Virtual display interaction system and method based on meta universe

Abstract

The invention relates to the field of digital data processing, and discloses a virtual display interaction system and method based on meta universe, and the method comprises the steps: capturing a weak interaction leading signal of a user in real time, converting the weak interaction leading signal into an interaction intention primitive carrying main body, target, action and intensity information through a lightweight rule, and broadcasting the interaction intention primitive; and each client integrates the elements to construct a dynamic interaction intention flow field, so that relevant data are preloaded prospectively, and fine auxiliary regulation and control are carried out on high-intention collaborative interaction. According to the method, the interaction intention flow field is used for estimating the group trend, intelligent preposition of resources and precise assistance of cooperation are achieved, virtual feedback time delay is remarkably shortened, and the success rate and naturalness of a complex cooperation task are improved.

Description

Technical Field

[0001] The present invention relates to a virtual display interaction system and method based on the metaverse, and belongs to the technical field of digital data processing. Background Art

[0002] Currently, the interaction of virtual systems such as the metaverse mostly relies on explicit user commands and synchronous resource loading; multi-person collaboration also mainly relies on explicit state synchronization to achieve consistent perception. However, in the metaverse display with a large number of users and complex interactions, the limitations of the traditional model are highlighted. The multi-link processing after the user command can easily lead to interaction delays, reducing smoothness and immersion. When multiple people work together, the prediction and fusion of user intentions are insufficient, which often leads to action conflicts or timing mismatches, making fine collaboration difficult and tacit cooperation difficult to form naturally.

[0003] The main technical bottlenecks are: 1. Interaction responses are generally delayed, lacking proactive prediction of potential user intent. Resource allocation and loading lag behind demand, resulting in operational lags. 2. Multi-person collaboration is poor, making it difficult to effectively perceive group trends and intentions, affecting the natural flow and success of interactions in collaborative scenarios. 3. Interaction information perception is limited, relying heavily on explicit user actions, and lacks effective capture and utilization of leading implicit signals that represent nascent intent. Therefore, how to design a model that can capture and analyze subtle user interaction precursors in real time, build a dynamic group interaction intention perception model, and then achieve intelligent resource allocation, proactive loading, and multi-person collaboration to significantly improve the immediacy, fluency, and collaborative efficiency of user experience in the metaverse has become a key issue to be addressed in this field. Summary of the Invention

[0004] The present invention provides a virtual display interaction system and method based on the metaverse, the main purpose of which is to solve the technical problems existing in the existing metaverse virtual display interaction, such as delayed interaction response, poor multi-person collaboration, and low level of interaction intelligence due to failure to fully utilize user leading implicit signals.

[0005] To achieve the above objectives, the present invention provides a virtual display interaction system based on the metaverse, comprising: The user client is configured to capture in real time interaction leading signals that represent the budding state of the user's interaction intention, wherein the interaction leading signals include a small displacement of the controller, a slight change in orientation, a preliminary movement of the eye focus, or a rate of change of button pressure; Through a preset lightweight behavior-intent mapping rule set, interaction leading signals are instantly converted into interaction intention primitives. Interaction intention primitives include: subject (the user himself), potential target (one or more virtual objects or areas ranked by the probability of the current user behavior), trend action type (one or more basic interaction action types related to the current leading signal), and the intention strength value that decays over time; Broadcasting the interaction intent primitive to other user clients within a predetermined logical or spatial range of the user client at a range and frequency determined based on the intent strength value; Receive interaction intent primitives broadcast from other user clients, and based on its own and the received interaction intent primitives, locally build and maintain a dynamic interaction intent flow field perception data. The interaction intent flow field is a probability distribution estimate of the interaction possibility and potential interaction direction in the current local area; Based on the intention convergence area with the intention intensity value exceeding the preset threshold or the intention streamline indicating the collaborative interaction trend identified in the locally perceived interactive intention flow field, before the relevant interactive event occurs, the three-dimensional model data, interactive script or sound effect data related to the high-probability interactive event will be preloaded into the local cache of the user client; and for the interaction whose collaborative interaction trend indicated in the interactive intention flow field reaches the preset collaborative threshold, the auxiliary interactive operation will be fine-tuned on the relevant user client. The fine-tuning processing includes adjusting the user's effective interaction judgment boundary within the user's active operation range or correcting the final state of the virtual object in accordance with the preset physical rules to improve the interaction success rate.

[0006] Preferably, the preset lightweight behavior-intent mapping rule set is implemented based on a lookup table or a finite state machine to ensure that the generation delay of the interaction intention primitive is less than a preset time threshold.

[0007] Preferably, the interaction intention primitive further includes a collaborative expectation identifier. When it is detected that the user is performing a predefined interactive task that requires collaboration with others, the collaborative expectation identifier is activated and a higher weight is given to the interaction intention primitive with the same or complementary collaborative expectation identifier in the interaction intention flow field.

[0008] Preferably, the size of the predetermined logical or spatial range and the frequency of the announcement are positively correlated with the intention intensity value.

[0009] Preferably, the user client is further configured to: when the number or frequency of received interaction intention primitives exceeds a preset density threshold or dynamic threshold, preprocess the received interaction intention primitives, the preprocessing including: calculating the reciprocity score IRSi of the interaction intention between the source user sending the interaction intention primitive and the client; if the interaction intention broadcast by the client within a preset time window and the interaction intention broadcast by the source user i within the same time window meet the preset spatial proximity or semantic correlation conditions in terms of potential targets or trend action types, then increase IRSi; otherwise, if the source user i continuously broadcasts low-intensity interaction intention primitives that are irrelevant to the current focus of the client or whose intentions change rapidly within the preset time window, then decrease IRSi; when constructing the local interaction intention flow field, the weight of the interaction intention primitive from the source user i is proportional to its IRSi; and performing spatiotemporal coherence evaluation on a sequence of continuous interaction intention primitives from the same source user, and giving a higher weight to interaction intention primitives whose duration exceeds a preset time length and whose potential targets change in space less than a preset distance threshold.

[0010] Preferably, the predetermined virtual object in the scene is further configured as follows: when its own state meets the preset trigger condition or when it perceives that the interaction intention primitive related to its preset theme in the interaction intention flow field of nearby users reaches a preset intensity, it actively generates and transmits an object-conditional interaction invitation beacon to user clients within a predetermined range. The object-conditional interaction invitation beacon contains the identifier of the virtual object and information indicating a specific interaction function point; when the user client constructs and perceives the interaction intention flow field, it further takes the received object-conditional interaction invitation beacon into consideration to enhance the intention guidance intensity directed to the specific interaction function point of the virtual object.

