Interaction method and system for virtual reality education scene

Abstract

The invention discloses an interaction method and system for a virtual reality education scene, and belongs to the technical field of man-machine interaction. The input rate of a user is collected in real time, the update response time of a virtual object is synchronously recorded, and the interaction speed of the virtual object is calculated based on the action rate of input equipment and the response time of the virtual object. Calculating the synchronous delay between the input device and the virtual environment, dividing the synchronous delay into a low delay level, a middle delay level and a high delay level, and maintaining the current interaction mode for the low delay; for high delay, an emergency optimization measure is started; for middle delay, a partial optimization mechanism is started, including adjustment of object update frequency in a virtual environment or reduction of dynamic elements, and automatic adjustment of an optimization strategy based on user interaction feedback data, the technology can significantly improve response precision and interaction experience of a virtual reality system, reduce distortion and unnatural feeling caused by delay, and improve user experience. And the immersion and interaction experience of the user are enhanced.

Description

Technical Field

[0001] The present invention relates to the field of human-computer interaction technology, and in particular to an interactive method and system for virtual reality education scenarios. Background Art

[0002] Virtual reality educational scenarios refer to immersive learning environments created using virtual reality (VR) technology. Within these environments, students can interact with various learning resources through virtual three-dimensional scenes, simulating complex real-world scenarios or experiencing scenarios that are difficult to achieve in reality. These educational scenarios provide a highly immersive learning experience, helping students better understand abstract concepts or complex operations, such as through virtual experiments and recreating historical scenes. Through VR, students can practice in a risk-free environment, significantly enhancing their engagement and effectiveness in learning.

[0003] The existing technology has the following shortcomings: In existing virtual reality (VR) education scenarios, when users interact with virtual objects using controllers, gloves, or other input devices, the interaction between the input devices (such as controllers and gloves) and the virtual environment requires high-precision synchronization. Any delay can cause a desynchronization between user actions and virtual objects, affecting immersion and the interactive experience. For example, when a user moves a controller, the position of the virtual object is updated slightly later, which can make the user feel unnatural or distorted. Summary of the Invention

[0004] The purpose of the present invention is to provide an interactive method and system for virtual reality education scenarios to address the shortcomings of the background technology.

[0005] To achieve the above-mentioned object, the present invention provides the following technical solution: an interactive method for a virtual reality education scene, comprising: The user's input rate is collected in real time through the input device, and the update response time of the virtual object is recorded synchronously; Based on the input device's motion rate and the virtual object's response time, the synchronization delay between the input device and the virtual environment is calculated and divided into three levels of delay: low, medium, and high. For low latency, maintain the current interaction mode; for high latency, initiate emergency optimization measures, including reducing the data processing accuracy of the input device; For medium latency, some optimization mechanisms are activated, including adjusting the update frequency of objects in the virtual environment or reducing dynamic elements in the virtual environment, and some optimization mechanisms are automatically adjusted based on user interaction feedback data.

[0006] Preferably, the input rate data refers to the movement rate and interaction frequency of the input device when the user interacts with the virtual reality environment, including: the position change rate is obtained by using an accelerometer and a gyroscope sensor to capture the position change of the user input device in real time and calculate the instantaneous velocity of the device; the angular velocity and acceleration are obtained by measuring the rotational speed and linear acceleration of the device through the gyroscope and the accelerometer; the frequency data is based on the button clicks and touch screen input events of the input device, recording the frequency of the operation; the update response time of the virtual object refers to the time interval from the start of user input to the change of the state or position of the object in the virtual environment.

[0007] Preferably, the device action rate deviation value is obtained by analyzing the action rate data of the input device within a fixed time period S, and the acquisition method is: Collect motion rate data of input devices ,in Represents the device at time Set a set of ideal rate data ; Set the device action rate and ideal rate Map it to the preset rate range to get the discrete value , calculate the device action rate and ideal rate The joint probability distribution P(X′, Y′) and marginal probability distributions P(X′) and P(Y′) are calculated. The joint probability distribution represents the probability of the device rate and the ideal rate occurring at the same time. The mutual information is calculated. The mutual information I(X;Y) measures the amount of information shared between the device action rate X′ and the ideal rate Y′. The mutual information is the logarithmically weighted sum of the ratio of the joint probability to the marginal probability between the device rate and the ideal rate. The mutual information I(X;Y) ranges from 0 to Imax, where Imax is the maximum mutual information. The deviation of the device action rate is calculated as follows: ; Where AS is the equipment action rate deviation value.

