CN117436152A

CN117436152A - Parameter-adjustable garment process modularized design method and system

Info

Publication number: CN117436152A
Application number: CN202311707348.XA
Authority: CN
Inventors: 张凤珍; 刘玲
Original assignee: Gaomi Zhenyoumei Garment Co ltd
Current assignee: Gaomi Zhenyoumei Garment Co ltd
Priority date: 2023-12-13
Filing date: 2023-12-13
Publication date: 2024-01-23
Anticipated expiration: 2043-12-13
Also published as: CN117436152B

Abstract

The invention relates to the field of clothing technology, in particular to a modularized design method and system for clothing technology with adjustable parameters. The method comprises the following steps: obtaining target client parameters; performing morphological analysis on the target client parameters to generate target client morphological data; dynamically simulating the target client form data to generate body dynamic data; performing motion trail identification on the body dynamic data to generate motion trail data; performing joint activity analysis on the motion trail data to generate joint activity parameters; dynamically adapting calculation is carried out on the body dynamic data according to the joint activity parameters so as to generate dynamic adapting data; performing behavior habit analysis on the target client parameters to generate client behavior habit data; and carrying out clothing demand characteristic analysis on the target client parameters according to the client behavior habit data and the dynamic adaptation data, thereby generating clothing demand data. The invention realizes efficient and accurate modular design of complex process.

Description

Parameter-adjustable garment process modularized design method and system

Technical Field

The invention relates to the field of clothing technology, in particular to a modularized design method and system for clothing technology with adjustable parameters.

Background

In modern apparel manufacturing, a high degree of automation and intelligence has become a dominant trend. In conventional garment production, process design is often a fixed process, and it is difficult to meet challenges of fast market changes and personalized needs. In order to improve the flexibility and efficiency of garment production, a new design method is needed that enables the modularization and parameter adjustment of the garment process. In the garment manufacturing process, technological parameters of each link such as cutting size, sewing thread density, textile materials and the like can influence the quality and appearance of a final product, and traditional garment technology modularized design is finished through manual means, so that the problems of low efficiency and inaccurate design effect are often caused, and therefore, in order to meet the requirements of modern garment technology modularized design, an intelligent garment technology modularized design method and system with adjustable parameters are required.

Disclosure of Invention

The invention provides a modularized design method and a modularized design system for a clothing process with adjustable parameters, which aim to solve at least one technical problem.

In order to achieve the above object, the present invention provides a modular design method for a garment process with adjustable parameters, comprising the following steps:

step S1: obtaining target client parameters; performing morphological analysis on the target client parameters to generate target client morphological data; dynamically simulating the target client form data to generate body dynamic data;

step S2: performing motion trail identification on the body dynamic data to generate motion trail data; performing joint activity analysis on the motion trail data to generate joint activity parameters; dynamically adapting calculation is carried out on the body dynamic data according to the joint activity parameters so as to generate dynamic adapting data;

step S3: performing behavior habit analysis on the target client parameters to generate client behavior habit data; performing clothing demand characteristic analysis on target client parameters according to client behavior habit data and dynamic adaptation data, so as to generate clothing demand data;

step S4: virtual module design is carried out on target customer parameters according to the clothing demand data so as to generate a clothing process module; performing module material parameterization on the clothing process module to generate module material parameters; performing material characteristic analysis on the module material parameters to generate module material characteristic data;

Step S5: performing motion performance analysis on the dynamic adaptation data based on the module material characteristic data to generate motion performance data; dynamic parameter adjustment is carried out on the clothing process module according to the athletic performance data, so that a dynamic parameter module is generated;

step S6: and performing expansion convolution on the dynamic parameter module by using a circular convolution network to construct a clothing dynamic process model so as to execute clothing process modularized design.

According to the invention, the morphological analysis is carried out by acquiring the target client parameters, and the system can accurately grasp the physical characteristics and morphological data of the client. This helps to improve the fit and comfort of the garment, meeting the personalized needs. By performing motion trajectory recognition and joint activity analysis on the somatic dynamic data, the system can more fully understand the physical changes of the customer in motion. The dynamic adaptation calculation enables the clothing to be flexibly adjusted along with the movement of the body, and the comfort and the movement performance of the clothing are improved. Through behavioral habit analysis and dynamic adaptation data, the system can understand the behavioral habits of clients and integrate the information into the garment design. This helps to create a garment that better fits customer behavior habits, improving customer experience and satisfaction. Based on the clothing demand data, virtual module design is carried out, and the system can quickly generate personalized clothing process modules. The material parameterization of the module enables the material of the clothing to be flexibly adjusted, adapts to different climates and occasions, and improves the practicability and the versatility of the clothing. Through athletic performance analysis, the system may optimize the design of the apparel process module, ensuring proper performance is maintained in different activities. The dynamic parameter adjustment enables the clothing to be adjusted in real time according to the exercise requirement, and comfort and adaptability during exercise are improved. The dynamic parameter module is subjected to expansion convolution by using a circular convolution network, and the system can better capture the change of clothing in movement. The method is beneficial to constructing a more accurate dynamic process model of the clothing, so that the clothing design is more intelligent and advanced, the modularized design of the clothing process is realized, the adaptability, individuation and comfort of the clothing are improved, and better wearing experience is provided for users.

