Virtual laboratory simulation system and method based on VR technology

Abstract

The invention relates to the field of virtual reality simulation, and discloses a virtual laboratory simulation system and method based on the VR technology, and the method comprises the steps: obtaining user interaction data through a sensor, carrying out the preprocessing, transmitting the user interaction data in real time through a network communication protocol, carrying out the data fusion and synchronous processing, and updating scene data, transmitting the updated scene data to the user; a scene synchronization value is calculated through network delay and equipment performance difference, scene data is fused with space-time three dimensions to perform synchronization verification, and a scene state synchronization algorithm based on a distributed hash table is adopted to monitor the change of a scene state and perform dynamic adjustment; detecting and compensating the operation delay of the user, establishing an operation delay model, predicting the operation action of the user, and executing the operation action in advance in the virtual scene; according to interaction data of an object in a virtual scene, an interactive friction tactile feedback signal is generated in real time, and tactile feedback is controlled by combining a pressure sensor and vibration motor hardware equipment.

Description

Technical Field

[0001] The present invention relates to the field of virtual reality simulation technology, and in particular to a virtual laboratory simulation system and method based on VR technology. Background Art

[0002] Virtual reality (VR) technology has brought advancements to education, scientific research, and industry through immersive interaction and three-dimensional visualization. In education, VR virtual laboratories have broken through the spatial and temporal limitations of traditional experiments, allowing students to conduct high-risk or high-cost experiments in a safe, low-cost environment. For example, hazardous chemical handling and nuclear reaction simulations. The School of Architecture's VR laboratory uses high-precision modeling and spatial positioning technology to help students intuitively understand building structures and light and shadow effects. The flight simulation laboratory combines tactile feedback and brainwave monitoring to improve the efficiency and safety of pilot training. It avoids the physical risks of real experiments and performs multi-degree-of-freedom operations in a virtual environment, such as disassembling an engine and adjusting experimental parameters. It supports simultaneous multi-user participation, allowing multiple people to collaborate on design or data analysis in the same virtual space. Cloud rendering technology enables real-time access across devices and regions. In the existing technology, traditional solutions mostly use centralized servers to synchronize scene states. Based on the simultaneous participation of multiple users, the server load is high and there is a risk of single point failure. When the user scale expands, the synchronization delay is prone to surge, and the performance differences of devices lead to inconsistent synchronization accuracy. Scene synchronization is only based on position coordinates, ignoring the object posture and timing logic, such as the order of operation execution, resulting in visual synchronization but misaligned actions. For example, when two people push and pull the same object at the same time, the local display action sequence is inconsistent with the actual physical logic; relying on simple timestamps or version number priorities, without combining experimental process compliance or device credibility, resulting in incorrect operations being adopted; linear prediction or basic Kalman filtering is commonly used to process uniform speed / uniform acceleration motion, which has large prediction errors for complex gestures or nonlinear trajectories, resulting in virtual action jams; the Coulomb friction formula with a fixed friction coefficient is used, ignoring the dynamic characteristics of the friction coefficient changing with sliding speed, and not considering the influence of contact area and surface roughness on friction, resulting in distorted touch of virtual objects; In view of this, it is necessary to provide a virtual laboratory simulation system and method based on VR technology. Summary of the Invention

[0003] The purpose of the present invention is to provide a virtual laboratory simulation system and method based on VR technology. In order to solve the above-mentioned problems in the prior art, the present invention is achieved through the following technical solutions: In a first aspect, an embodiment of the present invention provides a virtual laboratory simulation system based on VR technology, specifically including the following modules: Multi-person interaction module: obtains user interaction data through sensors for pre-processing, transmits the user interaction data in real time through network communication protocols, performs data fusion and synchronous processing to update scene data, and transmits the updated scene data to the user; Scene Adjustment Module: Calculates scene synchronization values ​​based on network latency and device performance differences, performs synchronization verification based on the fusion of spatiotemporal and temporal scene data, and uses a distributed hash table-based scene state synchronization algorithm to monitor changes in scene states and make dynamic adjustments. Delay compensation module: Detects and compensates for user operation delays. By building an operation delay model, it predicts user operation actions and executes them in advance in the virtual scene. Dynamic feedback module: Generates interactive friction tactile feedback signals in real time based on the interaction data of objects in the virtual scene, and controls tactile feedback by combining pressure sensors and vibration motor hardware devices.