[0008] Preferably, the update response time lag value is obtained after analyzing the update response time of the virtual object, and the acquisition method is: Response time refers to the time difference from the start of user input to the end of the update of the virtual object in the virtual environment; the timestamp tinput of the user input is collected, and the timestamp tupdate of the virtual object update is collected. For each interaction event, the response time of the virtual object is calculated, that is, the value of tupdate minus tinput is calculated; the update response time lag value refers to the delay between the virtual object update time and the user input time. The response time lag value is defined as the absolute value of the difference between the maximum response time and the minimum response time of the virtual object.

[0009] Preferably, the device action rate deviation value and the update response time lag value are normalized so that they are both between [0, 1], and the synchronization delay between the input device and the virtual environment is calculated based on the normalized device action rate deviation value and the update response time lag value.

[0010] Preferably, according to the size of the synchronization delay value, it is divided into three delay levels: low, medium and high: Low latency: Synchronization delay ∈ [0, 0.33], indicating that the synchronization between the input device and the virtual environment is very good, and there is no lag in the interaction; Medium delay: synchronization delay∈(0.33,0.66], indicating that there is a lag between the input device and the virtual environment, and the interaction experience is average; High latency: Synchronization delay ∈ (0.66, 1], indicating that the synchronization lag between the input device and the virtual environment is large, which seriously affects the user experience.

[0011] Preferably, some optimization mechanisms are activated in the case of medium delay, and some optimization mechanisms are automatically adjusted based on user interaction feedback data, including using the K-means clustering algorithm for optimization, specifically: Collect user interaction data and virtual environment performance data; Use K-means clustering algorithm to analyze user interaction data and identify different interaction patterns; Select the number of clusters K, randomly select K data points as the initial cluster centroids, assign each user interaction data point to the centroid closest to it, and update the centroid position to make it the average of all data points in the current cluster; iterate until the centroid no longer changes; Each cluster obtained by K-means clustering includes a low interaction intensity cluster and a high interaction intensity cluster.

[0012] Preferably, the optimization mechanism in the virtual environment is dynamically adjusted according to the clustering results: Low interaction intensity cluster: Reduce the rendering and updating frequency of virtual objects and reduce the number of dynamic objects or background objects in the virtual environment; High interaction intensity cluster: Increases the update frequency of virtual objects and adds dynamic elements or complex backgrounds to the virtual environment when users actively interact.

[0013] The present invention also provides an interactive system for a virtual reality education scene, comprising an input processing module, a delay calculation and evaluation module, a delay optimization module, and an adaptive adjustment module; Input processing module: collects the user's input rate in real time through the input device and simultaneously records the update response time of the virtual object; Latency calculation and evaluation module: Based on the input device's motion rate and the virtual object's response time, it calculates the synchronization delay between the input device and the virtual environment and classifies it into three levels: low, medium, and high. Latency optimization module: For low latency, maintain the current interaction mode; for high latency, initiate emergency optimization measures, including reducing the data processing accuracy of the input device; Adaptive Adjustment Module: For medium latency, some optimization mechanisms are activated, including adjusting the update frequency of objects in the virtual environment or reducing dynamic elements in the virtual environment, and some optimization mechanisms are automatically adjusted based on user interaction feedback data.

[0014] In the above technical solution, the technical effects and advantages provided by the present invention are: 1. The present invention solves the problems of interaction distortion and reduced immersion caused by synchronization delay in existing virtual reality education scenarios by collecting the user input rate and the update response time of virtual objects in real time and calculating the synchronization delay. By normalizing the device action rate deviation value and the virtual object response time lag value, the synchronization delay is divided into three levels: low, medium, and high. Corresponding optimization measures are taken according to different delay levels, thereby improving the system's real-time response capability and user experience. For high delays, emergency optimization measures are taken; for medium delays, some optimization mechanisms are activated and automatically adjusted based on user interaction feedback to ensure the efficient and stable operation of the system.

