A digital garment layout and style design system and method based on three-dimensional modeling

Abstract

This invention discloses a digital garment layout and style design system and method based on 3D modeling. Based on the construction of a fabric elasticity-pattern accuracy compensation effect feature database, and by establishing a mapping relationship matrix between fabric elasticity parameters and pattern accuracy deviation, it achieves a quantitative characterization of the nonlinear deformation characteristics of the fabric within a specific elasticity range. This provides a data foundation for precise control of pattern parameters. Intelligent calculation of pattern parameters based on the compensation effect is performed. By calling the mapping relationship in the compensation effect database to calculate the pre-deformation compensation amount, the fabric elasticity characteristics are transformed into quantifiable pattern adjustment parameters. This achieves a precise conversion from fabric physical properties to pattern geometric parameters, forming a closed-loop feedback mechanism between fabric characteristics and pattern design. This effectively solves the problem of pattern accuracy distortion caused by nonlinear deformation during 3D modeling of elastic knitted fabrics, significantly improving the accuracy and reliability of digital garment design.

Description

Technical Field

[0001] This invention relates to the field of garment design and pattern layout technology, and more particularly to a digital garment pattern layout and style design system and method based on three-dimensional modeling. Background Technology

[0002] With the deepening of digital transformation in the apparel industry, 3D modeling technology is increasingly widely used in garment layout and style design. By constructing virtual human and garment models, 3D modeling technology enables the digitalization, visualization, and intelligentization of the garment design process, effectively improving design efficiency, reducing sample garment production costs, and providing apparel companies with a complete digital solution from design to production.

[0003] In existing technologies, digital design of clothing typically follows this process: First, human body size data is acquired using a 3D human body scanning device to construct a parametric digital human body model; then, a 3D clothing model is created using clothing CAD software, and a virtual try-on simulation is performed using a physics engine to analyze the clothing's fit, wrinkle distribution, and dynamic effects on the human body; next, the 3D clothing model is unfolded into a 2D pattern, and fabric utilization is optimized using an intelligent nesting algorithm; finally, standardized production process documents are output, realizing a complete digital process from 3D design to 2D production.

[0004] However, existing technologies have significant limitations when dealing with elastic knitted fabrics with an elasticity range of 15%-25%. This elastic range is within a sensitive area of ​​fabric deformation, where the fabric exhibits obvious nonlinear deformation characteristics under stress. Traditional 3D modeling methods, which only simulate static mechanical parameters, cannot accurately capture the dynamic deformation behavior of the fabric within this elastic range, resulting in a large discrepancy between the garment shape predicted by virtual try-on and the actual wearing effect.

[0005] Therefore, existing technologies need to be improved to solve the above problems. Summary of the Invention

[0006] This invention overcomes the shortcomings of the prior art and provides a digital garment layout and style design system and method based on three-dimensional modeling.

[0007] To achieve the above objectives, the technical solution adopted by this invention is: a digital garment pattern layout and style design method based on three-dimensional modeling, characterized by comprising the following steps: S1: Obtain three-dimensional deformation data of fabrics with elasticity coefficients in the range of 15%-25%, establish a mapping relationship matrix between fabric elasticity parameters and pattern accuracy deviation, and store the characteristic values ​​of compensation coefficients of various fabrics under different stress states. S2: Input the elastic coefficient parameter of the target fabric, call the mapping relationship in the compensation effect database, calculate the pre-deformation compensation amount of each key part of the pattern, and generate a pattern optimization parameter set containing elastic compensation factors; S3: Based on the calculated compensation parameters, pre-deformation adjustments are made to key parts of the garment pattern, such as the shoulder line, waistline, and armhole, and compensation deformation that matches the fabric elasticity is implanted into the two-dimensional paper pattern. S4: Convert the adjusted pattern parameters into a standardized two-dimensional garment pattern file, generate a production process data package containing compensation effect identifiers, and output a pattern file that can be recognized by digital cutting equipment.

[0008] In a preferred embodiment of the present invention, in the step of collecting three-dimensional deformation data of fabrics with different elastic coefficients within the elastic range of 15%-25%, the elastic coefficient range is 15%-25%, and the three-dimensional deformation data includes the tensile deformation, resilience deformation, and nonlinear deformation characteristic parameters of the fabric under different stress states.

[0009] In a preferred embodiment of the present invention, in the step of establishing the mapping relationship matrix between fabric elasticity parameters and pattern accuracy deviation, the mapping relationship matrix includes the correspondence between fabric elasticity coefficient and accuracy deviation of key parts of the pattern, and the key parts of the pattern include shoulder line position deviation, waistline position deviation, armhole shape deviation and overall pattern outline deviation.

[0010] In a preferred embodiment of the present invention, in the step of calling the mapping relationship in the compensation effect database, the compensation effect database stores the compensation coefficient feature values ​​of different fabric types within the elasticity range of 15%-25%, and the compensation coefficient feature values ​​include the shoulder line compensation coefficient, waistline compensation coefficient, armhole compensation coefficient, and overall pattern compensation coefficient.