[0004] Furthermore, the method for data fusion and synchronous processing to update scene data is: Using data fusion algorithms, the same type of user interaction data from different users is integrated. Multi-user operation data of the same object is obtained. The operation data includes coordinate position and attitude quaternion. To obtain the coordinate position of the object, the weighted least squares method is used to fuse the position coordinates, assigning different weights according to the device accuracy, and calculating the comprehensive position of the object in the virtual scene. To obtain the attitude quaternion, the quaternion fusion method is used to fuse the attitude information of multiple users on the same object and update the object posture. The fused data forms the complete scene state data. Define the scene operation event structure and map the event to the DHT node through the synchronous hash algorithm; Furthermore, the method for calculating the scene synchronization value is: Get local scene coordinates With server status coordinates , calculate the Euclidean distance between the local scene and the server state, calculate the Euclidean distance between the local scene and the server state Normalized and dimensionless to obtain the position error value ; The attitude quaternion based on the local scene and server status is calculated by the formula: Calculate the quaternion spherical distance Normalize and de-dimension the attitude error value ; Obtain the local scene time and server time, subtract them and take the absolute value to get the clock offset. Ratio the obtained clock offset with the preset standard time to get the time error value. ; The obtained position error value, attitude error value and time error value are processed comprehensively and weighted fusion is performed to obtain the comprehensive error value ; Based on the obtained comprehensive error, calculate Get scene synchronization value ; Furthermore, the method for performing synchronization verification is: A global check is performed once during the check cycle. If the scene synchronization value is less than the synchronization threshold, the adjustment mechanism is triggered; The posture adjustment is completed through 3 rendering frames, and the adjustment amount per frame is ; Dynamically adjust the synchronization threshold based on network latency and device performance score, using the formula: ; Calculate the dynamically adjusted synchronization threshold ,in, is a one-way delay, To preset the basic synchronization threshold, 、 To preset the adjustable coefficient, Negative adjustment of the dominant one-way delay, Positive compensation of dominant equipment performance; Furthermore, the method for dynamic adjustment is: Obtain user ID and device fingerprint and hash them into 160-bit identifiers, which are mapped to the ring space; The scene state data is sharded by object ID hash, and the responsible node of each scene state data block is determined by its hash value; When the client detects a scene change, it generates a change log; Adjust based on one-way delay: If >100ms, reduce the rendering frame rate and predict the next position of the object based on historical data , render in advance: ; in, is the space-time vector at time t, is the speed at time t, is the acceleration at time t, t is the time index, To estimate delay; Adjustment based on device performance: Dynamically adjust the level of detail; Furthermore, the method for delaying detection is: The client adds a timestamp T1 when sending the operation data, which is recorded as T2 when the server receives it. After processing is completed, the server returns a timestamp T3, which is recorded as T4 when the client receives it. Real-time calculation of each segment delay , data processing delay ; Maintain a sliding window of the last K delay samples and calculate the delay mean and the standard deviation of delay ; Based on the obtained delay standard deviation, the delay pattern is identified. If , it is identified as a stable delay; if , it is identified as a fluctuation delay; Furthermore, the method for predicting the user's operation action is: Perform differential processing on the position coordinates / attitude quaternion to generate velocity ,in, is the space-time vector at time t, is the space-time vector at time t-1, is the sampling interval; Normalization techniques are used to scale numerical features to the [-1, 1] interval; For uniform speed / uniform acceleration motion scenes, the formula is used: ; Calculate the predicted space-time vector at time t+1 ; in, is the prediction time interval, is the acceleration at time t; For complex action prediction, a two-layer LSTM neural network and a fully connected layer are used to input historical data with a sequence length of N. The root mean square error is used as the loss function and optimized by the Adam optimizer. Furthermore, the method of early execution is: Collect M sets of real operation trajectories, judge the complexity of the action by calculating the action complexity index (i.e., acceleration variance), and dynamically select the prediction model; According to the total delay prediction value ,in, Data processing delay, Determine the pre-execution time advance for rendering output delay ; Based on the pre-execution time advance, the predicted trajectory in the future ΔT period is analyzed: ; in, is the number of prediction time intervals within the ΔT time period, is an integer; Furthermore, the method for controlling tactile feedback is: Generate interactive friction tactile feedback signals in real time based on the interaction data of objects in the virtual scene; The tactile feedback signal is divided into a high-frequency vibration signal and a low-frequency pressure signal; The intensity of the high-frequency vibration signal is proportional to the rate of change of the friction force, and the intensity of the low-frequency pressure signal is proportional to the magnitude of the friction force; The contact area and relative speed of objects interacting in real time are obtained through the physics engine in the virtual scene; Adjust the signal duration according to the contact area and modulate the phase of the vibration signal in combination with the relative velocity direction; A flexible pressure sensor array is used, evenly distributed on the palm and fingers of the data glove. The sensor output voltage is linearly related to the pressure. Generate a corresponding PWM control signal based on the calculated high-frequency vibration signal strength and low-frequency pressure signal strength; The micro vibration motor is an eccentric rotating mass ERM motor and a linear resonance actuator; the eccentric rotating mass motor generates centrifugal force by rotating the eccentric mass to achieve vibration.

