Virtual material interaction system with tactile feedback

Abstract

The invention relates to the technical field of tactile interaction, in particular to a virtual material interaction system with tactile feedback, which comprises a contact state sensing module, a potential gradient calculation module, a friction dynamic regulation and control module, a tactile feedback generation module and an interaction state monitoring module. According to the invention, dynamic mapping of contact pressure, sliding speed and node potential is realized through multi-source signal fusion, so that touch perception keeps continuous response in time sequence, potential difference calculation forms an electric field regulation basis for microscopic change of a material, and space-level adjustment of a surface friction coefficient is realized through charge redistribution and polarization direction switching. The tactile feedback changes adaptively according to material features, the friction spectrum is recombined into a multi-frequency-domain tactile signal after Fourier decomposition, texture, hardness and damping features of different materials are presented in a layered mode, the tactile feedback has more dynamic details and direction consistency, the sense of reality and the tactile recognition degree of virtual materials are improved, and the sense of reality and the sense of touch recognition degree of the virtual materials are improved. And stable and natural interaction touch in the virtual environment is ensured.

Description

Technical Field

[0001] This invention relates to the field of tactile interaction technology, and in particular to a virtual material interaction system with tactile feedback. Background Technology

[0002] Haptic interaction technology is a branch of human-computer interaction technology. It primarily studies how to use mechanical, electronic, and computer control methods to achieve information transmission between human tactile perception and a virtual environment. Its core aspects include the generation, transmission, and sensory response of tactile signals, involving force feedback devices, vibration drive structures, surface friction control units, and deformable material control. This technology integrates sensor detection, drive execution, and control strategies to enable users to simulate tactile information such as the texture, shape, and hardness of object surfaces in virtual space. It has wide applications in virtual reality, remote control, medical simulation, and educational training.

[0003] Among them, the traditional virtual material interaction system with tactile feedback refers to the device and method that generates physical stimulation on the human-machine interface through mechanical or electric drive components to simulate the tactile sensation of different virtual materials. It uses resistive or capacitive tactile sensors to collect information on the user's finger position and contact pressure, and changes the vibration frequency, surface texture or friction coefficient of the contact surface through a micro actuator array or friction control unit to correspond to changes in virtual materials, thereby realizing the tactile performance of material differences in the virtual environment.

[0004] Traditional haptic feedback systems mostly generate tactile sensation through mechanical drives or vibration structures. Signal acquisition is limited to contact pressure and position information, lacking coordinated processing of sliding speed and telecommunication characteristics. System response lag leads to a delay between haptic feedback and user actions. It is difficult to form continuous feedback when the touch point changes in different directions or force. Mechanical structures are prone to uneven texture transitions or force distortion during frequent switching. Friction control relies on fixed excitation modes and cannot adaptively adjust to local touch point changes. Tactile detail is limited, and the differences between materials are presented in a single way. The haptic recognition and natural continuity in virtual environments are insufficient. Summary of the Invention

[0005] To address the technical problems of traditional haptic feedback systems, which primarily rely on mechanical drives or vibration structures to generate tactile sensations, limit signal acquisition to contact pressure and position information, lack collaborative processing of sliding speed and telecommunication parameters, suffer from system response lag leading to delays between haptic feedback and user actions, struggle to generate continuous feedback under varying directions or force, and are prone to uneven texture transitions or force distortion during frequent mechanical switching, friction control depends on fixed excitation modes and cannot adaptively adjust to local touch point changes, resulting in limited tactile detail, uniform material differences, and insufficient haptic recognition and natural continuity in virtual environments, this invention provides a virtual material interaction system with haptic feedback. The technical solution is as follows: On the one hand, a virtual material interaction system with haptic feedback is provided, which includes: The contact state sensing module acquires real-time values ​​of contact pressure and sliding speed, collects instantaneous potential values ​​of nodes, uses a Kalman filter algorithm to filter out noise, generates virtual material contact state data, and transmits it to the potential gradient calculation module. The potential gradient calculation module receives the virtual material contact state data, performs potential difference calculation between adjacent nodes, analyzes the trend of potential difference change, calculates the material response requirements, generates a material electric field control scheme, and transmits it to the friction dynamic control module. The friction dynamic control module receives the material electric field control scheme, performs node charge redistribution, controls the switching of polarization direction between adjacent layers, calculates the friction coefficient distribution on the material surface, generates virtual material friction characteristic parameters, and transmits them to the tactile feedback generation module. The tactile feedback generation module receives the friction feature parameters of the virtual material, calculates the tactile intensity corresponding to different material textures, analyzes the friction force change spectrum using the Fourier transform algorithm, synthesizes tactile simulation signals of wood, metal and cloth materials, generates virtual material tactile feedback signals, and transmits them to the interactive state monitoring module.

[0006] As a further aspect of the present invention, the virtual material contact state data includes filtered contact pressure value, filtered sliding speed value, and filtered node potential value; the material electric field control scheme includes node charge distribution amount, polarization angle switching amount, and target potential gradient threshold; the virtual material friction characteristic parameters include surface friction coefficient value, dynamic friction force amplitude, and friction direction angle; and the virtual material tactile feedback signal includes wood texture surface roughness signal, metal surface roughness signal, fabric softness signal, and friction frequency characteristic value.

[0007] As a further aspect of the present invention, the contact state sensing module includes: The data acquisition submodule acquires the real-time values ​​of contact pressure and sliding speed, performs parallel sampling and time synchronization of the pressure signal, calculates the rate of change of sliding speed and corresponds it to the time index, acquires the instantaneous potential value of the node and performs normalization conversion, and generates the original contact signal set. The signal filtering submodule calls the potential amplitude sequence based on the original contact signal set, performs update correction using the Kalman filter algorithm, calculates the potential difference between adjacent points and compares the amplitude change rate, judges the fluctuation characteristics based on the noise fluctuation threshold and removes abnormal points, and generates a noise suppression signal sequence. The potential fluctuation threshold is set to 10% to 15% of the root mean square value of the potential amplitude sequence. The noise point judgment condition is determined by comparing the absolute value of the potential difference between adjacent sampling points with the potential fluctuation threshold. The potential feature generation submodule calculates the correlation coefficient between the pressure change rate and the potential change rate based on the noise suppression signal sequence, interpolates the sliding speed, fuses the correlation coefficient data, extracts the node potential gradient interval, integrates the mapping information, and generates virtual material contact state data.