[0011] In a preferred embodiment of the present invention, in the step of calculating the pre-deformation compensation amount of each key part of the pattern, the calculation formula of the pre-deformation compensation amount is: ΔL=k×E×L, where ΔL is the pre-deformation compensation amount, k is the compensation coefficient, E is the fabric elasticity coefficient, and L is the original size of the pattern.

[0012] In a preferred embodiment of the present invention, in the step of pre-deformation adjustment of key parts such as shoulder line, waistline, and armhole of the garment pattern, the pre-deformation adjustment range of the shoulder line is 0.5-3.0cm, the pre-deformation adjustment range of the waistline is 0.3-2.5cm, and the pre-deformation adjustment range of the armhole is 0.2-2.0cm.

[0013] In a preferred embodiment of the present invention, in the step of embedding a compensating deformation that matches the elasticity of the fabric into the two-dimensional paper pattern, the method of embedding the compensating deformation includes adding compensating markers at key nodes of the paper pattern, and the distribution position of the compensating markers is determined according to the complexity of the pattern.

[0014] In a preferred embodiment of the present invention, the complexity of the pattern is distinguished by the following criteria: the complexity of the pattern is obtained by extracting the geometric feature parameters and topological feature parameters of the two-dimensional paper pattern, and calculating it based on the feature parameters through a preset complexity evaluation model; the number of compensation marker points is determined by a preset mapping function based on the output value of the complexity evaluation model; and the distribution position of the compensation marker points is determined by filtering the candidate feature points of the paper pattern according to a priority strategy based on the geometric feature parameters.

[0015] In a preferred embodiment of the present invention, in the step of generating a production process data package containing a compensation effect identifier, the production process data package includes a pattern file, a compensation parameter file, a fabric characteristic file, and a cutting process parameter file, and the compensation effect identifier is used to identify and apply the corresponding compensation parameters in a digital cutting device.

[0016] To achieve the above objectives, the technical solution adopted by this invention is: a digital garment pattern layout and style design system based on three-dimensional modeling, comprising: The data acquisition module is used to collect three-dimensional deformation data of fabrics with different elastic coefficients within the elastic range of 15%-25%, establish a mapping relationship matrix between fabric elastic parameters and pattern accuracy deviation, and store the compensation coefficient characteristic values ​​of various fabrics under different stress states. The compensation calculation module is used to input the elastic coefficient parameters of the target fabric, call the mapping relationship in the compensation effect database, calculate the pre-deformation compensation amount of each key part of the pattern, and generate a pattern optimization parameter set containing elastic compensation factors. The pattern adjustment module is used to pre-deform key parts of the garment pattern, such as the shoulder line, waistline, and armhole, based on the calculated compensation parameters, and to embed compensation deformation that matches the elasticity of the fabric into the two-dimensional paper pattern. The file output module is used to convert the adjusted pattern parameters into standardized two-dimensional garment pattern files, generate production process data packages containing compensation effect identifiers, and output pattern files that can be recognized by digital cutting equipment.

[0017] This invention addresses the shortcomings of the prior art and has the following beneficial effects: This invention provides a digital garment layout and style design method based on 3D modeling. Based on the construction of a fabric elasticity-pattern accuracy compensation effect feature database, and by establishing a mapping relationship matrix between fabric elasticity parameters and pattern accuracy deviation, it achieves a quantitative characterization of the nonlinear deformation characteristics of the fabric within a specific elasticity range. This provides a data foundation for precise control of pattern parameters. Intelligent calculation of pattern parameters based on the compensation effect is performed. By calling the mapping relationship in the compensation effect database to calculate the pre-deformation compensation amount, the fabric elasticity characteristics are transformed into quantifiable pattern adjustment parameters. This achieves a precise conversion from fabric physical properties to pattern geometric parameters, forming a closed-loop feedback mechanism between fabric characteristics and pattern design. This effectively solves the problem of pattern accuracy distortion caused by nonlinear deformation during 3D modeling of elastic knitted fabrics, significantly improving the accuracy and reliability of digital garment design.

[0018] In this invention, by embedding compensation markers in a two-dimensional paper pattern and determining their distribution based on the pattern complexity, precise compensation control for patterns of varying complexity is achieved. Pattern complexity is quantified using two objective indicators: the number of seams and sew-through lines. This provides a clear basis for judging the distribution of compensation markers. Simple patterns use fewer compensation markers at key nodes; medium-complexity patterns increase the number of markers and expand their distribution range; and complex patterns have compensation markers placed at all seam intersections, curve inflection points, seam locations, and other key geometric features. This pattern complexity-based compensation marker distribution strategy, combined with the quantitative judgment standard for pattern complexity, forms a differentiated compensation scheme for patterns of different complexity. This ensures the targeted and effective nature of the compensation, avoids insufficient or excessive compensation, and improves the consistency of compensation for patterns of different complexity.