Preferably, step S1 comprises the steps of:

step S11: obtaining target client parameters;

step S12: body fat distribution analysis is carried out on target client parameters so as to generate body fat distribution parameters;

step S13: performing morphological analysis on the body fat distribution parameters to generate target client morphological data;

step S14: dynamically simulating the target client form data to obtain dynamic simulation data;

step S15: and performing body motion capture on the dynamic simulation data to generate body dynamic data.

The invention is achieved by collecting physical parameters of the target customer, such as height, weight, age, etc. This helps to personalize the garment, ensure the fit and comfort of the garment, and by analyzing the body fat distribution of the target customer, the system can learn about the body fat distribution. This helps to customize the garment more accurately to take into account the effect of fat distribution on the fit of the garment, especially for tight fitting garments or athletic wear, where this information is critical, based on body fat distribution parameters, the system can generate morphological data for the target customer. This helps to design garments, ensures that they fit the body form of the customer, improves the fit and appearance of the garment, and the acquisition of dynamic simulation data enables the system to simulate the body movements of the target customer in different activities. This helps to design garments to ensure that they remain comfortable and adaptable under different circumstances, such as sports, walking, etc., through the body motion capture, the system can generate body dynamics data for the customer, including posture, gait, etc. This is important for designing garments that require consideration of the customer's athletic needs, such as sportswear, professional uniforms, and the like. This ensures the fit and functionality of the garment, enhancing the customer's experience.

Preferably, step S2 comprises the steps of:

step S21: performing joint point detection on the body dynamic data to generate joint point data;

step S22: performing motion trail identification on the body dynamic data according to the node data to generate motion trail data;

step S23: performing joint activity analysis on the motion trail data to generate joint activity parameters;

step S24: performing body stability analysis on the body dynamic data according to the joint activity parameters to generate body balance data;

step S25: dynamically adapting and analyzing the body balance data by utilizing a joint movement dynamic adaptation calculation formula so as to generate dynamic adaptation data;

the system can identify key nodes of the body, such as shoulders, knees, ankles and the like through joint point detection. This helps to understand the basic structure of the body and provides important data for subsequent analysis. This step enables the system to capture the basic shape and structure of the body. The motion trajectory recognition helps to analyze the movement of the node over a period of time, i.e. the actual motion path of the body. This provides detailed information about the dynamic characteristics of the body in different activities, including changes in posture and the degree of joint movement. By analyzing the motion trajectories, the system may generate joint motion parameters that describe the degree and extent of motion of the joint in different motions. This is important for designing garments, such as sportswear and professional wear, that require a specific range of articulation. Body stability analysis the stability of the body in different movements was assessed by means of joint mobility parameters. This helps determine whether the body requires additional support or adjustment under different actions to provide better balance and comfort. This is particularly important for elderly people or people who require special support. The dynamic adaptation data is generated by using a joint movement dynamic adaptation calculation formula, which considers the activity degree and stability of the body. This helps to determine the suitability of the garment for different sports and activities to ensure that the garment does not restrict the movement of the customer and to provide the required support. This is important for custom-made sportswear and special purpose garments.

Preferably, the dynamic adaptive calculation formula of the joint movement in step S25 is specifically:

；

wherein,for the dynamic adaptation value of the joint movement,is the firstThe joints of the two-way joint are connected,as the total number of joints,is the firstThe weight of the individual joints in the motion,is the firstThe degree of mobility of the individual joints,is the firstThe maximum rotational moment experienced by the individual joints,the dynamic shear stress of the joint is that,is the firstThe damping forces of the individual joints and muscles,is the firstThe anti-transpiration force of the joints and muscles,for the radius of the twist of the joint,for the angular velocity of the joint movement,is the joint movement period.

The invention is realized byIndicating the degree of mobility of each jointAgainst its maximum bearing moment of rotationAnd multiplying the weight of each joint in motionThe effect of this is that, taking into account the relation between the flexibility of the joint and its load bearing capacity, the weights are used to distinguish the importance of the different joints, these information are summarized as a sum,the square root of the dynamic shear stress of the joint is represented. This can be used to quantify the stress levels experienced by the joint. Square roots are often used to reduce the effect of stress values to better represent joint safety, anddamping force of individual joints and muscles And anti-transpiration forceThis may reflect the effects of muscle on joint movement, including damping and support. This helps to comprehensively consider the regulating effect of muscles on joint movement,radius of articulationDynamic shear stressNatural logarithm of (a). This section can be used to characterize the relationship between joint stress and joint size. Logarithmic functions are typically used to describe non-linear relationships, taking into account the relationship between the velocity and frequency of joint motion, as well as the motion pattern of the joint. The ratio of angular velocity to angular frequency reflects the regulation performance of the joint motion, and the formula considers a plurality of factors of the joint motion, including the motion flexibility, the moment bearing capacity, the dynamic stress, the influence of muscles on the joint, the joint size and the motion speed. By combining these factors together, a dynamic adaptation value (D) of the joint motion can be derived, which is used to evaluate the overall performance and adaptation of the joint.

