Intelligent Interaction-Based Clothing Design System and Method

Abstract

The present invention discloses a clothing design system and method based on intelligent interaction, belonging to the field of clothing design, which specifically includes: collecting user body shape and motion data by using 3D scanning, wearable sensors and motion capture technology to construct a dynamic model; based on kinematic biomechanics analysis, identifying four key parts of clothing design, and analyzing their activity requirements and force conditions under different motion states; automatically optimizing clothing design through machine learning algorithms to generate fabric recommendations; determining the final fabric and clothing structure according to the fabric recommendations, and testing the clothing performance by using virtual simulation technology; realizing real-time fitting and dynamic adjustment through augmented reality technology to ensure that the design scheme meets the personalized needs and motion functionality requirements of users.

Description

Technical Field

[0001] The present invention belongs to the field of clothing design, and specifically relates to a clothing design system and method based on intelligent interaction. Background Art

[0002] Traditional clothing design often ignores the dynamic needs during human movement while meeting aesthetics, resulting in a sense of restraint or discomfort when wearing the clothes during activities. Moreover, it usually relies on the experience and manual operations of designers, with a long design cycle, high cost, and difficulty in meeting the increasing personalized needs of consumers. With the development of artificial intelligence and virtual reality technologies, there is an urgent need for an intelligent design method that can consider human body shape and movement principles in the field of clothing design.

[0003] For example, Chinese Patent with Publication No. CN118260819A discloses a visual clothing design system and a clothing design method. The system includes: a body shape import module for receiving the body shape data of a target user and establishing a three-dimensional model of the target user's body shape; a clothing piece import module for receiving clothing piece parameters and generating a flattened model of the clothing piece; an element configuration module for configuring clothing elements, where the clothing elements include element curling shapes, element alignment points, and element alignment point coordinates, and generating a three-dimensional model of the clothing piece based on the clothing elements and the flattened model of the clothing piece; a fusion module for receiving a movement operation instruction and fusing the three-dimensional model of the clothing piece with other clothing pieces and the three-dimensional model of the body shape based on the movement operation instruction to obtain a three-dimensional model of the clothing; an evaluation module for evaluating the color balance of the three-dimensional model of the clothing after receiving an evaluation instruction to obtain a reference evaluation result.

[0004] The above existing technologies have the following problems: lack of sports biomechanics analysis; more reliance on manual configuration and selection in clothing design and fabric selection, increasing the design time and cost, resulting in a low degree of automation; focusing on the display of the three-dimensional model of the clothing and the evaluation of color balance, and possibly lacking sufficient simulation and testing of the actual performance of the clothing under different movement states; limited in terms of user feedback and dynamic adjustment, mainly relying on the subjective judgment and experience of designers. Summary of the Invention

[0005] Aiming at the deficiencies of the existing technologies, the present invention proposes a clothing design system and method based on intelligent interaction, which uses 3D scanning, wearable sensors and motion capture technologies to collect user's body shape and motion data and construct a dynamic model; based on motion biomechanics analysis, it identifies four key parts of clothing design and analyzes their activity requirements and force conditions under different motion states; through machine learning algorithms, it automatically optimizes clothing design and generates fabric recommendations; according to the fabric recommendations, it determines the final fabric and clothing structure, and uses virtual simulation technology to test the clothing performance; through augmented reality technology, it realizes real-time fitting and dynamic adjustment to ensure that the design scheme meets the user's personalized needs and motion functionality requirements.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A clothing design method based on intelligent interaction, including:

[0008] Step S1: Collect multiple types of data and construct a human body dynamic model of the user; the multiple types of data include body shape data, motion data and motion trajectory data;

[0009] Step S2: Based on the motion biomechanics analysis module built in the system, according to the human body dynamic model, analyze the body mechanics changes of the user under different motion states, and at the same time, identify four parts in clothing design and determine the activity requirements and force conditions of the four parts under different motion states;

[0010] Step S3: Through machine learning algorithms, combined with the activity requirements of the four parts, automatically optimize clothing design, and according to the results of motion biomechanics analysis, generate an optimized clothing design scheme and a fabric recommendation list;

[0011] Step S4: According to the fabric recommendation list, combined with the design requirements, determine the final fabric, and according to the activity requirements of the four parts, design the clothing structure to obtain the final fabric selection scheme and clothing structure design drawing;

[0012] Step S5: Through virtual simulation technology, apply the final fabric selection scheme and clothing structure design drawing to the human body dynamic model, simulate the performance of the clothing under different motion states, and the system simulates the performance of the clothing under different motion states and conducts simulation tests;

[0013] Step S6: Through augmented reality method, try on the clothing design that passes the simulation test in real time and simulate the motion state in the virtual environment, and the system automatically adjusts the design scheme according to the user's feedback and dynamic performance.

[0014] Specifically, the specific steps of the step S1 include:

[0015] S1.1: Use 3D scanning technology to perform a full-body scan of the user to obtain the user's body shape data, and collect the user's motion data under specific actions through wearable sensors. At the same time, use an optical motion capture system to paste reflective marker points on the user's body key points to capture the motion trajectory data of the user under different motion states; the body shape data includes body type, contour, joint position, and muscle distribution; the motion data includes acceleration, angular velocity, and pressure distribution;

[0016] S1.2: Sort and match the body shape data, motion data, and motion trajectory data according to timestamps; if the timestamps of multiple types of data are inconsistent, use the improved linear interpolation formula for alignment, where y(t) represents the interpolation result, y1 and y2 represent multiple types of data at adjacent time points, t1 and t2 represent the timestamps corresponding to y1 and y2, t represents the current time, α represents the weight factor, and μ represents the smoothing parameter;

[0017] If there is a time offset between devices, calculate the time offset through the cross-correlation analysis method and perform correction; the cross-correlation analysis method is to calculate the cross-correlation function of two time series data and find the time delay τ that makes the function value reach the maximum in the cross-correlation function max , according to the found time delay τ max , adjust the timestamp of one type of data to match the timestamp of another type of data.

[0018] Specifically, the specific steps of step S1 further include:

[0019] S1.3: Based on the aligned body shape data and motion trajectory data, construct the user's bone model and use the inverse kinematics algorithm to calculate joint angles and motion ranges;

[0020] S1.4: Based on the aligned motion data and body shape data, construct the user's muscle model and skin model, simulate muscle contraction and skin extension, and use the finite element analysis method to simulate the deformation of the skin under different motion states;

[0021] S1.5: Use the bone model as the first-layer model, the muscle model as the second-layer model, and the skin model as the third-layer model, and integrate them to form the user's human dynamic model, and use the physics engine to simulate the performance of the model under different motion states.

