An interactive prop action control method and system in virtual-real fusion program production

Abstract

The application discloses an interactive prop action control method and system in virtual-real fusion program production, relates to the technical field of virtual reality and augmented reality fusion, and comprises the following steps: acquiring action data of an interactive prop, and constructing an action state machine corresponding to the interactive prop based on the action data; performing future trajectory prediction based on the action state machine, and generating program cooperative control data in advance according to a prediction result; performing virtual-real fusion linkage control based on the program cooperative control data, and performing reverse feedback control on the interactive prop based on a virtual scene state; and through state prediction and feedback closed-loop control, the application solves the problems of unstable action and virtual-real synchronization lag.

Description

Technical Field

[0001] This invention relates to the field of virtual reality and augmented reality fusion technology, and more specifically, to a method and system for controlling the motion of interactive props in the production of virtual-real fusion programs. Background Technology

[0002] With the development of virtual reality, augmented reality, and extended reality technologies, the production of hybrid virtual-real programs has gradually become an important direction for broadcast television, stage performances, and immersive content production. In such programs, interactive props, as key media connecting the physical world and virtual scenes, need to achieve high-precision real-time linkage between their actions and the characters, special effects, and resource systems in the virtual environment to construct an immersive and consistent audiovisual experience. However, under current technological conditions, interactive control in hybrid virtual-real program production still faces several technical bottlenecks. Therefore, how to construct an interactive prop action control method that can achieve stable modeling of action states, prediction of future trajectories, collaborative resource scheduling, and feedback closed-loop correction has become a key technical problem that urgently needs to be solved in the field of hybrid virtual-real program production.

[0003] For example, the invention patent application CN121501149A discloses an interactive game data processing method and system using virtual reality. The method includes: capturing in real time non-preset interactive action data with temporal node markers generated by the user through a VR interactive device; extracting action trend features, operation rhythm features, and intensity fluctuation features by combining temporal correlation and spatial continuity to generate a set of behavioral features; filtering effective behavioral features through the intent confidence set output by the interaction intent probability model; encoding the effective behavioral features according to semantic mapping rules to generate game system semantic information containing interactive intent descriptions; calling adaptation rules according to the virtual scene type, matching semantic information with scene requirements to determine fitting nodes, adjusting parameters to generate a scene dynamic adaptation scheme; adjusting the feedback logic triggering conditions based on the adaptation scheme and interactive intent description, updating the real-time dynamic response of the virtual scene and associating temporal node markers. This method improves the ability to understand non-preset action intents.

[0004] For example, patent application CN116958348A discloses a method and apparatus for generating interactive actions between a digital person and an object. The method includes: acquiring target condition data, including pose information of the digital person in its initial state, target object data, and pose information of the digital person interacting with the target object in the target state; determining N milestone data using a first diffusion model, where each milestone is a point on the trajectory of the digital person moving from its initial position to its target position; determining the posture of the digital person at any milestone using a second diffusion model; determining several trajectory point data of the digital person on the trajectory between adjacent milestones using a third diffusion model; and determining the posture of the digital person at any trajectory point using a fourth diffusion model. Then, based on the multiple trajectory point data and the posture of the digital person at any trajectory point, a sequence of actions for the digital person to move from its initial position to its target position is determined.

[0005] The above-disclosed embodiments have at least the following technical problems: In traditional technical solutions, the recognition of interactive prop actions relies on instantaneous data judgment, and the state switching is frequent and prone to jitter. At the same time, the virtual scene and physical props lack forward-looking coordination capabilities and usually rely on real-time triggering, resulting in response lag.

[0006] To address the above problems, this invention proposes a solution. Summary of the Invention

[0007] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide a method and system for controlling the motion of interactive props in the production of virtual-real integrated programs. Through state prediction and feedback closed-loop control, the problems of unstable motion and lag in virtual-real synchronization are solved.

[0008] To achieve the above objectives, the present invention provides the following embodiments: A method for controlling the motion of interactive props in the production of virtual-real integrated programs includes: acquiring motion data of the interactive props, generating motion sequences based on the motion data, determining the target motion state and the target state transition path, establishing a motion state diagram, and constructing a motion state machine of the interactive props based on the target state transition path and the state holding period; generating a future state evolution chain based on the motion state machine and predicting the future motion trajectory of the interactive props, performing program resource preloading and collaborative linkage processing according to the trajectory change stages, and generating program collaborative control data; performing virtual-real integrated linkage control based on the program collaborative control data, and performing reverse feedback control on the interactive props based on the virtual scene state.

[0009] In a preferred embodiment, the steps of acquiring the action data of the interactive prop, generating an action sequence based on the action data, determining the target action state and the target state transition path, establishing an action state diagram, and constructing the action state machine of the interactive prop based on the target state transition path and the state maintenance period are as follows: Acquiring the action data of the interactive prop and generating an action sequence and a set of candidate action states based on the action data; acquiring the current scene state and program state, generating state associations by combining the set of candidate action states, and determining the target action state of the interactive prop; constructing an action state diagram based on the target action state, and determining the target state transition path of the interactive prop based on the action state diagram; activating the target state transition path and generating a state maintenance period when the target action state of the target state transition path continuously meets preset stability conditions; maintaining the target state transition path in an active state within the state maintenance period, and generating an action state machine based on the active target state transition path; determining the state occupancy maintenance requirement of the current target state transition path based on the state maintenance period, and generating a state lease duration; when the state lease duration expires, readjusting the target state transition path in the action state diagram, and dynamically updating the action state machine.

[0010] In a preferred embodiment, determining the target state transition path of the interactive prop based on the action state diagram specifically involves: reading each action state node and each state transition edge in the action state diagram, and generating candidate state transition paths based on the connection relationships between the state transition edges; obtaining the path response features corresponding to each candidate state transition path, and generating path trigger identifiers; performing path activation filtering on the candidate state transition paths based on the path trigger identifiers of each candidate state transition path, and writing the candidate state transition paths that meet the path activation conditions into the path pre-activation set; obtaining the current action change stage of the interactive prop and the channel occupancy status of the current state transition channel, and combining the path pre-activation set to filter each candidate state transition path. The process involves performing path blocking detection on the state transition path to determine if there are any conditions prohibiting switching. If such conditions exist, the corresponding candidate state transition path is marked as blocked. If no such conditions exist, the candidate state transition path is added to the path activation set. The process also involves retrieving historical path activation records for each candidate state transition path in the activation set and generating path inertia stages based on these records. Finally, the process involves performing path elimination on the activation set based on the path inertia stages corresponding to each candidate state transition path. The final target state transition path that is not eliminated is then determined as the target state transition path for the interactive prop.

[0011] In a preferred embodiment, maintaining the target state transition path in an active state during the state holding period and generating an action state machine based on the active target state transition path specifically includes: performing path holding control on the target state transition path according to the state holding period to restrict the target state transition path from exiting the current continuously active state during the state holding period; continuously monitoring the path response characteristics and path activation trends corresponding to the target state transition path while the target state transition path is in a continuously active state, and maintaining the continuity of path activation corresponding to the target state transition path based on the path response characteristics and path activation trends; reading each action state node and each state transition edge in the target state transition path, and resolving the state switching order relationship between each action state node according to the connection direction of each state transition edge to generate a state node connection relationship; and performing time-based analysis on each action state node based on the state node connection relationship. Sequence calibration processes map the node connection sequence to entry and exit trigger points in the time dimension, forming a node temporal sequence mapping relationship. The entry and exit sequences of each action state node are obtained and aligned based on the temporal sequence mapping relationship to generate a state node transition sequence. The execution path hierarchy of each action state node is divided according to the state node transition sequence, generating a state path hierarchy structure, with different state path levels corresponding to different stage state transition layers. Path associations between each action state node are established based on the state path hierarchy structure, generating a state path mapping structure. State path driving relationships are established based on the state path mapping structure, and the action state machine corresponding to the interactive prop is generated according to the state path driving relationships. During the operation of the action state machine, the activation state of the current state node in the action state machine is dynamically maintained according to the path activation continuity corresponding to the target state transition path.

[0012] In a preferred embodiment, the step of generating a future state evolution chain based on an action state machine and predicting the future action trajectory of an interactive prop, performing program resource preloading and collaborative linkage processing according to the trajectory change stages, and generating program collaborative control data, specifically includes: determining the current state evolution direction of the interactive prop based on the target action state node currently in an active state in the action state machine and its corresponding target state transition path, and extracting the state progression trend; obtaining historical state flow records corresponding to the target state transition path and extracting the path evolution trajectory; generating a future state progression sequence based on the state progression trend and path evolution trajectory, and establishing a future state evolution chain; predicting the future action trajectory of the interactive prop based on the future state evolution chain, and dividing the trajectory change stages by the continuous change characteristics of the trajectory; obtaining virtual... The system simulates the current state of program resources in a given scenario and performs phased resource preloading based on the trajectory change stages. Based on the preloaded program resource states, it determines the resource response order among program resources and generates a resource response priority sequence. Based on the resource response priority sequence, it performs collaborative resource linkage processing on the program resource states to form a program collaborative response sequence. It obtains the current rhythm stage of the program system and adjusts the rhythm synchronization of the program collaborative response sequence accordingly. Based on the resource response connection relationships in the rhythm-synchronized program collaborative response sequence, it generates a resource control timing chain. Based on the resource control timing chain, it performs linkage control arrangement on the resource control order and resource control trigger times of each program resource to form program collaborative control data.

[0013] In a preferred embodiment, the prediction of the future action trajectory of the interactive prop based on the future state evolution chain, and the division of trajectory change stages by the continuous change characteristics of the trajectory, are as follows: The state advancement connection relationship and state advancement sequence in the future state evolution chain are obtained, and the future state advancement trajectory is extracted based on the state advancement connection relationship and state advancement sequence; the attitude change data and displacement change data corresponding to each state node are read based on the future state advancement trajectory to generate the future action trajectory; the trajectory advancement direction and trajectory advancement rate in the future action trajectory are obtained to generate a continuous trajectory change sequence; the trajectory change density and trajectory change diffusion range during the trajectory advancement process are analyzed based on the continuous trajectory change sequence to identify continuous trajectory clustering regions; the future action trajectory is divided into continuous advancement intervals based on the trajectory advancement boundary change relationship in each continuous trajectory clustering region to form continuous trajectory advancement intervals; the trajectory connection change relationship between each continuous trajectory advancement interval is obtained, and trajectory clustering is performed on the continuous trajectory advancement intervals based on the trajectory connection change relationship to form trajectory evolution segments; the trajectory change stages are divided based on the trajectory advancement continuity and trajectory change stability in each trajectory evolution segment.

[0014] In a preferred embodiment, the step of performing virtual-real fusion linkage control based on program collaborative control data and performing reverse feedback control on interactive props based on the virtual scene state specifically includes: acquiring resource linkage relationships in the program collaborative control data and establishing a program resource linkage control structure based on these relationships; controlling program resources in the virtual scene to execute resource linkage responses according to a preset linkage sequence based on the program resource linkage control structure, forming a continuous resource linkage state; acquiring the scene state change process of the virtual scene under the continuous resource linkage state, generating a scene state change chain; analyzing the scene response propagation direction and scene response diffusion trend based on the scene state change chain, generating a scene energy flow trajectory; and analyzing the scene energy in the virtual scene based on the scene energy flow trajectory. The system identifies the energy distribution area and the scene energy diffusion area, and establishes a scene energy distribution grid. It acquires the energy propagation direction and energy fluctuation continuity within the scene energy distribution grid, generating a scene feedback propagation chain. Based on the scene feedback propagation chain, it performs feedback fluctuation convergence on the feedback propagation path in the virtual scene. It acquires the scene feedback propagation chain after feedback fluctuation convergence and establishes a feedback coupling mapping structure. Based on the feedback coupling mapping structure, it performs feedback linkage adjustment on interactive props. Based on the motion offset change trajectory of interactive props during the feedback linkage adjustment process, it obtains the motion feedback drift band. Based on the motion feedback drift band, it performs drift compensation correction on the state advancement connection relationship in the motion state machine. Based on the drift compensation correction, it maintains continuous collaborative linkage between the virtual scene and interactive props.