[0019] In this invention, by generating a production process data package containing compensation effect identifiers, seamless data transfer from design to production is achieved. The production process data package integrates pattern files, compensation parameter files, fabric characteristic files, and cutting process parameter files, forming a complete digital production data system. The compensation effect identifier, as a key element in the data package, can be automatically identified and the corresponding compensation parameters can be applied in the digital cutting equipment, ensuring the accurate execution of pre-deformation compensation during the cutting process. The combination of the production process data package containing compensation effect identifiers and the automatic identification function of the digital cutting equipment establishes a direct mapping relationship between design parameters and production equipment, eliminating errors caused by manual intervention, improving the automation and consistency of the production process, and realizing the organic connection between digital design and intelligent manufacturing of clothing. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Figure 1 This is a flowchart illustrating the method steps of a preferred embodiment of the present invention. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein. Therefore, the scope of protection of the invention is not limited to the specific embodiments disclosed below.

[0023] Application Overview: Elastic knitted fabrics with an elasticity range of 15%-25% face significant technical challenges in 3D modeling. This elastic range falls within a sensitive area of ​​fabric deformation, exhibiting complex nonlinear deformation characteristics under stress, including tensile deformation, resilience deformation, and dynamic changes in nonlinear deformation characteristic parameters. Traditional 3D modeling methods rely solely on static mechanical parameters for simulation, failing to accurately capture the dynamic deformation behavior of the fabric within this elastic range. This results in significant discrepancies between the predicted garment shape in virtual try-on and the actual wearing effect. Pattern accuracy control fails within this elastic range, particularly at critical areas such as the shoulder line, waistline, and armhole, where deviations are particularly pronounced, leading to a severe mismatch between the actual wearing effect and design expectations. Existing technologies lack pre-deformation compensation mechanisms for fabrics within this elastic range, failing to consider the fabric's elasticity characteristics in advance during the pattern design stage, thus significantly compromising the accuracy and reliability of digital garment design.

[0024] This application addresses the aforementioned technical problems by constructing a fabric elasticity-pattern accuracy compensation effect feature database and performing pre-deformation compensation calculations. It acquires three-dimensional deformation data of fabrics with elasticity coefficients ranging from 15% to 25%, establishes a mapping matrix between fabric elasticity parameters and pattern accuracy deviations, and stores the compensation coefficient feature values ​​of various fabrics under different stress states. By inputting the elasticity coefficient parameters of the target fabric and calling the mapping relationship in the compensation effect database, it calculates the pre-deformation compensation amount for key parts of the pattern, generating a pattern optimization parameter set containing elasticity compensation factors. Based on the calculated compensation parameters, it adjusts the pre-deformation amount of key parts of the garment pattern, such as the shoulder line, waistline, and armhole, and embeds compensation deformation variables matching the fabric elasticity into the two-dimensional pattern. The adjusted pattern parameters are converted into standardized two-dimensional garment pattern files, generating a production process data package containing compensation effect identifiers, and outputting a pattern file recognizable by digital cutting equipment. Through this closed-loop feedback mechanism, it achieves precise conversion from fabric physical properties to pattern geometric parameters.

[0025] Based on the above principles, this application effectively solves the problem of pattern accuracy distortion caused by nonlinear deformation during 3D modeling of elastic knitted fabrics, significantly improving the accuracy and reliability of digital garment design. By embedding compensating deformation variables into the 2D pattern, effective coordination between fabric elasticity and pattern accuracy is achieved, ensuring that the garment's shape during actual wear is highly consistent with the design expectations. Generating a production process data package containing compensation effect identifiers enables seamless integration from design to production, improving production efficiency and product quality. The modular design of the system ensures smooth data interaction between modules, forming a complete closed-loop feedback system, improving the efficiency and accuracy of the entire digital design process. This technical solution provides garment enterprises with an efficient and reliable digital design solution, promoting the in-depth development of digital transformation in the garment industry.

[0026] like Figure 1 As shown, a digital garment pattern layout and style design method based on 3D modeling includes the following steps: S1: Obtain three-dimensional deformation data of fabrics with elasticity coefficients in the range of 15%-25%, establish a mapping relationship matrix between fabric elasticity parameters and pattern accuracy deviation, and store the characteristic values ​​of compensation coefficients of various fabrics under different stress states. S2: Input the elastic coefficient parameter of the target fabric, call the mapping relationship in the compensation effect database, calculate the pre-deformation compensation amount of each key part of the pattern, and generate a pattern optimization parameter set containing elastic compensation factors; S3: Based on the calculated compensation parameters, pre-deformation adjustments are made to key parts of the garment pattern, such as the shoulder line, waistline, and armhole, and compensation deformation that matches the fabric elasticity is implanted into the two-dimensional paper pattern. S4: Convert the adjusted pattern parameters into a standardized two-dimensional garment pattern file, generate a production process data package containing compensation effect identifiers, and output a pattern file that can be recognized by digital cutting equipment.