[0005] In a second aspect, an embodiment of the present invention provides a virtual laboratory simulation method based on VR technology, comprising the following steps: Step 1: Obtain user interaction data through sensors and pre-process it. Transmit the user interaction data in real time through network communication protocols, perform data fusion and synchronization to update scene data, and transmit the updated scene data to the user. Step 2: Calculate the scene synchronization value based on network latency and device performance differences, perform synchronization verification based on the spatiotemporal fusion of scene data, and use a scene state synchronization algorithm based on a distributed hash table to monitor changes in scene state and make dynamic adjustments. Step 3: Detect and compensate for user operation delays by building an operation delay model to predict user operation actions and execute them in advance in the virtual scene; Step 4: Generate interactive friction tactile feedback signals in real time based on the interaction data of objects in the virtual scene, and control the tactile feedback by combining pressure sensors and vibration motor hardware devices.

[0006] Beneficial effects of the present invention: 1. Multi-dimensional interactive data integration enhances immersion and accuracy. Real-time network transmission and distributed collaboration ensure smooth multi-person interaction. Dynamic scene synchronization and delay compensation adapt to complex network and device environments. Delay is broken down into four stages: acquisition, network, processing, and rendering. Models are used to predict fluctuating delays, and LSTM neural networks are used to process complex motion trajectories. Combined with motion prediction formulas, user operations can be pre-executed in advance, effectively reducing perceived delay. The physics engine-driven tactile feedback enhances the realism of virtual interactions. 2. A DHT node architecture is introduced to shard scene state data by hashing object IDs. The data is then mapped to ring space nodes using a synchronous hashing algorithm to achieve decentralized load balancing and data routing. Compared to traditional central server architectures, this improves system scalability and fault tolerance, making it suitable for large-scale multi-person virtual laboratory scenarios. A three-dimensional error model is constructed to quantify scene deviations using Euclidean distance and quaternion spherical distance. The synchronization threshold is dynamically adjusted based on network latency and device performance to achieve adaptability in the scene synchronization strategy, breaking through the limitations of traditional fixed-threshold synchronization. Stable and fluctuating delays are classified and processed: stable delays are predicted using mean values, while fluctuating delays are predicted using a time series model that combines historical delay data to predict the next cycle delay. Complex actions are learned through a two-layer LSTM neural network to learn trajectory features. This is more robust than a single prediction model and effectively addresses the dizziness caused by delays in VR user interaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0008] Figure 1 This is a structural diagram of a virtual laboratory simulation system based on VR technology provided by Example 1 of the present invention; Figure 2 This is a flowchart of the steps of a virtual laboratory simulation method based on VR technology provided in Example 2 of the present invention. DETAILED DESCRIPTION

[0009] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work should fall within the scope of protection of the present invention.