[0022] Specifically, the specific steps of S1.3 include:

[0023] S1.31: According to human anatomy knowledge, define the bone structure and use a tree structure to represent the bone hierarchy relationship, including joint position, bone length, and joint freedom;

[0024] S1.32: Initialize the parameters of the bone model according to the aligned human body shape data, and fit the bone model using the principal component analysis method;

[0025] S1.33: Define the target positions of the end effectors according to the aligned motion trajectory data; the end effectors include hands and feet;

[0026] S1.34: Use the inverse kinematics algorithm to calculate the joint angles θ = IK(p target , W, C, E, D), where θ represents the joint angles, p target represents the target position, W represents the weight vector, and W = [w1,..., w n , w n represents the weight of the nth joint, n represents the number of joints, C represents an n×2 constraint matrix, and each row represents the minimum and maximum angles [θ min , θ max of a joint, E represents the error tolerance vector, and E = [e1, e2, e3], where e1, e2, and e3 represent the error tolerances in the horizontal, vertical, and longitudinal directions respectively, D represents the damping coefficient vector, and IK(·) represents the inverse kinematics function;

[0027] S1.35: Define key frames according to the calculated joint angles, and generate a preliminary bone animation based on the key frames to simulate the user's performance in different motion states;

[0028] S1.36: Optimize the parameters of the bone model according to the aligned motion trajectory data and the preliminarily generated bone animation, and use an optimization algorithm to minimize the error between the bone model and the motion trajectory data;

[0029] If the error is greater than or equal to the preset error threshold, adjust the bone model parameters or the inverse kinematics algorithm;

[0030] S1.37: Generate the final bone animation based on the adjusted bone model.

[0031] Specifically, the specific steps of S1.35 include:

[0032] A1: Obtain the bone model from steps S1.31 and S1.32, including joint positions, bone lengths, and hierarchical relationships, and obtain the calculated joint angle data from step S1.34;

[0033] A2: Define key frames according to the joint angle data;

[0034] A3: Use the linear interpolation algorithm to generate intermediate frames between key frames, and use the formula Generate the interpolated joint angles, where σ represents the interpolation coefficient, q1 and q2 represent the quaternions of the key frames, and q(σ) represents the interpolated joint angles;

[0035] A4: According to the interpolated joint angles, update the rotation matrix R(θ) of each joint in the skeleton model, and use the rotation matrix to update the global coordinates of the joints. The formula is: P global = M parent ×R(θ)×P local , where P global represents the global coordinates of the child node, M parent represents the transformation matrix of the parent node, and P local represents the local coordinates of the child node;

[0036] A5: Bind the skeleton model to the human body shape data, and use the formula to calculate the final position Y of each vertex final , where represents the weight, n represents the number of vertices, M i represents the skeleton transformation matrix, and Y o represents the original position of the vertex;

[0037] A6: Use the graphics rendering engine to render the skeleton animation frame by frame, and render the skeleton pose of each frame onto the screen to generate an animation effect.

[0038] Specifically, the specific steps of S1.4 include:

[0039] S1.41: Obtain the aligned motion data and human body shape data, and extract the key points and regions; the key points include joint positions and muscle attachment points; the regions refer to muscle groups and skin regions;

[0040] S1.42: According to anatomical knowledge, define the path of each muscle, and represent the muscle path using a B-spline curve;

[0041] S1.43: Initialize the muscle parameters according to the motion data and human body shape data. The muscle parameters include length, cross-sectional area, and contraction force;

[0042] S1.44: Calculate the length change and contraction force of the muscle according to the joint angles and muscle paths, and simulate the mechanical behavior of the muscle using a muscle model;

[0043] S1.45: Construct the surface mesh model of the skin according to the human body shape data, and generate the skin mesh using a mesh generation algorithm;

[0044] S1.46: Initialize the skin parameters according to the biomechanical properties of the skin, and define the boundary conditions of the skin according to the muscle contraction force and joint movement;

[0045] S1.47: Discretize the skin mesh into finite elements, and based on the finite elements, establish a finite element equation according to the theory of elasticity mechanics, and solve the equation.

[0046] S1.48: Update the vertex positions of the skin mesh according to the solution results to generate a deformed skin model.

[0047] Specifically, the specific steps of step S2 include:

[0048] S2.1: Obtain multiple types of data and transmit the multiple types of data to the built-in sports biomechanics analysis module of the system.

[0049] S2.2: Use the coordinate transformation algorithm to adapt the multiple types of data to the human body dynamic model.

[0050] S2.3: Use the object detection algorithm based on deep learning to analyze the human body dynamic model, identify the four major parts in the clothing design, and extract the data of the four major parts during the movement process; the four major parts include the shoulder, elbow, knee, and waist; the movement process data includes position and morphological changes.

[0051] S2.4: Conduct a mechanical analysis on the identified four major parts according to the principles of kinematics and dynamics. The mechanical analysis includes calculating the displacement, velocity, acceleration, and the forces and torques received by each part during the movement process.

[0052] S2.5: Determine the activity requirements and force conditions of the four major parts in different motion states according to the mechanical analysis results and combined with the characteristics of different sports events.

[0053] Specifically, the specific steps of step S3 include:

[0054] S3.1: Extract the activity requirements of the four major parts from the sports biomechanics analysis results, and quantify the activity requirements into feature vectors to obtain activity requirement feature vectors; the activity requirements include activity range, force distribution, and ductility requirements.

[0055] S3.2: Load a pre-trained machine learning model, and the machine learning model is configured based on the random forest algorithm.

[0056] S3.3: Input the activity requirement feature vectors into the pre-trained machine learning model to generate a clothing design scheme that meets the optimization goal, and at the same time output clothing design parameters.

[0057] S3.4: Extract fabric features from the fabric database and match the fabric features with the clothing design parameters.

[0058] S3.5: Generate a fabric recommendation list based on the matching results and sort it by priority.

[0059] A clothing design system based on intelligent interaction includes: a data acquisition module, a sports biomechanics analysis module, a clothing design optimization module, a fabric and structure design module, a virtual simulation test module, and an augmented reality module;

[0060] The data acquisition module is used to collect various types of data of the user;

[0061] The sports biomechanics analysis module is used to analyze the body mechanics changes of the user in different motion states according to the collected various types of data, and identify the key parts of clothing design, their activity requirements and force conditions;

[0062] The clothing design optimization module is used to automatically optimize clothing design through machine learning algorithms, combine the activity requirements of the four major parts, and generate fabric recommendations;

[0063] The fabric and structure design module is used to determine the final fabric and design the clothing structure according to the fabric recommendation list and design requirements;

[0064] The virtual simulation test module is used to simulate the performance of clothing in different motion states through virtual simulation technology and conduct tests;

[0065] The augmented reality module is used to try on the clothing design that passes the simulation test in real time through augmented reality methods, and automatically adjust the design scheme according to user feedback and dynamic performance.