[0011] Preferably, the preset trigger conditions include the update of the virtual object's own data, the occurrence of a specific user interaction behavior, or the arrival of a preset time point.

[0012] Preferably, the preset physical rules include gravity, collision or friction models, and the amount of correction performed on the final state of the virtual object is linearly positively correlated with the degree of deviation of the optimal interaction area predicted by the interaction intention flow field.

[0013] Preferably, the user client is further configured to send relevant data of the interaction event successfully guided and completed through the interaction intention flow field to the authoritative node in a predetermined data format for final state synchronization and data recording.

[0014] A virtual display interaction system based on the metaverse, wherein a user client performs the following steps to implement a virtual display interaction method: Step a: capturing in real time interaction leading signals that represent the budding state of the user's interaction intention. Interaction leading signals include small displacements of the controller, subtle changes in orientation, initial movement of the eye focus, or the rate of change of button pressure. Step b: Using a preset lightweight behavior-intention mapping rule set, the interaction leading signal is instantly converted into an interaction intention primitive. The interaction intention primitive includes: the subject (the user), the potential target (one or more virtual objects or areas ranked by the probability of the current user behavior), the trend action type (one or more basic interaction action types related to the current leading signal), and the intention strength value that decays over time; Step c: broadcasting the interaction intent primitive to other user clients within a predetermined logical or spatial range of the user client at a range and frequency determined based on the intent strength value; Step d: Receive interaction intent primitives broadcast from other user clients and, based on its own and the received interaction intent primitives, locally construct and maintain a dynamic interaction intent flow field perception data. The interaction intent flow field is a probability distribution estimate of the interaction possibility and potential interaction direction in the current local area. Step e: Based on the intention convergence area with an intention intensity value exceeding a preset threshold or the intention streamline indicating a collaborative interaction trend identified in the locally perceived interaction intention flow field, the 3D model data, interaction script, or sound effect data related to the high-probability interaction event is preloaded into the local cache of the user client before the relevant interaction event occurs; Step f: For interactions where the collaborative interaction trend indicated in the interaction intention flow field reaches a preset collaborative threshold, fine-tuning of auxiliary interactive operations is performed on the relevant user client. The fine-tuning includes adjusting the user's effective interaction judgment boundary or correcting the final state of the virtual object in accordance with preset physical rules within the user's active operation range to improve the interaction success rate.

[0015] Compared with the background technology problems, the beneficial effects of the present invention are: 1. By capturing tiny displacements and leading signals during user operations and mapping them into lightweight intent primitives, the system can predict interaction trends before the user's actions are fully launched. Combined with the real-time construction and attenuation mechanism of the intent flow field, relevant three-dimensional resources and interaction scripts can be proactively loaded into the local cache, effectively avoiding interaction freezes caused by network delays or delayed resource loading in traditional synchronization mechanisms. The delay between user operations and virtual feedback is shortened to a perceptible range, significantly improving the smoothness of the interaction process.

[0016] 2. Based on the identification of intention convergence areas and collaborative streamlines in the dynamic intention flow field, the system fine-tunes the interaction judgment range or virtual object status in accordance with physical rules within the user's active operation boundary. This mechanism uses distributed perception of the complementarity of intentions between users to naturally guide the spatiotemporal matching of multiple actions without the need for central arbitration, transforming the difficult collaboration that originally relied on precise synchronization into smooth and tacit collaboration. It is particularly suitable for scenarios that require precise coordination, such as virtual assembly and joint exhibit operations.

[0017] 3. Virtual objects actively integrate into the intent flow field by emitting conditional invitation beacons, converting their own state changes into interaction attraction points that can be perceived by nearby users. Combining the spatial proximity and semantic association analysis of intent primitives, the system prioritizes preloading key resources for high-probability interaction areas. By dynamically screening regional resource broadcast objects based on intent intensity, the system prioritizes data distribution to high-interaction intensity areas, reducing the bandwidth load in low-activity areas.

[0018] 4. By evaluating the reciprocity and screening the spatiotemporal coherence of interaction intention primitives, the system automatically filters out noise signals in scenarios with dense users or frequent intention switching, and strengthens the weight of intentions with strong continuity and clear goals. This mechanism simulates the behavioral logic of humans focusing on effective information in noisy environments, ensuring that the intention flow field can still accurately reflect the actual interaction trends under high-concurrency interactions, avoiding misjudgments and resource mismatches caused by signal overload. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 A timing diagram for constructing the interactive intention flow field and collaborative interactive processing in the system of the present invention; Figure 2 A decision flow chart for interaction intention generation and dynamic range preloading in the interactive system of the present invention; Figure 3 This is a comparison chart of the interaction intention generation delay and collaborative operation success rate in the interactive system of the present invention.