[0015] 2. This invention also uses an adaptive adjustment module combined with a K-means clustering algorithm to dynamically analyze user interaction behavior and virtual environment performance data. It automatically optimizes the update frequency of virtual objects and the number of dynamic elements based on different interaction modes, thereby achieving a more intelligent and personalized optimization strategy. This method maximizes user immersion and interactive experience while ensuring system performance, making it particularly suitable for applications requiring high-precision synchronization, such as virtual reality education. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments described in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0017] Figure 1 This is a mind map of the method of the present invention.

[0018] Figure 2 This is a mind map of the system modules of the present invention. DETAILED DESCRIPTION

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0020] Example 1, please refer to Figure 1 As shown, the interactive method for a virtual reality education scene described in this embodiment includes: The user's input rate is collected in real time through the input device, and the update response time of the virtual object is recorded synchronously; Based on the input device's motion rate and the virtual object's response time, the synchronization delay between the input device and the virtual environment is calculated and divided into three levels of delay: low, medium, and high. For low latency, maintain the current interaction mode; for high latency, initiate emergency optimization measures, including reducing the data processing accuracy of the input device; For medium latency, some optimization mechanisms are activated, including adjusting the update frequency of objects in the virtual environment or reducing dynamic elements in the virtual environment, and some optimization mechanisms are automatically adjusted based on user interaction feedback data.

[0021] In virtual reality education scenarios, the user's input rate data is collected in real time through input devices, and the update response time of virtual objects is recorded synchronously, mainly to accurately evaluate the synchronization delay between the input device and the virtual environment.

[0022] Input rate data refers to the movement rate (such as the speed of change of position and direction) and interaction frequency (such as the frequency of operation) of the input device when the user interacts with the virtual reality environment. This includes: Position change rate: Use sensors such as accelerometers and gyroscopes to capture the position changes of user input devices in real time and calculate the instantaneous speed of the device (such as handle movement, head rotation, etc.).

[0023] Angular velocity and acceleration: Accurately measure the device's rotational speed (angular velocity) and linear acceleration using a gyroscope and accelerometer to assess the dynamic characteristics of user actions.

[0024] Frequency data: Based on events such as key clicks and touch screen input on input devices, this data records the frequency of operations (such as the number of clicks per second by the user, or the frequency of button presses and releases on a controller), reflecting the frequency of user interactions.

[0025] This data is collected in real time by specialized sensors (such as accelerometers, gyroscopes, and pressure sensors) and transmitted wirelessly or wired to a computing system or virtual reality platform. These sensors are typically built into input devices (such as VR helmets, controllers, and gloves).

[0026] The update response time of a virtual object is the interval between user input (such as moving a controller, clicking a button, or making a gesture) and a change in the state or position of the object in the virtual environment. This interval reflects the responsiveness of the virtual environment.

[0027] Every state change in the virtual environment (e.g., object displacement, rotation, color change, state update, etc.) generates a timestamp. The computing system pairs each user input with the state update time of the virtual object and calculates the time difference between the input and the response.

[0028] In dynamic scenarios (such as objects being subjected to forces, multiple objects changing simultaneously, etc.), the system records the response time of each object separately in order to analyze each object's independent reaction to user input.

[0029] After analyzing the input device action rate data within a fixed time period S, the device action rate deviation value is obtained. The acquisition method is: Set the motion rate data collected from a series of input devices ,in Represents the device at time Set a set of ideal rate data , which represents the ideal rate at which the device should move.

[0030] Discretize the continuous device rate data and ideal rate data. You can group the data by setting the discretization interval to get the discrete representation of the actual rate and ideal rate of the device. For example, the device action rate and ideal rate Map it to the preset rate range to get the discrete value The discretization process can also use equal interval partitioning method, K-means clustering and other methods.

[0031] Calculate device motion rate and ideal rate The joint probability distribution P(X′, Y′) and the marginal probability distributions P(X′) and P(Y′). The joint probability distribution represents the probability of the device rate and the ideal rate occurring at the same time, and the calculation formula is: ;in, Indicates that in the discretized data, and The number of times it appears at the same time, n is the total number of samples. Marginal probability distribution: ;in, and Respectively and The frequency of occurrence in the data.