[0015] In a preferred embodiment, the step of acquiring resource linkage relationships in program collaborative control data and establishing a program resource linkage control structure based on these relationships specifically involves: acquiring the resource response connection sequence and resource linkage progression relationship in the program collaborative control data; identifying resource connection links between program resources based on the resource response connection sequence; analyzing the linkage propagation direction between each resource connection link based on the resource linkage progression relationship; performing linkage serialization processing on each resource connection link based on the linkage propagation direction to form a resource linkage path; analyzing the resource linkage propagation direction and resource linkage continuity between different program resources based on each resource linkage path; and identifying the linkage between each program resource. Connection relationships; based on each linkage connection relationship, chain-connect the resource linkage paths between different program resources to form a resource linkage association chain; obtain the resource linkage aggregation area and resource linkage diffusion area in the resource linkage association chain, and identify the core nodes of resource linkage based on the resource linkage aggregation area; analyze the linkage propagation connection relationship between each core node of resource linkage based on the resource linkage diffusion area, and establish a resource linkage distribution network based on each linkage propagation connection relationship; perform linkage connection mapping processing on each resource linkage path based on the resource linkage distribution network to form linkage control connection relationships between program resources; establish a program resource linkage control structure based on the linkage control connection relationship after linkage connection mapping processing.

[0016] In a preferred embodiment, the step of obtaining the scene feedback propagation chain after feedback fluctuation convergence and establishing a feedback coupling mapping structure specifically includes: obtaining the node sequence and its temporal propagation relationship in the scene feedback propagation chain after feedback fluctuation convergence, and performing propagation consistency segmentation processing on the node sequence to generate stable propagation units; constructing a feedback propagation topology path based on the stable propagation units, and generating a feedback propagation coupling factor field by combining the directional offset and propagation intensity difference between nodes; performing spatial neighborhood expansion processing on the feedback propagation coupling factor field to extract local coupling enhancement regions and coupling attenuation regions, and generating a feedback coupling propagation distribution structure; performing coupling constraint reconstruction on the feedback propagation topology path based on the feedback coupling propagation distribution structure; and performing unified mapping and fusion on the reconstructed feedback propagation topology path and the feedback coupling propagation distribution structure to form a feedback coupling mapping structure.

[0017] A system for controlling the motion of interactive props in virtual-real fusion program production includes a construction module, a prediction module, and a feedback control module, with connections between the modules. The construction module acquires motion data of the interactive props, generates motion sequences based on the motion data, determines the target motion state and target state transition path, establishes a motion state diagram, and constructs a motion state machine for the interactive props based on the target state transition path and state holding period. The prediction module generates a future state evolution chain based on the motion state machine and predicts the future motion trajectory of the interactive props. It performs program resource preloading and collaborative linkage processing according to the trajectory change stages and generates program collaborative control data. The feedback control module performs virtual-real fusion linkage control based on the program collaborative control data and performs reverse feedback control on the interactive props based on the virtual scene state.

[0018] The technical effects and advantages of the interactive prop motion control method and system in virtual-real fusion program production according to the present invention are as follows: 1. This invention constructs an action state machine from motion data and performs future trajectory prediction based on the action state machine. It can generate program collaborative control data in advance before the interactive prop actually reaches the target action state, so that the virtual scene can complete the linkage preparation in advance, thereby improving the foresight of virtual-real fusion linkage control.

[0019] 2. This invention generates program coordination control data in advance based on predicted future action trajectories and drives program resources into a pre-response state, synchronizing resource scheduling with action progression, thereby shortening resource response time and improving program resource coordination efficiency. Furthermore, it continuously acquires virtual scene feedback results through reverse feedback control and dynamically corrects the state progression relationships in the action state machine, enabling timely correction of offset errors during action state progression, thus reducing the impact of accumulated feedback offset on subsequent action control. Attached Figure Description

[0020] Figure 1 This is a flowchart illustrating an interactive prop motion control method for virtual-real fusion program production according to the present invention.

[0021] Figure 2 This is a schematic diagram of the system structure of an interactive prop motion control method in the production of a virtual-real integrated program, according to the present invention. Detailed Implementation

[0022] The embodiments of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0023] Example 1, Figure 1 This invention provides a method for controlling the motion of interactive props in the production of virtual-real integrated programs, comprising: S1. Obtain the action data of the interactive prop, generate an action sequence based on the action data, determine the target action state and the target state transition path, establish an action state diagram, and construct the action state machine of the interactive prop based on the target state transition path and state maintenance period. In this embodiment, motion data of the interactive prop is acquired, and a motion sequence is generated based on the motion data. The target motion state and the target state transition path are determined, a motion state diagram is established, and the motion state machine of the interactive prop is constructed based on the target state transition path and the state holding period, as detailed below: Acquire the posture and displacement data of the interactive prop within a continuous time window, and generate an action sequence based on the posture and displacement change trends; The initial action state corresponding to the interactive prop is determined based on the action sequence, and a set of candidate action states is generated based on the initial action state. The current scene state of the virtual scene and the current program state of the program system are obtained, and a state association relationship is generated by combining the candidate action state set. The program state includes lighting linkage state, camera follow state, special effect trigger state and sound effect coordination state. The scene state includes battle scene state, performance scene state, interactive scene state and transition scene state. Output the state priority corresponding to each candidate action state based on the state association relationship, and determine the target action state of the interactive prop based on the state priority corresponding to each candidate action state. The target action state is used as the action state node, and the switching relationship between the target action state and the historical action state is used as the state transition edge to construct the action state graph of the interactive prop. Candidate state transition paths between each action state node are established based on the action state graph, and the target state transition path of the interactive prop is determined according to the triggering conditions of different candidate state transition paths. Determine whether the target action state corresponding to the target state transition path continuously meets the preset stability conditions. The preset stability conditions include: the current target state transition path has not entered the path blocking state, the path response characteristics corresponding to the current target state transition path are continuously within the path response boundary range, the state switching stage corresponding to the current target action state is continuously within the same stage range, and the path inertia stage corresponding to the current target state transition path has not experienced a stage jump. When the target action state continuously meets the preset stability conditions, the target state transition path is activated and a state maintenance period is generated. During the state maintenance period, the target state transition path is kept in an active state, and the action state machine of the interactive prop is generated based on the target state transition path that is in an active state. The state occupancy maintenance requirement corresponding to the current target state transition path is determined based on the state maintenance period, and the corresponding state lease duration is generated based on the state occupancy maintenance requirement. During the state lease duration, the switching of other state transition paths to the current state transition path is restricted. When the state lease expires, the state priority corresponding to each candidate action state is re-output, and the target state transition path in the action state graph is adjusted according to the updated state priority. The action state machine is dynamically updated based on the redefined target state transition path.

[0024] In this embodiment, the initial action state corresponding to the interactive prop is determined based on the action sequence, and a candidate action state set is generated based on the initial action state, as follows: The direction of continuous motion change in the motion sequence is segmented, and the corresponding motion feature patterns are extracted based on the posture change trend and displacement change trend of each motion segment. Each action feature pattern is matched with a preset action state template, and the initial action state corresponding to the interactive prop is determined according to the matching result. The preset action state template includes waving action, stationary action, lifting action, pressing action, and rotating action. Based on the initial action state, obtain the associated state set corresponding to the initial action state, and generate a candidate action state set according to the current action change direction of the interactive prop. The associated state set is used to represent other action states that have a state transition relationship with the current initial action state. The state continuity of each candidate action state is output based on the continuity of change between adjacent action segments in the action sequence, and the candidate priority corresponding to each candidate action state is adjusted based on the state continuity. The state continuity is used to characterize the degree of consistency between the current action change process of the interactive prop and the action continuity of the candidate action state. When the candidate priority corresponding to each candidate action state meets the preset candidate conditions, the corresponding candidate action state is written into the candidate action state set.

[0025] In this embodiment, the state priority corresponding to each candidate action state is output based on the state association relationship, and the target action state of the interactive prop is determined according to the state priority corresponding to each candidate action state, as follows: Based on the relationship between each state, a corresponding state trigger identifier is generated, wherein different state trigger identifiers correspond to different program coordination response types; Arrange the state trigger identifiers corresponding to each candidate action state in a state time sequence, and establish a state competition sequence based on the sequential triggering order between the state trigger identifiers; Obtain the current action change stage corresponding to the interactive prop, and filter the candidate action state execution stage in the state competition sequence according to the action change stage. Different action change stages correspond to different activatable action states. Based on the candidate action states after phase screening, a state occupancy marker is generated, and it is determined whether the current candidate action state occupies the current state transition channel. Specifically, the process involves: obtaining the state switching request corresponding to each candidate action state after phase screening, generating a corresponding state occupancy marker based on each state switching request, obtaining the state transition path currently in the active state in the action state graph, determining the channel occupancy status of the current state transition channel based on the currently active state transition path, matching the state occupancy marker corresponding to each candidate action state with the channel occupancy status of the current state transition channel, thereby determining whether the current state transition channel is in an idle state, i.e., the current candidate action state does not occupy the current state transition channel. When the current candidate action state does not occupy the current state transition channel, the corresponding candidate action state is written into the state queue to be activated. The process involves: acquiring historical state transition records for each candidate action state in the queue of states to be activated; generating state inertia identifiers based on these records; and characterizing the continuous activity trend of a candidate action state during historical action changes. Specifically, this includes: acquiring historical state transition records for each candidate action state in the queue of states to be activated; extracting the corresponding state entry time, state exit time, and number of state transitions based on these records; calculating the historical state duration for each candidate action state based on the state entry time and exit time; generating corresponding state inertia data based on the historical state duration and number of state transitions; and generating state inertia identifiers based on the state inertia data. Different state inertia identifiers correspond to different state retention capabilities. Based on the state inertia identifier, perform state elimination processing on the candidate action states in the queue of active states, and determine the target candidate action state after state elimination as the target action state corresponding to the interactive prop.

[0026] The process of obtaining the current action change stage corresponding to the interactive prop and filtering the candidate action state execution stages in the state competition sequence based on the action change stage is as follows: The continuous action change process in the action sequence is segmented into stages, and corresponding stage boundary markers are generated according to the inflection points of the action change trend. The stage boundary markers are used to characterize the stage switching position in the action change process of interactive props. Based on the corresponding action duration interval between adjacent stage boundary markers, a corresponding action change stage is generated, which includes an action start stage, an action transition stage, an action enhancement stage, and an action end stage. Obtain the stage response rules corresponding to each candidate action state, and establish a stage mapping relationship between the candidate action state and the action change stage based on the stage response rules. Different candidate action states correspond to different responsive action change stages. The stage response rules are used to characterize the corresponding triggering relationship between different candidate action states and action change stages, so as to limit the range of activatable stages for different candidate action states. Match the current action change stage with the stage mapping relationship corresponding to each candidate action state, and filter out candidate action states that do not match the current action change stage. Obtain the stage duration record corresponding to the candidate action state after stage filtering, and determine whether the corresponding candidate action state meets the preset stage maintenance condition based on the stage duration record. The stage duration record is used to characterize the continuous existence duration of the candidate action state in the current action change stage. Candidate action states that meet the preset stage retention conditions are written into the stage valid state set, thus completing the stage screening of candidate action states in the state competition sequence.