[0010] Example 1:

[0011] like Figure 1 As shown, the embodiment of the present invention provides a virtual laboratory simulation system based on VR technology, which specifically includes the following modules: Multi-person interaction module: obtains user interaction data through sensors for pre-processing, transmits the user interaction data in real time through network communication protocols, performs data fusion and synchronous processing to update scene data, and transmits the updated scene data to the user; It should be noted that the interaction data includes but is not limited to: coordinate position, posture quaternion, finger joint angle, palm pressure and voice data; In a specific embodiment, the method for obtaining user interaction data is: The user's head coordinate position based on the spatial positioning base station coordinate system is obtained through the built-in camera, gyroscope, and accelerometer sensors of the VR helmet and head pose quaternion ; Obtain the user's hand coordinate position through the sensors on the VR handle or data gloves , combined with the built-in IMU hand posture quaternion , collecting finger joint angles through flexible sensors and palm pressure p; obtaining the user's voice data through the microphone for voice communication and command input; It should be noted that the attitude quaternion includes: the cosine value of the user's rotation angle about the unit vector, the sine value of the pitch angle, the sine value of the yaw angle, and the sine value of the roll angle; The collected raw data is pre-processed, and the Kalman filter algorithm is used to smooth the position and posture data of the head and hands to remove noise interference and improve data accuracy and stability. The voice data is subjected to noise reduction and speech recognition processing, and the voice data is converted into a digital signal that can be recognized by the computer, laying the foundation for subsequent transmission and processing. Normalize the joint angles of the handle / glove to the range [0,1], and convert the pressure value to relative pressure with reference to standard atmospheric pressure; Normalize the hand posture quaternion to ensure ; The UDP protocol is used for data transmission. This protocol has strong real-time characteristics and meets the data transmission speed requirements of the virtual laboratory. Data is compressed and encrypted to ensure transmission reliability, reduce data transmission volume and ensure data security. Data is transmitted to the server or local computer via a wireless network. Before transmission, the transmission parameters are configured according to the network environment, such as setting the transmission port and IP address to ensure that the data is accurately delivered to the target address; A dedicated data transmission channel is established between the user terminal and the server. Each user corresponds to an independent channel identifier to ensure the uniqueness and accuracy of data transmission. During the channel establishment process, a handshake verification is performed to confirm that the status of both devices is normal and the conditions for transmission are met, preventing data transmission interruptions or errors. The server receives data from each user terminal through the data receiving module, parses it according to the preset data format, separates different types of interactive data, and stores them in the corresponding temporary data storage area for subsequent processing; Using data fusion algorithms, the same type of user interaction data from different users is integrated. Multi-user operation data of the same object is obtained. The operation data includes coordinate position and attitude quaternion. To obtain the coordinate position of the object, the weighted least squares method is used to fuse the position coordinates, assigning different weights according to the device accuracy, and calculating the comprehensive position of the object in the virtual scene. To obtain the attitude quaternion, the quaternion fusion method is used to fuse the attitude information of multiple users on the same object and update the object posture. The fused data forms the complete scene state data. Define the scene operation event structure and map the event to the DHT node through the synchronous hash algorithm to ensure load balancing; It should be noted that the DHT node is a decentralized distributed system architecture used to efficiently manage the resources and data routing of nodes in the network without a central node; Scene Adjustment Module: Calculates scene synchronization values ​​based on network latency and device performance differences, performs synchronization verification based on the fusion of spatiotemporal and temporal scene data, and uses a distributed hash table-based scene state synchronization algorithm to monitor changes in scene states and make dynamic adjustments. In a specific embodiment, the method for calculating the scene synchronization value is: The client and server send heartbeat packets with timestamps to each other every 50ms to calculate the round-trip delay (RTT). Specifically, the client records the sending time , the server records the receiving time and