[0008] As a further aspect of the present invention, the potential gradient calculation module includes: The contact data parsing submodule acquires the contact state data of the virtual material, parses the spatial coordinates and conductivity parameters of the nodes, calculates the distance between nodes to analyze the spatial mapping relationship, counts the contact duration, calculates the interaction weight distribution in combination with the node number, and generates node contact parameter data. The potential difference calculation submodule extracts the conductivity and contact area data of adjacent nodes based on the node contact parameter data, calculates the potential difference sequence between nodes, calculates the potential difference change ratio according to the time step, performs linear fitting to determine the change range, and generates the potential gradient change coefficient. The control scheme generation submodule calls the potential gradient change coefficient, calculates the response sensitivity parameter based on the node potential gradient distribution, compares the sensitivity with the response benchmark value to select node regions higher than the benchmark value, and overlays the node coordinates and the sensitivity to calculate the electric field intensity distribution map to generate the material electric field control scheme.

[0009] As a further aspect of the present invention, the potential gradient change coefficient is calculated based on the linear fitting results and the node potential distribution. The potential gradient change coefficient can further affect the calculation of the response sensitivity parameter and the selection of the node region in the electric field control scheme. The response benchmark value is determined based on the statistical characteristics of the node potential gradient change coefficient, which include the mean and standard deviation of the node potential gradient change coefficient.

[0010] As a further aspect of the present invention, the friction dynamic control module includes: The charge distribution calculation submodule calls the material electric field control scheme, extracts the initial charge density value of the node, calculates the charge transfer direction and transmission path between nodes based on the spatial distribution and potential difference distribution between nodes, corrects the charge value according to the potential difference ratio and balances the node potential, and generates the node charge distribution. The polarization direction control submodule detects the direction of electric dipole moments between adjacent layers based on the node charge distribution, compares the interlayer potential difference with the electrostatic potential threshold according to the interlayer spacing, determines the dipole rotation trigger condition when the threshold is exceeded and corrects the polarization reversal sequence, and generates an interlayer polarization direction sequence. The friction feature generation submodule calculates the contact point charge density and the friction force vector corresponding to the polarization orientation based on the interlayer polarization direction sequence and node charge distribution. It also calculates the material surface friction coefficient distribution by combining the interface pressure coefficient and shear rate integral, and establishes virtual material friction feature parameters.

[0011] As a further aspect of the present invention, the calculation of the charge transfer direction and transmission path between nodes based on the spatial distribution and potential difference distribution between nodes refers to, after extracting the initial charge density value of the nodes, performing a weighted comparison of the spatial distance between any two adjacent nodes, determining the preferred direction of charge transfer according to the numerical proportion of the potential difference between the nodes, and forming a charge transfer path matrix between the nodes. The electrostatic potential threshold refers to the electrostatic potential distribution of the same type of layered material under different interlayer spacing conditions, which is experimentally determined.

[0012] As a further aspect of the present invention, the haptic feedback generation module includes: The tactile input parsing submodule acquires the friction feature parameters of the virtual material, extracts the rate of change of friction coefficient and the amplitude of contact pressure, performs time synchronization comparison between the two within the sampling period, calculates the instantaneous force difference in the friction signal fluctuation segment, and generates friction intensity sequence data. The spectral feature calculation submodule, based on the friction intensity sequence data, performs spectral analysis on the periodic fluctuation segment using Fourier transform based on the amplitude sequence, calculates the frequency distribution value, divides the spectral segment according to the amplitude peak point, calculates the energy proportion of the spectral segment, and obtains the friction spectrum energy distribution coefficient. The tactile signal synthesis submodule, based on the friction spectrum energy distribution coefficient, calls the spectrum segment energy ratio and amplitude peak point parameters to adjust the weights of the wood, metal, and fabric texture signal templates and calculate the vibration intensity difference to generate virtual material tactile feedback signals.

[0013] As a further embodiment of the present invention, the interaction status monitoring module receives the tactile feedback signal of the virtual material, monitors and processes the interaction effect of the virtual material, detects the synchronization between the tactile feedback signal of the virtual material and the user operation, monitors the continuity of tactile feedback during material switching, judges the accuracy of virtual material simulation based on signal stability, and generates a virtual material interaction quality evaluation result. The virtual material interaction quality evaluation results include tactile response delay time, material transition smoothness value, and simulation accuracy level.

[0014] As a further aspect of the present invention, the interaction status monitoring module includes: The signal receiving submodule acquires multi-channel input data of the virtual material tactile feedback signal, analyzes the amplitude fluctuation range of the channel signal, calculates the difference in amplitude change of the sampling interval, segments the data for statistical analysis, calculates the amplitude variance and extracts the signal energy distribution, and generates tactile feedback signal stability parameter values. The synchronization detection submodule extracts the timing nodes of the operation trigger time and the feedback signal response time based on the stability parameter value of the tactile feedback signal and the user operation input sequence, calculates the time difference and performs normalization processing, determines the synchronization offset concentration, and generates the tactile interaction synchronization matching degree. The tactile accuracy assessment submodule calls the tactile interaction synchronization matching degree and tactile feedback signal stability parameter values, extracts the continuous change curve of the virtual material switching signal, calculates the tactile response difference rate and compares it with the benchmark value, calculates the interaction quality index vector, and generates the virtual material interaction quality assessment result.

[0015] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following: By fusing multi-source signals, dynamic mapping of contact pressure, sliding speed, and node potential is achieved, enabling tactile perception to maintain continuous temporal response. Potential difference calculation forms the basis for electric field control of microscopic changes in virtual materials. Charge redistribution and polarization direction switching enable spatial adjustment of surface friction coefficient, allowing tactile feedback to adapt to material characteristics. The friction spectrum is recombined into multi-frequency domain tactile signals after Fourier decomposition, allowing the texture, hardness, and damping characteristics of different materials to be presented in a layered manner. Tactile feedback is more dynamic, detailed, and directional, enhancing the realism and tactile recognition of virtual materials and ensuring the stability and naturalness of interactive tactile sensation in the virtual environment. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the accompanying drawings without creative effort.