[0027] The process involves eliminating candidate action states in the queue of states to be activated based on their state inertia identifiers, and then determining the target candidate action state after elimination as the target action state corresponding to the interactive prop. The details are as follows: Obtain the state inertia identifier corresponding to each candidate action state in the queue of states to be activated, and generate the corresponding state retention level according to different state inertia identifiers. Different state retention levels correspond to different state persistence capabilities. The candidate action states in the active state queue are sorted hierarchically, and the state elimination order is established according to the state maintenance level. Among them, the candidate action states with lower state maintenance levels are given priority to enter the elimination stage. Obtain the historical action states that are currently active in the action state graph, and generate a state occupancy stage based on the historical action states. The state occupancy stage is used to characterize the execution stage of the state transition path corresponding to the current historical action state. Determine whether the candidate action states in the queue of states to be activated meet the switching conditions of the corresponding state occupancy stage; If a candidate action state does not meet the switching conditions for the corresponding state occupation phase, the corresponding candidate action state will be removed from the queue of states to be activated. When multiple candidate action states simultaneously meet the switching conditions of the corresponding state occupation phase, the historical state continuity record corresponding to each candidate action state is obtained, and a state stability phase is generated based on the historical state continuity record. The state stability phase is used to characterize the stable continuity capability of the candidate action state during the historical state switching process. Based on the stable state phase corresponding to each candidate action state, a step-by-step state elimination is performed, and the final target candidate action state that has not been eliminated is retained. The target candidate action state that is not eliminated in the end is determined as the target action state corresponding to the interactive prop.

[0028] In this embodiment, candidate state transition paths between each action state node are established based on the action state graph, and the target state transition path of the interactive prop is determined according to the triggering conditions of different candidate state transition paths, as follows: Read each action state node and each state transition edge in the action state graph, and generate candidate state transition paths based on the connection relationship between the state transition edges. Each candidate state transition path corresponds to the state evolution process of the interactive prop switching from the current action state to different target action states. Obtain the path response features corresponding to each candidate state transition path, and generate path trigger identifiers based on the path response features. Different path trigger identifiers correspond to the allowed activation conditions of different state transition paths. Based on the path trigger identifier of each candidate state transition path, the candidate state transition path is filtered for path activation, and the candidate state transition paths that meet the path activation conditions are written into the path pre-activation set. Get the current action change stage of the interactive prop and the channel occupancy status of the current state transition channel, and perform path blocking detection on each candidate state transition path in combination with the path pre-activation set. The path blocking detection is used to determine whether there is a condition prohibiting switching on the corresponding candidate state transition path. When a candidate state transition path has a condition that prohibits switching, the corresponding candidate state transition path is marked as a path blocking state. When there is no prohibition condition for switching in the candidate state transition path, the corresponding candidate state transition path is written into the path to be activated set; Obtain the historical path activation records corresponding to each candidate state transition path in the path activation set, and generate a path inertia stage based on the historical path activation records. The path inertia stage is used to characterize the continuous activation capability of the candidate state transition path during the historical state switching process. Based on the path inertia stage corresponding to each candidate state transition path, the path elimination process is performed on the path activation set, and the final target state transition path that has not been eliminated is retained. The final target state transition path that is not eliminated is determined as the target state transition path of the interactive prop.

[0029] The process of obtaining the path response features corresponding to each candidate state transition path and generating a path trigger identifier based on the path response features is as follows: The state node change process in each candidate state transition path is obtained, and the path change features are extracted based on the state node change process. The state node change process is used to characterize the continuous switching process between each action state node in the candidate state transition path. Based on the path change characteristics, the path change rate and path duration stage corresponding to each candidate state transition path are output. The path change rate is used to characterize the speed of state switching change corresponding to the candidate state transition path, and the path duration stage is used to characterize the range of the continuous activation stage corresponding to the candidate state transition path. The path response mode corresponding to each candidate state transition path is determined based on the path change rate and path duration stage, and path response features are extracted based on the path response mode. The path response mode includes instantaneous trigger mode, continuous following mode, gradual change maintenance mode, and stage switching mode. Specifically, the extraction of path response features based on the path response mode involves: obtaining the path response mode corresponding to each candidate state transition path, reading the corresponding path behavior data set according to different path response modes, extracting the path behavior change trend corresponding to each candidate state transition path based on the path behavior data set, generating the corresponding path response contour based on the path behavior change trend, extracting the corresponding path response boundary based on the path response contour, and generating the corresponding path response feature based on the path response boundary. The path behavior data set includes path activation duration, path switching frequency, path continuous change, and path stage span. The path response contour is used to characterize the path behavior evolution of the candidate state transition path during the state switching process. Obtain the current scene change stage of the virtual scene, and perform stage mapping on the path response characteristics of each candidate state transition path according to the scene change stage. Different scene change stages correspond to different path response stage requirements. The system obtains the current program coordination state of the program system and performs path response adjustment on the path response features after the completion of stage mapping based on the program coordination state. Specifically, it obtains the path response features after the completion of stage mapping and establishes corresponding program response associations based on different program coordination states. It performs coordination response matching on the path response features based on the program response associations and generates path response adjustment parameters. Based on the path response adjustment parameters, it performs path response scaling processing on the path response features to adjust the path response range corresponding to the candidate state transition path. It obtains the current rhythm stage of the program system and performs path response synchronization processing on the path response features after the completion of path response scaling processing based on the rhythm stage. The rhythm stage is used to characterize the current resource response rhythm state and program linkage change stage of the program system, including the resource progressive response stage, the resource continuous maintenance stage, and the resource gradual switching stage. Based on the adjusted path response characteristics, the path activation trend corresponding to each candidate state transition path is determined, and a path trigger identifier is generated according to the path activation trend. The path trigger identifier is used to characterize whether the corresponding candidate state transition path meets the path activation condition corresponding to the current action state machine. Specifically, the following steps are taken: obtaining the path response characteristics after path response adjustment, extracting the corresponding path response change direction and path response duration state based on the path response characteristics, generating the corresponding path activation evolution process based on the path response change direction and path response duration state, determining the corresponding path activation trend based on the path activation evolution process, obtaining the current state transition stage of the action state machine, performing stage adaptation on the path activation trend corresponding to each candidate state transition path according to the state transition stage (different state transition stages correspond to different path activation stage requirements), generating the corresponding path trigger state based on the path activation trend after stage adaptation processing, and generating a path trigger identifier based on the path trigger state.

[0030] The process involves eliminating paths from the set of paths to be activated based on the path inertia stage corresponding to each candidate state transition path, and retaining the final target state transition paths that are not eliminated, as detailed below: Obtain the path inertia stage corresponding to each candidate state transition path in the path to be activated set, and determine the corresponding path continuous occupancy capacity based on different path inertia stages. A path competition sequence is generated based on the path persistence capability of each candidate state transition path, and path sorting is performed on each candidate state transition path in the path competition sequence according to the path persistence capability. The path switching span between adjacent candidate state transition paths in the path competition sequence is obtained, and the candidate state transition paths are eliminated based on the path switching span. The path switching span is used to characterize the degree of difference in state transition between different candidate state transition paths. When the path switching span of the corresponding candidate state transition path exceeds the preset span range, the corresponding candidate state transition path is marked as a path jump state. Based on the path transition state, perform path elimination processing on the candidate state transition paths in the path competition sequence, and retain the candidate state transition paths that have not been marked as path transition states; Obtain the remaining candidate state transition paths after the path elimination process is completed, and determine the final target state transition path that is not eliminated based on the path inertia stage corresponding to the remaining candidate state transition paths.

[0031] The specific formula for calculating the path elimination is as follows:

[0032] in, It is the path retention value of the i-th candidate state transition path. It is a path transition state identifier; when a candidate state transition path is marked as a path transition state... When a candidate state transition path is not marked as a path transition state ,when This indicates that the corresponding candidate state transition path is preserved, when This indicates that the corresponding candidate state transition path has been eliminated.

[0033] In this embodiment, when the target action state continuously meets the preset stability conditions, the target state transition path is activated and a state maintenance period is generated, as follows: When the target action state continuously meets the preset stability conditions, obtain the path stability duration corresponding to the target state transition path, and generate the corresponding path stability stage based on the path stability duration. According to the path stabilization phase, the target state transition path is subjected to path activation maintenance processing, so that the target state transition path enters a continuous activation state. The continuous activation state is used to characterize the stable occupancy state of the target state transition path in the current action state machine. The duration of continuous activation of the target state transition path after it enters the continuous activation state is obtained, and the corresponding state maintenance period is generated based on the duration of continuous activation of the path. The state maintenance period is used to characterize the effective maintenance time range of the target state transition path to maintain the continuous activation state.

[0034] In this embodiment, the target state transition path is kept active during the state maintenance period, and an action state machine for the interactive prop is generated based on the active target state transition path, as detailed below: Path retention control is performed on the target state transition path according to the state retention period to restrict the target state transition path from exiting the current continuously active state within the state retention period; When the target state transition path is in a continuously active state, the path response characteristics and path activation trend corresponding to the target state transition path are continuously monitored, and the path activation continuity corresponding to the target state transition path is maintained according to the path response characteristics and path activation trend, thereby ensuring that the target state transition path maintains a stable and usable activation propagation state in the time dimension. The path activation continuity is used to characterize the continuous effective state of the target state transition path during the action state switching process. Read each action state node and each state transition edge in the target state transition path, and parse the state switching order relationship between each action state node according to the connection direction of each state transition edge to generate a state node connection relationship. The state node connection relationship is used to characterize the state switching connection order between each action state node in the target state transition path. Based on the connection relationship of state nodes, the timing calibration of each action state node is performed, and the node connection order is mapped to the entry trigger point and exit trigger point in the time dimension to form a node timing mapping relationship. Obtain the node entry order and node exit order corresponding to each action state node, and align the order based on the node temporal mapping relationship to generate a state node transition sequence; The execution path hierarchy of each action state node is divided according to the state node transition sequence, and a state path hierarchy structure is generated. Different state path hierarchies correspond to different stages of state switching layers. The path association between each action state node is established based on the state path hierarchy, and a state path mapping structure is generated based on the path association. The state path mapping structure is used to characterize the path mapping relationship and state driving relationship between each action state node in the target state transition path. A state path driving relationship is established based on the state path mapping structure, and an action state machine corresponding to the interactive prop is generated according to the state path driving relationship. The state path driving relationship is used to characterize the state driving order and state switching logic between different action state nodes. During the operation of the action state machine, the activation state of the current state node in the action state machine is dynamically maintained according to the path activation continuity corresponding to the target state transition path.

[0035] The process of establishing a state path driving relationship based on the state path mapping structure and generating the action state machine corresponding to the interactive prop based on the state path driving relationship is as follows: Obtain the path mapping relationship between each action state node in the state path mapping structure, and determine the state driving direction corresponding to each action state node according to the path mapping relationship. The state driving direction is used to characterize the state switching driving order between different action state nodes. Establish a state path driving chain based on the state driving direction corresponding to each action state node, and generate a state path driving relationship based on the state path driving chain. The state path driving chain is used to characterize the continuous driving process between each action state node in the target state transition path. Obtain the path activation continuity corresponding to each state path driving relationship, and perform driving retention processing on each state path driving relationship according to the path activation continuity. The driving retention processing is used to maintain the continuous effective state of the corresponding state path driving relationship during the action state switching process. A state-driven mapping table is established based on the state path-driven relationship after the drive-keeping process is completed. The state-driven mapping table is used to record the state-driven association and state switching association between different action state nodes. A state-driven operation structure is established between each action state node based on a state-driven mapping table, and an action state machine for interactive props is generated based on the state-driven operation structure.