[0027] Step S1: In this step, high-precision 3D scanning equipment is used to acquire 3D deformation data of the fabric with an elastic modulus in the range of 15%-25%. Specifically, the fabric sample to be tested is fixed on a standard testing platform, and different tensile forces are applied to cause the fabric to deform to different degrees. Simultaneously, a 3D scanner is used to acquire real-time 3D morphological data of the fabric under different stress states. The acquired 3D deformation data includes the tensile deformation, resilience deformation, and nonlinear deformation characteristic parameters of the fabric under different stress states.

[0028] Based on the acquired 3D deformation data, a mapping matrix between fabric elasticity parameters and pattern accuracy deviations was established. This mapping matrix includes the correspondence between the fabric elasticity coefficient and the accuracy deviations of key parts of the pattern, including shoulder line position deviation, waistline position deviation, armhole shape deviation, and overall pattern outline deviation. Through statistical analysis, the patterns of accuracy deviations at each key part caused by fabrics with different elasticity coefficients were determined, forming the mapping matrix.

[0029] The established mapping matrix is ​​stored in the compensation effect database, along with the characteristic values ​​of compensation coefficients for various fabrics under different stress states. These characteristic values ​​include shoulder line compensation coefficients, waistline compensation coefficients, armhole compensation coefficients, and overall pattern compensation coefficients. These coefficients are used for subsequent intelligent calculations of pattern parameters.

[0030] By embedding compensation markers in two-dimensional patterns and determining their distribution based on the complexity of the pattern, precise compensation control for patterns of varying complexity is achieved. Pattern complexity is quantified using two objective indicators: the number of slits and seams. This provides a clear basis for judging the distribution of compensation markers. Simple patterns use fewer compensation markers distributed at key nodes, medium-complexity patterns increase the number of compensation markers and expand their distribution range, and complex patterns place compensation markers at all seam intersections, curve inflection points, slit locations, and other key geometric feature points. This compensation marker distribution strategy based on pattern complexity, combined with the quantitative judgment standard for pattern complexity, forms a differentiated compensation scheme for patterns of different complexity. This ensures the targetedness and effectiveness of the compensation effect, avoids the problems of insufficient or excessive compensation, and improves the consistency of compensation for patterns of different complexity.

[0031] The nonlinear deformation characteristic parameters are a set of indicators that quantify the degree to which the fabric deviates from the ideal elastomer within the elastic range. Specifically, they include: the hysteresis loop area ratio, which is the ratio of the area of ​​the closed loop enclosed by the stress-strain curves of the fabric during the loading-unloading cycle to the area of ​​the ideal elastomer under the same deformation, used to characterize the degree of rebound hysteresis; the tangent modulus change rate, which is the percentage change in the tangent slope at any point during the stretching process relative to the elastic modulus in the initial linear stage, used to characterize the nonlinear trend of the fabric becoming harder or softer; and the Poisson's ratio coefficient of variation, which is the ratio of the root mean square to the mean of the ratio of transverse shrinkage strain to longitudinal tensile strain during uniaxial stretching throughout the stretching process, used to characterize the non-uniformity of thickness and transverse dimension changes in three-dimensional deformation.

[0032] Furthermore, the process of transforming the above-mentioned three-dimensional surface deformation into two-dimensional pattern adjustment parameters is achieved through a reverse surface unfolding and deviation decoupling algorithm: First, the three-dimensional deformation point cloud of the fabric is constructed into a triangular mesh surface, and the three-dimensional spatial displacement vectors of the mesh vertices before and after deformation are calculated to form a three-dimensional displacement field; then, using the least squares conformal mapping algorithm, the three-dimensional garment surface with displacement vectors is reverse unfolded into a two-dimensional plane coordinate system, maintaining the local angle and area ratio of the mesh; finally, in the unfolded two-dimensional mesh, the mesh edges corresponding to the original two-dimensional pattern outline are extracted, and the length changes and endpoint position offsets of these mesh edges after unfolding are calculated, decoupling the nonlinear stretching and shear deformation of the three-dimensional surface in various directions into local adjustment amounts along the normal and tangential directions of the outline in the two-dimensional plane.

[0033] The mapping matrix M is a multidimensional data table. Its row dimension i is defined as different elastic coefficient ranges, divided in 1% increments: i=1 corresponds to 15%-16% to i=10 corresponds to 24%-25%. Its column dimension j is defined as the key parts of the pattern, including j=1 shoulder line, j=2 waistline, j=3 armhole, and j=4 overall silhouette. The element mij in the matrix represents the accuracy deviation of part j under a specific elastic coefficient i. The statistical analysis process of transforming massive data into matrix element mij specifically includes: registering the three-dimensional deformation point cloud of the fabric under different stress states with the standard three-dimensional clothing model through an iterative nearest-point algorithm, extracting the corresponding three-dimensional spatial boundary curves, and calculating the average normal distance from each point on the curve to the standard curve as the three-dimensional accuracy deviation; for the deviation sample set of the fabric under multiple stress levels within the same elastic coefficient range, principal component analysis is used to remove abnormal noise data, and the weighted average of the sample set is taken as the final matrix element mij and stored in the mapping matrix.