Preferably, step S23 comprises the steps of:

step S231: performing joint movement angle analysis on the movement track data to generate joint movement angle parameters;

step S232: performing motion amplitude analysis on the motion trail data according to the joint motion angle parameters to generate joint motion amplitude data;

Step S233: extracting joint body line contour from the joint point data to generate joint body line contour data;

step S234: performing joint coordination analysis on the joint movement amplitude data based on the joint body line profile data to generate joint coordination data;

step S235: joint motion analysis is performed on the joint coordination data to generate joint motion parameters.

The invention provides detailed information about joint motion by joint motion angle analysis, system measurement and recording of specific angle changes of the joint. This is important to determine the flexibility and range of motion of the joint. For example, it may help the designer determine which joints require more degrees of freedom to ensure that the garment is not constrained during movement. The motion amplitude analysis determines the range of motion of the joint in different motions based on the joint motion angle parameters. This helps to understand the flexibility and mobility of the body in various movements. This is important to ensure that the garment has sufficient extensibility to accommodate a variety of movements and postures, providing comfort and functionality. The joint body line contour extraction is used to capture the shape and contour of the joint. This provides information about the precise geometry of the joint, helping to customize the design of the garment, ensuring that it matches the shape and contour of the joint, providing better fit. Through the joint body line profile data, the system can analyze the coordination between the joints, i.e., how the joints cooperate with each other in motion. This helps to determine the balance and stability of the body in different movements to ensure that the garment does not affect the normal coordination of the joints. This is critical for athletic garments and for special garments requiring a high degree of coordination. The joint mobility analysis integrates the previous steps to generate joint mobility parameters, providing comprehensive information including joint angle, range of motion, shape and coordination. This helps the designer better understand the dynamic characteristics of the body to ensure that the garment is both adaptable and provides the desired support and comfort.

Preferably, step S34 includes the steps of:

step S31: performing behavior habit analysis on the target client parameters to generate client behavior habit data;

step S32: performing clothing demand association analysis on the target customer parameters according to the dynamic adaptation data to generate clothing demand association data;

step S33: performing morphological feature analysis on the target customer parameters based on the clothing demand association data to generate morphological feature data;

step S34: and analyzing the clothing demand characteristics of the morphological characteristic data according to the customer behavior habit data, thereby generating clothing demand data.

According to the invention, through behavior habit analysis, buying habits, favorites and frequency of the clients can be better understood, and after learning the behavior habits of the clients, the primary design of the clothing module which is more in line with the expectations of the clients can be provided, and the correlation between the client parameters and the dynamic adaptation data is analyzed by the system. This helps to determine the specific clothing needs of the customer, such as sports, formalism, leisure, etc. This ensures that the garment being designed matches the customer's activities and occasions, providing the proper style. Based on the garment demand correlation data, the system performs a morphological feature analysis to understand the client's body shape, posture and appearance features. This helps to determine the cut, length and style of the garment, ensuring that the garment is visually and functionally coordinated with the physical characteristics of the customer. And according to the customer behavior habit data and the physical characteristic data, the system performs clothing demand characteristic analysis. This helps determine the materials, colors, patterns and other details to ensure that the designed garment not only meets behavioral requirements, but also matches the physical characteristics and activity requirements of the customer. This helps provide a highly personalized clothing selection.

Preferably, step S4 comprises the steps of:

step S41: virtual module design is carried out on target customer parameters according to the clothing demand data so as to generate a clothing process module; the clothing process module comprises a head module, a shoulder module, an upper limb module, a hand module, a chest module, a waist module, a lower limb module, a foot module and a neck module;

step S42: performing module material parameterization on the clothing process module to respectively generate module material parameters;

step S43: and carrying out material characteristic analysis on the module material parameters to generate module material characteristic data, wherein the module material characteristic data comprises air permeability, expansion rate, glossiness, texture data and color characteristics.

According to the invention, by designing the virtual module according to the clothing demand data of the customer, the system can generate the clothing process module with strong adaptability, including the parts such as the head, the shoulders, the upper limbs, the hands, the chest, the waist, the lower limbs, the feet, the neck and the like. This helps ensure that the garment being designed is able to conform to the physical characteristics and needs of the customer, providing comfort and style. The system parameterizes the material of each module. This means that the appropriate materials are determined for each part, e.g. soft material for the head, flexibility for the hands, supportive material for the chest, etc. This helps ensure that the garment provides proper comfort and support at different locations. By performing a texture feature analysis on the module texture parameters, the system is able to generate module texture feature data, including air permeability, stretch rate, gloss, texture data, and color features. These feature data help ensure that the material of each module matches the corresponding body part and requirements. Air permeability can affect comfort, stretch rate affects freedom of movement, gloss and color characteristics affect appearance. Such information can be used to formulate custom-made garments to ensure that they meet customer expectations in appearance and function.