[0066] Compared with the prior art, the beneficial effects of the present invention are:

[0067] 1. The present invention proposes a clothing design method based on intelligent interaction. By comprehensively applying 3D scanning, motion capture and biomechanics analysis technologies, it accurately captures the human body shape and motion characteristics of users, realizes highly personalized clothing design, not only improves the comfort and functionality of clothing, but also ensures the fit and expressiveness of clothing in different motion states, meeting the personalized needs of users.

[0068] 2. The present invention proposes a clothing design method based on intelligent interaction. It uses machine learning algorithms to automatically optimize the design scheme, and combines virtual simulation and augmented reality technologies to realize a comprehensive digital process from fabric selection to clothing structure design. It not only improves the design efficiency, reduces the trial-and-error cost, but also enables users to experience and feedback the design effect in real time in a virtual environment, thus promoting the continuous optimization and improvement of the design scheme. Brief Description of the Drawings

[0069] Figure 1Schematic diagram of the clothing design method based on intelligent interaction of the present invention;

[0070] Figure 2 Principle flowchart of the clothing design method based on intelligent interaction of the present invention;

[0071] Figure 3 System architecture diagram of the clothing design system based on intelligent interaction of the present invention. Detailed implementation manners

[0072] Example 1

[0073] Please refer to Figure 1 and Figure 2 A kind of example provided by the present invention: A clothing design method based on intelligent interaction, including the following steps:

[0074] Step S1: Collect multiple types of data and construct a human body dynamic model of the user; the multiple types of data include human body form data, motion data and motion trajectory data;

[0075] Step S2: Based on the motion biomechanics analysis module built in the system, according to the human body dynamic model, analyze the body mechanics changes of the user in different motion states, and at the same time, identify four major parts in clothing design and determine the activity requirements and force conditions of the four major parts in different motion states;

[0076] Step S3: Through machine learning algorithms, combined with the activity requirements of the four major parts, automatically optimize the clothing design, and generate an optimized clothing design scheme and a fabric recommendation list according to the results of motion biomechanics analysis;

[0077] Step S4: According to the fabric recommendation list, combined with the design requirements, determine the final fabric, and design the clothing structure according to the activity requirements of the four major parts to obtain the final fabric selection scheme and clothing structure design drawing;

[0078] Furthermore, the specific steps of Step S4 include:

[0079] (1) Obtain fabric information from the fabric recommendation list generated by machine learning algorithms, including fabric type, elasticity, breathability, abrasion resistance and other characteristics. Among them, the fabric recommendation list is usually sorted by priority, and fabrics that meet the design requirements are recommended first;

[0080] (2) According to the clothing design requirements, such as comfort, freedom of movement, durability, screen the candidate fabrics in the fabric recommendation list;

[0081] (3) Match the fabric characteristics with the design requirements and select the fabric that best meets the requirements. For example, select a high-elasticity fabric to meet the freedom of movement requirements and select a breathable fabric to improve comfort;

[0082] Among them, the specific formula for matching fabric characteristics with design requirements is as follows: Among them, Match j represents the matching degree of the j-th fabric, F jk represents the k-th characteristic of the j-th fabric, D k represents the k-th characteristic of the design requirement, sim(·) represents the similarity function, and m represents the category of design requirement characteristics;

[0083] (4) Check the availability of the selected fabric, such as inventory, cost, and production cycle. If the fabric is unavailable, select the sub-optimal fabric and re-evaluate;

[0084] (5) Determine the final fabric based on the matching results and availability check, and output the final fabric selection plan;

[0085] (6) Integrate the final fabric selection plan with the clothing design plan to generate the final clothing structure design drawing, and generate production documents based on the clothing structure design drawing, such as cutting diagrams and sewing process descriptions.

[0086] Step S5: Through virtual simulation technology, apply the final fabric selection plan and clothing structure design drawing to the human body dynamic model to simulate the performance of the clothing under different motion states. The system simulates the performance of the clothing under different motion states and conducts simulation tests;

[0087] Furthermore, the specific steps of step S5 include:

[0088] (1) Obtain the human body dynamic model and motion trajectory data;

[0089] (2) Import the final fabric selection plan and clothing structure design drawing, and bind the clothing design data to the human body dynamic model;

[0090] (3) Define the motion states to be simulated, such as running, jumping, and bending;

[0091] (4) Define the simulation parameters, and set the physical parameters according to the fabric characteristics and clothing structure, such as the friction coefficient and elastic modulus;

[0092] (5) Generate the surface mesh model of the clothing according to the clothing structure design drawing, and use a mesh generation algorithm, such as Delaunay triangulation, to generate the clothing mesh. Among them, the Delaunay triangulation method is the prior art content in the field and is not the creative solution of this application, so it will not be elaborated here;

[0093] (6) Bind the clothing mesh to the human body dynamic model, use a physical engine to simulate the deformation of the clothing under different motion states, and calculate the deformation and force of the clothing according to the fabric characteristics and motion states;

[0094] (7) Run the simulation in the virtual simulation environment, record the performance of the clothing in different motion states, such as deformation amount and force distribution, and use the visualization tool Matplotlib to display the simulation results.

[0095] Step S6: Through the augmented reality method, try on the clothing design that passes the simulation test in real time, simulate the motion state in the virtual environment, and the system automatically adjusts the design scheme according to the user's feedback and dynamic performance.

[0096] Furthermore, the specific steps of step S6 include:

[0097] (1) Build an AR environment, load the clothing design model and the human body dynamic model that pass the simulation test;

[0098] (2) Obtain human body shape data, motion data and motion trajectory data;

[0099] (3) Bind the virtual clothing mesh to the user's human body dynamic model, update the deformation of the clothing mesh according to the user's real-time motion data, and at the same time, render it into the user's field of vision through the AR device and collect feedback data. Among them, the AR device rendering technology is the prior art content in this field and is not the creative solution of this application, so it will not be elaborated here;

[0100] (4) Quantify the feedback data, run the optimization algorithm to generate a new clothing design scheme, and update the clothing design model.