[0020] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0021] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0022] The present application provides a virtual display interaction system and method based on the Metaverse, including: The user client is configured to capture in real time interaction leading signals that represent the budding state of the user's interaction intention, wherein the interaction leading signals include a small displacement of the controller, a slight change in orientation, a preliminary movement of the eye focus, or a rate of change of button pressure; The specific method of capturing the controller's micro-displacement is to equip the user client with a high-precision motion sensor, such as a sensor based on an inertial measurement unit (IMU). The sensor collects the controller's three-dimensional spatial position data at a high frequency, and calculates the controller's micro-displacement by comparing the continuously collected position data. For example, when the user gently shakes the controller, the sensor can detect the change in the controller's displacement in the x, y, and z directions, Δx, Δy, and Δz. When the displacement change Δ x / Δ t , Δ y / Δ t , Δ z / Δ t When the preset threshold is reached, it can be judged as a valid micro-displacement interactive leading signal; Suppose the user gently shakes the controller in the virtual environment and the sensor captures the following data: initial position: (x0, y0, z0) = (0, 0, 0), position after 10 milliseconds: (x1, y1, z1) = (0.01, 0.02, 0.01), displacement vector: Δr = (0.01, 0.02, 0.01), displacement velocity: v = (Δx / Δt, Δy / Δt, Δz / Δt) = (1, 2, 1) m / s; Capturing subtle changes in orientation involves equipping the controller with a gyroscope and accelerometer, which are used to measure its orientation. The gyroscope measures the controller's angular velocities ωx, ωy, and ωz around each axis. The magnitude and direction of the angular velocity reflect the rotational speed and direction of the user's operation. The accelerometer measures the acceleration of the controller in the gravitational field. By fusing the data from these two sensors and using algorithms such as Kalman filtering, the controller's precise orientation is calculated. When the controller's orientation change angle exceeds a certain threshold and this change persists for a certain period of time, it can be determined as a subtle orientation change interaction leading signal. For example, if the user slightly tilts the controller upward from horizontal, the sensor detects that the orientation change meets the above conditions and captures this interaction leading signal. Assume that the user tilts the controller slightly upward from the horizontal direction, and the sensor captures the following data: initial Euler angle: (α0, β0, γ0) = (0, 0, 0), Euler angle after 10 milliseconds: (α1, β1, γ1) = (0, 5°, 0), angular velocity: ω = ((0 0) / 0.01, (5 0) / 0.01, (0 0) / 0.01)=(0,500,0) degrees / second; The initial movement capture of the eye focus is as follows: an eye tracking system is installed on the user client. The system captures the reflected light of the user's eyes through an infrared camera and combines it with the eye movement model algorithm to determine the position of the user's eye focus in the virtual scene in real time ( x , y , z ), when the user's gaze focuses on a virtual object or area ( x , y , z ) to another virtual object or area ( x1 , y1 , z1 ), and the moving speed exceeds a certain threshold, and the moving distance exceeds a certain distance (Δ x , Δ y , Δ z ), it is considered as the preliminary interactive leading signal of the eye focus movement. For example, when a user quickly moves his eyes from one painting to another in a virtual exhibition hall, the eye tracking system detects that this movement meets the conditions and captures the signal; Assume that a user quickly moves their gaze from one painting to another in a virtual exhibition hall. The eye tracking system captures the following data: initial gaze focus coordinates: (x0, y0) = (100, 200) pixels, gaze focus coordinates 10 milliseconds later: (x1, y1) = (300, 400) pixels, gaze focus movement vector: Δd = (200, 200) pixels, gaze focus movement speed: v = (200 / 0.01, 200 / 0.01) = (20000, 20000) pixels / second; Capturing the rate of change of key pressure involves installing pressure sensors on the controller's buttons. These pressure sensors can measure the pressure applied to the buttons in real time. By calculating the rate of change of continuously collected pressure data, when the rate of change of key pressure exceeds a preset threshold, it can be determined as a key pressure change rate interactive leading signal. For example, when a user presses a button on the controller hard, the pressure sensor detects a rapid increase in pressure, meeting the rate of change condition, and captures the signal. Suppose the user presses a button on the controller hard, and the pressure sensor captures the following data: Initial pressure value: P0 = 0.1 Newton, pressure value after 10 milliseconds: P1 = 0.5 Newton, pressure change rate: ΔP / Δt = (0.5 0.1) / 0.01=40 Newtons / second.

[0023] Through a preset lightweight behavior-intent mapping rule set, interaction leading signals are instantly converted into interaction intention primitives. Interaction intention primitives include: subject (the user himself), potential target (one or more virtual objects or areas ranked by the probability of the current user behavior), trend action type (one or more basic interaction action types related to the current leading signal), and the intention strength value that decays over time; A table lookup method is used to implement a lightweight behavior-intent mapping rule set. First, a mapping table is established to associate various interaction leading signals with corresponding interaction intention primitives; When a small displacement interaction signal of the controller is detected, it is converted into an interaction intention primitive according to the mapping table, where the subject is the user, the potential target is a virtual object within a certain distance in front of the controller's displacement direction, the trend action type is "move operation", and the intention strength value is calculated based on the displacement size and duration; Input signal: displacement vector , displacement speed , displacement acceleration ; Intent primitives are generated as: ;

[0024] User indicates that the subject is the user himself; PotentialTarget ... For the determined virtual objects or areas, use the spatial positioning algorithm to calculate the virtual objects or areas within a certain distance in the displacement direction and sort them by probability; The trend action type is move operation (Move); the intention intensity value is calculated based on the displacement speed and acceleration. k It is a tuning parameter used to control the range and sensitivity of the intent strength value, usually set to 0.1; For subtle orientation change interaction precursor signals, we convert them into interaction intention primitives based on a mapping table. The subject is the user, the potential target is the virtual object or area pointed by the orientation change, the orientation action type is "observation operation", and the intention strength value is calculated based on the orientation change angle and duration. Input signal: Euler angle change , angular velocity changes ; Intent primitives are generated as: ;

[0025] User indicates that the subject is the user himself; PotentialTarget indicates that the direction change vector The determined virtual objects or areas are calculated using a spatial positioning algorithm to determine the virtual objects or areas pointed to by the direction of the change and are sorted by probability. The trend action type is observation operation (Observe); the intention intensity value is calculated based on the angular velocity. k is a tuning parameter; The initial interaction leading signal of the gaze focus movement is converted into an interaction intention primitive according to the mapping table. The subject is the user, the potential target is the virtual object or area to which the gaze focus moves, the trend action type is "gaze operation", and the intention strength value is calculated based on the gaze focus movement speed and movement distance. Input signal: sight focus movement vector , moving speed ; Intent primitives are generated as: ;

[0026] User indicates that the subject is the user himself; PotentialTarget indicates the movement vector according to the focus of sight The determined virtual objects or areas are calculated using a spatial positioning algorithm to determine the virtual objects or areas pointed to by the direction of the line of sight and are sorted by probability. The trend action type is gaze operation (Gaze); the intention intensity value is calculated based on the movement speed. k is a tuning parameter; The key pressure change rate interaction leading signal is converted into an interaction intention primitive according to the mapping table. The subject is the user, the potential target is the virtual object associated with the key function (such as the operation object corresponding to the key), the trend action type is "trigger operation", and the intention strength value is calculated based on the key pressure change rate; Input signal: pressure change rate ; Intent primitives are generated as: ;

[0027] User indicates that the subject is the user himself; PotentialTarget indicates the virtual object associated with the button function; The trend action type is trigger operation (Trigger); the intention intensity value is calculated based on the pressure change rate. k is a tuning parameter.

