012] As a further solution of the present invention, the three-dimensional bounding boxes of the static obstacles and the dynamic obstacles are established as follows: For static obstacles: The contour coordinates are a set of vertices processed from the point cloud data The coordinates of each vertex are ; The obstacle bounding box vertices of the static obstacle are calculated as: ; Among them, the obstacle bounding box consists of vertices: , ; The only certainty; For dynamic obstacles: The real-time location is ; Movement status including speed and acceleration ; in They are , , The speed in three directions, They are , , Acceleration in three directions; The obstacle bounding box vertices of the dynamic obstacle at the current moment are calculated as: ; in, , , are the fixed width, height, and depth of the dynamic obstacle, respectively; The obstacle bounding box consists of vertices: , ; The only certainty.

[0013] As a further solution of the present invention, the calculation of the spatial overlap between the spatial coordinates of the user's predicted action at the corresponding time point and the three-dimensional bounding box of the obstacle is specifically as follows: Read the obstacle bounding box: ; Construct a human body bounding box: ; Calculate the length of the single axis overlap interval: ; ; ; in, , , Respectively expressed in axis, axis, In the axial direction, the length of the overlapping interval between the obstacle bounding box and the corresponding axial range of the human body bounding box; Calculate the ratio of the intersection volume to the union volume of the human body bounding box and the obstacle bounding box as the overlap: ; in, Represents the obstacle bounding box volume, Represents the volume of the human body bounding box; ; ; The calculation of the contact probability between each user's predicted action and the obstacle is specifically as follows: Probability of contact .

[0014] The beneficial effects of the present invention are: The system implements a complex collision detection mechanism. By establishing a three-dimensional bounding box, the system can accurately calculate the contact probability between the user's action and the obstacle. This technological breakthrough enables the system to provide safety protection in a real-time dynamic environment, allowing users to play games without obstacles and reducing the risk of accidental collisions.

[0015] The action correction instruction generation mechanism ensures that users can adjust their actions in time when a collision is about to occur. This intelligent guidance not only improves the safety of the game, but also enhances the user's immersion. Users can experience more natural and smoother operations in the game, and this feedback mechanism greatly improves the user's gaming experience.

[0016] The system creates a safe, flexible and immersive gaming environment for users. Users can not only enjoy higher interactivity, but also move freely in the real-time environment without worrying about potential collision risks. The intelligent and personalized features of the system enable each user to have a unique gaming experience, achieving higher participation and immersion in the game.

[0017] This game interaction system combined with body motion sensing represents the future direction of game interaction technology, providing a safer, more personalized and immersive gaming experience, and demonstrating significant progress in intelligence, real-time feedback and user experience design. These innovations and advances have pushed the entire industry to a higher level and laid the foundation for more complex and interactive gaming scenarios in the future. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A structural block diagram of a game interaction system combined with body motion sensing provided by an embodiment of the present invention; Figure 2 A flowchart of the execution steps of the environment perception module provided by an embodiment of the present invention; Figure 3 A flowchart of the execution steps of the game logic analysis module provided by an embodiment of the present invention; Figure 4 A flowchart of the execution steps of the action prediction module provided by an embodiment of the present invention; Figure 5 A flowchart of the execution steps of the collision risk assessment module provided by an embodiment of the present invention; Figure 6 A flowchart of the execution steps of the dynamic guidance generation module provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.

[0020] In the description of this application, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, "plurality" means two or more, unless otherwise clearly and specifically defined.

[0021] In the description of the present application, the term "for example" is used to mean "used as an example, illustration or description". Any embodiment described as "for example" in the present application is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is given to enable any technician in the field to implement and use the present invention. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art can recognize that the present invention can be implemented without using these specific details. In other examples, well-known structures and processes will not be elaborated in detail to avoid unnecessary details to obscure the description of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed in the present application.