Preferably, step S5 comprises the steps of:

step S51: performing dynamic matching simulation on the dynamic adaptation data based on the module material characteristic data to generate dynamic matching simulation data;

step S52: performing clothing module power demand analysis on the dynamic matching simulation data to acquire power demand data;

step S53: calculating the motion performance of the dynamic adaptation data by utilizing a module material motion performance calculation formula based on the power demand data so as to generate motion performance data;

step S54: dynamic parameter adjustment is carried out on the clothing process module according to the athletic performance data, so that a dynamic parameter module is generated;

according to the invention, the dynamic matching simulation data can be generated by simulating and matching the dynamic adaptation data based on the module material characteristic data. This enables the garment to adapt to different body positions and movements while exercising, providing better comfort and athletic performance. And carrying out clothing module power demand analysis on the dynamic matching simulation data to acquire power demand data. This means that the system can understand the support and motion characteristics required for each module in different motion scenarios, providing corresponding optimization and adjustment. In the calculation formulas of the dynamic demand data and the motion performance of the module materials, the system can calculate the motion performance of the dynamic adaptation data. This means that the performance of each module under different exercise scenarios, including elastic, torsional, tensile, etc., can be quantified. According to the athletic performance data, the system may perform dynamic parameter adjustments to the apparel process module. This means that the design in terms of materials, structures, etc. can be adjusted to the performance requirements of each module, thereby generating a dynamic parameter module. This ensures that the garment has optimal performance in different sports scenarios.

Preferably, in step S53, the calculation formula of the motion performance of the module material is specifically:

；

is the movement performance of the bulk material,is made of the elastic modulus of the material,the humidity is used for the best material quality,the temperature parameter is used for the best material quality,in order to achieve a gas permeability of the material,in order to achieve a degree of gloss,can be made ofThe speed of the stretching is such that,is made of the material with the maximum bearing tension,is the maximum deformation length of the material,is the maximum deformation width of the material,is made of the material with the density,is made of a material with a friction coefficient,is a motor performance adjusting factor.

The invention is realized byThe combined parameters related to humidity, temperature, air permeability and gloss are calculated, and the influence of these factors on the material properties is comprehensively considered, which can help evaluate the properties of the material under different humidity, temperature and air permeability conditions, +>Natural logarithm operation of material density, friction coefficient and motion performance regulating factor. This can be used to take into account the influence of the density and friction of the material on its movement properties, as well as the adjustment of the movement properties adjustment factor to the result, +.>Measuring the relationship between the strength of a material and its deformability can help evaluate the material's performance when subjected to stress, particularly when subjected to tensile forces. If this ratio is higher, this means that the material is able to withstand a greater tensile force under the given deformation conditions,/- >The material was evaluated for its properties in terms of friction and energy loss. A lower friction coefficient is generally beneficial for reducing energy losses, so this part can also be used to take into account the behaviour of the material under friction conditions, +.>The relationship between the maximum deformation length and width and the motion performance adjusting factors and the relationship between the humidity and the stretching speed are expressed, the deformation capacity and the motion performance related to the humidity of the material are taken into consideration, the factors such as strength, friction, energy loss and motion performance adjustment are taken into consideration by the formula, the comprehensive evaluation of the performance of the material under different stress and motion conditions is provided, and the motion performance of the material can be calculated and evaluated more accurately.

Preferably, step S52 includes the steps of:

step S521: performing material deformation detection on the dynamic matching simulation data to obtain material deformation data;

step S522: performing comfort analysis on the dynamic matching simulation data according to the material deformation data to generate material comfort data;

step S523: performing stability analysis on the material comfort data to generate material stability data;

step S524: performing adaptive range identification on the material stability data to generate material adaptive range parameters;

Step S525: and carrying out clothing module power demand analysis on the dynamic matching simulation data according to the material adaptation range parameters so as to acquire power demand data.

According to the invention, the deformation condition of the material under different dynamic simulation situations is detected, so that the material deformation data can be obtained. This helps to understand the elasticity, deformation and variation of the material in motion, providing important data for subsequent analysis. And carrying out comfort analysis according to the material deformation data. This allows the system to evaluate whether the material is causing discomfort or friction in movement, generating material comfort data. This is important to ensure that the garment provides good comfort during movement. Stability analysis was performed on the material comfort data. This helps to assess the stability of the material under different movement scenarios, such as whether the material is easily slipped, deformed or unstable. The generated material stability data may be used to improve the stability of the garment. By analyzing the material stability data, the application range of the material can be identified. This means that the applicability of the material under different dynamic simulation conditions is determined, and the material adaptation range parameters can be generated. This is critical to the selection of materials suitable for a particular athletic activity. And carrying out clothing module power demand analysis on the dynamic matching simulation data based on the material adaptation range parameters. This means that the support and power requirements required for the different modules can be better understood, ensuring that the garment has optimal performance in different sports scenarios.