[0028] Broadcasting the interaction intent primitive to other user clients within a predetermined logical or spatial range of the user client at a range and frequency determined based on the intent strength value; Receive interaction intent primitives broadcast from other user clients, and based on its own and the received interaction intent primitives, locally build and maintain a dynamic interaction intent flow field perception data. The interaction intent flow field is a probability distribution estimate of the interaction possibility and potential interaction direction in the current local area; Specifically, calculate the weighted average of the intention strength values ​​of all intention primitives in each virtual object or area. The formula is as follows: Interaction probability ;

[0029] Where O represents a virtual object or area, is the intent strength value of the i-th intent primitive, is the weight (which can be adjusted based on factors such as the timeliness and spatial distance of the intent primitive); Calculate the directional distribution of the trend action types of all intention primitives in each virtual object or area. The vector averaging method can be used to estimate the main interaction direction. The formula is as follows: Interaction direction probability ;

[0030] in, is the direction vector of the i-th intention primitive; In the locally perceived interaction intention flow field, the intention convergence areas with intention strength values ​​exceeding a preset threshold or intention streamlines indicating collaborative interaction trends (i.e., the path structure formed by multiple user interaction intention primitives showing a consistent trend in time and space) are identified. Specifically, the virtual environment is divided into multiple local areas, each with a unique identifier. Within each local area, the distribution of all intention primitives is counted. For each intention primitive, it is classified into the corresponding virtual object or area based on its potential target and tendency action type; In the dynamic interaction intention flow field, identify the intention convergence area where the intention strength value exceeds the preset threshold. The preset threshold can be set based on the statistical analysis results of typical user interaction behavior data. For example, the threshold is set to 0.6. The identification process is as follows: in each local area, compare the interaction possibility probability of each virtual object or area with the preset threshold. If the interaction possibility probability of the area is greater than 0.6, mark the virtual object or area as an intention convergence area; Identify the intention streamlines that indicate collaborative interaction trends, that is, the path structure formed by multiple user interaction intention primitives showing a consistent trend in time and space. The identification process is as follows: set a time window (such as 5 seconds), count all intention primitives within the time window, and for each virtual object or area, count the number of intention primitives pointing to the object or area and the type of trending action. If at least three users point to the same potential target in the same period and maintain intention, Figure 1If the consistency is high and the mean reciprocity score is not less than 0.5 and remains stable for more than 2 seconds, it is considered that there is a collaborative interaction trend; Before the relevant interactive event occurs, the 3D model data, interactive script or sound effect data related to the high-probability interactive event is preloaded into the local cache of the user client; For interactions where the collaborative interaction trend indicated in the interaction intention flow field reaches a preset collaborative threshold, fine-tuning of auxiliary interaction operations is performed on the relevant user client. The fine-tuning includes adjusting the user's effective interaction judgment boundary or correcting the final state of the virtual object in accordance with preset physical rules within the user's active operation range to improve the interaction success rate. The fine-tuning process specifically involves adjusting the user's effective interaction judgment boundary within the user's active operation range based on the collaborative interaction trend. For example, if multiple users tend to operate a virtual object, the effective interaction range of the object can be appropriately expanded to make it easier for users to interact. The final state of the virtual object is corrected in accordance with preset physical rules. For example, if the user operation causes the virtual object to deviate from the optimal interaction area, a linearly positive correction amount is calculated based on the degree of deviation to adjust the position or state of the virtual object. The formula is as follows:

[0031] in, is the correction amount, is the deviation vector, k is the correction coefficient; At the same time, in the system implementation, the setting of the intent intensity threshold and the collaborative trend threshold is based on the statistical analysis results of typical user interaction behavior data, combined with the interaction complexity in different scenarios, and adaptively adjusted. Among them, the intent intensity threshold is calculated by the weighted integral method of the frequency of occurrence of the intent primitive and the behavior amplitude per unit time. Its initial reference value is set to an average intensity of 0.6 (normalized scale range is 0-1) within a 3-second sliding window; The dynamic interaction intent flow field needs to be continuously updated according to the new interaction intent primitives. The update process is as follows: When a new intent primitive is received, it is added to the local data structure, and the interaction possibility probability and interaction direction probability in the local area are recalculated. According to the attenuation model of the intent strength value, when the intent strength value is lower than the threshold, the intent primitive is removed from the data structure, the entire flow field is updated regularly, and the interaction possibility probability and interaction direction probability in all local areas are recalculated.

[0032] The collaborative trend threshold is based on the fact that at least three users are in the same period, pointing to the same potential target and maintaining intention. Figure 1The system is triggered when the cumulative probability of consistency reaches a set significance level. The system defaults to setting the threshold to be when the mean reciprocity score is not less than 0.5 and remains stable for more than 2 seconds. All threshold parameters are open to adjustment, allowing for optimization and configuration according to actual scenarios during system deployment or operation. These are all extended implementation methods known to ordinary technicians in this field.

[0033] Preferably, the preset lightweight behavior-intent mapping rule set is implemented based on a lookup table or a finite state machine to ensure that the generation delay of the interaction intention primitive is less than a preset time threshold.

[0034] Preferably, the interaction intention primitive further includes a collaborative expectation identifier. When it is detected that the user is performing a predefined interactive task that requires collaboration with others, the collaborative expectation identifier is activated and a higher weight is given to the interaction intention primitive with the same or complementary collaborative expectation identifier in the interaction intention flow field.

[0035] Preferably, the size of the predetermined logical or spatial range and the frequency of the announcement are positively correlated with the intention intensity value.

[0036] Preferably, the user client is further configured to: when the number or frequency of received interaction intention primitives exceeds a preset density threshold or dynamic threshold, preprocess the received interaction intention primitives, the preprocessing including: calculating the reciprocity score IRSi of the interaction intention between the source user sending the interaction intention primitive and the client; if the interaction intention broadcast by the client within a preset time window and the interaction intention broadcast by the source user i within the same time window meet the preset spatial proximity or semantic correlation conditions in terms of potential targets or trend action types, then increase IRSi; otherwise, if the source user i continuously broadcasts low-intensity interaction intention primitives that are irrelevant to the current focus of the client or whose intentions change rapidly within the preset time window, then decrease IRSi; when constructing the local interaction intention flow field, the weight of the interaction intention primitive from the source user i is proportional to its IRSi; and performing spatiotemporal coherence evaluation on a sequence of continuous interaction intention primitives from the same source user, and giving a higher weight to interaction intention primitives whose duration exceeds a preset time length and whose potential targets change in space less than a preset distance threshold.

[0037] Preferably, the predetermined virtual object in the scene is further configured as follows: when its own state meets the preset trigger condition or when it perceives that the interaction intention primitive related to its preset theme in the interaction intention flow field of nearby users reaches a preset intensity, it actively generates and transmits an object-conditional interaction invitation beacon to user clients within a predetermined range. The object-conditional interaction invitation beacon contains the identifier of the virtual object and information indicating a specific interaction function point; when the user client constructs and perceives the interaction intention flow field, it further takes the received object-conditional interaction invitation beacon into consideration to enhance the intention guidance intensity directed to the specific interaction function point of the virtual object.

[0038] Preferably, the preset trigger conditions include the update of the virtual object's own data, the occurrence of a specific user interaction behavior, or the arrival of a preset time point.

[0039] Preferably, the preset physical rules include gravity, collision or friction models, and the amount of correction performed on the final state of the virtual object is linearly positively correlated with the degree of deviation of the optimal interaction area predicted by the interaction intention flow field.