[0101] Exemplarily, select a male runner with a height of 175 cm and a weight of 70 kg as the test object. Through 3D body scanning technology, collect his body shape data, including key dimensions such as height, weight, shoulder width, chest circumference, waist circumference, and hip circumference. At the same time, use the motion capture system to record his motion data and motion trajectory data when running at different speeds, such as step length, step frequency, and body swing amplitude. Based on these data, an accurate human body dynamic model is constructed; import the constructed human body dynamic model into the built-in motion biomechanics analysis module of the system. This module can simulate the body mechanics changes of the user when running at different speeds, such as muscle contraction and joint angle changes. At the same time, the system identifies four major parts in the clothing design: shoulders, chest, waist and legs, and analyzes the activity requirements and force conditions of these parts when running at different speeds. For example, when running at high speed, the activity requirements of the legs and waist increase, and higher flexibility and support are required; while the shoulders and chest are relatively stable, but good breathability and sweat discharge functions are required;

[0102] Taking the activity requirements and force conditions of the four major parts as inputs, the clothing design is optimized through machine learning algorithms. Among them, the machine learning algorithms combine ergonomic principles, fabric performance data, and past design experiences to automatically generate an optimized clothing design plan and a fabric recommendation list. For example, for the legs, the algorithm recommends fabrics with high elasticity and abrasion resistance; for the waist, materials with good support and breathability are selected. According to the fabric recommendation list and design requirements, the final fabric is selected. Then, combining the activity requirements of the four major parts, the clothing structure is designed. For example, tight-fitting pants are designed on the legs to improve muscle support, an elastic belt is used at the waist to ensure fit and comfort, and loose designs are adopted on the shoulders and chest to increase breathability and freedom. Finally, a fabric selection plan and a clothing structure design drawing are obtained.

[0103] The final fabric selection plan and clothing structure design drawing are imported into a virtual simulation system. The system simulates the clothing performance of a runner running at different speeds, including the stretching, deformation, and breathability of the fabric. Through the simulation test, the rationality and comfort of the clothing design are verified. Finally, augmented reality technology is used to let the runner try on the clothing design that passes the simulation test in real time. In the virtual environment, the runner can simulate running states at different speeds and observe the clothing performance in real time. The system automatically adjusts the design plan according to the runner's feedback and dynamic performance. For example, if it is found that the waist design is too tight and causes discomfort, the system can automatically adjust the tightness of the belt or the fabric selection. After multiple adjustments and tests, an intelligent clothing design plan that meets the runner's needs is finally obtained.

[0104] The specific steps of step S1 include:

[0105] S1.1: Use 3D scanning technology to perform a full-body scan of the user to obtain the user's body shape data, including body type, contour, joint positions, and muscle distribution. At the same time, collect the user's motion data under specific actions, such as acceleration, angular velocity, and pressure distribution, through wearable sensors. Also, use the optical motion capture system OptiTrack to paste reflective marker points at the key points of the user's body to capture the user's motion trajectories in different motion states.

[0106] S1.2: Sort and match the body shape data, motion data, and motion trajectory data according to timestamps. If the timestamps of multiple types of data are inconsistent, use the improved linear interpolation formula for alignment, where y(t) represents the interpolation result, y1 and y2 represent multiple types of data at adjacent time points, t1 and t2 represent the timestamps corresponding to y1 and y2, t represents the current time, α represents the weight factor used to adjust the weight of the interpolation result, and μ represents the smoothing parameter used to control the smoothness of the interpolation result changing with t.

[0107] It should be noted that, based on the original linear interpolation, the formula in the present invention aims to make the interpolation result closer to y1 or y2. Therefore, a weight factor α is introduced, which can adjust the weight of the interpolation result as needed, so as to more flexibly adapt to different application scenarios. In order to maintain the smoothness of the interpolation result near the midpoint of t1 and t2, a smoothing term is introduced. This reduces the problem of unstable interpolation results caused by fewer data points or large data fluctuations. Therefore, the formula in the present invention not only retains the simplicity and intuitiveness of the original linear interpolation formula, but also enhances its flexibility and smoothness by introducing additional parameters, making it applicable to more types of data and interpolation requirements.

[0108] If there is a time offset between devices, the time offset is calculated through the cross-correlation analysis method and corrected. The cross-correlation analysis method calculates the cross-correlation function of two time series data and finds the time delay τ that maximizes the function value in the cross-correlation function. max , according to the found time delay τ max , the timestamps of one type of data are adjusted to match the timestamps of the other type of data. Among them, the cross-correlation function is the prior art content in this field and is not the creative solution of this application, so it will not be elaborated here;

[0109] S1.3: According to the aligned human body form data and motion trajectory data, construct the user's bone model, including joint positions, bone lengths, and joint degrees of freedom, and use the inverse kinematics algorithm to calculate joint angles and motion ranges;

[0110] S1.4: According to the aligned motion data and human body form data, construct the user's muscle model and skin model, simulate muscle contraction and skin extension, and use the finite element analysis method to simulate the deformation of the skin under different motion states;

[0111] S1.5: Take the bone model as the first layer model, the muscle model as the second layer model, and the skin model as the third layer model, and integrate them to form the user's human body dynamic model, and use the physics engine to simulate the performance of the model under different motion states;

[0112] Among them, the physics engine simulation is to simulate the motion, deformation, and interaction of objects in the physical world through mathematical models and algorithms. In the human body dynamic model, the physics engine is used to simulate the performance of bones, muscles, and skin under different motion states. Specifically, the physics engine simulation method is the prior art content in this field and is not the creative solution of this application, so it will not be elaborated here.

[0113] The specific steps of S1.3 include:

[0114] S1.31: Define the bone structure according to human anatomy knowledge, and represent the bone hierarchical relationship using a tree structure, including joint positions, bone lengths, and joint degrees of freedom;

[0115] Further, the specific steps of S1.31 include:

[0116] (1) Determine the key joints according to human anatomy knowledge, such as the shoulder joint, elbow joint, knee joint, and hip joint. Each joint includes a position, a rotation axis, and a degree of freedom. For example, the shoulder joint has 3 degrees of freedom: flexion-extension, abduction-adduction, and rotation;

[0117] (2) Represent the bone hierarchical relationship using a tree structure, with the torso as the root node and the limbs as the child nodes;

[0118] Exemplarily, root node: torso

[0119] Child node 1: left shoulder → left upper arm → left elbow → left forearm → left hand

[0120] Child node 2: right shoulder → right upper arm → right elbow → right forearm → right hand

[0121] Child node 3: left hip → left thigh → left knee → left calf → left foot

[0122] Child node 4: right hip → right thigh → right knee → right calf → right foot.