[0034] To address the problem of converting three-dimensional spatial displacement vectors into accuracy deviations, a deviation quantization method based on local feature projection is adopted:

[0035] in, Let be the normal distance from the point cloud to the standard surface. For tangential displacement components, This represents the local curvature deviation. The weighting principle is based on the principal component contribution rate: let the contribution rates of the first three principal components obtained from the PCA analysis be... ,but For complex shapes such as armholes, shape descriptors are introduced. ( The centroid is used as an additional weighting factor to ensure accurate representation of shape distortion.

[0036] Step S2: In this step, the elasticity coefficient parameter of the target fabric is first entered. The elasticity coefficient of the target fabric can be obtained by measuring with fabric testing equipment or from the technical parameters provided by the fabric supplier. The input range for the elasticity coefficient parameter is 15%-25%, ensuring consistency with the data range in the compensation effect database.

[0037] The system retrieves the mapping relationships from the compensation effect database and finds the corresponding compensation coefficient feature value based on the input elasticity coefficient parameter. The compensation effect database stores compensation coefficient feature values ​​for different fabric types within the 15%-25% elasticity range, including shoulder line compensation coefficient, waistline compensation coefficient, armhole compensation coefficient, and overall pattern compensation coefficient.

[0038] Based on the found characteristic values ​​of the compensation coefficient, the pre-deformation compensation amount for each key part of the pattern is calculated. The formula for calculating the pre-deformation compensation amount is: ΔL=k×E×L, where ΔL is the pre-deformation compensation amount, k is the compensation coefficient, E is the fabric elasticity coefficient, and L is the original size of the pattern. This formula allows for the accurate calculation of the required pre-deformation compensation amount for each key part.

[0039] Generate a pattern optimization parameter set that includes an elastic compensation factor. The pattern optimization parameter set includes the pre-deformation compensation amount, compensation coefficient, and related process parameters for each key part, which are used to guide subsequent pattern adjustment operations.

[0040] During the system initialization and calibration phase, fabric with known elastic coefficient E and original size L is obtained. The actual deformation ΔLactual under actual wearing conditions is obtained through 3D scanning. The calibration coefficient is then calculated using the formula kcalibrated = ΔLactual / (E × L). For fabrics with an elasticity range of 15%-25% under different stress levels, multiple calibrations are performed to obtain a series of kcalibrated values. A cubic spline interpolation algorithm is then used to fit the kcalibrated values ​​to the stress state variables, establishing a dynamic response function for the kcalibrated value. In actual calculations, the system calculates the most suitable nonlinear compensation coefficient k for the current part based on the predicted local stress magnitude of the current pattern area in the 3D simulation, and then substitutes it into the dynamic response function for the kcalibrated value.

[0041] By generating a production process data package containing compensation effect identifiers, seamless data transfer from design to production is achieved. The production process data package integrates pattern files, compensation parameter files, fabric characteristic files, and cutting process parameter files, forming a complete digital production data system. As a key element in the data package, the compensation effect identifier can be automatically identified and the corresponding compensation parameters applied in the digital cutting equipment, ensuring the accurate execution of pre-deformation compensation during the cutting process. The combination of the production process data package containing compensation effect identifiers and the automatic identification function of the digital cutting equipment establishes a direct mapping relationship between design parameters and production equipment, eliminating errors caused by manual intervention, improving the automation and consistency of the production process, and realizing the organic connection between digital design and intelligent manufacturing of clothing.

[0042] A piecewise nonlinear response function model is adopted, designed for the 15%-25% elasticity range:

[0043] in These represent local stress values, and the coefficients are determined by least-squares fitting of experimental data. Typical curve characteristics should include three key points: hour , hour , hour This ensures that the function is monotonically increasing and its second derivative is continuous within the interval, reflecting the hardening characteristics of the knitted fabric in that interval.

[0044] Step S3: In this step, based on the compensation parameters calculated in step S2, pre-deformation adjustments are made to key parts of the garment pattern. Specifically, the shoulder line is pre-deformed within a range of 0.5-3.0 cm; the waistline within a range of 0.3-2.5 cm; and the armhole within a range of 0.2-2.0 cm. These adjustment ranges are optimal ranges derived from statistical analysis of extensive experimental data, effectively compensating for non-linear deformation of the fabric within the 15%-25% elasticity range.