return time , recorded when the client receives the return packet , then the one-way delay is: ; A sliding window of the most recent 20 round-trip time (RTT) samples is collected and the median is calculated as the current network delay to eliminate the impact of sudden jitter. Collect the CPU single-thread / multi-thread floating-point computing capabilities of the performance test program when the user first accesses , the sum of GPU triangle generation rate and pixel fill rate , memory bandwidth and latency ; Set the device parameter CPU single-thread / multi-thread floating-point computing capability , the sum of GPU triangle generation rate and pixel fill rate , memory bandwidth and latency Comprehensive calculation of performance scores mapped to [0,1] , the preset scale factors are 0.4, 0.4, 0.2; Collect object position , attitude quaternion , timestamp T, construct space-time vector ; Synchronize multi-sensor data through hardware timestamps to ensure spatiotemporal synchronization; Get local scene coordinates With server status coordinates , calculate the Euclidean distance between the local scene and the server state, using the formula: ; Calculate the Euclidean distance between the local scene and the server state Normalized and dimensionless to obtain the position error value ; The attitude quaternion based on the local scene and server status is calculated by the formula: ; Calculate the quaternion spherical distance Normalize and de-dimension the attitude error value ; Subtract the local scene time from the server time and take the absolute value to get the clock offset. Ratio the obtained clock offset with the preset standard time to get the time error value. ; The obtained position error value, attitude error value and time error value are processed comprehensively and weighted fusion is performed to obtain the comprehensive error value , the weights can be adjusted dynamically according to the scene. During fine operation, the weights are preset to: 0.6, 0.3, 0.1; Based on the obtained comprehensive error, calculate Get scene synchronization value ; A global check is performed once during the check cycle. If the scene synchronization value is less than the synchronization threshold, the adjustment mechanism is triggered; For example, for the pose error-dominated error, the local scene pose is calculated With target posture Interpolation posture : ; in, is the posture angle, , is the interpolation progress, ; The posture adjustment is completed through 3 rendering frames, and the adjustment amount per frame is , avoid visual jumps; Dynamically adjust the synchronization threshold based on network latency and device performance score, using the formula: ; Calculate the dynamically adjusted synchronization threshold ,in, To preset the basic synchronization threshold, 、 It is a preset adjustable coefficient, which is preset to 0.08 and 0.3. Negative adjustment of the dominant one-way delay, Positive compensation of dominant equipment performance; Obtain user ID and device fingerprint and hash them into 160-bit identifiers, which are mapped to the ring space; The scene state data is sharded by object ID hash, and the responsible node of each scene state data block is determined by its hash value; When the client detects a scene change, it generates a change log ; in, is the object ID hash, is the space-time vector change, is the change timestamp; Adjust based on one-way delay: If >100ms, reduce the rendering frame rate and predict the next position of the object based on historical data , render ahead of time to reduce perceived latency: ; in, is the space-time vector at time t, is the speed at time t, is the acceleration at time t, t is the time index, To estimate delay; Adjustment based on device performance: Dynamically adjust the level of detail, for example, if If it is greater than 80, it will be adjusted to high detail; if If it is greater than 50 and less than or equal to 80, it is adjusted to medium detail; if If it is less than or equal to 50, it is adjusted to low detail; Delay compensation module: Detects and compensates for user operation delays. By building an operation delay model, it predicts user operation actions and executes them in advance in the virtual scene. In a specific embodiment, the method for detecting and compensating for user operation delay is: Get the time from sensor data collection to data packaging to get input collection delay ; The network transmission delay is calculated by calculating 1 / 2 of the RTT from the client to the server through the two-way heartbeat packet. ; Get the time of server-side data fusion and physical simulation to get data processing delay ; Get the hardware rendering delay from scene data to VR helmet display, and get the rendering output delay ; The client adds a timestamp T1 when sending the operation data, which is recorded as T2 when the server receives it. After processing is completed, the server returns a timestamp T3, which is recorded as T4 when the client receives it. Real-time calculation of each segment delay , data processing delay ; Maintain a sliding window of the last K delay samples and calculate the delay mean and the standard deviation of delay ; Based on the obtained delay standard deviation, the delay