[0123] S1.32: Initialize the bone model parameters according to the aligned human body shape data, and fit the bone model using the principal component analysis method. The principal component analysis method is the prior art content in this field and is not the creative solution of this application, so it will not be elaborated here;

[0124] Further, the specific steps of S1.32 include:

[0125] (1) Obtain the bone hierarchical relationship;

[0126] (2) Extract key points from the human body shape data, such as joint positions and bone endpoints, and initialize the position coordinates of each joint according to the extracted key points. For example, the position of the shoulder joint is the coordinate of the left shoulder key point;

[0127] (3) Calculate the length of each bone segment, such as the upper arm length and the thigh length, according to the joint position coordinates using the Euclidean distance formula. The Euclidean distance formula is the prior art content in this field and is not the creative solution of this application, so it will not be elaborated here;

[0128] (4) Construct the key point coordinates of the human body shape data into a data matrix, where each row represents the three-dimensional coordinates of a key point;

[0129] (5) Standardize the data matrix so that the mean is 0 and the variance is 1;

[0130] (6) Calculate the covariance matrix of the standardized data matrix. The formula for the covariance matrix is prior art content in the field and is not the creative solution of this application, so it will not be elaborated here;

[0131] (7) Perform eigenvalue decomposition on the covariance matrix to obtain eigenvalues and eigenvectors. The formula for eigenvalue decomposition is prior art content in the field and is not the creative solution of this application, so it will not be elaborated here;

[0132] (8) Select the first M principal components according to the eigenvalue magnitudes, where the principal component directions represent the main stretching directions of the bones;

[0133] (9) Fit the bone model using the principal component directions to determine the orientation and length of the bones. For example, use the first principal component direction as the stretching direction of the bones.

[0134] S1.33: Define the target positions of the end effectors according to the aligned motion trajectory data; the end effectors include the hands and feet;

[0135] S1.34: Use the inverse kinematics algorithm θ = IK(p target , W, C, E, D) to calculate the joint angles required for the end effectors to reach the target positions, where θ represents the joint angles, p target represents the target positions, W represents the weight vector, and W = [w1,..., w n , w n represents the weight of the nth joint, n represents the number of joints, C represents the constraint condition matrix with n rows and 2 columns, and each row represents the minimum and maximum angles of a joint [θ min , θ max , E represents the error tolerance vector, and E = [e1, e2, e3], where e1, e2, and e3 respectively represent the error tolerances in the horizontal, vertical, and longitudinal directions, D represents the damping coefficient vector, and IK(·) represents the inverse kinematics function;

[0136] S1.35: Define key frames according to the calculated joint angles, and generate a preliminary bone animation based on the key frames to simulate the performance of the user in different motion states;

[0137] S1.36: Optimize the parameters of the bone model according to the aligned motion trajectory data and the preliminarily generated bone animation, and use an optimization algorithm to minimize the error between the bone model and the motion trajectory data. The optimization algorithm uses the least squares method, and the least squares method is prior art content in the field and is not the creative solution of this application, so it will not be elaborated here;

[0138] If the error is greater than or equal to a preset error threshold, adjust the bone model parameters or the inverse kinematics algorithm;

[0139] S1.37: Generate the final bone animation based on the adjusted bone model.

[0140] The specific steps of S1.35 include:

[0141] A1: Obtain the bone model from steps S1.31 and S1.32, including joint positions, bone lengths, and hierarchical relationships, and obtain the calculated joint angle data from step S1.34. Among them, the joint angle data is usually stored in the form of a time series, representing the motion state of the user at different time points;

[0142] A2: Define key frames according to the joint angle data. Each key frame represents the bone pose at a certain time point;

[0143] A3: Use the linear interpolation algorithm to generate intermediate frames between key frames to make the animation transition smoothly. For the rotation angle, use quaternion interpolation to avoid the gimbal lock problem and generate the interpolated joint angles. Among them, σ represents the interpolation coefficient, q1 and q2 represent the quaternions of the key frames, q(σ) represents the interpolated joint angle, and the linear interpolation algorithm is the prior art content in this field and not the creative solution of this application, so it will not be elaborated here;

[0144] A4: Update the rotation matrix R(θ) of each joint in the bone model according to the interpolated joint angles, and use the rotation matrix to update the global coordinates of the joints. The formula is: P global = M parent ×R(θ)×P local , where P global represents the global coordinates of the child node, M parent represents the transformation matrix of the parent node, and P local represents the local coordinates of the child node;

[0145] A5: Bind the bone model to the human body morphology data and use the formula to calculate the final position Y of each vertex final , where, represents the weight, n represents the number of vertices, M i represents the bone transformation matrix, and Y o represents the original position of the vertex;

[0146] A6: Use the graphics rendering engine to render the bone animation frame by frame, render the bone pose of each frame to the screen, and generate an animation effect. Among them, the graphics rendering engine is the prior art content in this field and not the creative solution of this application, so it will not be elaborated here.

[0147] The specific steps of S1.4 include:

[0148] S1.41: Obtain the aligned motion data and human body shape data, and extract key points and regions; the key points include joint positions and muscle attachment points; the regions refer to muscle groups and skin regions.

[0149] Furthermore, the specific steps of S1.41 include:

[0150] (1) Obtain the aligned motion data and human body shape data;

[0151] (2) Define key points according to human anatomy knowledge, and the key points include shoulder joints, elbow joints, knee joints, hip joints, wrists, and ankles;

[0152] (3) Use wearable sensors to extract the positions and movement trajectories of key points;

[0153] (4) Use feature point detection algorithms to label key points. Among them, the feature point detection algorithm is the prior art content in this field and is not the creative solution of this application, so it will not be elaborated here;

[0154] (5) Use region segmentation algorithms to extract regions corresponding to four major parts from the human body shape data. For example, use the K-means clustering algorithm to segment the shoulder region from the human body shape data. Among them, the region segmentation algorithm is the prior art content in this field and is not the creative solution of this application, so it will not be elaborated here;

[0155] (6) Extract data of regions corresponding to four major parts according to the positions and movement trajectories of key points. For example, extract the motion data of the shoulder region according to the movement trajectory of the shoulder joint;

[0156] (7) Integrate the key points and region data in the motion data and human body shape data, and compare the accuracy of the key points and region data to ensure data consistency.

[0157] S1.42: Define the path of each muscle according to anatomy knowledge, and use B-spline curves to represent the muscle paths;

[0158] Furthermore, the specific steps of S1.42 include:

[0159] (1) Determine the origin and insertion points of each muscle according to anatomy knowledge. For example, the origin of the biceps brachii is on the scapula and the insertion point is on the radius;

[0160] (2) Determine the intermediate path points according to the anatomical structure of the muscle, such as the path points bypassing joints. For example, the biceps brachii has an obvious bend at the elbow joint;

[0161] (3) Obtain human body shape data, ensuring that the data contains the position information of the muscle origin, insertion, and intermediate path points;

[0162] (4) Use feature point detection algorithms to extract the three-dimensional coordinates of the muscle origin, insertion, and intermediate path points from the human body shape data;

[0163] (5) Take the muscle origin, insertion, and intermediate path points as the control points of the B-spline curve, where the number of control points depends on the complexity of the muscle path;

[0164] (6) Use the B-spline curve formula to calculate the muscle path, where G(u) represents the B-spline curve, Boor l,v (u) represents the B-spline basis function, Q l represents the control points, q represents the number of control points, and v represents the curve order.