[0040] Preferably, the user client is further configured to send relevant data of the interaction event successfully guided and completed through the interaction intention flow field to the authoritative node in a predetermined data format for final state synchronization and data recording.

[0041] A virtual display interaction system based on the metaverse, wherein a user client performs the following steps to implement a virtual display interaction method: Step a: capturing in real time interaction leading signals that represent the budding state of the user's interaction intention. Interaction leading signals include small displacements of the controller, subtle changes in orientation, initial movement of the eye focus, or the rate of change of button pressure. Step b: Using a preset lightweight behavior-intention mapping rule set, the interaction leading signal is instantly converted into an interaction intention primitive. The interaction intention primitive includes: the subject (the user), the potential target (one or more virtual objects or areas ranked by the probability of the current user behavior), the trend action type (one or more basic interaction action types related to the current leading signal), and the intention strength value that decays over time; Step c: broadcasting the interaction intent primitive to other user clients within a predetermined logical or spatial range of the user client at a range and frequency determined based on the intent strength value; Step d: Receive interaction intent primitives broadcast from other user clients and, based on its own and the received interaction intent primitives, locally construct and maintain a dynamic interaction intent flow field perception data. The interaction intent flow field is a probability distribution estimate of the interaction possibility and potential interaction direction in the current local area. Step e: Based on the intention convergence area with an intention intensity value exceeding a preset threshold or the intention streamline indicating a collaborative interaction trend identified in the locally perceived interaction intention flow field, the 3D model data, interaction script, or sound effect data related to the high-probability interaction event is preloaded into the local cache of the user client before the relevant interaction event occurs; Step f: For interactions where the collaborative interaction trend indicated in the interaction intention flow field reaches a preset collaborative threshold, fine-tuning of auxiliary interactive operations is performed on the relevant user client. The fine-tuning includes adjusting the user's effective interaction judgment boundary or correcting the final state of the virtual object in accordance with preset physical rules within the user's active operation range to improve the interaction success rate.

[0042] Example 1: In a virtual exhibition scene built in a metaverse virtual environment and oriented to multi-user collaborative interaction, in order to effectively reduce the response delay of user operations, enhance group collaboration efficiency and improve the intelligent level of resource allocation, this embodiment provides a specific system deployment and operation process; in the specific deployment process, the built-in interactive perception module of the user client first collects the weak leading signals generated during the user operation in real time. Such signals include but are not limited to: the micro-displacement information generated by the input control device in a very short time, the rate of change of the posture angle, the rate of change of the button trigger pressure, and the time series trajectory formed by the user's visual focus shifting to a specific virtual target. After the signal is collected, the client performs a preliminary analysis of the leading signal through a preset lightweight behavior-intention mapping rule set; The rule set is built on a finite state recognition structure and can convert each leading signal into a standard format interaction intention primitive within a delay of no more than fifty milliseconds. The primitive contains four basic elements: user identification (i.e., the intention subject), a set of potential target candidates (usually virtual objects or areas within the current perceptible range), trend action type (such as approach, gaze, grab, click, and other basic interaction trends), and an intention strength value that decreases over time. It is worth noting that the initial value of the intention strength is graded based on the combined characteristics of different action types and leading signal parameters. For example, when the two types of signals, gaze maintenance for three seconds and rapid changes in the direction of the handle, appear together, the initial intention strength value is significantly higher than the case where there is only a light touch and click signal, which is closer to the psychological characteristics of human natural interactive behavior.

[0043] After generating the intent primitive, the user client will broadcast the intent primitive to its logical neighborhood according to the set time frequency. The above frequency is initially set to twice per second, and the broadcast frequency is positively correlated with the current intent strength value. The range of the logical neighborhood is dynamically expanded according to the user's spatial position in the current scene. The initial default is a fan-shaped area with a radius of five meters within the horizontal viewing angle of one hundred and twenty degrees in front. The system can also make adaptive adjustments based on user density and scene complexity. At the same time, the client synchronously receives intent primitives broadcast from other user clients, and combines with the set time window strategy to continuously build a perception model of the interactive intent flow field locally. The intent flow field model is constructed through the local density function and the multi-dimensional weight fusion mechanism, which not only takes into account the various intent primitives The spatial position and direction vector of the element are calculated, and a function of the decay of intent intensity over time, as well as the semantic similarity and reciprocity scores between different intents are introduced. In the specific calculation process, the system performs a joint verification of spatial proximity and target pointing consistency on continuous intent primitives from the same source user within each refresh cycle. If the intent sequence continues to point to the same potential target within a set time window (such as three seconds) and the trend action remains stable, the sequence is given a higher continuity weight. In addition, when the system determines that there is a two-way pointing between the client and a source user within the same time window (that is, the intentions are mutually related), its reciprocity score will be increased, thereby enhancing the weight of the user's intent primitive in the overall flow field to ensure priority response to high-correlation intent trends. When there is a high-density intention aggregation or a clear collaborative trend in the intention flow field (for example, multiple users simultaneously have a high-intensity synchronous rotation intention on the same exhibit model), the client will automatically preload the three-dimensional model data, interaction scripts and sound effect resources related to the high-probability event locally based on the preset collaborative threshold and probability evaluation model. For example, in a typical exhibition collaborative operation scenario, when the system recognizes that three or more users show a synchronous high-intensity interaction trend on a virtual component in different client environments, the resource preloading process is initiated, and the high-definition rendering data, physical constraint parameters and collaborative operation scripts of the relevant models are pre-placed in the client cache area, and a set of fine-tuning parameter sets for subsequent interaction assistance are simultaneously loaded to support subsequent refined multi-user collaborative interaction.

[0044] During actual collaborative operations, in order to ensure the consistency of the physical state of multiple people's actions and the naturalness of interactive feedback, the client will make appropriate adjustments to the effective judgment boundaries within the user's operation range based on the trend information of the intention flow field; for example, if the user's click operation deviates slightly from the target center, and the system detects that the current intention streamline aggregation trend points to the target, and the deviation is within the set threshold, it can automatically determine that the operation is a valid hit, and perform unified corrections on the state of the virtual object according to the physical rules preset by the system, such as adsorption alignment, slight elastic response, etc., thereby providing a natural and coherent interactive feedback effect. Furthermore, when a virtual object configured to have active behavior in the scene (such as an exhibit explanation terminal, an interactive information module, etc.) meets specific preset trigger conditions in its own state, it can actively broadcast an object conditional interaction invitation beacon to the user client in the adjacent area. The beacon includes the unique identification information of the virtual object and a description of its functional nodes for interaction. When the user client receives the beacon, it integrates it into the local intention flow field calculation model, and appropriately enhances the intention guidance intensity directed to the virtual object based on the degree of semantic matching and spatial proximity between the beacon content and the potential target in the current intention primitive, thereby promoting the formation of active interactive relationships under the guidance of the system.