[0022] Figure 1 A structural block diagram of a game interaction system combined with body motion sensing provided by an embodiment of the present invention, such as Figure 1 As shown, the system comprises: The environment perception module 100 is used to collect the three-dimensional spatial data of the user's environment in real time, identify the location of obstacles and the movement trajectory of dynamic objects in the environment, and build a dynamic environment model; This module obtains environmental point cloud data and uses laser scanning, depth cameras or other 3D sensing devices to identify the outlines of static obstacles in the environment. These static obstacles may include walls, furniture, equipment, etc., and their accurate identification is crucial for the safety and feasibility of subsequent user actions. At the same time, the system also needs to monitor the position and movement status of dynamic obstacles in real time, such as moving objects and pedestrians, to ensure that changes in the environment can be updated and reflected in a timely manner.

[0023] Once this information is collected, the system will fuse the data of static and dynamic obstacles to build a comprehensive dynamic environment model. This model not only contains the geometric information of obstacles, but also the movement trajectory and dynamic change characteristics of objects, and can dynamically adapt to the real-time changes of the user's environment. Through such a dynamic model, the system can provide users with accurate environmental feedback and effectively avoid potential collision risks in subsequent action planning.

[0024] This module ensures that the system can reflect the complex environment in which the user is in real time through precise environmental data collection and dynamic model construction. This not only improves the user experience, allowing them to play games in a safer and more immersive environment, but also enhances the system's ability to adapt to environmental changes, thereby improving the accuracy and safety of user actions. Through continuous environmental monitoring, the system can dynamically adjust the execution plan of user actions to ensure that users can interact without obstacles during the game, reduce the risk of accidental collisions, and greatly enhance the fun and sense of participation in the game.

[0025] like Figure 2 As shown, the execution steps of the environment perception module 100 include: S110, acquiring environmental point cloud data and identifying obstacle information, including static obstacle contours, and detecting the real-time position and movement status of dynamic obstacles; S120: The information of the identified static obstacles and dynamic obstacles is integrated to establish a comprehensive dynamic environment model.

[0026] The motion capture module 200 is used to obtain the user's body motion data in real time through a multimodal sensor, analyze the user's current motion features, and store them as the user's historical motion pattern; The game logic analysis module 300 is used to connect to the game content database and analyze the required action sequence, target movement path and game action requirement parameters that the user needs to complete within the subsequent preset time period according to the current game scene; Obtain the status information of the current game scene, including the real-time status of various dynamic elements in the scene, such as moving enemies, interactive items, NPC (non-player character) behavior, etc. By using computer vision and environmental perception technology, the system can efficiently identify and track these dynamic elements, thereby establishing a dynamic game environment view.

[0027] After identifying the dynamic elements of the current scene, the system further analyzes the game logic to determine the target tasks that the user needs to complete in a specific scene. This may be a complex multi-step task, such as in a competitive game where players need to dodge enemy attacks, find props, and complete specific kill targets at the same time. The system not only identifies these tasks, but also extracts the action requirement parameters required for each task, such as the required force, speed, and movement method. For example, if the user needs to cross an obstacle area, the system may require the user to jump at a faster speed while precisely controlling the jump height and landing posture.

[0028] After the target task is determined, the system will analyze the user's historical behavior patterns based on the tasks the user has completed, and predict the next 5 to 10 action sequences to be performed. The prediction in this step is mainly to clarify the subsequent actions required by the system, that is, the actions that the user is about to perform, so as to provide a basis for subsequent action analysis.

[0029] like Figure 3 As shown, the execution steps of the game logic analysis module 300 include: S310, connecting to the game content database, obtaining the status information of the current game scene, identifying the dynamic elements in the current game scene, parsing the game logic, and determining the target tasks that the user needs to complete in the current scene and the corresponding action requirement parameters; S320, based on the target task to be completed in the current scene and the corresponding action requirement parameters, combined with the task currently completed by the user, identifying the action sequence that the game requires the user to perform within a future preset time period, and defining it as a required action sequence; S330, analyzing the required actions of the game based on the dynamic environment model and the required action sequence, and combining them to form a target movement path; S340, reading the game content database, using the required actions of the game as tags, identifying the required parameters of each required action, including strength, speed and movement mode, and combining them with the target movement path.