[0036] S2 generates a future state evolution chain based on the action state machine and predicts the future action trajectory of interactive props. It performs program resource preloading and collaborative linkage processing according to the trajectory change stage and generates program collaborative control data. In this embodiment, a future state evolution chain is generated based on the action state machine, and the future action trajectory of the interactive prop is predicted. Based on the trajectory change stages, program resource preloading and collaborative linkage processing are performed, and program collaborative control data is generated, as detailed below: Based on the target action state node that is currently active in the action state machine and the corresponding target state transition path, determine the current state evolution direction of the interactive prop and extract the state advancement trend. The process involves obtaining historical state transition records corresponding to the target state transition path and extracting the path evolution trajectory. Specifically, this includes: obtaining state node transition logs of the target state transition path during historical state switching; extracting the state switching time sequence and state dwell stage between each state node based on the state node transition logs; performing path timing splicing on the historical state transition process based on the state switching time sequence and state dwell stage to generate a state transition timing chain; obtaining the state advancement change density and state switching cluster state in the state transition timing chain; dividing the path evolution active segment based on the state advancement change density and state switching cluster state; performing path continuous extension on the state transition timing chain based on the state connection relationship between each path evolution active segment to generate a path evolution extension chain; obtaining the path advancement direction change state in the path evolution extension chain; and performing path convergence stabilization processing based on the path advancement direction change state to reduce discrete path jumps during historical state transitions; and extracting the path evolution trajectory based on the path evolution extension chain after path convergence stabilization processing. Generate a future state progression sequence based on the state progression trend and path evolution trajectory, and establish a future state evolution chain to describe the continuous progression relationship between future states; Predict the future action trajectory of interactive props based on the future state evolution chain, and divide the trajectory change stages by the continuous change characteristics of the trajectory. The system acquires the current program resource status of the virtual scene and performs staged resource preloading based on the trajectory change stages, enabling different program resources to enter a ready-to-respond state in advance. The program resource status includes camera resource status, lighting resource status, sound effect resource status, and special effects resource status. Specifically, it acquires the current operating status of camera resources, lighting resources, sound effect resources, and special effects resources in the virtual scene, generates a program resource status set based on the operating and occupancy relationships of each program resource, acquires the trajectory change stages corresponding to future action trajectories, and extracts the trajectory advancement intensity and trajectory change rhythm in each trajectory change stage. Based on the trajectory advancement intensity, it preloads resources from the program resource status set... Each program resource is classified into resource response levels to determine the pre-response priority of different program resources. The resource linkage timing between program resources is analyzed according to the trajectory change rhythm to generate a program resource preloading sequence. Based on the program resource preloading sequence, resource pre-activation processing is performed on each program resource to enable each program resource to enter the ready-to-respond state in advance. The resource response continuity of each program resource after the resource pre-activation processing is obtained, and resource buffer adjustment is performed on the program resource preloading sequence according to the resource response continuity to reduce the response switching delay between different program resources. Based on the program resource preloading sequence after the resource buffer adjustment is completed, staged resource preloading processing is performed on the program resource status. Based on the program resource status after resource preloading, determine the resource response order among the program resources, and generate a resource response priority sequence based on the resource response order to describe the response progression order of different program resources; Based on the resource response priority sequence, collaborative resource linkage processing is performed on the program resource status to form a program collaborative response sequence, which is used to characterize the linkage relationship between program resources. The system acquires the current rhythm stage of the program system and adjusts the rhythm of the program collaborative response sequence according to the rhythm stage to match the overall program operation rhythm. Specifically, this involves: acquiring the resource response advancement order, resource response interval, and resource linkage switching rhythm in the program collaborative response sequence, generating a program response rhythm chain; acquiring the rhythm advancement rate and rhythm change cycle corresponding to the current rhythm stage of the program system, generating a program rhythm baseline chain based on the rhythm advancement rate and rhythm change cycle; performing rhythm synchronization adjustment on the resource response interval and resource linkage switching order in the program collaborative response sequence based on the rhythm offset relationship between the program response rhythm chain and the program rhythm baseline chain to reduce the rhythm offset during the program resource response process; acquiring the rhythm advancement continuity in the program collaborative response sequence after rhythm synchronization adjustment; and performing rhythm smoothing transition processing on the program collaborative response sequence according to the rhythm advancement continuity to maintain the continuous stability of the overall program operation rhythm. Based on the resource response connection relationship in the program collaborative response sequence after rhythm synchronization processing, a resource control timing chain is generated. Based on the resource control timing chain, the resource control sequence and resource control trigger time of each program resource are linked and arranged to form program collaborative control data.

[0037] In this embodiment, based on the target action state node currently in an active state in the action state machine and its corresponding target state transition path, the current state evolution direction of the interactive prop is determined, and the state progression trend is extracted, as follows: Obtain the target action state node that is in an active state in the action state machine, and read the connection relationship between the predecessor node and the successor node in the state path mapping structure of the target action state node to form the set of reachable states of the current state. Based on the connection depth and state transition order between each state node in the reachable state set, determine the state advancement direction of the target action state node in the state evolution chain, and generate the current state evolution direction of the interactive prop; Obtain the historical activation duration and state switching interval of each state node in the target state transition path, and analyze the continuous advancement relationship between each state node based on the historical activation duration and state switching interval to generate a state advancement continuity sequence. A state evolution buffer is generated based on the state progression continuity sequence, and the state progression changes of the target action state within a preset time range are recorded to form a continuous state evolution record. Specifically, this involves: obtaining the state progression order and state dwell stage of each state node in the state progression continuity sequence; dividing the continuous state progression into segments based on the state progression order and state dwell stage; establishing a state cache mapping relationship based on each continuous state progression segment; and writing each continuous state progression segment into the state evolution buffer according to the state progression order to form a state progression cache chain arranged continuously in time. The progression of each continuous state progression segment in the state evolution buffer is then obtained. The frequency of changes and the density of stage switching are determined, and the buffer retention time of each continuous state advancement segment in the state evolution buffer is adjusted according to the frequency of changes and the density of stage switching. Based on the adjusted buffer retention time, buffer retention processing is performed on each continuous state advancement segment, and continuous state advancement segments that meet the buffer retention conditions are written into the state advancement retention chain. Chain connection processing is performed on each continuous state advancement segment in the state advancement retention chain according to the state advancement order to form a continuous state evolution record of the target action state within a preset time range. The buffer retention time is used to control the continuous retention time of different continuous state advancement segments in the state evolution buffer. Based on continuous state evolution records, state progression fluctuation states are extracted, and state evolution jitter features are generated. The state evolution jitter features include state switching fluctuation frequency, state progression fluctuation amplitude, fluctuation diffusion range, and abnormal jump trend. Specifically, the process involves: obtaining the state switching change states between each continuous state progression segment in the continuous state evolution record; extracting the fluctuation change trajectory during the state progression process based on the state switching change states; analyzing the state switching fluctuation frequency and state progression fluctuation amplitude between each continuous state progression segment based on the fluctuation change trajectory; generating a state fluctuation distribution interval based on the state switching fluctuation frequency and state progression fluctuation amplitude; obtaining the state fluctuation diffusion state within the state fluctuation distribution interval; generating a state fluctuation diffusion trend based on the state fluctuation diffusion state; and analyzing abnormal jump states during the state progression process based on the state fluctuation distribution interval and state fluctuation diffusion trend to generate state evolution jitter features. State evolution jitter energy is generated based on state evolution jitter characteristics, and evolution stability compensation is performed on the state evolution direction to suppress short-term abnormal jumps during state advancement and maintain the continuous stability of the state evolution direction. The process involves obtaining the state evolution direction after stability compensation and generating a state propulsion inertia interval. Specifically, this includes: obtaining the state evolution direction after stability compensation and extracting the direction maintenance duration and direction deviation rate of the state evolution direction during continuous state propulsion; analyzing the direction stability stage of the state evolution direction during continuous propulsion based on the direction maintenance duration and direction deviation rate; dividing the state propulsion stable segment according to the direction stability stage; obtaining the direction connection change state between each state propulsion stable segment; generating a state propulsion inertia continuation stage based on the direction connection change state; and performing inertia aggregation on each state propulsion stable segment based on the state propulsion inertia continuation stage to form a state propulsion inertia interval. The inertia aggregation process is used to continuously merge multiple state propulsion stable segments with continuous propulsion inertia to maintain the direction continuation characteristic during the state propulsion process. Based on the state propulsion inertial interval, state inertial extension processing is performed on the target state transition path to maintain the continuous extension capability of the state evolution direction in future state propulsion processes, forming a state propulsion trend. Specifically, this involves: obtaining the inertial duration stage and directional continuation range in the state propulsion inertial interval; extracting the inertial propulsion segment in the target state transition path based on the inertial duration stage and directional continuation range; performing inertial path splicing on the target state transition path based on the directional connection relationship between each inertial propulsion segment to generate a state inertial extension chain; obtaining the direction change density and direction decay state in the state inertial extension chain; and generating a state inertial maintenance interval based on the direction change density and direction decay state. A continuous directional convergence process is performed on the state inertia extension chain within the state inertia maintenance interval to reduce directional abrupt changes during future state advancement. A state advancement trend is generated based on the state inertia extension chain after the continuous directional convergence process. The inertia advancement segment is used to characterize the state advancement segment with continuous directional continuity capability in the target state transition path. The state inertia extension chain is used to characterize the continuous extension relationship of the state evolution direction during future state advancement. The state inertia maintenance interval is used to limit the range of directional discrete changes of the state evolution direction during future state advancement. The continuous directional convergence process is used to maintain the directional aggregation stability of the state evolution direction during continuous state advancement.

[0038] The formula for calculating the state evolution jitter energy is as follows:

[0039] in, It is the energy of state evolution jitter. The influence weights of the jitter characteristics of each state evolution are... These are the values ​​representing the state evolution jitter characteristics, including the frequency of state transition fluctuations, the amplitude of state progression fluctuations, the range of fluctuation spread, and the trend of abnormal jumps. It is a nonlinear amplification factor used to enhance the effects of anomalous jumps. It is the natural base.

[0040] The process involves determining the state progression direction of the target action state node in the state evolution chain based on the connection depth and state transition order between state nodes in the reachable state set, and generating the current state evolution direction of the interactive prop, as detailed below: Obtain the node connection level between each state node in the reachable state set, and determine the state advancement distance of each state node in the state evolution chain based on the node connection level. The state advancement distance is used to characterize the state transition span required for the current target action state to advance to different state nodes. Obtain the arrival order of each state node in the historical state transition process, and construct a state transition advancement sequence based on the node arrival order to represent the continuous advancement relationship between different state nodes; Based on the state advancement distance and state transition advancement sequence, state advancement trend analysis is performed on each state node to generate the state advancement active interval for each state node. The state advancement active interval is used to characterize the continuous activity level of the target action state when advancing to the corresponding state node. Specifically, this involves: obtaining the number of consecutive occurrences of each state node in the state transition advancement sequence and the node advancement interval, and generating the state advancement frequency sequence for each state node in combination with the state advancement distance; extracting the advancement peak segment and advancement trough segment of each state node in the continuous state advancement process based on the state advancement frequency sequence; generating the state advancement oscillation interval based on the change span between the advancement peak segment and the advancement trough segment; obtaining the interval dwell time of each state node in the state advancement oscillation interval; generating the state advancement dwell intensity based on the interval dwell time; determining the state advancement dwell intensity threshold based on the state advancement dwell intensity of all state nodes; determining the state advancement oscillation interval with the state advancement dwell intensity greater than or equal to the state advancement intensity threshold as the stable advancement segment; and performing advancement stabilization aggregation processing on adjacent stable advancement segments to form the state advancement active interval corresponding to each state node. Obtain the state advancement change density in the active state advancement interval, and generate a state advancement cluster region based on the state advancement change density. The state advancement cluster region is used to characterize the concentrated distribution area of ​​state advancement behavior in the state evolution chain. State advancement convergence processing is performed on each state node based on the state advancement cluster region to reduce discrete state jumps in the state evolution chain; Based on the state advancement results after state advancement convergence processing, determine the state advancement direction of the target action state node in the state evolution chain, and generate the current state evolution direction of the interactive prop.