[0032] Calculate the mutual information. The mutual information I(X;Y) measures the amount of information shared between the device action rate X′ and the ideal rate Y′. The calculation expression is: Mutual information represents the logarithmically weighted sum of the ratios of the joint probability and marginal probability between the device rate and the ideal rate. A larger mutual information value indicates a stronger correlation between the two, meaning a smaller deviation; a lower mutual information value indicates a larger deviation between the device rate and the ideal rate.

[0033] The mutual information I(X;Y) ranges from 0 to Imax, where Imax is the maximum possible mutual information. The deviation of the device action rate is calculated as follows: Where AS is the deviation value of the device's motion rate. A deviation value closer to 1 indicates a larger deviation; a deviation value closer to 0 indicates a better degree of synchronization between the device's motion rate and the ideal rate.

[0034] After analyzing the update response time of the virtual object, the update response time lag value is obtained. The acquisition method is: Response time refers to the time difference from the start of user input (such as controller movement, button click, etc.) to the end of the update of the virtual object in the virtual environment (such as position, rotation, state change, etc.).

[0035] Collect the timestamps of user input, which can be provided by sensors of input devices (such as controllers, VR helmets, etc.).

[0036] Collect the timestamp tupdate of the virtual object update, that is, the time point when the virtual object state changes after the system receives user input.

[0037] For each interaction event (such as the user moving the handle, clicking a button, etc.), calculate the response time of the virtual object, that is, calculate the value of tupdate minus tinput; Update response time lag refers to the delay between the virtual object's update and the user input. Statistical analysis of response times over a period of time can reveal the response lag characteristics of the entire system. Response time lag can be defined as the absolute value of the difference between the maximum and minimum response times of a virtual object.

[0038] Low response time lag values indicate very rapid virtual object updates, resulting in a good user experience. Higher response time lag values indicate a slower system response, potentially impacting the interactive experience. This is particularly true in high-precision or real-time VR education scenarios, where user actions may become out of sync with virtual objects.

[0039] The device action rate deviation value and the update response time lag value are normalized so that they are both between [0, 1]. The synchronization delay between the input device and the virtual environment is calculated based on the normalized device action rate deviation value and the update response time lag value.

[0040] For example, the present invention may use the following formula to calculate the synchronization delay between the input device and the virtual environment, and the calculation expression is: Where, is the synchronization delay between the input device and the virtual environment, AS is the device action rate deviation value, To update the response time lag value, is the weight coefficient of the device action rate deviation value and the update response time lag value (which can be optimized based on experimental experience or machine learning), and Both are greater than 0.

[0041] Since both parameters are normalized between [0, 1], the final synchronization delay value will also be between [0, 1]. A lower synchronization delay (closer to 0) indicates a more accurate system response with less latency; a higher synchronization delay (closer to 1) indicates a larger synchronization error between the device and the virtual environment.

[0042] If you need to emphasize the influence of one factor (for example, the response time of virtual object updates is more critical in educational scenarios), you can adjust the weight , making one of the parameters have a greater impact on the synchronization delay.

[0043] Based on the size of the synchronization delay value, it is divided into three delay levels: low, medium, and high. Since the synchronization delay value is between [0, 1], it can be divided as follows: Low latency: The synchronization delay value is close to 0, indicating that the synchronization between the input device and the virtual environment is very good and the interaction is almost lag-free, under the condition that the synchronization delay ∈ [0, 0.33]; Medium delay: The synchronization delay value is in the medium range, indicating that there is a certain lag between the input device and the virtual environment, and the interaction experience is average. The condition is synchronization delay∈(0.33,0.66]; High latency: A synchronization delay value close to 1 indicates a large synchronization lag between the input device and the virtual environment, which may seriously affect the user experience. The condition is synchronization delay ∈ (0.66, 1].

[0044] Different optimization strategies are adopted for different latency levels to improve the user interaction experience: Low latency (synchronization delay between [0, 0.33]): Optimization strategy: The system response is very fast and the user experience is good, so no special optimization is required. Solution: Maintain the current interaction method and VR system configuration without making any additional adjustments.