[0045] Compensating deformations that match the fabric's elasticity are incorporated into the two-dimensional pattern. The incorporation of these compensation deformations involves adding compensation markers at key nodes in the pattern. The steps for determining the pattern's complexity are as follows: The outer contour lines and internal structural lines in a two-dimensional paper pattern are abstracted into a topological graph model, with the intersections and endpoints of all line segments as the set of nodes in the graph. The set of edges of a graph is the line segments that connect the nodes. Calculate the topological structure characteristics, including the total number of nodes. Total number of sides And the number of closed loops formed by the internal structural lines. Construct topological complexity feature vectors ; Discrete sampling is performed on the outer contour curve of the two-dimensional paper pattern, and the curvature value of each sampling point on the contour line is calculated using the discrete curvature algorithm. Extract geometric feature parameters, including the sum of absolute values ​​of curvature. and the standard deviation of curvature Construct geometric complexity feature vectors ; The aforementioned feature vectors are input into a pre-defined complexity evaluation model, and the publication complexity index is calculated using a weighted fusion algorithm. The calculation formula is: ,in and These are the normalization functions for topological features and geometric features, respectively. and The preset weighting coefficients, and ; Based on the calculated pattern complexity index Through a pre-defined piecewise linear mapping function Calculate the number of compensation marker targets required for the current paper pattern, where With complexity exponent The increase of shows a monotonically increasing relationship; A weighting method based on information entropy is used to assign scientific weights to different deformation modes: .

[0046] in For the first Information entropy of various transformation modes For the first in this mode The probability density of each sample. Recommended base proportion: shoulder line area. Waistline armhole This ratio is based on statistical analysis of 1000 sets of human kinematic data to ensure the coordination of compensation amounts in different parts of the body. For highly elastic fabrics, the weighting coefficient should be adjusted linearly with the elasticity coefficient. .

[0047] All nodes in the topological graph model are merged with the local curvature extrema points on the outer contour curve to construct a candidate feature point set. When determining the distribution location, the local curvature change gradient of each point in the candidate feature point set is calculated, and the points are sorted in descending order of gradient value. At the same time, a minimum distance constraint is introduced to eliminate redundant points that are too close, and finally, the top-ranked points are selected. The algorithm selects candidate points as the final distribution locations of compensation markers. This algorithm achieves objective quantification of pattern complexity and automated, precise configuration of the number and location of compensation markers.

[0048] Implanting compensating deformation variables that match the fabric elasticity into a two-dimensional paper pattern is achieved by driving the two-dimensional paper pattern to undergo substantial deformation reconstruction through geometric processing algorithms. The specific technical path is as follows: First, normal offset is performed to generate control points. The calculated pre-deformation compensation amount ΔL of each key part is offset along the normal direction of the original two-dimensional paper pattern outline at the corresponding compensation mark point to generate new spatial coordinate points as compensation control points. Second, spline curve interpolation reconstruction is performed. The endpoints of the original paper pattern outline are used as boundary constraints, and the newly added compensation control points are used as shape value points. The original paper pattern outline is re-fitted and calculated using a cubic B-spline interpolation algorithm. In this process, a tangent continuity constraint condition with continuous first derivative is set, which requires that the tangent direction of the reconstructed curve is consistent with the tangent direction of the original paper pattern contour at the point when entering and leaving the compensation control point, so as to avoid sharp corners or abrupt broken lines caused by local compensation. Finally, the internal mesh is re-divided. For the closed two-dimensional paper pattern area enclosed by the reconstructed contour line, the Delaunay triangulation algorithm is used to re-divide the mesh and map the coordinates of the paper pattern, so that the internal filling area adapts to the new contour boundary, and finally completes the complete implantation of the compensation deformation into the geometric topology of the two-dimensional paper pattern.

[0049] The improved least-squares conformal mapping algorithm should introduce an anisotropic weight matrix:

[0050] in For anisotropic penalties, Triangular facet The primary and secondary stretching rates. Decoupling mathematical logic employs singular value decomposition: Let the deformation gradient tensor... The local adjustment amount in the two-dimensional plane ,in , The shrinkage rate in the thickness direction is expressed by Poisson's ratio. Related: .

[0051] The compensation effect database should be obtained through three-stage training: 1. Data Acquisition Phase: Using a six-axis force sensor platform, apply... ( A periodic load (where the spring constant is constant) was applied, and 1000 deformation cycle data were collected at a frequency of 0.1 Hz. 2. Feature extraction stage: Calculate the Hausdorff distance matrix for each period. And extract the Fourier descriptor

[0052] 3. Model training phase: Gaussian process regression is used. covariance function Hyperparameters are estimated by maximizing marginal likelihood. The training set to validation set ratio is 8:2, and the cross-validation error should be less than 5%.