[0041] The formula for calculating the state propulsion dwell intensity is as follows:

[0042] in, It is the state progression dwell strength of the i-th state node. It is the duration of the i-th state node's stay within the state progression oscillation interval. It is the span of the state-driven oscillation interval corresponding to the i-th state node, that is, the span of change between the propulsion peak segment and the propulsion trough segment. It is the state propulsion dwell intensity threshold. This represents the number of state nodes participating in this state progression analysis.

[0043] The process of performing evolutionary stability compensation on the state evolution direction based on state evolution jitter characteristics to suppress short-term abnormal jumps during state advancement and maintain the continuous stability of the state evolution direction is as follows: The state evolution jitter characteristics are obtained, including the frequency of state transition fluctuations, the amplitude of state advancement fluctuations, the trend of fluctuation diffusion, and the trend of abnormal jumps. The state fluctuation level is divided according to the frequency of state transition fluctuations and the amplitude of state advancement fluctuations. The state fluctuation level is used to characterize the intensity level of fluctuations during the state advancement process. Based on the state fluctuation level and fluctuation diffusion trend analysis, the directional deviation state in the state evolution direction is analyzed, and the state evolution deviation segment is generated according to the directional deviation state. The state evolution deviation segment is used to characterize the abnormal deviation range of the state evolution direction in the continuous advancement process. The abnormal transition duration stage in the state evolution offset segment is obtained, and a state offset mitigation interval is generated based on the abnormal transition duration stage. The state offset mitigation interval is used to limit the range of continuous impact of the abnormal transition on the subsequent state advancement process. Based on the state offset mitigation interval, directional smoothing and extension processing is performed on the state evolution direction to reduce sudden state offsets during the state advancement process. The directional smoothing and extension processing is used to maintain the directional continuity of the state evolution direction during the continuous state advancement process. The state evolution direction after the completion of the directional smoothing and extension process is obtained, and the stability compensation convergence process is performed according to the stability of the state evolution direction during the continuous state advancement process. The stability compensation convergence process is used to suppress the directional discrepancy diffusion caused by short-term abnormal jumps. A stable state propagation trajectory is generated based on the state evolution direction after stability compensation convergence processing, so as to maintain the continuous stability of the state evolution direction.

[0044] In this embodiment, a future state progression sequence is generated based on the state progression trend and path evolution trajectory, and a future state evolution chain is established, as follows: The process involves: acquiring the state propagation inertia intervals and directional continuity ranges within the state propagation trend; extracting candidate segments for future state propagation based on the inertial extension changes within these intervals; specifically: acquiring the state propagation inertia intervals within the state propagation trend; extracting the directional maintenance duration and directional continuity range within each interval; analyzing the inertial extension changes during the state propagation process based on the directional maintenance duration and directional continuity range; generating state inertial extension distribution intervals based on these inertial extension changes; acquiring the inertial connection changes between each state inertial extension distribution interval; and further analyzing the inertial extension changes based on the inertial extension changes. The system generates a state inertial extension continuous chain by connecting the state transitions. Based on the state inertial extension continuous chain, it analyzes the inertial propulsion aggregation state in each state propulsion inertial interval and divides the future state propulsion active region according to the inertial propulsion aggregation state. It then obtains the directional propulsion dense change state in the future state propulsion active region and performs inertial propulsion convergence processing on the state inertial extension continuous chain according to the directional propulsion dense change state to reduce the discrete directional diffusion during the future state propulsion process. Based on the state inertial extension continuous chain after completing the inertial propulsion convergence processing, it identifies continuous inertial propulsion regions and generates future state propulsion candidate segments according to the continuous inertial propulsion regions. Obtain the historical path advancement sequence and active path evolution segments in the path evolution trajectory, and perform path mapping processing on each future state advancement candidate segment according to the historical path advancement sequence and active path evolution segments to generate a state advancement association sequence; Based on the state-progression association sequence analysis, the state connection changes between each future state-progression candidate segment are analyzed, and a future state-progression rhythm is generated. Specifically, the following steps are taken: the state connection order and state-progression interval between each future state-progression candidate segment in the state-progression association sequence are obtained, and the state-progression connection relationship is extracted based on the state connection order and state-progression interval. Based on the state-progression connection relationship, the frequency of advancement switching and the continuous change of advancement between each future state-progression candidate segment are analyzed, and a state-progression beat sequence is generated. The beat aggregation state and beat interval change in the state-progression beat sequence are obtained, and the state-progression rhythm segments are divided. Based on the rhythm connection changes between each state-progression rhythm segment, the state-progression beat sequence is continuously integrated. The future state-progression rhythm is generated based on the state-progression beat sequence after the continuous integration process. Obtain the dense state propagation segment and the buffer state propagation segment in the future state propagation rhythm, and perform propagation rhythm shaping on the state propagation associated sequence; The process involves generating a future state advancement sequence based on the state advancement association sequence after advancement rhythm shaping. Specifically, this includes: obtaining the state advancement association sequence after advancement rhythm shaping, extracting the advancement connection order and advancement transition span between each future state advancement candidate segment, performing continuous advancement arrangement on each future state advancement candidate segment according to the advancement connection order to form a state advancement sorting chain, coordinating the advancement span of adjacent future state advancement candidate segments in the state advancement sorting chain based on the advancement transition span to reduce discrete advancement jumps during the future state advancement process, obtaining the state advancement sorting chain after advancement span coordination, generating a state advancement evolution sequence based on the advancement connection continuity between each future state advancement candidate segment, analyzing the state advancement dense region and state advancement buffer region during the future state advancement process based on the state advancement evolution sequence, and performing advancement rhythm balancing on the state advancement evolution sequence based on the state advancement dense region and state advancement buffer region to maintain continuous advancement stability during the future state advancement process, thereby generating the future state advancement sequence. Obtain the state progression connection relationships in the future state progression sequence, and establish the future state evolution chain based on the state progression connection relationships.

[0045] The process of obtaining the dense state propagation segments and buffer segments in the future state propagation rhythm, and performing propagation rhythm shaping on the state propagation association sequence, is as follows: Obtain the rhythmic variation distribution in the future state propulsion rhythm, and identify regions where the state propulsion frequency continuously increases and regions where the state propulsion frequency continuously decreases based on the rhythmic variation distribution; The region where the state advancement frequency continuously increases is defined as the state advancement dense region, and the region where the state advancement frequency continuously decreases is defined as the state advancement buffer region. The state advancement dense region is used to characterize the continuous advancement region where state switching is relatively concentrated in the future state advancement process, and the state advancement buffer region is used to characterize the continuous transition region where state advancement gradually decreases in the future state advancement process. The state advancement sequence and advancement interval change of each future state advancement candidate segment in the state advancement association sequence are obtained, and a state advancement rhythm distribution chain is generated based on the state advancement sequence and advancement interval change. The state advancement rhythm distribution chain is used to characterize the continuous distribution relationship of different state advancement rhythms during the future state advancement process. Based on the dense state-propelling segments, the state-propelling rhythm distribution chain is subjected to propulsion aggregation to reduce the discrete propulsion intervals in future state-propelling processes. The propulsion aggregation process is used to enhance the rhythm concentration in continuous state-propelling processes. Specifically, it involves: obtaining the state-propelling arrangement order and state switching interval in the dense state-propelling segments, identifying continuous state-propelling segments based on the state-propelling arrangement order and state switching interval, determining continuous state-propelling segments with state switching intervals less than a preset propulsion interval threshold as high-density propulsion segments, generating a propulsion aggregation chain based on the propulsion connection relationship between each high-density propulsion segment, obtaining the propulsion direction change and propulsion rhythm concentration degree of each high-density propulsion segment in the propulsion aggregation chain, and performing propulsion compression processing on the propulsion aggregation chain based on the propulsion direction change and propulsion rhythm concentration degree. Based on the propulsion aggregation chain after propulsion compression processing, the continuous state-propelling segments in the state-propelling rhythm distribution chain are rhythmically clustered to form a concentrated and continuous state-propelling structure. Based on the state advancement buffer segment, the state advancement rhythm distribution chain is subjected to advancement mitigation to reduce sudden advancement jumps in the future state advancement process. The advancement mitigation process is used to maintain a smooth transition of the state advancement rhythm in the future state advancement process. Specifically, it involves: obtaining the state advancement arrangement order and state switching interval in the state advancement buffer segment, identifying continuous state buffer segments according to the state advancement arrangement order and state switching interval, determining continuous state buffer segments with continuously increasing state switching intervals as gradual advancement segments, generating an advancement mitigation chain according to the advancement connection relationship between each gradual advancement segment, obtaining the advancement slowdown magnitude and advancement rhythm diffusion degree of each gradual advancement segment in the advancement mitigation chain, and performing advancement expansion on the advancement mitigation chain according to the advancement slowdown magnitude and advancement rhythm diffusion degree to reduce sudden state switching aggregation in the future state advancement process. Based on the advancement mitigation chain after advancement expansion, rhythm smoothing processing is performed on continuous state advancement segments in the state advancement rhythm distribution chain to maintain the continuity of state advancement transition in the future state advancement process. Based on the state propulsion rhythm distribution chain after propulsion aggregation and propulsion slow release, the propulsion connection interval and propulsion transition span between each future state propulsion candidate segment in the state propulsion association sequence are coordinated to form a continuous and stable state propulsion rhythm structure.

[0046] In this embodiment, the future trajectory of the interactive prop is predicted based on the future state evolution chain, and the trajectory change stages are divided by the continuous change characteristics of the trajectory, as follows: Obtain the state progression connection relationships and state progression order in the future state evolution chain, and extract the future state progression trajectory based on the state progression connection relationships and state progression order; Based on the future state advancement trajectory, the attitude change data and displacement change data corresponding to each state node are read, and the future motion trajectory is generated based on the attitude change data and displacement change data. Specifically, each state node in the future state advancement trajectory is obtained, and the attitude change data and displacement change data corresponding to each state node are aligned in the time dimension so that different types of change information correspond to a unified state time slice. Based on the time-aligned attitude change data and displacement change data, the motion state of each state node is jointly expressed to generate a node motion state description, which is used to characterize the spatial attitude change process of the node in continuous state advancement. Based on the node motion state description, the motion change between adjacent state nodes is interpolated and smoothed, and the path connection is reconstructed according to the temporal order of the state advancement trajectory to generate a continuously changing future motion trajectory. Obtain the trajectory propagation direction and trajectory propagation rate in the future action trajectory, and generate a continuous trajectory change sequence based on the trajectory propagation direction and trajectory propagation rate; Based on the analysis of the trajectory change sequence, the trajectory change density and trajectory change diffusion range during the trajectory advancement process are analyzed to identify the continuous trajectory clustering region; Based on the relationship between the trajectory advancement boundary changes in the continuous clustering regions of each trajectory, the future action trajectory is divided into continuous advancement intervals to form continuous trajectory advancement intervals. The process involves: acquiring the trajectory connection and change relationships between continuous trajectory advancement intervals; performing trajectory clustering on these intervals to form trajectory evolution segments; specifically: acquiring the trajectory advancement direction, trajectory advancement rate, and trajectory boundary connection order of each continuous trajectory advancement interval; extracting the trajectory connection relationships between adjacent continuous trajectory advancement intervals based on these relationships; analyzing the continuity and offset of trajectory advancement based on these relationships; generating trajectory connection change chains based on these relationships; acquiring trajectory advancement clustered and discrete regions within these chains; performing trajectory aggregation processing on the continuous trajectory advancement intervals based on the clustered regions to form continuous trajectory clustering chains; performing trajectory boundary separation processing on the continuous trajectory clustering chains based on the discrete regions to reduce trajectory cross-interference between different trajectory advancement directions; acquiring the continuous trajectory clustering chains after trajectory boundary separation processing; and performing trajectory chain splicing based on the trajectory advancement association relationships between the continuous trajectory advancement intervals to form trajectory evolution segments. The trajectory change stages are divided based on the continuity of trajectory advancement and the stability of trajectory changes in each trajectory evolution segment.