[0045] High latency (synchronization delay between 0.66 and 1) optimization strategy: In this case, the system response lag is significant, which may seriously affect the user experience, especially in real-time interactive scenarios such as education and gaming. Emergency optimization measures: Reduce the data processing accuracy of input devices: Temporarily reduce the sampling frequency or resolution of input devices to reduce the data processing burden. For example, reduce the button response accuracy of a controller or the finger capture accuracy of a glove to reduce the pressure on the system's real-time computing. For example, if the original processing speed is 1000 data points per second (high resolution), it can be reduced to 500 data points per second (low resolution), thereby reducing the real-time computing pressure on the system.

[0046] Optimize virtual object update strategies: Reduce the frequency of virtual object updates. For example, reduce the frequency of virtual object updates under high load conditions, or reduce the frequency of object detail rendering without affecting the user experience. For example, for virtual objects that do not require frequent updates, reduce their update frequency to reduce the amount of computation required.

[0047] Enable prediction algorithms: Predict the user's next action or the future state of virtual objects and render them in advance. This approach can reduce interaction lag caused by latency. For example, if the system predicts that the user is about to perform an action (such as rotating a controller), it can render that action in advance, thereby reducing response time.

[0048] Data compression and transmission optimization: Compress input device data to reduce transmission delays, especially in wireless data transmission. Example: Using a more efficient compression algorithm to reduce input data transmission bandwidth usage and improve response speed.

[0049] Medium latency (synchronization latency between 0.33 and 0.66) optimization strategy: In this case, system response time is moderate, but the user experience may be affected, especially in high-precision interactive scenarios. Solution: Appropriately adjust the update frequency of the virtual environment, reduce dynamic elements in the environment, or reduce the complexity of virtual object updates to reduce system burden and improve response speed. Some optimization mechanisms are automatically adjusted based on user interaction feedback.

[0050] To enable optimization mechanisms in moderate latency scenarios and automatically adjust some of these mechanisms based on user interaction feedback, the K-means clustering algorithm can be used. This algorithm identifies user behavior patterns through cluster analysis and dynamically adjusts the frequency of object updates in the virtual environment or reduces dynamic elements. By clustering user interaction data, the K-means algorithm can help determine adjustments to optimization strategies for different interaction patterns.

[0051] First, collect user interaction data and virtual environment performance data, including the following: User interaction data: records user input frequency, interaction duration, controller or device movement speed, gesture frequency, etc.

[0052] Virtual environment data: including the update frequency of virtual objects, rendering load, number of dynamic elements, etc.

[0053] The data is normalized so that features of different magnitudes (such as input frequency and object update frequency) can be compared at the same scale.

[0054] Use the K-means clustering algorithm to analyze user interaction data and identify distinct interaction patterns. The K-means algorithm's primary goal is to group user interaction behavior data into clusters, each representing a specific interaction pattern. Based on these patterns, corresponding optimization strategies can be implemented.

[0055] Initialize the number of clusters: The number of clusters K can be determined based on the complexity of the interaction behavior. For example, K = 3 can be set to correspond to low, normal, and high interaction intensity modes respectively.

[0056] Assign initial centroids: Randomly select K data points as the initial cluster centroids.

[0057] Clustering iteration: Assign each user interaction data point to the nearest centroid. Update the centroid position to be the average of all data points in the current cluster. Repeat until the centroid no longer changes or changes very little.

[0058] For each cluster (i.e., interaction pattern) obtained through K-means clustering, the following key information can be obtained: Low-interaction clusters: These clusters represent patterns where users have less interaction or operate slowly. For these users, the frequency of dynamic element updates in the virtual environment can be reduced to reduce system load.

[0059] High interaction intensity clusters: These clusters represent patterns of users who frequently interact. For these users, the frequency of dynamic element updates in the virtual environment can be increased to provide a better interactive experience.

[0060] Based on the clustering results, the optimization mechanism in the virtual environment is dynamically adjusted: Low interaction intensity clusters (e.g., the frequency of user actions within the cluster is low): Reduce the update frequency of virtual objects: Reduce the rendering and update frequency of virtual objects to save system resources.

[0061] Reduce dynamic elements: Reduce the number of dynamic objects or background objects in the virtual environment to reduce the computational burden.

[0062] High interaction intensity clusters (for example, users in the cluster frequently move their devices or interact quickly): Improve the update frequency of virtual objects: Increase the update frequency of virtual objects to ensure that feedback after user interaction is immediate.