pattern is identified. If , it is identified as a stable delay; if , it is identified as a fluctuation delay; For stable delay, use the average As a predicted value; For the fluctuation delay, the ARIMA time series model is used to predict the next period delay, using the formula: ; Calculate the predicted delay at time t+1; in, is the network transmission delay at time t, is the network transmission delay at time t-1, is the network transmission delay at time t-2, t is the sampling time point, 、 、 is the correlation coefficient obtained through least squares training, is white noise; Perform differential processing on the position coordinates / attitude quaternion to generate velocity ,in, is the space-time vector at time t, is the space-time vector at time t-1, is the sampling interval; Normalization techniques are used to scale numerical features to the [-1, 1] interval; For uniform speed / uniform acceleration motion scenes, the formula is used: ; Calculate the predicted space-time vector at time t+1 ; in, is the prediction time interval, is the acceleration at time t; For complex action prediction, a two-layer LSTM neural network and a fully connected layer are used to input historical data with a sequence length of N. The root mean square error is used as the loss function and optimized by the Adam optimizer. Collect M sets of real operation trajectories, judge the complexity of the action by calculating the action complexity index (i.e., acceleration variance), and dynamically select the prediction model; According to the total delay prediction value , determine the pre-execution time advance ; Based on the pre-execution time advance, the predicted trajectory in the future ΔT period is analyzed: ; in, is the number of prediction time intervals within the ΔT time period, is an integer; Dynamic feedback module: Generates interactive friction tactile feedback signals in real time based on the interaction data of objects in the virtual scene, and controls tactile feedback by combining pressure sensors and vibration motor hardware devices; According to the interaction data of objects in the virtual scene, interactive friction tactile feedback signals are generated in real time, and the object interaction information is obtained in real time through the physical engine in the virtual scene, including: the coordinates of the contact points between objects , contact area A, relative speed , relative acceleration , record the friction coefficient of the object material surface roughness ; The pressure sensor on the data glove is used to collect pressure data of each area of ​​the hand, and the palm is divided into 9 pressure sensing areas, each area corresponds to a pressure value ,..., ; The hand posture quaternion and hand position coordinates are obtained through the inertial measurement unit (IMU). The sampling frequency is preset to 200Hz to ensure real-time data. The 3σ principle is used to remove abnormal values ​​from the pressure sensor data. If the pressure value in the pressure sensing area is satisfy ,in, is the mean pressure in the area, is the standard deviation of pressure in the region, If it is a regional index, the pressure value of the pressure-sensing area is regarded as an abnormal value and replaced by the weighted average of the pressures of the adjacent areas; Normalize the pressure data to the interval [0,1], and perform unit conversion and standardization on the relative velocity and acceleration; Construct a friction calculation model and calculate the basic friction force at the contact point according to Coulomb's friction law ; Introducing the speed-related dynamic friction coefficient, through the formula Calculate the dynamic friction coefficient ; in, is the sliding friction coefficient, is the coefficient of kinetic friction, is the relative speed, is the speed threshold; The tactile feedback signal is divided into a high-frequency vibration signal and a low-frequency pressure signal; The intensity of the high-frequency vibration signal is proportional to the rate of change of the friction force, and the intensity of the low-frequency pressure signal is proportional to the magnitude of the friction force; The signal duration is adjusted according to the contact area A. The larger the contact area, the longer the signal duration T. The phase of the vibration signal is modulated in combination with the relative velocity direction to give the tactile feedback a sense of direction. A flexible pressure sensor array is evenly distributed on the palm and fingers of the data glove. The sensor output voltage is linearly related to the pressure. The analog signal is converted into a digital signal through A / D conversion and input into the control system. Generate a corresponding PWM control signal based on the calculated high-frequency vibration signal strength and low-frequency pressure signal strength; The micro vibration motor is an eccentric rotating mass ERM motor and a linear resonance actuator. The eccentric rotating mass motor generates centrifugal force by rotating the eccentric mass to achieve vibration. When multiple vibration motors and pressure sensors work simultaneously, time division multiplexing technology is used for control to avoid signal interference.