[0030] The action prediction module 400 is used to generate the user's future predicted action sequence and the spatial coordinate change trajectory of the corresponding body parts through a time series prediction algorithm based on the historical action patterns stored in the action capture module, the current action features analyzed in real time, and the action requirement parameters output by the game logic analysis module; This module extracts the user's historical action patterns. This process uses machine learning and data mining techniques to extract features from the user's past action records, including action frequency, type, and success rate. Combined with the dynamic environment model, the system can identify the user's location coordinates in the environment at the current moment. This location coordinate is updated in real time to ensure that the system makes predictions based on the user's actual status.

[0031] Next, the system reads the current action requirement parameters and combines them with the user's previous action sequence to establish a time series prediction algorithm. Through such a mathematical model, the system can predict the spatial coordinates of future actions and generate future action sequences. For example, if the user needs to move quickly and avoid obstacles in the current game, the system will identify the user's possible actions and possible spatial coordinates in the next 5-10 action sequences in real time based on historical data and environmental changes.

[0032] Through a highly intelligent timing prediction algorithm, it can provide users with a forward-looking gaming experience. This ability to predict in advance not only improves the fluency of the game, but also brings the user experience to a new level. Through prediction and simulation, the system can effectively reduce the risk of collision and increase the user's sense of security and immersion.

[0033] For example, in some action games, users may need to perform a series of rapidly changing actions in succession. Through the prediction mechanism, the system can identify potential risks in these actions in advance. This active feedback mechanism not only enhances the interaction between users and the game, but also improves the overall gaming experience quality, allowing users to remain flexible and agile in a constantly changing environment.

[0034] like Figure 4 As shown, the execution steps of the action prediction module 400 include: S410, extracting the user's historical action pattern, combining it with the dynamic environment model, and identifying the position coordinates of the user in the dynamic environment model at the current moment; S420, reading action demand parameters, combining the current demand action sequence and the position coordinates of the user in the dynamic environment model at the previous moment, establishing a time series prediction algorithm, identifying the user predicted action corresponding to each demand action in the demand action sequence, and combining them into a predicted action sequence; In this step, specifically: Prediction Spatial coordinates of time It is expressed as: ; in, express The user's position coordinates in the dynamic environment model at the moment, Represents the instantaneous velocity vector corresponding to the current demand action, Represents the acceleration vector corresponding to the current demand action, is a preset time interval; Then for the first Action, its predicted position recursively calculates the cumulative motion of all previous actions: .

[0035] S430, based on the predicted action sequence, combined with the dynamic environment model and the current position coordinates of the user in the dynamic environment model, simulate the user's limb movements in the dynamic environment model and combine them into a predicted motion trajectory, and at the same time identify the spatial coordinates corresponding to each user's predicted action in the predicted action sequence.

[0036] The collision risk assessment module 500 is used to compare the spatial coordinate trajectory of the predicted action sequence with the dynamic environment model in real time, and calculate the contact probability and collision risk level of each predicted action with the obstacle; For static obstacles, the system will identify the boundary points of the obstacle through point cloud data processing to form an accurate three-dimensional bounding box. The calculation formula of this bounding box shows how to determine its spatial range through the geometric information of the static obstacle (such as the point coordinates in the vertex set G). This enables the system to effectively locate obstacles in three-dimensional space and provide a reliable reference frame for subsequent dynamic collision detection.

[0037] For dynamic obstacles, the system adopts a similar strategy to update their position and motion status in real time. The implementation of this part relies on sensor data and motion models to ensure that no matter how the obstacle moves, the system can always accurately reflect its position in space. This dynamic update mechanism greatly improves the realism and responsiveness of the game environment, allowing users to obtain the most realistic environmental feedback when performing actions.