[0017] Figure 1 This is a system schematic diagram of the present invention; Figure 2 This is a schematic diagram of the system framework of the present invention; Figure 3 This is a flowchart of the contact state sensing module in this invention; Figure 4 This is a flowchart of the potential gradient calculation module in this invention; Figure 5 This is a flowchart of the friction dynamic control module in this invention; Figure 6 This is a flowchart of the haptic feedback generation module in this invention; Figure 7 This is a flowchart of the interactive status monitoring module in this invention. Detailed Implementation

[0018] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0019] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0020] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning.

[0021] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0022] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0023] This invention provides a virtual material interaction system with haptic feedback, such as... Figure 1-2 The diagram shown illustrates a virtual material interaction system with haptic feedback. The system includes: The contact state sensing module acquires real-time values ​​of contact pressure and sliding speed, collects instantaneous potential values ​​of nodes, uses a Kalman filter algorithm to filter out noise, generates virtual material contact state data, and transmits it to the potential gradient calculation module. The potential gradient calculation module receives virtual material contact state data, performs potential difference calculation between adjacent nodes, analyzes the trend of potential difference change, calculates material response requirements, generates material electric field control scheme, and transmits it to the friction dynamic control module. The friction dynamic control module receives the material electric field control scheme, executes the nodal charge redistribution, controls the switching of polarization direction between adjacent layers, calculates the friction coefficient distribution on the material surface, generates virtual material friction characteristic parameters, and transmits them to the tactile feedback generation module. The haptic feedback generation module receives the friction feature parameters of the virtual material, calculates the tactile intensity corresponding to different material textures, analyzes the friction force change spectrum using the Fourier transform algorithm, synthesizes the tactile simulation signals of wood, metal and cloth materials, generates the virtual material haptic feedback signal, and transmits it to the interactive state monitoring module. The interaction status monitoring module receives tactile feedback signals from virtual materials, monitors and processes the interaction effects of virtual materials, detects the synchronization between the tactile feedback signals of virtual materials and user operations, monitors the continuity of tactile feedback during material switching, judges the accuracy of virtual material simulation based on signal stability, and generates virtual material interaction quality assessment results. The virtual material contact state data includes filtered contact pressure value, filtered sliding speed value, and filtered node potential value. The material electric field control scheme includes node charge distribution, polarization angle switching amount, and target potential gradient threshold. The virtual material friction characteristic parameters include surface friction coefficient value, dynamic friction force amplitude, and friction direction angle. The virtual material tactile feedback signals include wood texture surface roughness signal, metal surface roughness signal, fabric softness signal, and friction frequency characteristic value. The virtual material interaction quality evaluation results include tactile response delay time, material transition smoothness value, and simulation accuracy level.

[0024] Specifically, such as Figure 2 , 3 As shown, the contact state sensing module includes: The data acquisition submodule acquires the real-time values ​​of contact pressure and sliding speed, performs parallel sampling and time synchronization of the pressure signal, calculates the rate of change of sliding speed and corresponds it to the time index, acquires the instantaneous potential value of the node and performs normalization conversion, and generates the original contact signal set. The real-time pressure values ​​of the user's fingertips on multiple touch points on the touch surface are obtained through a pressure sensor array, for example... At that moment, the pressure values ​​collected from the three contact points were as follows: , , Meanwhile, the position tracking unit obtains the contact point's location. Position in coordinate system The positions corresponding to the times are respectively , , The unit is millimeters, and the system combines parallel pressure signals with a unified timestamp. Binding is performed to form a synchronized data frame, and then... Time (e.g.) New location data was collected again. , , The system calculates the sliding speed by measuring the displacement change between two consecutive time stamps. For example, for the first contact point, its speed at... Inside directional displacement is , directional displacement is The total displacement is The corresponding sliding speed is Next, the system calculates the rate of change of the sliding speed, assuming that in Previous moments arrive The velocity calculated within the time interval is Then in The rate of change of velocity at time t is and the rate of change value With time index Associative storage is performed, and simultaneously, the instantaneous potential value of each node in the array due to material deformation is acquired, for example, in At a certain moment, the potential at a node is Considering that the potential output range of this sensor node is to The system performs a normalization transformation on it, specifically by subtracting the minimum value from the current value and then dividing by the range width. This yields a dimensionless potential value. Finally, at each sampling time, the synchronized average pressure (e.g.) The calculated rate of change of sliding speed and the normalized potential value of the associated node are combined to form a time-series multidimensional dataset, namely the original contact signal set.

[0025] The signal filtering submodule calls the potential amplitude sequence based on the original contact signal set, performs update correction using the Kalman filter algorithm, calculates the potential difference between adjacent points and compares the amplitude change rate, judges the fluctuation characteristics based on the noise fluctuation threshold and removes abnormal points, and generates a noise suppression signal sequence. The potential fluctuation threshold is set to 10% to 15% of the root mean square value of the potential amplitude sequence. The noise point judgment condition is determined by comparing the absolute value of the potential difference between adjacent sampling points with the potential fluctuation threshold. First, retrieve the potential amplitude sequence over a period of time from the generated original contact signal set. For example, construct the sequence by continuously collecting the normalized potential values ​​at five time points: ; For this sequence, the system performs a prediction and update correction process, processing the third data point. At that time, the system first bases its decisions on the first two smoothed values ​​(assuming they are 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19 ... and Predict the value of the third point, for example, predict the value as... Then compare this predicted value with the actual measured value. Weighted fusion is performed, and because the actual measured value deviates too much from the predicted value, the system will assign a higher weight to the predicted value (e.g., ...). ), and give the measured value a lower weight (e.g. The corrected value is This suppresses sudden noise. Next, to identify and eliminate outliers that haven't been fully corrected, the system needs to set a potential fluctuation threshold. This threshold is set with reference to the root mean square (RMS) value of the potential amplitude sequence itself. For example, let's first calculate the sum of squares of the multiple terms: ; Then calculate the mean: Finally, taking the square root yields an RMS value of approximately The system sets the threshold to 12 percent of the RMS value, i.e. Subsequently, the system identifies noise points by calculating the absolute value of the potential difference between adjacent sampling points; for example, it calculates the absolute value of the difference between the second and third points. This value Greater than the set threshold Therefore, the system will use the third data point. If a point is identified as an outlier, the system performs a removal operation, such as replacing it with the average of its two nearest neighbors. To replace Through the above process, the original potential sequence is processed into a sequence with smoother fluctuations and suppressed outliers, that is, a noise-suppressed signal sequence is generated.