In this specification, there is provided a modular design system for a garment process with adjustable parameters, comprising:

the morphological analysis module is used for acquiring target client parameters; performing morphological analysis on the target client parameters to generate target client morphological data; dynamically simulating the target client form data to generate body dynamic data;

the dynamic adaptation module is used for identifying the motion trail of the body dynamic data so as to generate motion trail data; performing joint activity analysis on the motion trail data to generate joint activity parameters; dynamically adapting calculation is carried out on the body dynamic data according to the joint activity parameters so as to generate dynamic adapting data;

the clothing demand feature module is used for carrying out behavior habit analysis on the target client parameters so as to generate client behavior habit data; performing clothing demand characteristic analysis on target client parameters according to client behavior habit data and dynamic adaptation data, so as to generate clothing demand data;

the material characteristic module is used for carrying out virtual module design on target customer parameters according to the clothing demand data so as to generate a clothing process module; performing module material parameterization on the clothing process module to generate module material parameters; performing material characteristic analysis on the module material parameters to generate module material characteristic data;

The motion performance module is used for performing motion performance analysis on the dynamic adaptation data based on the module material characteristic data so as to generate motion performance data; dynamic parameter adjustment is carried out on the clothing process module according to the athletic performance data, so that a dynamic parameter module is generated;

and the convolution model module is used for performing expansion convolution on the dynamic parameter module by utilizing a circular convolution network so as to construct a clothing dynamic process model and execute clothing process modularized design.

The invention obtains the physical parameters of the target client through the morphological analysis module. This allows the garment design system to tailor the personalized design to each customer's physical form, ensuring that the garment matches the customer's physical features. The dynamic adaptation module is used for analyzing the body dynamic data and the joint activity parameters, and the system can know the dynamic requirements of the client under different exercise situations. This helps ensure that the garment is not only suitable in a resting state, but also provides sufficient freedom and adaptability in movement. And the garment demand characteristic module is used for carrying out behavior habit analysis and dynamic adaptation data to help understand the behavior demands of clients. This helps to design the garment so that it can provide comfort, behavioral connectivity, and meet customer behavioral needs. The material characteristic module is used for carrying out virtual module design and material parameterization, and generating module material parameters according to the requirements of clients. This helps ensure that the material of the garment is compatible with the customer's needs and body dynamics, providing optimal comfort and performance, and the athletic performance module analyzes the athletic performance of the garment in different situations. This helps ensure that the garment provides optimal performance in sports, such as proper support and adaptability, and the dynamic parameter module is subjected to expansion convolution using a convolutional neural network to construct a garment dynamic process model. This helps to automate the design process, ensure that the garment meets customer needs in different situations, and provides optimal performance and comfort.

Drawings

FIG. 1 is a schematic flow chart of the steps of a modular design method and system for a parameter-adjustable garment process according to the present invention;

FIG. 2 is a detailed implementation step flow diagram of step S1;

FIG. 3 is a detailed implementation step flow diagram of step S2;

fig. 4 is a detailed implementation step flow diagram of step S3.

Detailed Description

It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.

The embodiment of the application provides a modularized design method and system for a clothing process with adjustable parameters. The execution main body of the parameter-adjustable clothing technology modularized design method and system comprises, but is not limited to, the system: mechanical devices, data processing platforms, cloud server nodes, network uploading devices, etc. may be considered general purpose computing nodes of the present application, including but not limited to: at least one of an audio image management system, an information management system and a cloud data management system.

Referring to fig. 1 to 4, the present invention provides a modular design method for a garment process with adjustable parameters, the method comprising the following steps:

In the embodiment of the present invention, as described with reference to fig. 1, a step flow diagram of a parameter-adjustable garment process modular design method of the present invention is provided, and in this example, the steps of the parameter-adjustable garment process modular design method include:

in this embodiment, relevant data of the customer is collected, including physiological data such as height, weight, age, sex, bone structure, etc., and other information possibly related to the design of the garment, such as style preference, activity level, occupation, etc. This may be done by way of coefficient data acquisition, questionnaires, measurements, interviews, etc., digitizing the acquired data for subsequent analysis and processing, using specialized morphological analysis tools such as body measurement software or specially designed algorithms to analyze the customer's morphological data. These tools can help determine the size, scale, and shape of the various body parts, and in the morphological analysis process, based on the client parameters, generate the body morphological data of the target client, including the size, curve, volume, etc. of the various parts. These data will be used for subsequent garment design and simulation, integrating the generated morphological data with dynamic parameters to account for the physical changes of the customer under different actions and postures. This may include data of different dynamic behavior habits such as walking, sitting, bending, etc., in which dynamic simulation body dynamic data of the customer under different actions and situations is generated, including changes in volume, changes in posture, and relative positions of body parts, etc. These data will be used in custom-made garment designs to ensure that the garment will fit comfortably against the body of the customer in a variety of situations.

in this embodiment, the motion trajectory recognition algorithm is used to analyze the body dynamic data to extract the motion trajectory of the customer. This may involve tracking the position, direction and path of movement of various parts of the body. Typically, this can be done by computer vision techniques, motion capture systems or 3D modeling software, after which you get motion trajectory data for the customer under different actions, which describe the change of the position of the body part over time, and the extracted motion trajectory data is used to identify the joint position of the customer. This may involve joint detection algorithms to determine critical joints of the body, such as the shoulder, elbow, knee, etc., using joint position data to calculate the mobility of the joint in different movements. This may include angular measurements, degree of joint bending, joint rotation, etc. These mobility parameters describe the range and manner in which the client's joint moves. And associating the joint activity parameters with the body dynamic data to know the relation between the body change and the joint activity of the client under different actions, and developing or using an adaptation model based on the joint activity parameters, wherein the model can adjust the body dynamic data according to the joint activity of the client to generate dynamic adaptation data. This may involve adjusting the shape of the body volume, curve or key part to accommodate different movements or gestures, calculating body dynamic data from the dynamic adaptation model to generate dynamic adaptation data. These data describe the physical form changes of the client under different actions and situations, taking into account the influence of the joint mobility.