[0053] The elastic modulus should be explicitly defined as the tensile strain rate under standard test conditions:

[0054] in This is a specific stress value, determined based on ASTM D5034 standard. The actual test conditions corresponding to the 15%-25% range are: specimen size... Clamping distance 100mm, stretching speed Ambient temperature and humidity The formula for calculating the elasticity coefficient should be revised as follows:

[0055] in This is a typical Poisson's ratio for knitted fabrics. For lateral strain, ensure the accuracy and repeatability of the elastic range definition.

[0056] The least squares conformal mapping algorithm should be supplemented with a gradient optimization step:

[0057] The decoupling algorithm employs a hierarchical projection strategy: 1. First layer: Global rigid transformation, solution

[0058] 2. Second layer: Local affine transformation, for each vertex calculate

[0059] 3. Third layer: Nonlinear optimization, energy function

[0060] in , The formula for converting a three-dimensional displacement field to a two-dimensional adjustment amount is as follows:

[0061] in For the strain tensor components, This refers to the outline curve of the paper pattern.

[0062] Step S4: In this step, the pattern parameters adjusted in step S3 are converted into standardized two-dimensional garment pattern files. During the conversion process, the geometric accuracy of the pattern and the integrity of the compensation parameters are maintained to ensure that the output pattern file accurately reflects the shape of the pattern after compensation and adjustment.

[0063] Generate a production process data package containing compensation effect identifiers. This data package includes pattern files, compensation parameter files, fabric property files, and cutting process parameter files. The compensation effect identifiers are used to identify and apply the corresponding compensation parameters in the digital cutting equipment, ensuring that pre-deformation compensation is automatically applied during the cutting process.

[0064] Output pattern files that can be recognized by digital cutting equipment. Pattern file formats include one or more of DXF, PLT, ASTM, and Gerber formats. The pattern file includes a compensation effect identifier layer for automatically applying pre-deformation compensation during the cutting process. Standardized file formats ensure that pattern files can be correctly recognized and applied on digital cutting equipment from different brands.

[0065] To achieve the above objectives, the technical solution adopted by this invention is: a digital garment pattern layout and style design system based on three-dimensional modeling, comprising: The data acquisition module is used to collect three-dimensional deformation data of fabrics with different elastic coefficients within the elasticity range of 15%-25%, establish a mapping relationship matrix between fabric elasticity parameters and pattern accuracy deviation, and store the compensation coefficient characteristic values ​​of various fabrics under different stress states. The compensation calculation module is used to input the elastic coefficient parameters of the target fabric, call the mapping relationship in the compensation effect database, calculate the pre-deformation compensation amount of each key part of the pattern, and generate a pattern optimization parameter set containing elastic compensation factors. The pattern adjustment module is used to pre-deform key parts of the garment pattern, such as the shoulder line, waistline, and armhole, based on the calculated compensation parameters, and to embed compensation deformation that matches the elasticity of the fabric into the two-dimensional paper pattern. The file output module is used to convert the adjusted pattern parameters into standardized two-dimensional garment pattern files, generate production process data packages containing compensation effect identifiers, and output pattern files that can be recognized by digital cutting equipment.

[0066] Exemplary electronic devices To achieve the above objectives, the third technical solution adopted by the present invention is: an electronic device, comprising: Processor; a processor may be a central processing unit (CPU) or other form of processing unit with data processing capabilities and / or instruction execution capabilities.

[0067] The memory stores computer program instructions, which, when executed by the processor, cause the processor to perform a digital garment layout and style design method based on three-dimensional modeling.

[0068] The memory may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory and / or cache memory. Non-volatile memory may include, for example, read-only memory, hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and a processor may execute the program instructions to implement a digital garment pattern layout and style design method based on three-dimensional modeling, as described in the various embodiments of this application above, and / or other desired functions.

[0069] To achieve the above objectives, the fourth technical solution adopted by the present invention is: a computer-readable storage medium storing computer program instructions thereon, wherein the computer program instructions, when run by a processor, cause the processor to execute a digital garment layout and style design method based on three-dimensional modeling.

[0070] Computer-readable storage media can take the form of any combination of one or more readable media. A readable medium can be a readable signal medium or a readable storage medium. A readable storage medium can be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media include: electrical connections having one or more wires, portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0071] Based on the preferred embodiments of the present invention described above, those skilled in the art can make various changes and modifications without departing from the inventive concept. The technical scope of this invention is not limited to the contents of the specification, but must be determined according to the scope of the claims.

Claims

1. A digital garment pattern layout and style design method based on 3D modeling, comprising: Its features are, S1: Obtain three-dimensional deformation data of fabrics with elasticity coefficients in the range of 15%-25%, establish a mapping relationship matrix between fabric elasticity parameters and pattern accuracy deviation, and store the characteristic values ​​of compensation coefficients of various fabrics under different stress states. S2: Input the elastic coefficient parameter of the target fabric, call the mapping relationship in the compensation effect database, calculate the pre-deformation compensation amount of each key part of the pattern, and generate a pattern optimization parameter set containing elastic compensation factors; S3: Based on the calculated compensation parameters, pre-deformation adjustments are made to key parts of the garment pattern, such as the shoulder line, waistline, and armhole, and compensation deformation that matches the fabric elasticity is implanted into the two-dimensional paper pattern. S4: Convert the adjusted pattern parameters into a standardized two-dimensional garment pattern file, generate a production process data package containing compensation effect identifiers, and output a pattern file that can be recognized by digital cutting equipment.