[0165] S1.43: Initialize muscle parameters according to the motion data and human body shape data, where the muscle parameters include length, cross-sectional area, and contractile force;

[0166] S1.44: Calculate the length change and contractile force of the muscle according to the joint angle and muscle path, and use a muscle model to simulate the mechanical behavior of the muscle. Among them, the muscle model adopts the Hill-type model, and the Hill-type model is the prior art content in this field and is not the creative solution of this application, so it will not be elaborated here;

[0167] Further, the specific steps for calculating the length change and contractile force of the muscle according to the joint angle and muscle path include:

[0168] (1) Obtain joint angle data and muscle path data;

[0169] (2) According to the joint angle and muscle path, use the arc length formula of the B-spline curve to calculate the length of the muscle in different motion states where G′(u) represents the first derivative of the B-spline curve, and u1 and u2 represent the control points;

[0170] (3) Use numerical integration methods to calculate the arc length H L , where the numerical integration method is the prior art content in this field and is not the creative solution of this application, so it will not be elaborated here;

[0171] (4) Obtain the muscle length change ΔL according to the difference between the initial length and the current length;

[0172] (5) Calculate the muscle contraction force according to the muscle length change and activation degree, where the Hill-type muscle model is the prior art content in the field and not the creative solution of this application, so it will not be elaborated here.

[0173] S1.45: Construct the surface mesh model of the skin according to the human body morphology data, and generate the skin mesh using the mesh generation algorithm. The mesh generation algorithm is the prior art content in the field and not the creative solution of this application, so it will not be elaborated here;

[0174] Further, the specific steps for constructing the surface mesh model of the skin include:

[0175] (1) Obtain the human body morphology data, and extract the point cloud data of the skin surface from the human body morphology data using the surface extraction algorithm. The surface extraction algorithm is the prior art content in the field and not the creative solution of this application, so it will not be elaborated here;

[0176] (2) Generate the triangular mesh of the skin surface based on the point cloud data of the skin surface using the Delaunay triangulation algorithm. The Delaunay triangulation algorithm is the prior art content in the field and not the creative solution of this application, so it will not be elaborated here.

[0177] S1.46: Initialize the skin parameters according to the biomechanical properties of the skin, and define the boundary conditions of the skin according to the muscle contraction force and joint movement;

[0178] S1.47: Discretize the skin mesh into finite elements, and establish the finite element equation based on the finite elements according to the theory of elasticity, and solve the equation. The finite element analysis method and the finite element equation are the prior art content in the field and not the creative solution of this application, so it will not be elaborated here;

[0179] S1.48: Update the vertex positions of the skin mesh according to the solution result, and generate the deformed skin model.

[0180] Further, the specific steps of S1.48 include:

[0181] (1) Obtain the displacement vector from the finite element analysis result;

[0182] (2) Obtain the surface mesh model of the skin, including vertex coordinates, edge and face information;

[0183] (3) Update the coordinates of each vertex according to the displacement vector;

[0184] (4) Use a topology checking algorithm to check whether the updated mesh maintains a correct topology, such as no self-intersection and no degenerate faces. The topology checking algorithm is the prior art content in this field and is not the creative solution of this application, so it will not be elaborated here;

[0185] (5) Write the updated vertex coordinates into the mesh data file and use a visualization tool to display the deformed skin model. In this invention, the visualization tool adopts ParaView.

[0186] The specific steps of step S2 include:

[0187] S2.1: Obtain multiple types of data and transfer the multiple types of data to the built-in motion biomechanics analysis module of the system;

[0188] S2.2: Use a coordinate transformation algorithm to adapt the multiple types of data to the human body dynamic model. The coordinate transformation algorithm is the prior art content in this field and is not the creative solution of this application, so it will not be elaborated here;

[0189] S2.3: Use a deep learning-based object detection algorithm to analyze the human body dynamic model, identify the four major parts in the clothing design, and extract the data of the four major parts during the movement process; the four major parts include the shoulders, elbows, knees, and waist; the movement process data includes position and morphological changes. The deep learning-based object detection algorithm is the prior art content in this field and is not the creative solution of this application, so it will not be elaborated here;

[0190] S2.4: According to the principles of kinematics and dynamics, perform a mechanical analysis on the identified four major parts. The mechanical analysis includes calculating the displacement, velocity, acceleration, and the forces and torques received by each part during the movement process;

[0191] S2.5: According to the results of the mechanical analysis and combined with the characteristics of different sports events, determine the activity requirements and force conditions of the four major parts in different motion states.

[0192] The specific steps of step S3 include:

[0193] S3.1: Extract the activity requirements of the four major parts from the results of the motion biomechanics analysis and quantify the activity requirements into feature vectors to obtain activity requirement feature vectors; the activity requirements include activity range, force distribution, and ductility requirements;

[0194] S3.2: Load a pre-trained machine learning model. The machine learning model is configured based on the random forest algorithm. The machine learning model is the prior art content in this field and is not the creative solution of this application, so it will not be elaborated here;

[0195] S3.3: Input the activity requirement feature vector into the pre-trained machine learning model to generate a clothing design solution that meets the optimization objective, and output the clothing design parameters at the same time.

[0196] S3.4: Extract the fabric features from the fabric database and match the fabric features with the clothing design parameters.

[0197] S3.5: Generate a fabric recommendation list according to the matching result and sort it according to the priority.

[0198] Embodiment 2

[0199] Please refer to Figure 3 , another embodiment provided by the present invention: a clothing design system based on intelligent interaction, including:

[0200] A data acquisition module, a sports biomechanics analysis module, a clothing design optimization module, a fabric and structure design module, a virtual simulation test module, and an augmented reality module;

[0201] The data acquisition module is used to collect various types of data of the user to provide a basis for subsequent analysis and design.

[0202] The sports biomechanics analysis module is used to analyze the body mechanics changes of the user in different motion states according to the collected various types of data, and identify the key parts of clothing design, their activity requirements and force conditions.

[0203] The clothing design optimization module is used to automatically optimize the clothing design through machine learning algorithms, combined with the activity requirements of the four major parts, and generate fabric recommendations.

[0204] The fabric and structure design module is used to determine the final fabric according to the fabric recommendation list and design requirements, and design the clothing structure.