[0038] Next, the system will spatially map the user's predicted motion trajectory to the bounding boxes of these obstacles. By calculating the motion trajectory of the body parts corresponding to the user's motion, the system can build a three-dimensional bounding box of the human body and match it with the bounding box of the obstacle. The key to this step is that it can calculate the contact probability between the user's motion and the obstacle through spatial overlap, and then evaluate the collision risk level. At this point, the system will calculate the spatial overlap between the spatial coordinates of each predicted motion at the corresponding time point and the obstacle bounding box, forming a contact probability calculation model.

[0039] After determining the contact probability of each user's predicted action, the system will compare it with the preset collision risk level threshold. This evaluation process helps determine which actions have a higher collision risk, allowing the system to perform accurate risk management. For those actions whose contact probability exceeds the safety threshold, the system can respond in a timely manner and generate corresponding collision risk assessment results.

[0040] By implementing real-time dynamic detection, users are ensured to be in a safe environment at all times during the game. This active collision detection mechanism not only enhances the user's sense of security, but also greatly enhances the immersion and participation of the game. Users can freely perform complex actions without worrying about the risk of collision, so they can focus more on game strategy and operation.

[0041] In a virtual reality combat game, the user needs to shuttle between enemies and attack. By combining the user's future predicted action sequence obtained by the action prediction module and the spatial coordinate change trajectory of the corresponding body parts, and executing the collision detection mechanism, the system can predict the relationship between the user's movement path and the enemy's position in real time, and issue a warning or adjust the preset action in time when the user is about to enter a high-risk area. This not only ensures the smoothness of the game, but also improves the user's participation and the fun of the game experience.

[0042] like Figure 5 As shown, the execution steps of the collision risk assessment module 500 include: S510, obtaining the spatial coordinates and body part motion trajectory corresponding to each user's predicted action in the predicted action sequence, and simultaneously obtaining the static obstacle contour coordinates, the dynamic obstacle real-time position and movement status data in the dynamic environment model, and establishing the three-dimensional obstacle bounding boxes of the static obstacles and the dynamic obstacles; In this step, the three-dimensional bounding boxes of the static obstacles and the dynamic obstacles are established as follows: For static obstacles: The contour coordinates are a set of vertices processed from the point cloud data The coordinates of each vertex are ; The obstacle bounding box vertices of the static obstacle are calculated as: ; The obstacle bounding box consists of vertices: , ; The only certainty; For dynamic obstacles: The real-time location is ; Movement status including speed and acceleration ; in They are , , The speed in three directions, They are , , Acceleration in three directions; The obstacle bounding box vertices of the dynamic obstacle at the current moment are calculated as: ; in, , , are the fixed width, height, and depth of the dynamic obstacle, respectively; Among them, the obstacle bounding box consists of vertices: , ; The only certainty.

[0043] S520, spatially mapping the motion trajectory of the body part corresponding to the user's predicted action in the dynamic environment model, establishing a three-dimensional bounding box of the human body containing the motion range of the human body part based on the spatial coordinates for each user's predicted action, and performing spatial position matching with the three-dimensional bounding box of the obstacle in the dynamic environment model; S530, calculating the spatial overlap between the spatial coordinates of the user's predicted action at the corresponding time point and the three-dimensional bounding box of the obstacle based on the contour coordinates of the static obstacle and the real-time position and movement status of the dynamic obstacle, and constructing a contact probability calculation model based on the spatial overlap to calculate the contact probability between each user's predicted action and the obstacle; In this step, the calculation of the spatial overlap between the spatial coordinates of the user's predicted action at the corresponding time point and the three-dimensional bounding box of the obstacle is specifically as follows: Read the obstacle bounding box: ; Construct a human body bounding box: ; Calculate the length of the single axis overlap interval: ; ; ; in, , , Respectively expressed in axis, axis, In the axial direction, the length of the overlapping interval between the obstacle bounding box and the corresponding axial range of the human body bounding box; Calculate the ratio of the intersection volume to the union volume of the human body bounding box and the obstacle bounding box as the overlap: ; in, Represents the obstacle bounding box volume, Represents the volume of the human body bounding box; ; ; The calculation of the contact probability between each user's predicted action and the obstacle is specifically as follows: Probability of contact .