in this embodiment, behavior habit data is classified into different behavior categories, such as positive, negative, neutral, etc., using a behavior analysis algorithm. The method can be realized by a natural language processing technology, character feedback or other behavior data possibly provided by the client are analyzed, the client behavior habit data is associated with the dynamic adaptation data generated before so as to know the physical change and the demand of the client in different behavior states, and the data analysis is performed by combining the behavior habit data and the dynamic adaptation data of the client so as to determine the clothing demand of the client in different behavior states. For example, in some behavioral habitual states, customers may require looser or warmer clothing, while in other behavioral habitual states, they may pay more attention to fashion or comfort, and features of clothing needs are extracted from the correlation analysis, which may include clothing style, material, color, fitness, etc. These features will guide subsequent garment design and manufacture, and based on the demand features, tailoring garment demand data describing the specific needs of the customer for the garment in different behavioral states. This may take the form of structured data so that subsequent manufacturing and design teams can understand and apply these requirements.

in this embodiment, the garment module is designed using a Computer Aided Design (CAD) tool or other virtual design tool. These tools may provide 3D modeling functionality that enables a designer to visualize the appearance and structure of the module, define details of the module, such as the type of sewing thread, the location and type of trim, and the like. These details should be matched with the customer's needs and behavior states, and appropriate materials are selected according to the design of the virtual module. This includes the physical properties of fabric, texture, color, and material, such as elasticity, breathability, etc., using parametric modeling tools or software to associate the selected material with the virtual module. This means that the properties of the material, such as gloss, softness, elasticity, etc. of the material are defined so that the subsequent analysis and production steps can accurately apply these parameters, analyze the selected module material, and extract various feature data. These characteristics may include thermal conductivity, transparency, strength, weight, etc. of the material, and the performance and characteristics of the module are evaluated using simulation and calculation tools. This may include analyzing the suitability of the material for different behavioral conditions, such as temperature control, humidity management, comfort, etc.

in this embodiment, features relating to the performance of the sport are extracted from the dynamic adaptation data. This may include joint angles, muscle activity, body position, etc., using a motion biomechanical model or computer simulation to analyze motion performance. This may help understand physical burden during exercise, range of articulation, etc., taking into account the effects of behavioral states on exercise performance, such as anxiety, excitement, fatigue, etc. The behavioral state data may be feedback or physiological indicators from the subject, integrating the analyzed data into a athletic performance data set. The data should include performance parameters in various exercise scenarios, such as comfort, exercise efficiency, thermoregulation, etc., the design of the virtual module is parameterized to have adjustable dynamic parameters. These parameters may include elasticity of the material, air permeability, thermal insulation, etc., and the athletic performance data is used to adjust the dynamic parameters of the virtual module. This may require the establishment of a parameter optimization algorithm to maximize performance parameters, such as comfort and athletic efficiency, to verify and test the tuned virtual module to ensure that it provides optimal performance under various behavioral states and athletic scenarios.

In this embodiment, the RNN is a neural network architecture suitable for processing time series data. Here, RNNs will be used in the dilation convolution to take into account the timing characteristics of the dynamic parameters, and the appropriate RNN type is selected according to the task requirements, such as LSTM (long short-time memory network) or GRU (gate loop unit), the inputs of the RNNs being the dynamic parameter data sequences and the outputs being the characteristic sequences of the process model. The dilation convolution is a convolution operation that expands the receptive field of the convolution kernel to capture features of different scales, defining a dilation convolution kernel of appropriate size for each level in the RNN model, ensuring that the input and output data shapes match correctly. These kernels will be used to perform convolution operations on dynamic parameter data, using defined dilation convolution kernels, on time series data. This will generate a series of feature maps, each corresponding to a different time scale, integrating the information extracted from the feature maps of different scales into one comprehensive garment dynamics process model. This model may include various process parameters such as stitching, cutting, sewing, etc. to simulate the garment manufacturing process, performing modular design using the constructed garment dynamics process model. This involves selecting different process parameters and methods during the garment manufacturing process to accommodate different design requirements and dynamic parameters.