[0205] The virtual simulation test module is used to simulate the performance of the clothing in different motion states through virtual simulation technology and conduct tests.

[0206] The augmented reality module is used to try on the clothing design that passes the simulation test in real time through the augmented reality method, and automatically adjust the design scheme according to the user feedback and dynamic performance.

[0207] The data acquisition module includes: a 3D scanning unit, a wearable sensor unit, and a motion capture unit;

[0208] The 3D scanning unit is used to obtain the body shape data of the user using 3D scanning technology.

[0209] The wearable sensor unit collects the motion data of the user through wearable devices.

[0210] A motion capture unit for accurately recording the user's actions and movement trajectories using a motion capture device.

[0211] The sports biomechanics analysis module includes: a mechanics analysis unit, a part recognition unit, and an activity requirement and force analysis unit;

[0212] A mechanics analysis unit for analyzing the changes in the user's body mechanics during exercise;

[0213] A part recognition unit for identifying the four major parts of clothing design, such as the shoulders, chest, waist, and legs;

[0214] An activity requirement and force analysis unit for determining the activity requirements and forces on the four major parts in different exercise states.

[0215] The clothing design optimization module includes: a machine learning algorithm unit and a fabric recommendation unit;

[0216] A machine learning algorithm unit for optimizing clothing design using machine learning techniques;

[0217] A fabric recommendation unit for generating a fabric recommendation list based on the analysis results.

[0218] The fabric and structure design module includes:

[0219] A fabric selection unit for selecting the final fabric by combining the recommendation list and design requirements;

[0220] A structure design unit for designing the clothing structure according to the activity requirements of the four major parts.

[0221] The virtual simulation test module includes: a simulation unit and a test evaluation unit;

[0222] A simulation unit for applying the fabric and structure design to a human dynamic model for simulation;

[0223] A test evaluation unit for testing and evaluating the simulation results.

[0224] The augmented reality module includes: an augmented reality try-on unit and a design scheme adjustment unit;

[0225] An augmented reality try-on unit for simulating the exercise state in a virtual environment and real-time displaying the clothing design effect;

[0226] A design scheme adjustment unit for automatically adjusting the design scheme according to user feedback and dynamic performance.

[0227] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific embodiments. The above-mentioned specific embodiments are merely illustrative and not restrictive. Under the guidance of the present invention, ordinary technicians in this field can also change, modify, replace and modify the above-mentioned embodiments without departing from the purpose and scope of protection of the present invention. These are all protected by the present invention.

[0228] If the technical solution disclosed herein involves personal information, the product using the technical solution disclosed herein has clearly informed the individual of the personal information processing rules and obtained the individual's voluntary consent before processing the personal information. If the technical solution disclosed herein involves sensitive personal information, the product using the technical solution disclosed herein has obtained the individual's separate consent before processing the sensitive personal information and at the same time meets the "explicit consent" requirement. For example, on personal information collection devices such as cameras, a clear and prominent sign is set to inform that the individual has entered the personal information collection scope and that personal information will be collected. If the individual voluntarily enters the collection scope, it is deemed that they agree to the collection of their personal information; or on the personal information processing device, when the personal information processing rules are notified by obvious signs / information, the individual's authorization is obtained through pop-up information or by asking the individual to upload their personal information. The personal information processing rules may include information such as the personal information processor, the purpose of personal information processing, the processing method, and the type of personal information processed.

Claims

1. A clothing design method based on intelligent interaction, characterized in that: include: Step S1: Collect multiple types of data to construct a user's human body dynamic model; the multiple types of data include human body shape data, motion data and motion trajectory data; Step S2: Based on the system's built-in sports biomechanics analysis module and the human body dynamic model, the user's body mechanics changes under different motion states are analyzed. At the same time, the four major parts of the clothing design are identified, and the activity requirements and stress conditions of the four major parts under different motion states are determined; Step S3: Automatically optimize clothing design through machine learning algorithms and combining the activity requirements of the four major body parts. Generate an optimized clothing design plan and a fabric recommendation list based on the results of sports biomechanics analysis. Step S4: Determine the final fabric based on the fabric recommendation list and the design requirements, and design the garment structure based on the activity requirements of the four major parts to obtain the final fabric selection plan and garment structure design drawing; Step S5: Using virtual simulation technology, the final fabric selection plan and clothing structure design drawing are applied to the human body dynamic model to simulate the performance of the clothing in different motion states and conduct simulation tests; Step S6: Using augmented reality, the clothing design scheme that has passed the simulation test is tried on in real time, and the movement state is simulated in a virtual environment. The system automatically adjusts the clothing design scheme based on the user's feedback and dynamic performance; The four major parts include shoulders, elbows, knees, and waist; The specific steps of step S3 include: S3.1: Extract the activity requirements of the four major body parts from the results of the sports biomechanics analysis and quantify them into feature vectors to obtain the activity requirement feature vectors. The activity requirements include range of motion, force distribution, and ductility requirements. S3.2: Load a pre-trained machine learning model, where the machine learning model is configured based on a random forest algorithm; S3.3: Input the activity demand feature vector into the pre-trained machine learning model to generate a clothing design solution that meets the optimization goal and output the clothing design parameters; S3.4: Extract fabric features from the fabric database and match the fabric features with clothing design parameters; S3.5: Generate a fabric recommendation list based on the matching results and sort them by priority.

2. The clothing design method based on intelligent interaction according to claim 1, characterized in that: The specific steps of step S1 include: S1.1: Use 3D scanning technology to perform a full-body scan of the user to obtain the user's anatomy data. Wearable sensors will also be used to collect motion data of the user during specific movements. An optical motion capture system will also be used to attach reflective markers to key points on the user's body to capture the user's motion trajectory data during different motion states. The anatomy data includes body shape, outline, joint position, and muscle distribution. The motion data includes acceleration, angular velocity, and pressure distribution. S1.2: Sort and match the human body shape data, motion data, and motion trajectory data according to timestamps; If the timestamps of multiple types of data are inconsistent, the improved linear interpolation formula is used Align, where y(t) represents the interpolation result, y1 and y2 represent multi-class data at adjacent time points, t1 and t2 represent the timestamps corresponding to y1 and y2, t represents the current time, α represents the weight factor, and μ represents the smoothing parameter; If there is a time offset between devices, the time offset is calculated and corrected by a cross-correlation analysis method; the cross-correlation analysis method is to calculate the cross-correlation function of two time series data and find the time delay τ that makes the function value reach the maximum in the cross-correlation function. max , according to the time delay τ found max , adjust the timestamp of one type of data to match the timestamp of the other type of data.