[0063] Add dynamic elements: When users are actively interacting, add dynamic elements or complex backgrounds to the virtual environment to enhance the immersive experience.

[0064] To achieve real-time optimization, the optimization mechanism is dynamically adjusted using user interaction feedback data. User interaction data, such as frequency of operation and speed of controller or device movement, is continuously monitored. If the system detects a change in user behavior (e.g., a shift from low to high interaction frequency), the optimization strategy is automatically adjusted. The virtual environment's update strategy is dynamically adjusted based on clustering results. For example, if user interaction frequency suddenly increases, the system can automatically switch to a higher update frequency and more dynamic elements. If interaction decreases, the system load is reduced and the number of dynamic elements is reduced.

[0065] In this paper, a K-means clustering algorithm is used to analyze user interaction data, effectively identifying different interaction patterns and dynamically adjusting the object update frequency and the number of dynamic elements in the virtual environment based on these patterns. This automatic adjustment mechanism based on interaction behavior can optimize the VR experience by reducing system burden or enhancing interaction accuracy in medium-latency situations.

[0066] Example 2, please refer to Figure 2 As shown, the interactive system for virtual reality education scenarios described in this embodiment includes an input processing module, a delay calculation and evaluation module, a delay optimization module, and an adaptive adjustment module; Input processing module: collects the user's input rate in real time through the input device and simultaneously records the update response time of the virtual object; Latency calculation and evaluation module: Based on the input device's motion rate and the virtual object's response time, it calculates the synchronization delay between the input device and the virtual environment and classifies it into three levels: low, medium, and high. Latency optimization module: For low latency, maintain the current interaction mode; for high latency, initiate emergency optimization measures, including reducing the data processing accuracy of the input device; Adaptive Adjustment Module: For medium latency, some optimization mechanisms are activated, including adjusting the update frequency of objects in the virtual environment or reducing dynamic elements in the virtual environment, and some optimization mechanisms are automatically adjusted based on user interaction feedback data.

[0067] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0068] It should be understood that the term "and / or" as used herein simply describes a relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A alone, A and B together, or B alone. A and B can be singular or plural. Furthermore, the character " / " as used herein generally indicates an "or" relationship between the associated objects, but it may also indicate an "and / or" relationship. For specific understanding, please refer to the context.

[0069] Those skilled in the art will appreciate that the units and algorithm steps of each example 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 performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0070] The above is only a specific implementation method of the present application, but the scope of protection of the present application is not limited thereto. Any technician familiar with this technical field can easily think of changes or replacements within the technical scope disclosed in this application, which should be covered by the scope of protection of the present application.

Claims

1. An interactive method for a virtual reality educational scene, characterized by: include: The user's input rate is collected in real time through the input device, and the update response time of the virtual object is recorded synchronously; Based on the input device's motion rate and the virtual object's response time, the synchronization delay between the input device and the virtual environment is calculated and divided into three levels of delay: low, medium, and high. For low latency, maintain the current interaction mode; for high latency, initiate emergency optimization measures, including reducing the data processing accuracy of the input device; For medium latency, some optimization mechanisms are activated, including adjusting the update frequency of objects in the virtual environment or reducing dynamic elements in the virtual environment, and some optimization mechanisms are automatically adjusted based on user interaction feedback data.

2. The interactive method for virtual reality education scene according to claim 1, characterized in that: Input rate data refers to the movement rate and interaction frequency of the input device when the user interacts with the virtual reality environment, including: the position change rate is obtained by using accelerometers and gyroscopes to capture the position changes of the user input device in real time and calculate the instantaneous velocity of the device; the angular velocity and acceleration are obtained by measuring the rotational speed and linear acceleration of the device through gyroscopes and accelerometers; the frequency data is based on the button clicks and touch screen input events of the input device, recording the frequency of operations; the update response time of the virtual object refers to the time interval from the start of user input to the change of the state or position of the object in the virtual environment.