[0026] The potential feature generation submodule calculates the correlation coefficient between the pressure change rate and the potential change rate based on the noise suppression signal sequence, interpolates the sliding speed and fuses the correlation coefficient data, extracts the node potential gradient interval and integrates the mapping information to generate virtual material contact state data. Based on the generated noise-suppressed signal sequence, such as a processed potential sequence and the pressure sequence synchronized with it. (Unit: N), calculate the correlation coefficient between the rates of change of the two. First, calculate the correlation coefficient at each time interval (assuming N). The rate of change within ) yields the potential change rate sequence. (unit ) and pressure change rate series (unit Subsequently, the system calculates the Pearson correlation coefficient by performing covariance and standard deviation calculations on the two rate-of-change sequences. For example, the calculated correlation coefficient value for the two sequences is... The value is between arrive Between, among which close This indicates a strong positive correlation. Simultaneously, the system acquires sliding velocity data within the corresponding time period. If the sampling rate of the velocity data is inconsistent with the potential and pressure data, interpolation processing is required, for example, in... and The velocities at time t are respectively and Then the interpolation calculation yields The speed at time is Next, the calculated correlation coefficients Interpolated sliding speed data The data is fused to form a data point containing multi-dimensional information. Then, the system extracts the gradient interval of the node potential based on the magnitude of the rate of change of potential. The interval division criterion here is: the rate of change is lower than... Defined as a low gradient interval, in arrive The interval between these two is the medium gradient range, and the interval above this range is... For the high gradient region, use the calculated potential change rate sequence For example, they belong to the low gradient, medium gradient, and low gradient intervals, respectively. Finally, the system integrates the information and performs information transformation according to preset mapping rules. For example, one rule is: when the pressure is greater than... The sliding speed is arrive Within the specified range, and when the potential gradient is in the medium gradient region, the current state is mapped to a tactile feedback instruction that "simulates rough wood grain," which includes the vibration frequency. and amplitude The parameters are calculated, fused, and mapped to generate virtual material contact state data that can drive the haptic feedback actuator.

[0027] Specifically, such as Figure 2 , 4 As shown, the potential gradient calculation module includes: The contact data parsing submodule acquires virtual material contact state data, parses node spatial coordinates and conductivity parameters, calculates the distance between nodes to analyze spatial mapping relationships, counts contact duration, calculates interaction weight distribution based on node numbers, and generates node contact parameter data. The system acquires the generated virtual material contact state data, which includes commands such as "simulate rough wood grain" and corresponding node activation information. The system first parses the spatial coordinates of the activated nodes from this data, such as the node... The coordinates are ,node The coordinates are All units are in millimeters. Simultaneously, the conductivity parameters associated with the "rough wood grain" material are extracted and set as follows: Next, the system calculates the Euclidean distance between nodes to analyze their spatial mapping relationship. With nodes The distance between them is Subsequently, the system calculates the contact duration by tracking the activation state of nodes within consecutive time frames. If the interval of a time frame is... ,node Continuous If it remains active for 1 time frame, then its contact duration is 1 / 2. The system combines the unique identifier of the node, for example, node Number ,node Number Calculate the interaction weight distribution and interaction weights. The calculation process involves taking the real-time pressure value of the node. With a contact duration-based Multiplying by the gain factor, specifically: ,in It is a weighting coefficient, and its setting is based on the typical short swipe interaction (approximately arrive In this context, the effect of duration should increase the weight. arrive , here is set If node pressure value The duration is Then its interaction weight If node pressure value The duration is Then its weight By performing this calculation on the activated node, a set of weight values ​​is obtained. This is the interaction weight distribution. Finally, the parsed node coordinates, conductivity parameters, distance between nodes, contact duration, and calculated interaction weights are integrated into a dataset to generate node contact parameter data.

[0028] The potential difference calculation submodule extracts the conductivity and contact area data of adjacent nodes based on node contact parameter data, calculates the potential difference sequence between nodes, calculates the potential difference change ratio according to the time step, performs linear fitting to determine the change range, and generates the potential gradient change coefficient. Based on the generated node contact parameter data, neighboring nodes are extracted, such as nodes. With nodes conductivity And obtain its contact area data, contact area The acquisition process is to combine it with pressure. To establish a correlation, a conversion coefficient is set. This coefficient was calibrated based on pressing test data of typical silicone material contacts. When the node... The pressure is At that time, its contact area is The system extracts nodes from the noise-suppressed signal sequence. and nodes In four consecutive time steps (each step) The normalized potential values ​​of ) are respectively and The potential difference between nodes is calculated at each time step to obtain the potential difference sequence. Subsequently, based on The time step is used to calculate the ratio of the change in potential difference between adjacent time points. The ratio of the change within the first time interval is... The second interval is The third interval is The change ratio sequence is obtained. The system performs a linear fit on this change ratio sequence to determine its overall change range or trend. The fitting process here simplifies to calculating the slope at the first and last points of the sequence, i.e., at time points... arrive (span The trend between ) is However, to more stably reflect the overall trend, the system uses least squares fitting of the data points, and the slope is calculated to be... The slope value is determined as an indicator to describe the dynamic trend of potential difference changes, and the potential gradient change coefficient is generated.

[0029] The control scheme generation submodule calls the potential gradient change coefficient, calculates the response sensitivity parameter based on the node potential gradient distribution, compares the sensitivity with the response benchmark value to filter the node region higher than the benchmark value, and overlays the node coordinates and the sensitivity to calculate the electric field intensity distribution map to generate the material electric field control scheme. Call the calculated potential gradient change coefficient, for example And calculate its response sensitivity parameter based on the potential gradient distribution of the node, response sensitivity The calculation method is to use the interaction weight of the nodes. With potential gradient coefficient Multiply the absolute values, that is For interaction weights nodes Its sensitivity is For weights of nodes Its sensitivity is Next, the system needs to set a response benchmark value to filter out the node regions that require priority response. The determination of this response benchmark value is based on the statistical characteristic analysis of the potential gradient change coefficients calculated for node pairs within the current interaction region. Assuming the system has currently analyzed five node pairs, a set of coefficients is obtained. First, calculate the mean of this set of data. Then calculate the standard deviation. Response baseline value The calculation formula is ,in The adjustment coefficient is set to [value] based on the statistical definition of significant change. ,but The system compares the node's sensitivity parameter with the benchmark value. If the sensitivity value is higher than the benchmark value, the node is filtered out. In this example, and All below Assuming there is another node Its sensitivity ,but ,node Selected.