In this embodiment, as described with reference to fig. 2, a detailed implementation step flow diagram of the step S1 is described, and in this embodiment, the detailed implementation step of the step S1 includes:

step S11: obtaining target client parameters;

In this embodiment, relevant data of the customer is collected, including physiological data such as height, weight, age, sex, bone structure, etc., and other information possibly related to the design of the garment, such as style preference, activity level, occupation, etc. This may be done by way of coefficient data acquisition, questionnaires, measurements, interviews, etc., digitizing the acquired data for subsequent analysis and processing, using specialized morphological analysis tools such as body measurement software or specially designed algorithms to analyze the customer's morphological data. These tools can help determine the size, scale, and shape of the various body parts, and in the morphological analysis process, based on the client parameters, generate the body morphological data of the target client, including the size, curve, volume, etc. of the various parts. Professional body composition analysis software or tools are used to analyze the body fat profile of the customer. These tools typically provide graphics and charts showing the distribution of fat throughout the body, and generate three-dimensional morphological data of the customer through mathematical modeling and computer graphics techniques. This may include generating geometric, proportional and cosmetic body features, using specialized dynamic simulation software that is typically used to simulate the motion and movements of a human body, importing morphological data of a customer into the simulation software to create virtual agents for the customer, defining activities of the customer in different sports scenarios, such as walking, running, weightlifting, etc., running simulations to generate dynamic behavior habits of the customer in different activities, including gestures, movements and body changes, using a body motion capture system, such as an optical sensor, inertial sensor or camera array, to capture the actual movements of the customer, placing sensors or marker points on the customer's body to enable the system to accurately track the movements of the customer, the sensor system recording the movement data, integrating the movement capture data with previous dynamic simulation data to create accurate body dynamic data.

In this embodiment, as described with reference to fig. 3, a detailed implementation step flow diagram of the step S2 is shown, and in this embodiment, the detailed implementation step of the step S2 includes:

In this embodiment, body dynamics data is obtained by a motion capture system, which typically includes three-dimensional coordinate points representing the position of different body parts over time, and the joints in the body dynamics data are detected using computer vision techniques or motion capture software. These joints are typically critical parts of the human body, such as the head, neck, shoulders, elbows, knees, and the position information of each joint is extracted to generate joint data. These data will include coordinates, velocity, acceleration, etc. of each joint point, and computer vision or pattern recognition techniques are used to analyze the joint point data to identify the motion trajectories of the different joints. This may include the path and direction of movement of the limb in space, and the identified movement trace data is recorded for subsequent analysis. These data may describe the manner and path of movement of the joints, and the mobility parameters of each joint are calculated using mathematical methods. This may include rotation angle, velocity, acceleration, etc. of the joint, and the calculated joint mobility parameters are recorded for subsequent analysis and application, using physical principles and biomechanical principles, to analyze the physical stability of the customer. This may include detecting center of gravity position, support area, posture, etc., generating body balance data from the analysis results, describing the balance status of the customer in different movements or activities, applying appropriate calculation formulas and algorithms, analyzing body balance data and joint mobility parameters to assess the dynamic fitness of the customer. This may include recording calculated dynamic adaptation data describing the physical fitness level of the customer in different situations, taking into account balance adjustments and adaptations in different sports or activities.

In this embodiment, the dynamic adaptive calculation formula of the joint motion in step S25 specifically includes:

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The invention is realized byIndicating the degree of mobility of each jointAgainst its maximum bearing moment of rotationAnd multiplying the weight of each joint in motionThe effect of this is that, taking into account the relation between the flexibility of the joint and its load bearing capacity, the weights are used to distinguish the importance of the different joints, these information are summarized as a sum,the square root of the dynamic shear stress of the joint is represented. This can be used to quantify the joint supportStress level to which the test piece is subjected. Square roots are often used to reduce the effect of stress values to better represent joint safety, anddamping force of individual joints and muscles And anti-transpiration forceThis may reflect the effects of muscle on joint movement, including damping and support. This helps to comprehensively consider the regulating effect of muscles on joint movement,radius of articulationDynamic shear stressNatural logarithm of (a). This section can be used to characterize the relationship between joint stress and joint size. Logarithmic functions are typically used to describe non-linear relationships, taking into account the relationship between the velocity and frequency of joint motion, as well as the motion pattern of the joint. The ratio of angular velocity to angular frequency reflects the regulation performance of the joint motion, and the formula considers a plurality of factors of the joint motion, including the motion flexibility, the moment bearing capacity, the dynamic stress, the influence of muscles on the joint, the joint size and the motion speed. By combining these factors, a dynamic adaptation value D of the joint motion can be derived, which is used to evaluate the overall performance and adaptation of the joint.

In this embodiment, step S23 includes the steps of:

In this embodiment, the movement angle of each joint is calculated using three-dimensional coordinate data. This may involve calculating the angle of flexion, rotation, etc. of the joint using the relationship between the vector and the joint point, and recording the calculated angle of articulation to form an angle of articulation parameter. These parameters will describe the angular change of the joint in motion, and the range of motion of the joint is calculated using the joint motion angle parameters. This may involve measuring the range of variation, speed, etc. of the angle, recording the calculated range of motion of the joint, and forming range of motion data of the joint. These data will describe the breadth and speed of joint motion, and joint line contours are extracted from the joint point data using computer vision techniques or image processing techniques. This may be the connection line between the joint points, forming the contour of the joint, and recording the extracted joint body line contour data, forming the joint body line contour data. These data will describe the connection and morphology between the joints and the joint body line profile and joint movement amplitude data are analyzed using mathematical methods or algorithms to assess coordination between the joints. This may include detecting whether there is a coordination pattern between different joint movements, generating joint coordination data from the analysis results, describing the coordination level of the joint in motion, analyzing the joint coordination data using statistical or machine learning methods to evaluate the degree of joint movement. This may include the frequency, stability, etc. of joint movement, and the analyzed joint movement parameters are recorded to form the final joint movement parameters. These parameters will provide a comprehensive assessment of joint movement performance.