[0012] Example 2:

[0013] like Figure 2 As shown, an embodiment of the present invention provides a virtual laboratory simulation method based on VR technology, comprising the following steps: Step 1: Obtain user interaction data through sensors and pre-process it. Transmit the user interaction data in real time through network communication protocols, perform data fusion and synchronization to update scene data, and transmit the updated scene data to the user. Step 2: Calculate the scene synchronization value based on network latency and device performance differences, perform synchronization verification based on the spatiotemporal fusion of scene data, and use a scene state synchronization algorithm based on a distributed hash table to monitor changes in scene state and make dynamic adjustments. Step 3: Detect and compensate for user operation delays by building an operation delay model to predict user operation actions and execute them in advance in the virtual scene; Step 4: Generate interactive friction tactile feedback signals in real time based on the interaction data of objects in the virtual scene, and control the tactile feedback by combining pressure sensors and vibration motor hardware devices.

[0014] An embodiment of the present invention is described in detail above, but the content described is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention; the above formulas are all dimensionless and numerical calculations, and the formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field based on actual conditions and historical experience, and can be adjusted according to actual conditions; the above description is only a preferred embodiment of the present invention and is not used to limit the present invention. All equal changes and improvements made according to the scope of application of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A virtual laboratory simulation system based on VR technology, characterized in that: Includes the following modules: Multi-person interaction module: obtains user interaction data through sensors for pre-processing, transmits the user interaction data in real time through network communication protocols, performs data fusion and synchronous processing to update scene data, and transmits the updated scene data to the user; Scene Adjustment Module: Calculates scene synchronization values ​​based on network latency and device performance differences, performs synchronization verification based on the fusion of spatiotemporal and temporal scene data, and uses a distributed hash table-based scene state synchronization algorithm to monitor changes in scene states and make dynamic adjustments. Delay compensation module: Detects and compensates for user operation delays. By building an operation delay model, it predicts user operation actions and executes them in advance in the virtual scene. Dynamic feedback module: Generates interactive friction tactile feedback signals in real time based on the interaction data of objects in the virtual scene, and controls tactile feedback by combining pressure sensors and vibration motor hardware devices.

2. A virtual laboratory simulation system based on VR technology according to claim 1, characterized in that: The method for data fusion and synchronous processing to update scene data is: Use data fusion algorithms to integrate the same type of user interaction data from different users; Acquire multi-user operation data of the same object. The operation data includes coordinate position and attitude quaternion. To obtain the coordinate position of the object, the weighted least squares method is used to fuse the position coordinates, assigning different weights according to the device accuracy, and calculating the comprehensive position of the object in the virtual scene. To obtain the attitude quaternion, the quaternion fusion method is used to fuse the attitude information of multiple users on the same object and update the object posture. The fused data forms the complete scene state data. Define the scene operation event structure and map the event to the DHT node through the synchronous hash algorithm.

3. A virtual laboratory simulation system based on VR technology according to claim 1, characterized in that: The method for calculating the scene synchronization value is: Get local scene coordinates With server status coordinates , calculate the Euclidean distance between the local scene and the server state, calculate the Euclidean distance between the local scene and the server state Normalized and dimensionless to obtain the position error value ; The attitude quaternion based on the local scene and server status is calculated by the formula: Calculate the quaternion spherical distance Normalize and de-dimension the attitude error value ; Obtain the local scene time and server time, subtract them and take the absolute value to get the clock offset. Ratio the obtained clock offset with the preset standard time to get the time error value. ; The obtained position error value, attitude error value and time error value are processed comprehensively and weighted fusion is performed to obtain the comprehensive error value ; Based on the obtained comprehensive error, calculate Get scene synchronization value .

4. The virtual laboratory simulation system based on VR technology according to claim 1, characterized in that: The method for performing synchronization verification is: A global check is performed once during the check cycle. If the scene synchronization value is less than the synchronization threshold, the adjustment mechanism is triggered; The posture adjustment is completed through 3 rendering frames, and the adjustment amount per frame is ; Dynamically adjust the synchronization threshold based on network latency and device performance score, using the formula: ； Calculate the dynamically adjusted synchronization threshold ,in, is a one-way delay, To preset the basic synchronization threshold, 、 To preset the adjustable coefficient, Negative adjustment of the dominant one-way delay, Positive compensation for dominant device performance.

5. The virtual laboratory simulation system based on VR technology according to claim 1, characterized in that: The method for dynamic adjustment is: Obtain user ID and device fingerprint and hash them into 160-bit identifiers, which are mapped to the ring space; The scene state data is sharded by object ID hash, and the responsible node of each scene state data block is determined by its hash value; When the client detects a scene change, it generates a change log; Adjust based on one-way delay: If >100ms, reduce the rendering frame rate and predict the next position of the object based on historical data , render in advance: ； in, is the space-time vector at time t, is the speed at time t, is the acceleration at time t, t is the time index, To estimate delay; Adjustment based on device performance: Dynamically adjust levels of detail.