3. The clothing design method based on intelligent interaction according to claim 2, characterized in that: The specific steps of step S1 also include: S1.3: Based on the aligned human body shape data and motion trajectory data, a skeletal model of the user is constructed, and the joint angles and range of motion are calculated using the inverse kinematics algorithm. S1.4: Based on the aligned motion data and human morphology data, construct a muscle model and skin model of the user, simulate muscle contraction and skin extension, and use finite element analysis to simulate skin deformation under different motion states; S1.5: The skeleton model is used as the first layer model, the muscle model as the second layer model, and the skin model as the third layer model. Through integration, a dynamic human body model of the user is formed, and the physics engine is used to simulate the performance of the model in different motion states.

4. The clothing design method based on intelligent interaction according to claim 3, characterized in that: The specific steps of S1.3 include: S1.31: Based on human anatomy, define the skeletal structure and use a tree structure to represent the skeletal hierarchy, including joint positions, bone lengths, and joint degrees of freedom. S1.32: Initialize the skeleton model parameters based on the aligned human morphological data and fit the skeleton model using principal component analysis. S1.33: Defining a target position of an end effector according to the aligned motion trajectory data; the end effector includes a hand and a foot; S1.34: Use the inverse kinematics algorithm to calculate the joint angle θ = IK (p target ,W,C,E,D), where θ represents the joint angle, p target represents the target position, W represents the weight vector, and W=[w1,...,w n ],w n represents the weight of the nth joint, n represents the number of joints, C represents the constraint matrix with n rows and 2 columns, and each row represents the minimum and maximum angles of a joint [θ min ,θ max ], E represents the error tolerance vector, and E = [e1, e2, e3], e1, e2, e3 represent the error tolerance in the horizontal, vertical, and vertical directions respectively, D represents the damping coefficient vector, and IK(·) represents the inverse kinematics function; S1.35: Define keyframes based on the calculated joint angles and generate preliminary skeletal animations based on the keyframes to simulate the user's performance in different motion states. S1.36: Based on the aligned motion trajectory data and the initially generated skeletal animation, optimize the parameters of the skeletal model and use an optimization algorithm to minimize the error between the skeletal model and the motion trajectory data; If the error is greater than or equal to a preset error threshold, the skeletal model parameters or the inverse kinematics algorithm are adjusted; S1.37: Generate the final skeletal animation based on the adjusted skeletal model.

5. The clothing design method based on intelligent interaction according to claim 4, characterized in that: The specific steps of S1.35 include: A1: Obtain the skeleton model from steps S1.31 and S1.32, including joint positions, bone lengths, and hierarchical relationships, and obtain the calculated joint angle data from step S1.34; A2: Define keyframes based on joint angle data; A3: Use linear interpolation to generate intermediate frames between key frames, and use the formula Generate interpolated joint angles, where σ represents the interpolation coefficient, q1 and q2 represent the quaternions of the keyframes, and q(σ) represents the interpolated joint angles; A4: Update the rotation matrix R(θ) of each joint in the skeleton model according to the interpolated joint angle, and use the rotation matrix to update the global coordinates of the joint. The formula is: P global =M parent ×R(θ)×P local , where P global Indicates the global coordinates of the child node, M parent Represents the transformation matrix of the parent node, P local Represents the local coordinates of the child node; A5: Bind the skeleton model to the human body shape data and use the formula Calculate the final Y position of each vertex final ,in, represents weight, n represents the number of vertices, M i Represents the bone transformation matrix, Y o Indicates the original position of the vertex; A6: Use a graphics rendering engine to render skeletal animation frame by frame, rendering the skeletal posture of each frame to the screen to generate animation effects.

6. The clothing design method based on intelligent interaction according to claim 5, characterized in that: The specific steps of S1.4 include: S1.41: Acquire the aligned motion data and human body shape data, and extract key points and regions; the key points include joint positions and muscle attachment points; the regions include muscle groups and skin areas; S1.42: Based on anatomical knowledge, define the path of each muscle and use B-spline curves to represent the muscle path; S1.43: Initializing muscle parameters based on the motion data and the human body morphology data, wherein the muscle parameters include length, cross-sectional area, and contraction force; S1.44: Calculate muscle length changes and contraction forces based on joint angles and muscle paths, and simulate muscle mechanical behavior using muscle models. S1.45: Construct a surface mesh model of the skin based on human body morphology data and generate a skin mesh using a mesh generation algorithm; S1.46: Initialize skin parameters based on the biomechanical properties of the skin and define the boundary conditions of the skin based on muscle contraction forces and joint motions; S1.47: Discretize the skin mesh into finite elements. Based on the finite elements and the theory of elasticity, establish the finite element equations and solve the equations. S1.48: Based on the solution results, update the vertex positions of the skin mesh to generate a deformed skin model.

7. The clothing design method based on intelligent interaction according to claim 6, characterized in that: The specific steps of step S2 include: S2.1: Acquire multiple types of data and transmit the multiple types of data to the system's built-in sports biomechanics analysis module; S2.2: Use coordinate transformation algorithms to adapt multi-class data to the human body dynamic model; S2.3: Use a deep learning-based object detection algorithm to analyze the human body dynamic model, identify the four major parts of the clothing design, and extract the motion data of the four major parts; the motion data includes position and shape changes; S2.4: Perform a mechanical analysis of the four identified parts based on the principles of kinematics and dynamics. The mechanical analysis includes calculating the displacement, velocity, acceleration, and forces and moments applied to each part during motion. S2.5: Based on the results of mechanical analysis and the characteristics of different sports, determine the activity requirements and force conditions of the four major parts under different sports conditions.

8. A clothing design system based on intelligent interaction, which is used to implement the clothing design method based on intelligent interaction according to any one of claims 1 to 7, characterized in that: include: Data acquisition module, sports biomechanics analysis module, clothing design optimization module, fabric and structure design module, virtual simulation test module, augmented reality module; The data collection module is used to collect multiple types of data from users; The sports biomechanics analysis module is used to analyze the changes in the user's body mechanics under different sports conditions based on the collected multiple types of data, and to identify the key parts of the clothing design and their activity requirements and stress conditions; The clothing design optimization module is used to automatically optimize clothing designs and generate fabric recommendations based on the activity requirements of the four major body parts through machine learning algorithms; The fabric and structure design module is used to determine the final fabric and design the garment structure based on the fabric recommendation list and design requirements; The virtual simulation test module is used to simulate the performance of the garment in different motion states through virtual simulation technology and perform tests; The augmented reality module is used to try on clothing designs that have passed simulation tests in real time through augmented reality methods, and automatically adjust the design scheme based on user feedback and dynamic performance.

Images