[0045] Example 2: In a large-scale multi-user collaborative interaction metaverse exhibition scenario, the explicit operation drive and synchronous loading mechanism relied on in the prior art often lead to resource scheduling lags and interactive feedback delays. In particular, in an environment with high interaction complexity and high concurrency density, problems such as action conflicts and timing misalignment are prone to occur between users, thereby reducing collaborative efficiency. The experiment of this example was conducted in a standardized virtual exhibition environment with a total scene area of ​​300 square meters. The simulation deployment included 16 typical exhibit models and 6 virtual interactive terminals. The overall configuration was as follows: the user client device was a customized XR client that uniformly used an integrated head display, handle, and eye tracking module; the system version deployed the latest version of the core module of the present invention, including the intent primitive generation engine (generation delay control target ≤ 50ms), the interactive intent flow field modeling module, and the resource preloading scheduling mechanism; the user setting was 32 participating users, divided into 8 groups of 4 people each, performing collaborative operation tasks in a typical exhibition scenario; the network communication delay was maintained within the fluctuation range of 50ms±10ms, simulating a conventional commercial metaverse platform environment. The key variables involved in the experiment are set as follows: the types of interactive leading signals include tiny displacements of the controller, the rate of change of posture, changes in eye movement trajectory, and the rate of change of button pressure; the level of intention intensity is divided into five levels of intensity set according to behavioral characteristics, from weak gaze to rapid approach + strong press; the collaborative task types include typical interactive behaviors such as multi-person collaborative dragging of display boards, joint unlocking of virtual devices, and synchronous adjustment of exhibit status.

[0046] Based on 2,000 natural interactions completed by 32 users, we recorded and calculated the system response delays for converting various interaction preamble signals into standardized intent primitives. The average, maximum, and minimum response times for typical signal combinations are as follows (in milliseconds):

[0047] The response delay in all combinations was controlled within 50 milliseconds, indicating that the system has efficient response capabilities in intent recognition and primitive generation. Users were set to initiate interactive tendencies towards designated exhibits within a limited time window. The evaluation was conducted by recording whether resource preloading was successful and the time interval between loading completion and actual interaction (i.e., the preload duration).

[0048] In all three typical tasks, resources can be loaded before the user actually performs an interactive action, indicating that the intention flow mechanism has good forward-looking scheduling capabilities. Comparing the completion success rate of collaborative operation tasks and the changes in user subjective experience scores when the intention flow mechanism is enabled and not enabled is as follows:

[0049] Test data shows that after enabling this mechanism, the completion rate of collaborative tasks and the interaction consistency are both improved compared with the control group, which has certain application value; the results of this experiment show that: the generation delay of the interaction intention primitive is significantly lower than 50 milliseconds, meeting the requirements of the low-latency interaction system; the interaction intention flow field mechanism shows good stability in predicting user trend intentions and driving resource preloading; the completion rate of multi-user collaborative operations and the naturalness of user interaction are significantly improved after enabling this mechanism.

[0050] Example 3: This example combines Figures 1 to 3 , a virtual display interaction system and method based on the metaverse is described. Figure 1 As shown in the figure, user client A first captures the interactive leading signal representing the budding state of the user's interactive intention in real time, converts the signal into an interactive intention primitive, and then transmits it to other user clients by broadcasting; after receiving the interactive intention primitive, user client B integrates its own interactive intention primitive with the received intention primitive, and constructs perception data that maintains the dynamic interactive intention flow field. In this process, the local intention flow field perception module of user client A and the local intention flow field perception module of user client B work together to collaboratively construct and maintain dynamic interactive intention flow field data through real-time exchange of interactive intention primitives, thereby optimizing the user interaction experience in the virtual display and preloading related resources and data in advance.

[0051] like Figure 2As shown in the figure, first, the system captures interactive leading signals (such as small displacements, changes in line of sight, changes in button pressure, etc.), and these signals are converted through lightweight rules to generate interactive intention primitives. The generated interactive intention primitives contain subject, target, action type and intensity information. Next, the system dynamically broadcasts the range based on this information and constructs a dynamic interactive intention flow field. According to the current intention flow field, the system will decide whether to make pre-loading decisions or collaborative fine-tuning decisions, thereby realizing intelligent and forward-looking resource scheduling for virtual display interactive scenes and enhancing the user's interactive experience.

[0052] like Figure 3 As shown in the figure, the left chart shows the comparison of the generation delay of interaction intention primitives. The figure shows the maximum, minimum and average values ​​of interaction intention generation through five different signal types. The average generation delay is 40 milliseconds. Specifically, the generation delay of signal types numbered 1 to 5 varies under different signal types, which affects the performance of the generation delay; the right chart shows the comparison of the success rate of collaborative operations with and without the flow field mechanism enabled. The success rate of collaborative operations in task types numbered I to IV is generally higher after the flow field mechanism is enabled, with the highest success rate being 32.5%, while the highest success rate when the flow field mechanism is not enabled is 25.3%.

[0053] Example 4: In a digital cultural tourism exhibition project built on the Metaverse platform, the system design aims to enhance the immersion and collaboration efficiency of visitors through immersive interactive means. There are multiple interactive exhibit units in the virtual exhibition hall. Each exhibit supports two modes: single-person exploration and multi-person collaborative operation. The system needs to realize the early perception of users' weak interactive intentions, the pre-allocation of relevant resources, and the auxiliary alignment of multi-user collaborative actions to ensure that in situations with a large number of users or frequent behavior switching, a high system responsiveness and natural collaboration can still be maintained. In this embodiment, each user client is equipped with a head-mounted visual terminal, a handle controller, and an eye tracking module. The system includes an interactive leading signal acquisition module, a behavioral intention parsing unit, an intention primitive generation engine, an interactive intention flow field modeling and update module, a local resource preloader, and a collaborative interaction fine-tuning scheduling module. The system startup process is as follows: When the user accesses the virtual exhibition hall and enters the designated exhibition area, the system immediately activates the interactive leading signal acquisition process. The acquisition module continuously obtains input signals such as controller micro-displacement, orientation angular rate, eye movement trajectory change and button pressure change rate at a frequency of 20 frames per second, and performs normalization processing on each frame signal to eliminate background noise and non-interactive interference information. The cleaned leading signal is input into the behavior intention analysis unit, which performs intention classification judgment based on a set of preset lightweight behavior mapping rule sets. The rule set is constructed using a finite state graph structure. During the establishment process, it refers to the prior data experience of more than 50 common user interaction paths, and is based on the combination of signal types, change amplitude and duration. The system matches the most likely trend action type based on factors such as user identification, potential target set, trend action type, and intention intensity. For example, if a user's eye movement focuses on the control part of a virtual mechanical device for two consecutive seconds and controls the device to point stably to the area, the system determines that the user has a high tendency intention to start the device. Combined with the parameter settings corresponding to this type of action mode, the system sets the initial value of the intention intensity to a medium-high level. This intention primitive is then encapsulated into a structured data unit, which includes four basic elements: user identification, potential target set, trend action type, and intention intensity. It is broadcast to the logical neighborhood range within the current user's field of view according to the broadcast cycle determined by its intention intensity level (initially set to three times per second).