6. The virtual laboratory simulation system based on VR technology according to claim 1, characterized in that: The method for delaying detection is: The client adds a timestamp T1 when sending the operation data, which is recorded as T2 when the server receives it. After processing is completed, the server returns a timestamp T3, which is recorded as T4 when the client receives it. Real-time calculation of each segment delay , data processing delay ; Maintain a sliding window of the last K delay samples and calculate the delay mean and the standard deviation of delay ; Based on the obtained delay standard deviation, the delay pattern is identified. If , it is identified as a stable delay; if , it is identified as a fluctuation delay.

7. The virtual laboratory simulation system based on VR technology according to claim 1, characterized in that: The method for predicting the user's operation action is: Perform differential processing on the position coordinates / attitude quaternion to generate velocity ,in, is the space-time vector at time t, is the space-time vector at time t-1, is the sampling interval; Normalization techniques are used to scale numerical features to the [-1, 1] interval; For uniform speed / uniform acceleration motion scenes, the formula is used: ； Calculate the predicted space-time vector at time t+1 ; in, is the prediction time interval, is the acceleration at time t; For the prediction of complex actions, a two-layer LSTM neural network and a fully connected layer are used to input historical data with a sequence length of N. The root mean square error is used as the loss function and optimized by the Adam optimizer.

8. The virtual laboratory simulation system based on VR technology according to claim 1, characterized in that: The method of early execution is: Collect M sets of real operation trajectories, judge the complexity of the action by calculating the action complexity index (i.e., acceleration variance), and dynamically select the prediction model; According to the total delay prediction value ,in, Data processing delay, Determine the pre-execution time advance for rendering output delay ; Based on the pre-execution time advance, the predicted trajectory in the future ΔT period is analyzed: ； in, is the number of prediction time intervals within the ΔT time period, is an integer.

9. The virtual laboratory simulation system based on VR technology according to claim 1, characterized in that: The method for controlling tactile feedback is: Generate interactive friction tactile feedback signals in real time based on the interaction data of objects in the virtual scene; The tactile feedback signal is divided into a high-frequency vibration signal and a low-frequency pressure signal; The intensity of the high-frequency vibration signal is proportional to the rate of change of the friction force, and the intensity of the low-frequency pressure signal is proportional to the magnitude of the friction force; The contact area and relative speed of objects interacting in real time are obtained through the physics engine in the virtual scene; Adjust the signal duration according to the contact area and modulate the phase of the vibration signal in combination with the relative velocity direction; A flexible pressure sensor array is used, evenly distributed on the palm and fingers of the data glove. The sensor output voltage is linearly related to the pressure. Generate a corresponding PWM control signal based on the calculated high-frequency vibration signal strength and low-frequency pressure signal strength; The micro vibration motor is an eccentric rotating mass ERM motor and a linear resonance actuator; the eccentric rotating mass motor generates centrifugal force by rotating the eccentric mass to achieve vibration.

10. A virtual laboratory simulation method based on VR technology, applied to a virtual laboratory simulation system based on VR technology according to any one of claims 1 to 9, characterized in that: The following steps are involved: Step 1: Obtain user interaction data through sensors and pre-process it. Transmit the user interaction data in real time through network communication protocols, perform data fusion and synchronization to update scene data, and transmit the updated scene data to the user. Step 2: Calculate the scene synchronization value based on network latency and device performance differences, perform synchronization verification based on the spatiotemporal fusion of scene data, and use a scene state synchronization algorithm based on a distributed hash table to monitor changes in scene state and make dynamic adjustments. Step 3: Detect and compensate for user operation delays by building an operation delay model to predict user operation actions and execute them in advance in the virtual scene; Step 4: Generate interactive friction tactile feedback signals in real time based on the interaction data of objects in the virtual scene, and control the tactile feedback by combining pressure sensors and vibration motor hardware devices.

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