[0044] S540, preset a collision risk level threshold interval, and compare and match the contact probability between each user's predicted action and the obstacle with the collision risk level threshold interval according to the contact probability calculation result to determine the corresponding collision risk level; S550: Integrate the contact probability and collision risk level corresponding to each user's predicted action to form a collision risk assessment result corresponding to the predicted action sequence.

[0045] The dynamic guidance generation module 600 is used to generate action correction instructions according to the collision risk assessment results, and generate subsequent action guidance that meets the game objectives and avoids obstacles.

[0046] This module will conduct an in-depth analysis of the collision risk assessment results to identify predicted actions with a high collision risk level. These actions may face the risk of contact with static or dynamic obstacles due to the user's movement path or execution method. In this step, the system not only identifies potential dangers, but also specifically identifies those action nodes and corresponding body parts that come into contact with obstacles. For example, if the user's knee is likely to contact the wall next to him when performing a high leg lift, the system will accurately identify this risk.

[0047] Next, combining the game goal and the status information of the current game scene, the system analyzes the role and necessity of each high-risk action in the game logic. This analysis process ensures that the system can judge which actions can be adjusted and which are indispensable. For example, if an action is necessary to complete a task, the system will give priority to adjusting it instead of simply deleting or replacing the action. This judgment relies on the understanding of the game logic, allowing the system to maintain the consistency of the game experience.

[0048] For high-risk actions that are adjustable and do not affect the game objectives, the system will use motion planning algorithms to re-plan the spatial trajectory and motion parameters of the action based on the dynamic environment model and the position and state information of obstacles. This process ensures that when adjusting the action, the user can still achieve the goal and effectively avoid obstacles. For example, if the user's action requires moving from one area to another, the system may suggest that the user move sideways instead of moving straight forward to reduce the risk of collision with obstacles, or by expanding the user's movement feedback, only guiding the user to move in small segments, but feedback to the game is a large segment of displacement.

[0049] After generating the revised action plan, the system will merge it with the original required action sequence. At this time, the system will also consider the timing requirements and coherence of the game, and adjust the execution order, action direction, and action range of the required action sequence. This comprehensive adjustment ensures that when users perform subsequent actions, they can complete the game tasks smoothly and naturally without feeling abrupt or uncoordinated.

[0050] Through intelligent dynamic feedback and real-time adjustments, users can operate more safely and confidently in complex gaming environments. First, the system can promptly identify and reduce collision risks, ensure the safety of users in the game, and reduce the chance of accidents. Second, by understanding the user's behavior and game logic, the system ensures that the adjusted actions will not affect the user's game goals, thereby improving the overall gaming experience.

[0051] For example, in an action-adventure game, if the user is about to collide with a moving enemy while running, the system will immediately calculate the best avoidance plan and guide the user to move sideways or jump, ensuring that the user can not only avoid the collision but also continue to move towards the goal. This real-time feedback mechanism greatly enhances the user's sense of immersion and participation, allowing them to enjoy greater safety and freedom while experiencing the fun of the game.

[0052] like Figure 6 As shown, the execution steps of the dynamic guidance generation module 600 include: S610, analyzing the collision risk assessment results, identifying predicted actions with high collision risk levels, and identifying action nodes and corresponding body parts that may come into contact with obstacles; S620, analyzing the role and necessity of each user predicted action above the collision risk level threshold interval in the game logic in combination with the game goal and the status information of the current game scene, and determining whether the action can be adjusted without affecting the game goal; S630, for the user's predicted action that is adjustable and does not affect the game goal and is above the collision risk level threshold range, based on the dynamic environment model and the position and state information of the obstacle, a motion planning algorithm is used to re-plan the spatial trajectory and motion parameters of the action while ensuring that the obstacle is avoided, and a revised action plan is generated; S640, the revised action plan is integrated with the original required action sequence, and the execution order, action direction and action range of the required action sequence are adjusted according to the timing requirements and continuity of the game.