[0030] Table 1: Node Sensitivity Screening Table ; As shown in Table 1, this table lists the parameter calculation and filtering results for some nodes. Node 003 was selected because its response sensitivity is higher than the calculated response baseline value. Subsequently, the system will select nodes (such as node 003). coordinates Its sensitivity By superimposing these values, a local reference value for the electric field intensity can be calculated, such as the intensity. And set the strength of the unselected nodes to This constructs an electric field intensity distribution map, which is essentially a set of instructions containing coordinate positions and corresponding control intensities, generating a material electric field control scheme.

[0031] Specifically, such as Figure 2 , 5 As shown, the friction dynamic control module includes: The charge distribution calculation submodule calls the material electric field control scheme, extracts the initial charge density value of the nodes, calculates the charge transfer direction and transmission path between nodes based on the spatial distribution and potential difference distribution between nodes, corrects the charge value according to the potential difference ratio and balances the node potential, and generates the node charge distribution. The generated material electric field manipulation scheme is invoked, which specifies the nodes. The electric field strength reference value is The remaining nodes are The system first extracts the initial charge density values ​​of the nodes from memory and sets them to a uniform value. Subsequently, the charge transfer path and direction are calculated based on the spatial distribution and potential difference distribution between nodes, and based on the nodes... coordinate and adjacent nodes coordinate The system identifies the two as a charge-interacting pair and retrieves their potential values ​​from the noise suppression signal sequence, such as the node. electric potential is And nodes electric potential is The charge will flow from the node with higher potential. To the node with lower potential The shift is necessary, but the regulatory plan requires strengthening the nodes. The electric field, therefore the system reverses this natural tendency, setting from the node To the node Charge transfer is performed, and then the system corrects the amount of charge transfer based on the potential difference ratio. First, the maximum potential difference between adjacent nodes in the region is calculated, assuming it is... And nodes With nodes The potential difference is The potential difference ratio is The system will transfer a reference charge amount, for example Multiplying this by the ratio, we get the amount of charge transferred in this instance. The system performs charge transfer at the nodes. The charge density decreases to ,node The charge density increases to This process is repeated on multiple neighboring nodes within the controlled area until the node... The charge density reaches the target electric field strength Corresponding preset values, for example This allows for the balance adjustment of the node potential, ultimately resulting in a non-uniform charge distribution map and generating the node charge distribution.

[0032] The polarization direction control submodule detects the direction of electric dipole moments between adjacent layers based on the node charge distribution, compares the interlayer potential difference with the electrostatic potential threshold according to the interlayer spacing, determines the dipole rotation trigger condition when the threshold is exceeded and corrects the polarization reversal sequence, and generates an interlayer polarization direction sequence. Based on the generated node charge distribution data, such as node The charge density is Unregulated nodes charge density is The system detects the direction of the electric dipole moment between adjacent material layers at the node location. Initially, the electric dipole moments of the nodes all point downwards, in vector form. This indicates that the system then, based on the set interlayer spacing, for example... The interlayer potential difference is calculated and compared with a preset electrostatic potential threshold. The electrostatic potential threshold is set by comparing the same type of layered piezoelectric materials under different conditions. to Experiments were conducted by applying varying electric fields and measuring electrostatic potential at different interlayer spacings, revealing that... At a given spacing, when the potential difference reaches At this point, the polarization direction of the material begins to undergo an unstable reversal, therefore the electrostatic potential threshold is... Set as The system calculates the interlayer potential difference for each node, and the calculation process involves taking the surface charge density of the node. Divide by the dielectric constant ε of the material (let it be ε). Multiply by the interlayer spacing. For nodes Its potential difference is For nodes Its potential difference is The system will calculate the potential difference and compare it with the threshold. Comparison, because and The system determined that none of the dipole rotation triggering conditions for the nodes were met, therefore no correction of the polarization reversal order was performed, and the polarization direction of the nodes remained unchanged. Without changing, the system eventually outputs a sequence that records the current polarization direction vector of the node, generating an interlayer polarization direction sequence.

[0033] The friction feature generation submodule calculates the contact point charge density and the friction force vector corresponding to the polarization orientation based on the interlayer polarization direction sequence and the nodal charge distribution. It also calculates the material surface friction coefficient distribution by combining the interface pressure coefficient and the shear rate integral, and establishes the virtual material friction feature parameters. Based on the generated interlayer polarization direction sequence, the polarization directions of the nodes are all and the nodal charge distribution, where the nodes charge density is ,node for The calculation logic here involves determining the frictional force vector corresponding to the contact point charge density and polarization orientation. This is done by using a base friction coefficient... Multiply by an adjustment factor determined by both charge density and polarization direction, which is calculated as follows: ,in The electrostatic effect coefficient is set to . , For the Z component of the polarization direction, for the node The adjustment factor is The corresponding coefficient of friction is For nodes The adjustment factor is The corresponding coefficient of friction is Next, the system integrates the friction coefficient value by combining the interface pressure coefficient and the shear rate. The integration process here is simplified to multiplying by a gain coefficient related to the shear rate, first by the sliding speed of the finger on the touchpad. and depth of action The calculated shear rate is approximately The range of shear rates is divided into: below For low shear rates, between and Between these ranges is the medium shear rate, and above... For high shear rates, the current rate is in the medium shear rate range, and the corresponding gain coefficient is set to [value missing]. The interface stress coefficient directly uses the normalized stress value of the node, such as the node's stress value. The pressure is In one After normalization within the range, it becomes The final coefficient of friction is calculated as follows: By performing this process on the contact nodes, a complete distribution of the friction coefficient of the material surface can be obtained.

[0034] Table 2: Table of Friction Characteristic Parameters of Virtual Material ; As shown in Table 2, this table displays the friction characteristic parameters calculated for different nodes based on their electrical properties. These parameters together establish the friction characteristic parameters of the virtual material.