[0054] The setting principle of the logical neighborhood range comprehensively considers the user's orientation angle and spatial position. In the current scenario, it is defined as a fan-shaped area with the user's orientation as the axis, a horizontal angle of 110 degrees, and a radius of 6 meters. This parameter setting is based on the platform's statistical analysis of the historical collaborative behavior density distribution, aiming to cover high-probability collaborative areas while avoiding information redundancy. After receiving the intention primitives broadcast by other users in the neighborhood, the client will perform reciprocity scoring and continuity evaluation based on the local broadcast records. The reciprocity score is calculated based on the target pointing similarity and trending action consistency between the current user and the source user within the preset time window. If the set semantic relevance and spatial proximity thresholds are met at the same time, the weight of the source user's intention primitive in the local interactive intention flow field will be increased. In a specific collaborative exhibit control operation, three users stood on different sides of a virtual cultural relic installation. The system detected that all three users continuously output trend intention primitives pointing to the cultural relic control panel within ten seconds, and their mutual reciprocity scores exceeded the system-set threshold (the threshold is set to 0.6 based on the platform's interaction mode for efficient collaborative behavior). At the same time, the intensity of their respective intentions remained at a medium-high level or above. Based on this, the system triggered the collaborative streamline recognition module and marked the area as a high-collaboration trend area. The system then started the local resource preloading mechanism, and prioritized loading various control state models, sound effect files, multi-role linkage scripts and other resources related to the device into the local cache of each client.

[0055] When the user actually enters the operation stage, taking into account the possible interaction accuracy errors in multi-person operations, for example, a user's handle click position is slightly offset, the system will evaluate the effectiveness of the operation based on the spatial matching results between the density center of the current intention flow field and the physical properties of the target virtual object. When the offset is within the set threshold (the set value is thirty centimeters) and the direction is consistent, the system determines that the operation is valid, and according to the preset physical model (including rigid body collision rules and adsorption logic), the state of the virtual button is feedback-corrected within the physical constraints, resulting in an adsorption response and natural elastic feedback effect. For other users participating in the collaborative operation, their client systems dynamically align and correct their own behavioral trajectories based on the locally received sequence of intention primitives. For example, when jointly rotating an exhibit structure, if one user's operating rhythm is slightly slower than others, the system will appropriately relax the user's effective action judgment window and adjust the response frequency of their real-time rotation feedback without violating its active operation principle to ensure the coordination and consistency of the overall action rhythm, thereby enhancing the naturalness and collaborative tacit understanding of the interaction process. During the scenario operation, the system also verified the rationality of the setting of the intention flow field modeling parameters. The verification results show that when the broadcast frequency of the intention primitive is set to three times per second, the intention intensity threshold is set to 60% of the average intensity within a three-second sliding window, and the collaborative trend judgment standard is that three or more people point to the same target and maintain it for more than two seconds, the system can effectively identify the true collaborative behavior trend under high concurrency conditions and successfully complete resource pre-loading, significantly reducing the risk of interaction interruption caused by misjudgment rate and loading delay.

[0056] Example 5: In a digital cultural exhibition platform that supports multi-person collaborative interaction, in order to further ensure the system's prediction accuracy and collaborative stability in high-concurrency and complex interactive scenarios, this example specifically describes multiple core variables in the aforementioned interactive intention flow field mechanism. For example, during system operation, the client continuously obtains interactive leading signals from the user through the acquisition module, such as the slight displacement of the handle, the change trajectory of the eye focus, and the rate fluctuation of button pressure. After preprocessing and normalization, each frame signal will be mapped into a structured interactive intention primitive. The intention primitive contains four main components: the intention subject, that is, the user to whom the current client belongs; the potential target, which is one or more virtual objects that the user's operation behavior may point to; the tendency action type, which indicates the user's operation trend, such as approaching, observing, or trying to activate a certain interactive node; and the intention intensity value, which is used to measure the significance of the intention.

[0057] Furthermore, regarding the source and calculation logic of intent strength, a weighted integration mechanism based on time window statistics can be employed, taking into account the frequency, duration, and complexity of leading signals. For example, if a user continuously generates a sequence of leading signals with the same direction within three seconds, with the signal interval below a preset gap threshold, the system will determine their intent as persistent and assign a higher initial strength level. Conversely, if the signals are intermittent or the target frequently changes, the intent is considered less stable and assigned a relatively lower initial strength level. Intent strength naturally decays over time to prevent the system from responding ineffectively to outdated actions. During the construction of the interactive intent flow field, the system dynamically evaluates the spatiotemporal consistency between intent primitives from different users. To avoid misjudgments due to dense or frequently changing intent, this embodiment introduces a reciprocity scoring mechanism. This mechanism calculates whether different users' potential targets overlap and their directional movements are similar within the same time window. If two users point to the same virtual object three times in a row within two seconds, with consistent operational trends, the system increases their reciprocity score, thereby increasing the weight of that user's intent primitive in the overall intent flow field.

[0058] Furthermore, given that resource loading in virtual scenes is often limited by client performance and network bandwidth, the system implements a resource scheduling priority mechanism. When a high-intensity intent convergence area appears in the intent flow field, the system marks this area as a priority for resource preloading and triggers the local cache preparation process for the corresponding resources in the background. This mechanism ensures that resource preloading decisions are strictly based on multiple triggering conditions, such as intent intensity exceeding a threshold, stable intent convergence duration, and the relevant user intent having a clear target, rather than single or sporadic signal triggers. This effectively avoids the system burden caused by invalid preloading. Furthermore, during collaborative interaction, to address interaction failures caused by gesture deviation or response time differences among multiple users, the system appropriately adjusts the user interaction decision boundary based on the directionality and density distribution of the current intent flow lines. For example, during the operation of a virtual switch device, if the system detects three users simultaneously sending high-intensity trend signals toward the device, even if one user's operating position deviates slightly, as long as their direction matches the trend direction and the distance does not exceed the set tolerance threshold, the system will include that user's operation within the valid operation range and execute the interactive response synchronously. These are all extended implementations known to those skilled in the art.