[0053] It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and for parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0054] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0055] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0056] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0057] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0058] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0059] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A game interaction system combined with body motion sensing, characterized in that: The system comprises: The environmental perception module is used to collect the three-dimensional spatial data of the user's environment in real time, identify the location of obstacles and the movement trajectory of dynamic objects in the environment, and build a dynamic environment model; The motion capture module is used to obtain the user's body motion data in real time through multimodal sensors, analyze the user's current motion characteristics, and store them as the user's historical motion pattern; The game logic analysis module is used to connect to the game content database and analyze the required action sequence, target movement path and game action requirement parameters that the user needs to complete within the subsequent preset time period according to the current game scene; An action prediction module is used to generate a user's future predicted action sequence and a spatial coordinate change trajectory of the corresponding body parts through a time series prediction algorithm based on the historical action patterns stored in the action capture module, the current action features analyzed in real time, and the action requirement parameters output by the game logic analysis module; The collision risk assessment module is used to compare the spatial coordinate trajectory of the predicted action sequence with the dynamic environment model in real time, and calculate the contact probability and collision risk level of each predicted action with the obstacle; The dynamic guidance generation module is used to generate action correction instructions based on the collision risk assessment results, and generate subsequent action guidance that meets the game objectives and avoids obstacles.

2. The system according to claim 1, characterized in that The execution steps of the environment perception module include: Acquire environmental point cloud data and identify obstacle information, including static obstacle outlines, and detect the real-time position and movement status of dynamic obstacles; The information of identified static obstacles and dynamic obstacles is integrated to establish a comprehensive dynamic environment model.

3. The system according to claim 2, characterized in that The execution steps of the game logic analysis module include: Connect to the game content database, obtain the status information of the current game scene, identify the dynamic elements in the current game scene, analyze the game logic, and determine the target tasks that the user needs to complete in the current scene and the corresponding action requirement parameters; Based on the target tasks to be completed in the current scene and the corresponding action requirement parameters, combined with the tasks currently completed by the user, identify the action sequence that the game requires the user to perform within a preset time period in the future, which is defined as the required action sequence; Based on the dynamic environment model and the required action sequence, the required actions of the game are analyzed and combined to form the target movement path; The game content database is read, and the required actions of the game are used as labels to identify the required parameters of each required action, including strength, speed and movement mode, and combine them with the target movement path.

4. The system according to claim 3, characterized in that The execution steps of the action prediction module include: Extract the user's historical action pattern, combine it with the dynamic environment model, and identify the user's current position coordinates in the dynamic environment model; Read the action demand parameters, combine the current demand action sequence and the position coordinates of the user in the dynamic environment model at the previous moment, establish a time series prediction algorithm, identify the user predicted action corresponding to each demand action in the demand action sequence, and combine them into a predicted action sequence; According to the predicted action sequence, combined with the dynamic environment model and the current position coordinates of the user in the dynamic environment model, the user's limb movements are simulated in the dynamic environment model and combined into a predicted motion trajectory, and the spatial coordinates corresponding to each user's predicted action in the predicted action sequence are identified.