3. The interactive method for virtual reality education scene according to claim 2, characterized in that: After analyzing the input device action rate data within a fixed time period S, the device action rate deviation value is obtained. The acquisition method is: Collect motion rate data of input devices ,in Represents the device at time Set a set of ideal rate data ; Set the device action rate and ideal rate Map it to the preset rate range to get the discrete value , calculate the device action rate and ideal rate The joint probability distribution P(X′, Y′) and marginal probability distributions P(X′) and P(Y′) are calculated. The joint probability distribution represents the probability of the device rate and the ideal rate occurring at the same time. The mutual information is calculated. The mutual information I(X;Y) measures the amount of information shared between the device action rate X′ and the ideal rate Y′. The mutual information is the logarithmically weighted sum of the ratio of the joint probability to the marginal probability between the device rate and the ideal rate. The mutual information I(X;Y) ranges from 0 to Imax, where Imax is the maximum mutual information. The deviation of the device action rate is calculated as follows: ; Where AS is the equipment action rate deviation value.

4. The interactive method for virtual reality education scene according to claim 3, characterized in that: After analyzing the update response time of the virtual object, the update response time lag value is obtained. The acquisition method is: Response time refers to the time difference between the start of user input and the end of the update of the virtual object in the virtual environment. The timestamp of user input, tinput, and the timestamp of virtual object update, tupdate, are collected. For each interaction event, the response time of the virtual object is calculated as tupdate minus tinput. The update response time lag value refers to the delay between the virtual object update time and the user input time. The response time lag value is defined as the absolute value of the difference between the maximum response time and the minimum response time of the virtual object.

5. The interactive method for virtual reality education scene according to claim 4, characterized in that: The device action rate deviation value and the update response time lag value are normalized so that they are both between [0, 1]. The synchronization delay between the input device and the virtual environment is calculated based on the normalized device action rate deviation value and the update response time lag value.

6. The interactive method for virtual reality education scene according to claim 5, characterized in that: According to the size of the synchronization delay value, it is divided into three delay levels: low, medium, and high: Low latency: Synchronization delay ∈ [0, 0.33], indicating that the synchronization between the input device and the virtual environment is very good, and there is no lag in the interaction; Medium delay: synchronization delay∈(0.33,0.66], indicating that there is a lag between the input device and the virtual environment, and the interaction experience is average; High latency: Synchronization delay ∈ (0.66, 1], indicating that the synchronization lag between the input device and the virtual environment is large, which seriously affects the user experience.

7. The interactive method for virtual reality education scene according to claim 6, characterized in that: In the case of medium latency, some optimization mechanisms are activated and automatically adjusted based on user interaction feedback data, including the use of the K-means clustering algorithm for optimization. Specifically: Collect user interaction data and virtual environment performance data; Use K-means clustering algorithm to analyze user interaction data and identify different interaction patterns; Select the number of clusters K, randomly select K data points as the initial cluster centroids, assign each user interaction data point to the centroid closest to it, and update the centroid position to make it the average of all data points in the current cluster; iterate until the centroid no longer changes; Each cluster obtained by K-means clustering includes a low interaction intensity cluster and a high interaction intensity cluster.

8. The interactive method for virtual reality education scene according to claim 7, characterized in that: Based on the clustering results, the optimization mechanism in the virtual environment is dynamically adjusted: Low interaction intensity cluster: Reduce the rendering and updating frequency of virtual objects and reduce the number of dynamic objects or background objects in the virtual environment; High interaction intensity cluster: Increases the update frequency of virtual objects and adds dynamic elements or complex backgrounds to the virtual environment when users actively interact.

9. An interactive system for a virtual reality education scene, used to implement the interactive method for a virtual reality education scene according to any one of claims 1 to 8, characterized in that: It includes input processing module, delay calculation and evaluation module, delay optimization module and adaptive adjustment module; Input processing module: collects the user's input rate in real time through the input device and simultaneously records the update response time of the virtual object; Latency calculation and evaluation module: Based on the input device's motion rate and the virtual object's response time, it calculates the synchronization delay between the input device and the virtual environment and classifies it into three levels: low, medium, and high. Latency optimization module: For low latency, maintain the current interaction mode; for high latency, initiate emergency optimization measures, including reducing the data processing accuracy of the input device; Adaptive Adjustment Module: For medium latency, some optimization mechanisms are activated, including adjusting the update frequency of objects in the virtual environment or reducing dynamic elements in the virtual environment, and some optimization mechanisms are automatically adjusted based on user interaction feedback data.

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