[0059] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0060] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A virtual display interaction system based on the metaverse, characterized in that: include: The user client is configured to capture in real time interaction leading signals that represent the budding state of the user's interaction intention, wherein the interaction leading signals include a small displacement of the controller, a slight change in orientation, a preliminary movement of the eye focus, or a rate of change of button pressure; Through a preset lightweight behavior-intention mapping rule set, the interaction leading signal is instantly converted into an interaction intention primitive, which includes: a subject, a potential target, a trending action type, and an intention strength value that decays over time; Broadcasting the interaction intent primitive to other user clients within a predetermined logical or spatial range of the user client at a range and frequency determined based on the intent strength value; Receive interaction intent primitives broadcast from other user clients, and based on its own and the received interaction intent primitives, locally build and maintain a dynamic interaction intent flow field perception data. The interaction intent flow field is a probability distribution estimate of the interaction possibility and potential interaction direction in the current local area; Based on the locally perceived interaction intention flow field, the system identifies areas of intent convergence where the intensity of intent exceeds a preset threshold, or intent streamlines that indicate collaborative interaction trends. Before the relevant interaction event occurs, the system preloads the 3D model data, interaction scripts, or sound effect data related to the high-probability interaction event into the local cache of the user client. For interactions where the collaborative interaction trend indicated in the interaction intention flow field reaches the preset collaborative threshold, fine-tuning of auxiliary interactive operations is performed on the relevant user client. The fine-tuning includes adjusting the user's effective interaction judgment boundary or correcting the final state of the virtual object in accordance with preset physical rules within the user's active operation range.

2. The virtual display interaction system based on the metaverse according to claim 1 is characterized in that: The preset lightweight behavior-intent mapping rule set is implemented based on a lookup table or a finite state machine to ensure that the generation delay of the interaction intention primitive is less than a preset time threshold.

3. The virtual display interaction system based on the metaverse according to claim 1 is characterized in that: The interaction intention primitive further includes a collaborative expectation identifier. When it is detected that the user is performing a predefined interaction task that requires collaboration with others, the collaborative expectation identifier is activated and a higher weight is given to the interaction intention primitive with the same or complementary collaborative expectation identifier in the interaction intention flow field.

4. The virtual display interaction system based on the metaverse according to claim 1 is characterized in that: The size of the predetermined logical or spatial scope and the frequency of broadcasting are positively correlated with the intention intensity value.

5. The virtual display interaction system based on the metaverse according to claim 1 is characterized in that: The user client is further configured to: when the number or frequency of received interaction intention primitives exceeds a preset density threshold or dynamic threshold, preprocess the received interaction intention primitives, the preprocessing including: calculating the reciprocity score IRSi of the interaction intention between the source user sending the interaction intention primitive and the client; if the interaction intention broadcast by the client within a preset time window and the interaction intention broadcast by the source user i within the same time window meet the preset spatial proximity or semantic relevance conditions in terms of potential targets or trend action types, then increase IRSi; otherwise, if the source user i continuously broadcasts low-intensity interaction intention primitives that are irrelevant to the current focus of the client or whose intentions change rapidly within the preset time window, then decrease IRSi; when constructing the local interaction intention flow field, the weight of the interaction intention primitive from the source user i is proportional to its IRSi; and performing spatiotemporal coherence evaluation on a sequence of continuous interaction intention primitives from the same source user, and assigning a higher weight to interaction intention primitives that last longer than a preset time length and whose potential targets change in space less than a preset distance threshold.

6. The virtual display interaction system based on the metaverse according to claim 1 is characterized in that: The predetermined virtual object in the scene is further configured to: when its own state meets the preset trigger condition or when it senses that the interaction intention primitive related to its preset theme in the interaction intention flow field of nearby users reaches a preset strength, it actively generates and transmits an object conditional interaction invitation beacon to the user client within the predetermined range. The object conditional interaction invitation beacon contains the identifier of the virtual object and information indicating a specific interaction function point; When constructing and perceiving the interaction intention flow field, the user client further takes into account the received object conditional interaction invitation beacon to enhance the intensity of intention guidance directed to specific interaction function points of the virtual object.

7. The virtual display interaction system based on the metaverse according to claim 6 is characterized in that: The preset trigger conditions include the update of the virtual object's own data, the occurrence of specific user interaction behavior, or the arrival of a preset time point.

8. The virtual display interaction system based on the metaverse according to claim 1 is characterized in that: The preset physical rules include gravity, collision or friction models, and the amount of correction made to the final state of the virtual object is linearly positively correlated with the degree of deviation from the optimal interaction area predicted by the interaction intention flow field.

9. The virtual display interaction system based on the metaverse according to claim 1, characterized in that: The user client is further configured to send relevant data of the interaction events successfully guided and completed through the interaction intention flow field to the authoritative node in a predetermined data format for final state synchronization and data recording.

10. A virtual display interactive system based on the metaverse, characterized in that: The user client performs the following steps to implement the virtual presentation interaction method: Step a: capturing in real time interaction leading signals that represent the budding state of the user's interaction intention. Interaction leading signals include small displacements of the controller, subtle changes in orientation, initial movement of the eye focus, or the rate of change of button pressure. Step b: Using a preset lightweight behavior-intention mapping rule set, the interaction leading signal is instantly converted into an interaction intention primitive. The interaction intention primitive includes: a subject, a potential target, a trending action type, and an intention strength value that decays over time. Step c: broadcasting the interaction intent primitive to other user clients within a predetermined logical or spatial range of the user client at a range and frequency determined based on the intent strength value; Step d: Receive interaction intent primitives broadcast from other user clients and, based on its own and the received interaction intent primitives, locally construct and maintain a dynamic interaction intent flow field perception data. The interaction intent flow field is a probability distribution estimate of the interaction possibility and potential interaction direction in the current local area. Step e: Based on the intention convergence area with an intention intensity value exceeding a preset threshold or the intention streamline indicating a collaborative interaction trend identified in the locally perceived interaction intention flow field, the 3D model data, interaction script, or sound effect data related to the high-probability interaction event is preloaded into the local cache of the user client before the relevant interaction event occurs; Step f: For interactions where the collaborative interaction trend indicated in the interaction intention flow field reaches a preset collaborative threshold, fine-tuning of auxiliary interactive operations is performed on the relevant user client. The fine-tuning includes adjusting the user's effective interaction judgment boundary or correcting the final state of the virtual object in accordance with preset physical rules within the user's active operation range to improve the interaction success rate.

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