[0047] In this embodiment, collaborative resource linkage processing is performed on the program resource status based on the resource response priority sequence to form a program collaborative response sequence, as follows: Obtain the resource response order and resource linkage relationship of each program resource in the resource response priority sequence, and generate a resource linkage advancement chain based on the resource response order and resource linkage relationship; Based on the resource linkage advancement chain, read the resource trigger connection relationship between each program resource, and establish a resource linkage mapping structure according to the resource trigger connection relationship; Based on the resource linkage mapping structure, the resource response output of the preceding program resource is controlled to trigger the linkage of the following program resource, so that the following program resource enters the linkage waiting response state after the preceding program resource enters the response state. The resource response time interval and resource response progress order between each program resource are obtained, and the resource linkage mapping structure is adjusted in a linkage synchronization manner according to the resource response time interval and resource response progress order to reduce the response misalignment between different program resources. Based on the resource linkage mapping structure after linkage and synchronization adjustment, collaborative resource linkage processing is performed on the program resource status to form a continuous collaborative program resource response process. Obtain the resource response connection relationship between each program resource after collaborative resource linkage processing, and generate a resource response timing chain based on the resource response connection relationship; Based on the resource response timing chain, the resource response order of each program resource is continuously arranged to form a program collaborative response sequence.

[0048] S3 performs virtual-real fusion linkage control based on program collaborative control data, and performs reverse feedback control on interactive props based on the virtual scene state; In this embodiment, virtual-real fusion linkage control is performed based on program collaborative control data, and reverse feedback control is performed on interactive props based on the virtual scene state, as detailed below: Acquire the resource linkage relationships in the program collaborative control data, and establish a program resource linkage control structure based on the resource linkage relationships; Based on the program resource linkage control structure, the program resources in the virtual scene are controlled to execute resource linkage responses in a preset linkage order, forming a continuous resource linkage state in the virtual scene; The process involves acquiring the scene state change process of a virtual scene under continuous resource linkage and generating a scene state change chain. Specifically, this includes: acquiring the scene state change process of a virtual scene under continuous resource linkage and extracting the change nodes of the scene state in the time dimension; analyzing the state evolution order and state switching continuity between each node based on the scene state change nodes and constructing the scene state temporal relationship; discretizing and segmenting the scene state change process according to the scene state temporal relationship to form continuous state change segments; and chaining each continuous state change segment according to the state evolution order to generate a scene state change chain. Based on the analysis of the scene state change chain, the propagation direction and diffusion trend of the scene response are analyzed to generate the scene energy flow trajectory. Based on the analysis of scene energy flow trajectory, the scene energy accumulation area and scene energy diffusion area in the virtual scene are analyzed, and a scene energy distribution grid is established; The process involves obtaining the energy propagation direction and energy fluctuation continuity in the scene energy distribution grid, and generating a scene feedback propagation chain. Specifically, this includes: obtaining the energy propagation direction of each grid cell in the scene energy distribution grid, constructing an energy propagation direction association relationship based on the spatial adjacency relationship between adjacent grid cells, analyzing the energy change stability between each grid cell based on the energy fluctuation continuity, performing continuity filtering on the energy propagation direction association relationship to obtain effective propagation connection relationships, performing path concatenation processing on the grid cells in the scene energy distribution grid based on the effective propagation connection relationships, and organizing them sequentially according to the energy propagation direction to form a scene feedback propagation chain. This approach uses a scene feedback propagation chain to converge feedback fluctuations in virtual scenes, reducing local feedback oscillations during continuous interaction. Specifically, it involves: acquiring the path node sequence and propagation direction relationships between nodes in the scene feedback propagation chain; constructing a feedback propagation path topology; analyzing the propagation continuity and degree of change between adjacent nodes based on the feedback propagation path topology; identifying unstable segments in the feedback propagation path; performing propagation intensity attenuation and path offset correction on unstable segments; and continuously reorganizing the corrected path nodes to form a stable feedback propagation path. Based on the stable feedback propagation path, it performs convergence control on local feedback propagation in the virtual scene, reducing local feedback oscillations during continuous interaction. Obtain the scene feedback propagation chain after the feedback fluctuation convergence, and establish a feedback coupling mapping structure; Based on the feedback coupling mapping structure, feedback linkage adjustment is performed on interactive props to maintain the linkage synchronization stability between interactive props and virtual scenes. Specifically, the process involves: obtaining the correspondence between path nodes and distribution units in the feedback coupling mapping structure, analyzing the coupling propagation relationship and spatial action relationship, generating interactive control units to characterize the influence of different structural regions on interactive props, analyzing the intensity and direction of the effect of each structural region on the state change of interactive props based on the interactive control units, converting the structural relationship into action control rules, forming the feedback linkage action path of interactive props, and performing adjustment processing on the action state machine of interactive props based on the feedback linkage action path, so that the interactive props produce corresponding action adjustments under the action of different feedback coupling regions, and forming a state update process that is synchronized with the virtual scene. Based on the trajectory of the motion offset change of the interactive props during the feedback linkage adjustment process, the motion feedback drift band is obtained; The drift compensation correction is performed on the state advancement connection relationship in the action state machine based on the action feedback drift band to reduce the feedback accumulation offset during the action state advancement process. Specifically, the spatial offset distribution and temporal offset trajectory in the action feedback drift band are obtained, and the offset deviation degree between each state node is calculated based on the ideal state path in the state advancement process to form a state advancement offset identifier. The state transition edge in the state advancement connection relationship is matched and associated with the state advancement offset identifier, and the state transition path segment with offset accumulation is identified to generate the drift-affected path segment. Based on the drift-affected path segment, the connection strength and transition direction of the state transition edge are adjusted. The path convergence correction is performed on the path segment with high offset accumulation, and the path smoothing correction is performed on the path segment with low offset, so that the state advancement path gradually returns to the target state evolution trajectory. The motion state machine, corrected by drift compensation, maintains continuous collaborative interaction between the virtual scene and interactive props.

[0049] Furthermore, the current state node and corresponding state transition path in the motion state machine after drift compensation correction are obtained, and the action execution command of the interactive prop is generated according to the current state node. In the virtual concert program scene, when the virtual character performs stage movement action, the interactive prop is controlled to complete the position switching action synchronously according to the action execution command, and the action response state of the interactive prop is obtained in real time. The action execution command corresponding to the next state node is triggered according to the matching result between the action response state and the current action state of the virtual character, so that the action change process of the interactive prop is synchronized with the action evolution process of the virtual character, thereby realizing real-time collaborative control between the virtual character and the interactive prop.

[0050] In this embodiment, the resource linkage relationships in the program collaborative control data are obtained, and a program resource linkage control structure is established based on the resource linkage relationships, as detailed below: The system acquires the resource response connection sequence and resource linkage advancement relationship from the program collaborative control data, and identifies the resource connection links between each program resource based on the resource response connection sequence. Specifically, it acquires the resource response connection sequence from the program collaborative control data, extracts the forward response relationship and backward response relationship between each program resource, analyzes the resource connection continuity in the resource response transmission process based on the forward response relationship and backward response relationship between each program resource, identifies resource response transmission nodes based on the resource connection continuity, acquires the resource response propagation direction and resource response advancement interval between each resource response transmission node, and performs response connection concatenation on each resource response transmission node based on the resource response propagation direction and resource response advancement interval to form the resource connection links between each program resource. Based on the analysis of the linkage and promotion relationship of resources, the linkage propagation direction between each resource connection link is analyzed, and linkage and serial processing is performed on each resource connection link according to the linkage propagation direction to form a resource linkage path; Based on the analysis of the resource linkage paths, the direction of resource linkage propagation and the continuity of resource linkage between different program resources are analyzed, and the linkage connection relationship between program resources is identified. Based on the various linkage relationships, the resource linkage paths between different program resources are chained together to form a resource linkage association chain. Obtain the resource linkage aggregation area and resource linkage diffusion area in the resource linkage association chain, and identify the core nodes of resource linkage based on the resource linkage aggregation area; Based on the analysis of resource linkage diffusion areas, the linkage propagation connection relationships between core nodes of each resource linkage are analyzed, and a resource linkage distribution network is established based on these connections. Specifically, this involves: obtaining the resource linkage distribution status in the resource linkage cluster area, extracting the resource linkage connection density and resource linkage propagation concentration between each program resource, outputting the connection strength of each program resource in the resource linkage cluster area based on the connection density and propagation concentration, obtaining the connection strength distribution, constructing a resource linkage strength mapping sequence based on the connection strength distribution, performing intensity projection processing on the spatial location within the resource linkage cluster area based on the resource linkage strength mapping sequence, obtaining a resource linkage strength distribution map, merging regions based on the spatial connectivity of continuous high-intensity distribution intervals in the resource linkage strength distribution map to form active resource linkage areas, obtaining the resource propagation coverage and resource linkage diffusion direction in each active resource linkage area, determining the main connection points of resource linkage based on the resource propagation coverage and diffusion direction, and performing core screening on each main connection point of resource linkage based on the resource linkage propagation strength and continuous activity of resource linkage to form core nodes of resource linkage. Based on the resource linkage distribution network, linkage connection mapping processing is performed on each resource linkage path to form linkage control connection relationship between program resources. Based on the linkage control connection relationship after linkage connection mapping, a program resource linkage control structure is established. Specifically, this involves: obtaining the resource nodes and linkage connection paths between nodes in the linkage control connection relationship, constructing a resource connection topology based on the linkage connection paths, analyzing the linkage hierarchy and control dependency relationship between each resource node based on the resource connection topology relationship, generating a resource control topology chain based on the linkage hierarchy and control dependency relationship, and performing structured organization processing on the linkage control connection relationship based on the resource control topology chain to form a program resource linkage control structure with hierarchical control relationship.