[0035] Specifically, such as Figure 2 , 6 As shown, the haptic feedback generation module includes: The tactile input parsing submodule acquires the friction feature parameters of the virtual material, extracts the rate of change of the friction coefficient and the amplitude of the contact pressure, performs time-synchronous comparison of the two within the sampling period, calculates the instantaneous force difference in the friction signal fluctuation segment, and generates friction intensity sequence data. Obtain the friction feature parameters of the aforementioned generated virtual material, which include the values ​​of node C in the model. The final coefficient of friction at time t is As the user's finger continues to move slightly, the system will make the following two sampling cycles (each cycle) ), and continue to calculate to get The coefficient of friction at time t is , Time for This allows for the extraction of a friction coefficient variation sequence. The system calculates the rate of change of the sequence. arrive The rate of change between ,exist arrive The rate of change between Simultaneously, the system extracts the synchronous contact pressure amplitudes within the same sampling period, and their sequence is as follows: The system will display the friction coefficient change rate sequence. The system synchronously compares the pressure amplitude sequence at specific time points. Then, it identifies fluctuating segments of the friction signal by comparing the absolute value of the rate of change of the friction coefficient with a preset fluctuation threshold. This fluctuation threshold is set based on the statistical average of the rate of change of the friction coefficient measured when simulating an absolutely smooth surface; here, it is set to [value missing]. Because of arrive Interval, absolute value of the rate of change Above the threshold This segment was identified as a fluctuating segment, while arrive Interval, absolute value of the rate of change Below the threshold If a segment is not identified as a fluctuation zone, the system calculates the instantaneous force difference within the identified fluctuation zone. Specifically, it first calculates the frictional force at the start and end points of the segment. The frictional force is obtained by multiplying the friction coefficient by the contact pressure. The frictional force at time t is ,exist The frictional force at time t is The instantaneous force difference is By repeating this calculation over the fluctuating segments throughout the entire contact process, the system obtains a sequence consisting of multiple instantaneous force difference values, generating friction intensity sequence data.

[0036] The spectrum feature calculation submodule, based on friction intensity sequence data, uses Fourier transform based on amplitude sequence to perform spectrum analysis on periodic fluctuation segments, calculates frequency distribution values, divides spectrum segments according to amplitude peaks, calculates the energy proportion of spectrum segments, and obtains friction spectrum energy distribution coefficient; Based on the generated friction intensity sequence data, such as a segment collected from a continuous interaction process and containing Sequence of force difference samples (unit The system first determines whether there are periodic fluctuation segments by analyzing the autocorrelation function of the amplitude sequence. If a significant autocorrelation peak appears after a specific delay, the segment is considered periodic, and then spectral analysis is performed on it. This spectral analysis is achieved by performing a Fourier transform on the segment data, thereby obtaining a set of frequency distribution values ​​consisting of frequencies and their corresponding amplitudes. For example, the analysis results show... There exists an amplitude peak at a certain point, the amplitude of which is ,exist There is another amplitude peak at that point, with an amplitude of At the same time There is another amplitude at that point. The system then divides the spectrum into segments based on the frequency location of the amplitude peak. The segmentation criteria are: frequencies within... to The area between these two points is defined as the low-frequency range. to The area between these frequencies is the mid-frequency range. to The area between these two points is a high-frequency band; therefore, The peak point was classified into the low-frequency range. The peak points are classified into the mid-frequency range, while The peak points are assigned to the high-frequency segment. Subsequently, the system calculates the energy proportion of each spectral segment. The segment energy is calculated by summing the squares of the amplitudes of the frequency components within that segment. Assuming that after calculation, the total energy of the low-frequency segment is... (mainly by) (Contribution), the total energy in the mid-frequency range is (mainly by) (Contribution), the total energy in the high-frequency band is (mainly by) (Contribution), then the total energy of the spectrum is The energy proportions of the sections are as follows: low-frequency section Mid-frequency band High frequency band This set consists of percentage values. The friction spectrum energy distribution coefficient is obtained.

[0037] The tactile signal synthesis submodule, based on the friction spectrum energy distribution coefficient, calls the spectrum segment energy ratio and amplitude peak point parameters to adjust the weights of the wood, metal, and fabric texture signal templates and calculate the vibration intensity difference to generate virtual material tactile feedback signals. Based on the calculated friction spectrum energy distribution coefficients, i.e., the energy proportions of the low, medium, and high frequency bands are respectively... , and The system retrieves the energy percentage data and the corresponding amplitude peak frequency parameters. , and The weights of various standard texture signal templates pre-existing in the signal library are adjusted.

[0038] Table 3: Texture Signal Template Weight Table ; As shown in Table 3, the system will calculate the energy distribution coefficient vector in real time. The dot product operation is performed with the preset weight vector of each template in the table to determine the current tactile sensation. The matching weight for the wood template is: The matching weight of the metal template is The matching weight of the fabric template is By comparison, the matching weight of the wooden template At its highest level, the system determines that the tactile feel of the currently interacting virtual material is closest to that of wood. Subsequently, the system calculates and synthesizes the final vibration signal intensity. The specific process involves setting a reference vibration intensity (for example, denoted as ) as the reference intensity. The frequency band weights of the wood template with the highest matching degree are modulated, and in The vibration intensity at that point is ,exist The vibration intensity at that point is ,exist The vibration intensity at that point is Ultimately, this set of multiple specific frequencies and their corresponding vibration intensity values ​​generates virtual material tactile feedback signals.