2. The method for digital garment layout and style design based on three-dimensional modeling according to claim 1, characterized in that: In the step of collecting three-dimensional deformation data of fabrics with different elastic coefficients within the elasticity range of 15%-25%, the elastic coefficient range is 15%-25%, and the three-dimensional deformation data includes the tensile deformation, elastic deformation, and nonlinear deformation characteristic parameters of the fabric under different stress states.

3. The digital garment layout and style design method based on three-dimensional modeling according to claim 1, characterized in that: In the step of establishing the mapping relationship matrix between fabric elasticity parameters and pattern accuracy deviation, the mapping relationship matrix includes the correspondence between fabric elasticity coefficient and accuracy deviation of key parts of the pattern, and the key parts of the pattern include shoulder line position deviation, waistline position deviation, armhole shape deviation and overall pattern outline deviation.

4. The digital garment layout and style design method based on three-dimensional modeling according to claim 1, characterized in that: In the step of calling the mapping relationship in the compensation effect database, the compensation effect database stores the compensation coefficient feature values ​​of different fabric types in the range of 15%-25% elasticity. The compensation coefficient feature values ​​include the shoulder line compensation coefficient, waistline compensation coefficient, armhole compensation coefficient, and overall pattern compensation coefficient.

5. The digital garment layout and style design method based on three-dimensional modeling according to claim 1, characterized in that: In the step of calculating the pre-deformation compensation amount of each key part of the pattern, the calculation formula for the pre-deformation compensation amount is: ΔL=k×E×L, where ΔL is the pre-deformation compensation amount, k is the compensation coefficient, E is the fabric elasticity coefficient, and L is the original size of the pattern.

6. The digital garment layout and style design method based on three-dimensional modeling according to claim 1, characterized in that: In the step of pre-deformation adjustment of key parts such as shoulder line, waistline, and armhole of the garment pattern, the pre-deformation adjustment range of the shoulder line is 0.5-3.0cm, the pre-deformation adjustment range of the waistline is 0.3-2.5cm, and the pre-deformation adjustment range of the armhole is 0.2-2.0cm.

7. The method for digital garment layout and style design based on three-dimensional modeling according to claim 1, characterized in that: In the step of embedding a compensating deformation that matches the elasticity of the fabric into the two-dimensional paper pattern, the method of embedding the compensating deformation includes adding compensating markers at key nodes of the paper pattern, and the distribution position of the compensating markers is determined according to the complexity of the pattern.

8. The digital garment layout and style design method based on three-dimensional modeling according to claim 7, characterized in that: The complexity of the pattern is distinguished by the following criteria: the complexity of the pattern is obtained by extracting the geometric feature parameters and topological feature parameters of the two-dimensional paper pattern, and calculating it based on the feature parameters through a preset complexity evaluation model. The number of compensation marker points is determined by a preset mapping function based on the output value of the complexity evaluation model. The distribution position of the compensation marker points is determined by filtering the candidate feature points of the paper pattern according to a priority strategy based on the geometric feature parameters.

9. The digital garment layout and style design method based on three-dimensional modeling according to claim 1, characterized in that: In the step of generating a production process data package containing a compensation effect identifier, the production process data package includes a pattern file, a compensation parameter file, a fabric characteristic file, and a cutting process parameter file. The compensation effect identifier is used to identify and apply the corresponding compensation parameters in the digital cutting equipment.

10. A digital garment layout and style design system based on 3D modeling, comprising the digital garment layout and style design method based on 3D modeling as described in any one of claims 1-9, characterized in that, include: The data acquisition module is used to collect three-dimensional deformation data of fabrics with different elastic coefficients within the elastic range of 15%-25%, establish a mapping relationship matrix between fabric elastic parameters and pattern accuracy deviation, and store the compensation coefficient characteristic values ​​of various fabrics under different stress states. The compensation calculation module is used to input the elastic coefficient parameters of the target fabric, call the mapping relationship in the compensation effect database, calculate the pre-deformation compensation amount of each key part of the pattern, and generate a pattern optimization parameter set containing elastic compensation factors. The pattern adjustment module is used to pre-deform key parts of the garment pattern, such as the shoulder line, waistline, and armhole, based on the calculated compensation parameters, and to embed compensation deformation that matches the elasticity of the fabric into the two-dimensional paper pattern. The file output module is used to convert the adjusted pattern parameters into standardized two-dimensional garment pattern files, generate production process data packages containing compensation effect identifiers, and output pattern files that can be recognized by digital cutting equipment.

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