[0051] In this embodiment, the scene response propagation direction and scene response diffusion trend are analyzed based on the scene state change chain to generate the scene energy flow trajectory, as follows: Obtain the state transition relationship between adjacent state nodes in the scene state change chain, and construct the scene state propagation association sequence based on the state transition relationship; Based on the scene state propagation association sequence analysis, the propagation direction relationship between each state node is analyzed, and the scene response propagation direction is generated; Based on the state transition relationships and state change continuity between adjacent state nodes in the scene state change chain, a state change feature sequence is extracted. Specifically, this involves: obtaining the state transition relationships between adjacent state nodes in the scene state change chain, performing temporal sorting on the state transition relationships to form a state transition sequence chain, performing continuous association analysis on adjacent state nodes in the state transition sequence chain based on the state change continuity to generate state continuous association units, performing structured segmentation on the state change process according to the state continuous association units, extracting change description information from each structured segment, and serializing and organizing the change description information according to the state transition sequence chain to generate a state change feature sequence. The change description information includes the state change intensity and state duration rhythm. Based on the state change feature sequence analysis, the clustering degree and propagation trend of each state node in the continuous change process are analyzed to generate a scene response diffusion mapping relationship. Specifically, this involves: obtaining the change distribution density and change clustering region of each state node in the state change feature sequence, and constructing state change aggregation units based on the change distribution density and change clustering region; analyzing the change propagation direction and continuity between each state node based on the state change feature sequence, and constructing state propagation association chains based on the change propagation direction and continuity; obtaining the correspondence between the state change aggregation units and the state propagation association chains, and generating a state change spatial mapping structure based on the correspondence; obtaining the spatial distribution topology in the state change spatial mapping structure, and constructing a state change spatial association grid based on the spatial distribution topology; spatially relocating the state change features based on the distribution position and flow path of the state change feature sequence in the spatial association grid to form a spatialized state reconstruction sequence; analyzing the spatial adjacency relationship and cross-regional flow relationship between each state node based on the spatialized state reconstruction sequence to generate a state diffusion propagation connection chain; and performing cross-regional association fusion of the spatialized state reconstruction sequence based on the state diffusion propagation connection chain to form a scene response diffusion mapping relationship. Obtain the high-density change intervals and low-density buffers in the scene response diffusion mapping relationship, and construct the diffusion distribution structure based on the spatial correlation between the high-density change intervals and low-density buffers; Based on the diffusion distribution structure, spatial organization processing is performed on the state change features in the state change chain to form a scene response diffusion representation field with spatiotemporal diffusion expression capability. Specifically, this involves: obtaining spatial distribution units in the diffusion distribution structure, and spatially partitioning and mapping the state change features according to the spatial distribution units to form state change spatial mapping units; analyzing the spatial adjacency and diffusion association relationships between each state change feature based on the state change spatial mapping units, and generating a state diffusion association network; performing temporal fusion on the state change features according to the diffusion propagation path in the state diffusion association network, obtaining the distribution results of the state change features after temporal fusion processing in the spatial distribution units, and mapping the state change features to corresponding spatial locations; forming a local diffusion association structure based on the adjacency relationship and change continuity between spatial locations; and performing overall fusion and continuous integration processing on the state change features in the spatial distribution units based on the local diffusion association structure to form a scene response diffusion representation field with spatiotemporal diffusion expression capability. Based on the scene response propagation direction and the scene response diffusion characterization field, a spatial field coupling mapping is performed on the scene state change chain to form a scene response energy distribution field sequence. Specifically, this involves: obtaining each state node in the scene state change chain and its temporal relationship; combining the scene response propagation direction, directional alignment processing is performed on the spatial position of the state node in the scene response diffusion characterization field to establish a mapping relationship between the state node and the diffusion characterization field; based on the mapping relationship, the distribution of the state node in the diffusion characterization field is coupled and fused; and the spatial distribution results are organized according to the temporal order of the state change chain to form a scene response energy distribution field sequence with temporal evolution characteristics. Based on the spatiotemporal evolution gradient of the scene response energy distribution field sequence, path reconstruction and trajectory extension are performed to generate scene energy flow trajectories. Specifically, this involves: obtaining the distribution change relationship between adjacent fields in the scene response energy distribution field sequence, extracting the spatiotemporal evolution gradient based on spatial location changes and temporal evolution differences, analyzing the dominant direction and continuous enhancement region of energy change based on the spatiotemporal evolution gradient, generating an energy propagation path skeleton, obtaining discrete path nodes and their spatial adjacency relationships in the energy propagation path skeleton, and performing connectivity matching on the corresponding distribution units in the scene response energy distribution field sequence based on the spatial adjacency relationships. For path nodes with breaks or jumps, neighborhood completion and path reorganization processing are performed to form a continuous energy propagation path structure. Based on the continuous energy propagation path structure, the path nodes are temporally aligned according to the temporal evolution order, and interpolation smoothing and direction consistency adjustment processing are performed on the path turning points to maintain the continuous evolution characteristics of the path in the time dimension, thereby generating the scene energy flow trajectory.

[0052] In this embodiment, the scene energy accumulation area and scene energy diffusion area in the virtual scene are analyzed based on the scene energy flow trajectory, and a scene energy distribution grid is established, as follows: The sequence of trajectory points and their temporal distribution relationship in the scene energy flow trajectory are obtained, and the spatial coordinates of the trajectory points are normalized to generate a standardized trajectory temporal sequence. A local energy distribution density model is constructed based on standardized trajectory time series, and trajectory points are mapped to a unified spatial coordinate system to form a continuous energy density distribution map, which is used to characterize the spatial energy change trend. Based on the continuous energy density distribution map, spatial clustering is performed on the trajectory points to identify high-density clusters and low-density diffusion clusters. The clustering results are then mapped to the temporal trajectory advancement direction to form an energy propagation structure chain, which is used to describe the energy transfer path in space. Based on the energy propagation structure chain, high-density clusters are continuously screened, and spatial clusters with continuous high density characteristics are identified as energy accumulation regions. The energy diffusion regions are determined by combining the extension direction of low-density diffusion clusters in the propagation structure chain, thus forming a dual-region division result with spatiotemporal evolution attributes. Based on the dual-region division results, the virtual scene space is divided into regular grids, and the energy gathering region and energy diffusion region are mapped to the corresponding grid cells. Specifically, the boundary range of the energy gathering region and the boundary range of the energy diffusion region in the dual-region division results are obtained, and the spatial coverage range of the virtual scene is determined according to the boundary range. Within the spatial coverage range, a regular grid division baseline is generated according to the preset grid spacing, and an initial regular grid structure is constructed. Based on the spatial distribution of the energy gathering region and the energy diffusion region, the initial regular grid structure is subjected to region constraint mapping processing to form a regular grid division result that matches the dual-region division results. The scene energy distribution grid is generated by weighting each grid cell based on trajectory coverage frequency, spatial dwell intensity, and propagation traversal number.

[0053] The local energy distribution density model is constructed based on the standardized trajectory time series, mapping the trajectory points to a unified spatial coordinate system to form a continuous energy density distribution map, as detailed below: Obtain the coordinates of trajectory points in the standardized trajectory time series, and perform spatial coordinate normalization processing on trajectory points from different sources or at different scales, so that all trajectory points are uniformly mapped to the same spatial coordinate reference system, forming a spatially aligned set of trajectory points; Based on the set of trajectory points, a spatial neighborhood radius is set with each trajectory point as the center. In a unified spatial coordinate system, neighboring trajectory points whose distances satisfy the neighborhood range constraints are searched to form a local neighborhood point set with a single trajectory point as the core. Based on the spatial overlap and connection relationships between local neighborhood point sets, the local neighborhood point sets are structured and organized to form a local spatial neighborhood structure, which is used to express the local spatial relationship between trajectory points; The number and concentration of trajectory points in each local neighborhood structure are obtained. Combined with the number of consecutive occurrences of trajectory points in the time dimension, the spatial distribution and temporal persistence are fused and mapped to generate a spatial-temporal joint density representation result. The number of spatial distributions is used to characterize the local spatial occupancy intensity, and the number of consecutive occurrences in time is used to characterize the continuous stability of trajectory points in the local region. Based on the joint spatial-temporal density characteristics, a weighted fusion process is performed on the local neighborhood structure to uniformly map the spatial distribution intensity with temporal continuity, generating a local energy distribution density model to characterize the energy accumulation level in different local spatial regions.

[0054] In this embodiment, the scene feedback propagation chain after feedback fluctuation convergence is obtained, and a feedback coupling mapping structure is established, as follows: Obtain the node sequence and its temporal propagation relationship in the scene feedback propagation chain after the feedback fluctuation convergence, and perform propagation consistency segmentation processing on the node sequence to generate stable propagation units; A feedback propagation topology path is constructed based on stable propagation units, and a feedback propagation coupling factor field is generated by combining the directional offset and propagation intensity difference between nodes. Specifically, the node sequence and its temporal order relationship in the stable propagation unit are obtained, and a feedback propagation topology path is constructed based on the propagation connection relationship between nodes. The change in the propagation direction angle between adjacent nodes is calculated based on the feedback propagation topology path, and the change in propagation intensity between nodes is obtained to form directional offset and propagation intensity difference, respectively. The directional offset and propagation intensity difference are jointly mapped to generate the coupling relationship distribution value between nodes. Based on the coupling relationship distribution value between nodes, spatial expansion and continuous mapping are performed on the feedback propagation topology path to form a feedback propagation coupling factor distribution field, which is used to characterize the spatial distribution characteristics of coupling strength in the propagation path. The feedback propagation coupling factor field is spatially expanded to extract local coupling enhancement and attenuation regions, and a feedback coupling propagation distribution structure is generated. Specifically, the distribution expression of the feedback propagation coupling factor field on the feedback propagation topology path is obtained and discretized into field distribution units corresponding to the propagation nodes. A spatial neighborhood range is set with each propagation node as the center. Based on the spatial coverage relationship of the field distribution units within the neighborhood range, the feedback propagation coupling factor field is locally expanded to form local spatial coupling expansion units. Based on the local spatial coupling expansion units, the coupling change gradient and spatial diffusion trend of the field distribution units within the neighborhood are analyzed to obtain a local coupling change sequence. Based on the local coupling change sequence, coupling strength enhancement segments and coupling strength attenuation segments are identified. The enhanced and attenuation segments are then structurally organized according to their distribution relationship in the spatial neighborhood to generate the feedback coupling propagation distribution structure. Based on the feedback coupled propagation distribution structure, coupling constraint reconstruction processing is performed on the feedback propagation topology path to enable the path structure to undergo adaptive convergence and enhancement adjustment under the propagation consistency constraint. Specifically, the spatial distribution relationship between the coupling enhancement region and the coupling decay region in the feedback coupled propagation distribution structure is obtained, and path constraint rules are extracted based on the regional distribution differences to characterize the propagation control direction of different path segments. The path constraint rules are mapped to each path node in the feedback propagation topology path, and a path control correspondence is established according to the spatial position relationship of the nodes in the distribution structure, so that the path nodes have regional constraint attributes. Based on the path control correspondence, the node connection relationship in the feedback propagation topology path is reorganized, and connection strengthening is performed on the path segments in the coupling enhancement region, and connection convergence adjustment is performed on the path segments in the coupling decay region. A unified mapping and fusion process is performed on the reconstructed feedback propagation topology and the feedback coupled propagation distribution structure to form a feedback coupled mapping structure. Specifically, this involves: obtaining the node sequence and spatial positional relationship in the reconstructed feedback propagation topology and the spatial distribution units in the feedback coupled propagation distribution structure, aligning them under a unified spatial coordinate system, establishing a spatial correspondence between path nodes and distribution units, analyzing the coverage and influence of path nodes in the distribution structure based on the spatial correspondence, and simultaneously obtaining the constraint influence range of distribution units on path nodes, forming a bidirectional mapping relationship from path to distribution structure and from distribution structure to path. The bidirectional mapping relationship is then structurally integrated, fusing the path connection relationship and the distribution influence relationship, so that the path topology and distribution structure form coupled related units under the same expression framework. Based on the coupled related units, the path nodes and distribution units are reconstructed as a whole to generate the feedback coupled mapping structure, which is used to characterize the unified mapping relationship between the propagation path structure and the coupled distribution structure.

[0055] Example 2, Figure 2 The present invention provides a system for controlling the motion of interactive props in the production of virtual-real integrated programs, comprising a construction module, a prediction module, and a feedback control module, with connections between the modules; The module is used to acquire the action data of interactive props, generate action sequences based on the action data, determine the target action state and the target state transition path, establish an action state diagram, and construct the action state machine of interactive props based on the target state transition path and state holding period. The prediction module is used to generate future state evolution chains based on the action state machine and predict the future action trajectory of interactive props. It performs program resource preloading and collaborative linkage processing according to the trajectory change stages and generates program collaborative control data. The feedback control module is used to perform virtual-real fusion linkage control based on program collaborative control data, and to perform reverse feedback control on interactive props based on the virtual scene state.

[0056] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0057] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the implementation. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this application.