[0039] Specifically, such as Figure 2 , 7 As shown, the interaction status monitoring module includes: The signal receiving submodule acquires multi-channel input data of the virtual material tactile feedback signal, analyzes the amplitude fluctuation range of the channel signal, calculates the difference in amplitude variation of the sampling interval, segments the data for statistical analysis, calculates the amplitude variance and extracts the signal energy distribution, and generates tactile feedback signal stability parameter values. Acquire the haptic feedback signal of the generated virtual material, which consists of multiple frequency channels, such as a channel containing... , and For multi-channel input data with three channels, the system first parses a single channel, such as... The channel, at four consecutive sampling points (sampling interval) The amplitude fluctuation range of the signal, and the extracted amplitude sequence are: Its fluctuation range is and The difference, that is Next, the system calculates the difference in amplitude changes between adjacent sampling intervals; the change in the first interval is... The change in the second interval is The change in the third interval is Subsequently, the system treats the data from these four sampling points as a segment for statistical analysis, calculates the amplitude variance of this segment, and first calculates the mean, i.e. Then calculate the variance as Simultaneously, the system extracts the energy distribution of the signal from the preceding steps, specifically the energy proportions of the low, mid, and high frequency bands. , and The variance value here This variance is used as a key indicator to measure signal stability. The smaller the value, the smoother the amplitude fluctuation. The system calculates a weighted average of this variance value with the variance values ​​calculated from the other channels. The weights are determined based on the energy proportion of each channel. The resulting weighted average variance value is... Assume the variances of the other two channels are respectively and The final weighted variance is Generate tactile feedback signal stability parameter values.

[0040] The synchronization detection submodule extracts the timing nodes of the operation trigger time and the feedback signal response time based on the stability parameter value of the tactile feedback signal and the user operation input sequence, calculates the time difference and performs normalization processing, determines the synchronization offset concentration, and generates the tactile interaction synchronization matching degree. Stability parameter values ​​based on generated tactile feedback signals The system also records a sequence of user input actions, from which it extracts the time points that trigger the actions, such as the time when the user's finger first touches the screen. Simultaneously, the response time of the feedback signal is monitored. This response time is defined as the time when the amplitude of the feedback signal first exceeds a minimum perceptible threshold (e.g., ...). The time point is recorded as... The system calculates the time difference between the two. This is the delay time for a single interaction. Next, the system normalizes this time difference. The reference range for normalization is the ideal tactile feedback delay range determined in human factors engineering experiments, with the lower limit of this range being... The upper limit is ,Exceed The delay is considered to be a perceptible delay, and the normalized value is... Subsequently, the system analyzed the delay time series generated from five consecutive interactions. To analyze and determine the concentration of synchronization shifts, the specific process involves calculating the standard deviation of the sequence, starting with the mean. Then calculate the standard deviation. The standard for determining concentration is: the standard deviation is lower than... Determined to have "high" concentration, arrive The concentration between these two values ​​is considered "medium," and is higher than that of other values. For a concentration level of "low", the current calculation results This indicates a high degree of concentration. Finally, the system converts this concentration index into a... arrive The matching score between them is calculated as follows: ,in It is the maximum acceptable standard deviation of delay, set to ,but Generate haptic interaction synchronization matching degree.

[0041] The haptic accuracy assessment submodule calls the haptic interaction synchronization matching degree and haptic feedback signal stability parameter values, extracts the continuous change curve of the virtual material switching signal, calculates the haptic response difference rate and compares it with the benchmark value, calculates the interaction quality index vector, and generates the virtual material interaction quality assessment result. Call the calculated haptic interaction synchronization matching degree and tactile feedback signal stability parameter values The system first extracts the continuity curve of the feedback signal during a virtual material switching operation. For example, when the user's finger slides from the simulated "wood" area to the "metal" area, the system records the dominant frequency in the feedback signal before and after the switch (e.g., from the initial value of the material to the final value of the material). Switch to The intensity change of the "wood" signal before the switch was: The actual generated "metallic" signal strength after the switch is The system calculates the tactile response difference rate, which requires comparison with an ideal template signal. The target intensity of the ideal "metal" template under the same input is... The response difference rate is ,Right now The system compares this difference rate with a preset benchmark value, which is set based on the average threshold of imperceptible vibration intensity changes, derived from statistical analysis of numerous user tests; this threshold is known as the "just-perceptible difference" (JND). Due to the calculated difference rate Slightly higher than the benchmark value This indicates a slight difference between the user's perceived actual feedback and the expected metallic feel. Next, the system integrates multiple evaluation dimensions to calculate an interaction quality index vector, which contains three normalized components: stability, synchronicity, and accuracy. The stability component is... Synchronization components directly use synchronization matching degree The accuracy component is The index vector is .

[0042] Table 4: Virtual Material Interaction Quality Assessment Table ; As shown in Table 4, this table lists multiple evaluation dimensions of interaction quality, their calculation results, and preset weights. The system sums the weighted scores of the multiple dimensions, i.e. Generate virtual material interaction quality assessment results.

[0043] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A virtual material interaction system with haptic feedback, characterized in that, The system includes: The contact state sensing module acquires real-time values ​​of contact pressure and sliding speed, collects instantaneous potential values ​​of nodes, uses a Kalman filter algorithm to filter out noise, generates virtual material contact state data, and transmits it to the potential gradient calculation module. The potential gradient calculation module receives the virtual material contact state data, performs potential difference calculation between adjacent nodes, analyzes the trend of potential difference change, calculates the material response requirements, generates a material electric field control scheme, and transmits it to the friction dynamic control module. The friction dynamic control module receives the material electric field control scheme, performs node charge redistribution, controls the switching of polarization direction between adjacent layers, calculates the friction coefficient distribution on the material surface, generates virtual material friction characteristic parameters, and transmits them to the tactile feedback generation module. The tactile feedback generation module receives the friction feature parameters of the virtual material, calculates the tactile intensity corresponding to different material textures, analyzes the friction force change spectrum using the Fourier transform algorithm, synthesizes tactile simulation signals of wood, metal and cloth materials, generates virtual material tactile feedback signals, and transmits them to the interactive state monitoring module.

2. The virtual material interaction system with haptic feedback according to claim 1, characterized in that, The virtual material contact state data includes filtered contact pressure value, filtered sliding speed value, and filtered node potential value. The material electric field control scheme includes node charge distribution, polarization angle switching amount, and target potential gradient threshold. The virtual material friction characteristic parameters include surface friction coefficient value, dynamic friction force amplitude, and friction direction angle. The virtual material tactile feedback signal includes wood texture surface roughness signal, metal surface roughness signal, fabric softness signal, and friction frequency characteristic value.