5. The system according to claim 4, characterized in that The execution steps of the collision risk assessment module include: Obtain the spatial coordinates and body part motion trajectory corresponding to each user's predicted action in the predicted action sequence, and at the same time obtain the static obstacle contour coordinates, dynamic obstacle real-time position and movement status data in the dynamic environment model, and establish the three-dimensional obstacle bounding boxes of static obstacles and dynamic obstacles; The motion trajectory of the body parts corresponding to the user's predicted action is spatially mapped in the dynamic environment model. For each user's predicted action, a three-dimensional bounding box of the human body containing the motion range of the human body parts is established based on the spatial coordinates, and the spatial position is matched with the three-dimensional bounding box of the obstacle in the dynamic environment model. According to the contour coordinates of static obstacles and the real-time position and movement status of dynamic obstacles, the spatial overlap between the spatial coordinates of the user's predicted action at the corresponding time point and the three-dimensional bounding box of the obstacle is calculated. Based on the spatial overlap, a contact probability calculation model is constructed to calculate the contact probability between each user's predicted action and the obstacle. A collision risk level threshold interval is preset, and according to the contact probability calculation result, the contact probability of each user's predicted action with the obstacle is compared and matched with the collision risk level threshold interval to determine the corresponding collision risk level; The contact probability and collision risk level corresponding to each user's predicted action are integrated to form a collision risk assessment result corresponding to the predicted action sequence.

6. The system according to claim 5, characterized in that The execution steps of the dynamic guidance generation module include: Analyze the collision risk assessment results, identify the predicted actions with high collision risk levels, and identify the action nodes and corresponding body parts that will come into contact with obstacles; Combined with the game goal and the status information of the current game scene, analyze the role and necessity of each user's predicted action above the collision risk level threshold interval in the game logic, and determine whether the action can be adjusted without affecting the game goal; For user predicted actions that are above the collision risk level threshold range and are adjustable and do not affect the game objectives, the motion planning algorithm is used to replan the spatial trajectory and motion parameters of the action while ensuring obstacle avoidance, and generate a revised action plan based on the dynamic environment model and the position and state information of the obstacles. The revised action plan is integrated with the original required action sequence, and the execution order, action direction and action range of the required action sequence are adjusted according to the timing requirements and continuity of the game.

7. The system according to claim 4, characterized in that The time series prediction algorithm is established to identify the user prediction action corresponding to each demand action in the demand action sequence and combine them into a prediction action sequence, specifically: Prediction Spatial coordinates of time It is expressed as: ； in, express The user's position coordinates in the dynamic environment model at the moment, Represents the instantaneous velocity vector corresponding to the current demand action, Represents the acceleration vector corresponding to the current demand action, is a preset time interval; Then for the first Action, its predicted position recursively calculates the cumulative motion of all previous actions: 。 8. The system according to claim 5, characterized in that The three-dimensional bounding boxes of the static obstacles and the dynamic obstacles are established as follows: For static obstacles: The contour coordinates are a set of vertices processed from the point cloud data The coordinates of each vertex are ; The obstacle bounding box vertices of the static obstacle are calculated as: ； Among them, the obstacle bounding box consists of vertices: ， ； The only certainty; For dynamic obstacles: The real-time location is ; Movement status including speed and acceleration ; in They are , , The speed in three directions, They are , , Acceleration in three directions; The obstacle bounding box vertices of the dynamic obstacle at the current moment are calculated as: ； in, , , are the fixed width, height, and depth of the dynamic obstacle, respectively; in, The obstacle bounding box at this moment consists of vertices: ， ； The only certainty.

9. The system according to claim 5, characterized in that The calculation of the spatial overlap between the spatial coordinates of the user's predicted action at the corresponding time point and the three-dimensional bounding box of the obstacle is specifically: Read the obstacle bounding box: ; Construct a human body bounding box: ; Calculate the length of the single axis overlap interval: ； ； ； in, , , Respectively expressed in axis, axis, In the axial direction, the length of the overlapping interval between the obstacle bounding box and the corresponding axial range of the human body bounding box; Calculate the ratio of the intersection volume to the union volume of the human body bounding box and the obstacle bounding box as the overlap: ； in, Represents the obstacle bounding box volume, Represents the volume of the human body bounding box; ； ； The calculation of the contact probability between each user's predicted action and the obstacle is specifically as follows: Probability of contact .

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