[0058] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0059] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0060] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for controlling the motion of interactive props in the production of a virtual-real integrated program, characterized in that, include: Acquire the action data of the interactive prop, generate an action sequence based on the action data, determine the target action state and the target state transition path, establish an action state diagram, and construct the action state machine of the interactive prop based on the target state transition path and state holding period. Based on the action state machine, the future state evolution chain is generated and the future action trajectory of the interactive prop is predicted. According to the trajectory change stage, program resource preloading and collaborative linkage processing are performed, and program collaborative control data is generated. The virtual-real fusion linkage control is executed based on program collaborative control data, and the interactive props are subjected to reverse feedback control based on the virtual scene state. Among them, the motion feedback drift band is obtained based on the motion offset change trajectory of the interactive props in the feedback linkage adjustment process. The drift compensation correction is performed on the state advancement connection relationship in the motion state machine based on the motion feedback drift band. The motion state machine after drift compensation correction maintains the continuous collaborative linkage between the virtual scene and the interactive props. This includes preloading program resources and coordinating processes based on the trajectory change phases, and generating program coordination control data, specifically including: The future action trajectory is divided into trajectory change stages based on the continuous change characteristics of the trajectory; Obtain the current program resource status of the virtual scene, and perform phased resource preloading processing on the program resource status according to the trajectory change stage; Based on the program resource status after resource preloading, determine the resource response order among the program resources, and generate a resource response priority sequence based on the resource response order; Based on the resource response priority sequence, collaborative resource linkage processing is performed on the program resource status to form a program collaborative response sequence; Obtain the current rhythm stage of the program system and adjust the rhythm synchronization of the program's coordinated response sequence according to the rhythm stage; Based on the resource response connection relationship in the program collaborative response sequence after rhythm synchronization processing, a resource control timing chain is generated. Based on the resource control timing chain, the resource control sequence and resource control trigger time of each program resource are linked and arranged to form program collaborative control data.

2. The interactive prop motion control method in the production of a virtual-real fusion program according to claim 1, characterized in that, The process involves acquiring the action data of the interactive prop, generating an action sequence based on the action data, determining the target action state and the target state transition path, establishing an action state diagram, and constructing the action state machine of the interactive prop based on the target state transition path and state maintenance period, as detailed below: Acquire the action data of interactive props, and generate action sequences and candidate action state sets based on the action data; Obtain the current scene state and program state, combine the candidate action state set to generate state association relationships, and determine the target action state of the interactive props; An action state graph is constructed based on the target action state, and the target state transition path of the interactive prop is determined based on the action state graph. When the target action state of the target state transition path continuously meets the preset stability conditions, the target state transition path is activated and a state maintenance period is generated. During the state maintenance period, the target state transition path is kept in an active state, and an action state machine is generated based on the target state transition path that is in an active state. The state occupancy maintenance requirement of the current target state transition path is determined based on the state maintenance period, and the state lease duration is generated. When the state lease expires, the target state transition path in the action state diagram is readjusted, and the action state machine is dynamically updated.

3. The interactive prop motion control method in the production of a virtual-real integrated program according to claim 2, characterized in that, The determination of the target state transition path for interactive props based on the action state diagram is as follows: Read each action state node and each state transition edge in the action state graph, and generate candidate state transition paths based on the connection relationships between the state transition edges; Obtain the path response characteristics corresponding to each candidate state transition path and generate a path trigger identifier; Based on the path trigger identifier of each candidate state transition path, the candidate state transition path is filtered for path activation, and the candidate state transition paths that meet the path activation conditions are written into the path pre-activation set. Get the current action change stage of the interactive prop and the channel occupancy status of the current state transition channel, and perform path blocking detection on each candidate state transition path in combination with the path pre-activation set to determine whether there is a condition prohibiting switching on the corresponding candidate state transition path. When a candidate state transition path has a condition that prohibits switching, the corresponding candidate state transition path is marked as a path blocking state. When there is no prohibition condition for switching in the candidate state transition path, the corresponding candidate state transition path is written into the path to be activated set; Obtain the historical path activation records of each candidate state transition path in the path activation set, and generate the path inertia stage based on the historical path activation records; Based on the path inertia stage corresponding to each candidate state transition path, the path elimination process is performed on the set of paths to be activated. The final target state transition path that is not eliminated is determined as the target state transition path of the interactive prop.

4. The interactive prop motion control method in the production of a virtual-real fusion program according to claim 2, characterized in that, The process of maintaining the target state transition path in an active state during the state maintenance period and generating an action state machine based on the active target state transition path is as follows: Based on the state retention period, path retention control is performed on the target state transition path to restrict the target state transition path from exiting the current continuously active state within the state retention period; When the target state transition path is in a continuously active state, continuously monitor the path response characteristics and path activation trend corresponding to the target state transition path, and maintain the path activation continuity corresponding to the target state transition path according to the path response characteristics and path activation trend. Read each action state node and each state transition edge in the target state transition path, and parse the state switching order relationship between each action state node according to the connection direction of each state transition edge to generate the state node connection relationship. Based on the connection relationship of state nodes, the timing calibration of each action state node is performed, and the node connection order is mapped to the entry trigger point and exit trigger point in the time dimension to form a node timing mapping relationship. Obtain the node entry order and node exit order corresponding to each action state node, and align the order based on the node temporal mapping relationship to generate a state node transition sequence; The execution path hierarchy of each action state node is divided according to the state node transition sequence, and a state path hierarchy structure is generated. Different state path hierarchies correspond to different stages of state switching layers. Establish the path association relationship between each action state node based on the state path hierarchy structure, and generate a state path mapping structure. Establish state path driving relationships based on state path mapping structure, and generate action state machines corresponding to interactive props based on state path driving relationships. During the operation of the action state machine, the activation state of the current state node in the action state machine is dynamically maintained according to the path activation continuity corresponding to the target state transition path.

5. The interactive prop motion control method in the production of a virtual-real integrated program according to claim 1, characterized in that, The process of generating future state evolution chains based on action state machines and predicting the future action trajectories of interactive props is as follows: Based on the target action state node that is currently active in the action state machine and the corresponding target state transition path, determine the current state evolution direction of the interactive prop and extract the state advancement trend. Obtain the historical state transition records corresponding to the target state transition path and extract the path evolution trajectory; Generate a future state progression sequence based on the state progression trend and path evolution trajectory, and establish a future state evolution chain; Predict the future action trajectory of interactive props based on the future state evolution chain.

6. The interactive prop motion control method in virtual-real fusion program production according to claim 5, characterized in that, The method predicts the future action trajectory of interactive props based on future state evolution chains, and divides the trajectory change stages by continuous trajectory change characteristics, as detailed below: Obtain the state progression connection relationships and state progression order in the future state evolution chain, and extract the future state progression trajectory based on the state progression connection relationships and state progression order; Based on the future state, the attitude change data and displacement change data corresponding to each state node are read to generate the future motion trajectory. Obtain the trajectory propagation direction and trajectory propagation rate in the future action trajectory, and generate a continuous trajectory change sequence; Based on the analysis of the trajectory change sequence, the trajectory change density and trajectory change diffusion range during the trajectory advancement process are analyzed to identify the continuous trajectory clustering region; Based on the relationship between the trajectory advancement boundary changes in the continuous clustering regions of each trajectory, the future action trajectory is divided into continuous advancement intervals to form continuous trajectory advancement intervals. Obtain the trajectory connection and change relationship between each continuous trajectory advancement interval, and perform trajectory clustering on the continuous trajectory advancement interval according to the trajectory connection and change relationship to form trajectory evolution segments; The trajectory change stages are divided based on the continuity of trajectory advancement and the stability of trajectory changes in each trajectory evolution segment.

7. The method for controlling the motion of interactive props in the production of a virtual-real fusion program according to claim 1, characterized in that, The virtual-real fusion linkage control based on program collaborative control data, and the reverse feedback control of interactive props based on the virtual scene state, are as follows: Acquire the resource linkage relationships in the program collaborative control data, and establish a program resource linkage control structure based on the resource linkage relationships; Based on the program resource linkage control structure, the program resources in the virtual scene are controlled to execute resource linkage responses in a preset linkage order, forming a continuous resource linkage state. Obtain the process of scene state change in a virtual scene under continuous resource linkage, and generate a scene state change chain; Based on the analysis of the scene state change chain, the propagation direction and diffusion trend of the scene response are analyzed to generate the scene energy flow trajectory. Based on the analysis of scene energy flow trajectory, the scene energy accumulation area and scene energy diffusion area in the virtual scene are analyzed, and a scene energy distribution grid is established; Obtain the energy propagation direction and energy fluctuation continuity in the scene energy distribution grid, and generate the scene feedback propagation chain; Based on the scene feedback propagation chain, feedback fluctuation convergence is performed on the feedback propagation path in the virtual scene. Obtain the scene feedback propagation chain after the feedback fluctuation convergence, and establish a feedback coupling mapping structure; The interactive props are adjusted based on a feedback coupling mapping structure. Based on the trajectory of the motion offset change of the interactive props during the feedback linkage adjustment process, the motion feedback drift band is obtained; Based on the motion feedback drift band, drift compensation correction is performed on the state advancement connection relationship in the motion state machine; The motion state machine, corrected by drift compensation, maintains continuous collaborative interaction between the virtual scene and interactive props.

8. The method for controlling the motion of interactive props in the production of a virtual-real fusion program according to claim 7, characterized in that, The acquisition of resource linkage relationships in program collaborative control data, and the establishment of a program resource linkage control structure based on these relationships, are detailed as follows: Obtain the resource response connection order and resource linkage advancement relationship in the program collaborative control data, and identify the resource connection links between each program resource based on the resource response connection order; Based on the analysis of the linkage and promotion relationship of resources, the linkage propagation direction between each resource connection link is analyzed, and linkage and serial processing is performed on each resource connection link according to the linkage propagation direction to form a resource linkage path; Based on the analysis of the resource linkage paths, the direction of resource linkage propagation and the continuity of resource linkage between different program resources are analyzed, and the linkage connection relationship between program resources is identified. Based on the various linkage relationships, the resource linkage paths between different program resources are chained together to form a resource linkage association chain. Obtain the resource linkage aggregation area and resource linkage diffusion area in the resource linkage association chain, and identify the core nodes of resource linkage based on the resource linkage aggregation area; Based on the analysis of the resource linkage diffusion area, the linkage propagation connection relationship between each resource linkage core node is analyzed, and a resource linkage distribution network is established based on each linkage propagation connection relationship; Based on the resource linkage distribution network, linkage connection mapping processing is performed on each resource linkage path to form linkage control connection relationship between program resources. Establish a program resource linkage control structure based on the linkage control connection relationship after linkage connection mapping processing.

9. The method for controlling the motion of interactive props in the production of a virtual-real fusion program according to claim 7, characterized in that, The process of obtaining the scene feedback propagation chain after feedback fluctuation convergence and establishing a feedback coupling mapping structure is as follows: Obtain the node sequence and its temporal propagation relationship in the scene feedback propagation chain after the feedback fluctuation convergence, and perform propagation consistency segmentation processing on the node sequence to generate stable propagation units; A feedback propagation topology path is constructed based on stable propagation units, and a feedback propagation coupling factor field is generated by combining the directional offset and propagation intensity difference between nodes. The feedback propagation coupling factor field is subjected to spatial neighborhood expansion processing to extract local coupling enhancement regions and coupling attenuation regions, and a feedback coupling propagation distribution structure is generated. Based on the feedback-coupled propagation distribution structure, the topology path of the feedback propagation is reconstructed by coupling constraints. The reconstructed feedback propagation topology path and the feedback coupling propagation distribution structure are uniformly mapped and fused to form a feedback coupling mapping structure.

10. A system using the interactive prop motion control method in virtual-real fusion program production as described in any one of claims 1-9, characterized in that, It includes a construction module, a prediction module, and a feedback control module, and there are connections between the modules; The module is used to acquire the action data of interactive props, generate action sequences based on the action data, determine the target action state and the target state transition path, establish an action state diagram, and construct the action state machine of interactive props based on the target state transition path and state holding period. The prediction module is used to generate future state evolution chains based on the action state machine and predict the future action trajectory of interactive props. It performs program resource preloading and collaborative linkage processing according to the trajectory change stages and generates program collaborative control data. The feedback control module is used to perform virtual-real fusion linkage control based on program collaborative control data, and to perform reverse feedback control on interactive props based on the virtual scene state.

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