3. The virtual material interaction system with haptic feedback according to claim 1, characterized in that, The contact state sensing module includes: The data acquisition submodule acquires the real-time values ​​of contact pressure and sliding speed, performs parallel sampling and time synchronization of the pressure signal, calculates the rate of change of sliding speed and corresponds it to the time index, acquires the instantaneous potential value of the node and performs normalization conversion, and generates the original contact signal set. The signal filtering submodule calls the potential amplitude sequence based on the original contact signal set, performs update correction using the Kalman filter algorithm, calculates the potential difference between adjacent points and compares the amplitude change rate, judges the fluctuation characteristics based on the noise fluctuation threshold and removes abnormal points, and generates a noise suppression signal sequence. The potential feature generation submodule calculates the correlation coefficient between the pressure change rate and the potential change rate based on the noise suppression signal sequence, interpolates the sliding speed, fuses the correlation coefficient data, extracts the node potential gradient interval, integrates the mapping information, and generates virtual material contact state data.

4. The virtual material interaction system with haptic feedback according to claim 1, characterized in that, The potential gradient calculation module includes: The contact data parsing submodule acquires the contact state data of the virtual material, parses the spatial coordinates and conductivity parameters of the nodes, calculates the distance between nodes to analyze the spatial mapping relationship, counts the contact duration, calculates the interaction weight distribution in combination with the node number, and generates node contact parameter data. The potential difference calculation submodule extracts the conductivity and contact area data of adjacent nodes based on the node contact parameter data, calculates the potential difference sequence between nodes, calculates the potential difference change ratio according to the time step, performs linear fitting to determine the change range, and generates the potential gradient change coefficient. The control scheme generation submodule calls the potential gradient change coefficient, calculates the response sensitivity parameter based on the node potential gradient distribution, compares the sensitivity with the response benchmark value to select node regions higher than the benchmark value, and overlays the node coordinates and the sensitivity to calculate the electric field intensity distribution map to generate the material electric field control scheme.

5. The virtual material interaction system with haptic feedback according to claim 4, characterized in that, The potential gradient change coefficient is calculated based on the linear fitting results and the node potential distribution. The potential gradient change coefficient can further affect the calculation of the response sensitivity parameter and the selection of the node region in the electric field control scheme. The response benchmark value is determined based on the statistical characteristics of the node potential gradient change coefficient, which include the mean and standard deviation of the node potential gradient change coefficient.

6. The virtual material interaction system with haptic feedback according to claim 1, characterized in that, The friction dynamic control module includes: The charge distribution calculation submodule calls the material electric field control scheme, extracts the initial charge density value of the node, calculates the charge transfer direction and transmission path between nodes based on the spatial distribution and potential difference distribution between nodes, corrects the charge value according to the potential difference ratio and balances the node potential, and generates the node charge distribution. The polarization direction control submodule detects the direction of electric dipole moments between adjacent layers based on the node charge distribution, compares the interlayer potential difference with the electrostatic potential threshold according to the interlayer spacing, determines the dipole rotation trigger condition when the threshold is exceeded and corrects the polarization reversal sequence, and generates an interlayer polarization direction sequence. The friction feature generation submodule calculates the contact point charge density and the friction force vector corresponding to the polarization orientation based on the interlayer polarization direction sequence and node charge distribution. It also calculates the material surface friction coefficient distribution by combining the interface pressure coefficient and shear rate integral, and establishes virtual material friction feature parameters.

7. The virtual material interaction system with haptic feedback according to claim 6, characterized in that, The calculation of charge transfer direction and transmission path between nodes based on the spatial distribution and potential difference distribution between nodes refers to, after extracting the initial charge density value of the nodes, performing a weighted comparison of the spatial distance between any two adjacent nodes, determining the preferred direction of charge transfer according to the numerical proportion of the potential difference between the nodes, and forming a charge transfer path matrix between the nodes. The electrostatic potential threshold refers to the electrostatic potential distribution of the same type of layered material under different interlayer spacing conditions, which is experimentally determined.

8. The virtual material interaction system with haptic feedback according to claim 1, characterized in that, The haptic feedback generation module includes: The tactile input parsing submodule acquires the friction feature parameters of the virtual material, extracts the rate of change of friction coefficient and the amplitude of contact pressure, performs time synchronization comparison between the two within the sampling period, calculates the instantaneous force difference in the friction signal fluctuation segment, and generates friction intensity sequence data. The spectral feature calculation submodule, based on the friction intensity sequence data, performs spectral analysis on the periodic fluctuation segment using Fourier transform based on the amplitude sequence, calculates the frequency distribution value, divides the spectral segment according to the amplitude peak point, calculates the energy proportion of the spectral segment, and obtains the friction spectrum energy distribution coefficient. The tactile signal synthesis submodule, based on the friction spectrum energy distribution coefficient, calls the spectrum segment energy ratio and amplitude peak point parameters to adjust the weights of the wood, metal, and fabric texture signal templates and calculate the vibration intensity difference to generate virtual material tactile feedback signals.

9. The virtual material interaction system with haptic feedback according to claim 1, characterized in that, The interactive state monitoring module receives the tactile feedback signal of the virtual material, monitors and processes the interactive effect of the virtual material, detects the synchronization between the tactile feedback signal of the virtual material and the user's operation, monitors the continuity of tactile feedback during material switching, judges the accuracy of virtual material simulation based on signal stability, and generates a virtual material interactive quality evaluation result. The virtual material interaction quality evaluation results include tactile response delay time, material transition smoothness value, and simulation accuracy level.

10. The virtual material interaction system with haptic feedback according to claim 9, characterized in that, The interaction status monitoring module includes: The signal receiving submodule acquires multi-channel input data of the virtual material tactile feedback signal, analyzes the amplitude fluctuation range of the channel signal, calculates the difference in amplitude change of the sampling interval, segments the data for statistical analysis, calculates the amplitude variance and extracts the signal energy distribution, and generates tactile feedback signal stability parameter values. The synchronization detection submodule extracts the timing nodes of the operation trigger time and the feedback signal response time based on the stability parameter value of the tactile feedback signal and the user operation input sequence, calculates the time difference and performs normalization processing, determines the synchronization offset concentration, and generates the tactile interaction synchronization matching degree. The tactile accuracy assessment submodule calls the tactile interaction synchronization matching degree and tactile feedback signal stability parameter values, extracts the continuous change curve of the virtual material switching signal, calculates the tactile response difference rate and compares it with the benchmark value, calculates the interaction quality index vector, and generates the virtual material interaction quality assessment result.

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