In this embodiment, as described with reference to fig. 4, a detailed implementation step flow diagram of the step S3 is shown, and in this embodiment, the detailed implementation step of the step S3 includes:

In this embodiment, data analysis tools (such as pandas and numpy in Python) are used to search data, find information such as purchasing mode, preference class, shopping time, and the like of clients, generate client behavior habit data according to analysis results, including information such as purchasing preference, shopping frequency, class preference, and the like, and analyze dynamic adaptation data of target clients by using data mining and machine learning techniques so as to know their clothing requirements. This may include identifying purchase patterns, preference garment types and colors, etc., generating garment demand correlation data based on the analysis results, correlating customer demands with specific garment characteristics to guide subsequent garment recommendations and designs, analyzing customer posture characteristics, such as stature, skin tone, facial form, etc., using computer vision techniques and image processing algorithms. This may include detecting body contours and facial features, generating body state feature data based on the analysis results to describe the physical and appearance features of the customer, analyzing the customer behavioral habits and body state feature data using data analysis and machine learning techniques to learn the customer's demand features for apparel. This may include an association between behavioral habits and physical characteristics, and based on the analysis results, final garment demand data is generated, which may include recommended garment types, colors, styles to meet the customer's behavioral and physical characteristics needs.

In this embodiment, step S4 includes the following steps:

In this embodiment, apparel process modules are designed that represent different portions of the apparel, such as the head, shoulders, hands, and the like. The shape, size and style of each module are customized according to the requirements and characteristics of customers, and the designed modules are integrated together to construct the complete virtual garment. The transition and connection between the various modules is ensured to be naturally smooth so as to ensure the comfort and the aesthetic property of the final garment, and each module is respectively assigned with material parameters including material type (such as cotton, silk, leather, etc.), thickness, density and other properties related to the material. These parameters will define the look and feel of each module, ensuring that the module material parameters match the garment demand data and customer characteristics. For example, depending on customer preference and season, appropriate material type and thickness are selected, module material characteristics are defined, including but not limited to air permeability (degree of air permeation through the material), stretch (elasticity and ductility of the material), gloss (surface reflectance of the material), texture data (surface texture or pattern of the material), and color characteristics, and the material parameters of each module are analyzed using a texture testing instrument and image processing techniques to obtain relevant material characteristic data. For example, the values of these characteristics are obtained by measuring air permeability and stretch ratio, and the material characteristic data of the modules are integrated together to describe the material characteristics of the whole garment. This may cover the features of the individual modules, as well as their combination in the garment.

In this embodiment, step S5 includes the following steps:

In this embodiment, dynamic adaptation data describing changes in the body under different motion and activities, such as stretching, bending and twisting of body parts, are acquired or created, and model material characterization data is combined with the dynamic adaptation data using simulation software or algorithms. This may involve simulating deformation of the garment by the body during movement, such as stretching of the cloth and shape changes. The generated dynamic matching simulation data describes the adaptability of the garment in various dynamic situations, defining power demand data reflecting the strength and dynamic demands on the garment, such as stretch, pressure and friction, in different movement situations, the power demand of each module being analyzed using the generated dynamic matching simulation data. This includes determining the forces and stresses that each module needs to withstand under a particular athletic or activity scenario, defining athletic performance calculation formulas that will calculate the athletic performance of each module based on the power demand data, module material characteristic data, and dynamic adaptation data. This may include the elasticity, durability, tensile strength, etc. characteristics of the modules, with each module being subjected to a athletic performance analysis using an athletic performance calculation formula. This can help determine the durability of the module, how to respond to the force and dynamic needs, and whether additional reinforcement or material adjustments are needed, redesign the apparel process module based on athletic performance data to meet the required dynamic performance. This may include changing materials, reinforcing structures, adjusting the shape or size of the modules, etc., adjusting each module to ensure that they remain comfortable, durable, and effective in various athletic situations. This may require re-evaluation of the combination of material parameters and modules, generating new dynamic parameter modules based on the adjusted design. These modules have been optimized to meet dynamic performance requirements while taking into account module materials and power requirements.

In this embodiment, the calculation formula of the motion performance of the module material in step S53 is specifically:

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is the movement performance of the bulk material,is made of the elastic modulus of the material,the humidity is used for the best material quality,the temperature parameter is used for the best material quality,in order to achieve a gas permeability of the material,in order to achieve a degree of gloss,the speed of the material can be stretched,is made of the material with the maximum bearing tension,is the maximum deformation length of the material,is the maximum deformation width of the material,is made of the material with the density,is made of a material with a friction coefficient,is a motor performance adjusting factor.

The invention is realized byThe combined parameters related to humidity, temperature, air permeability and gloss are calculated, and the influence of these factors on the material properties is comprehensively considered, which can help evaluate the properties of the material under different humidity, temperature and air permeability conditions, +>Natural logarithm operation of material density, friction coefficient and motion performance regulating factor. This can be used to take into account the effects of material density and friction on its athletic performance, as well as the adjustment of the athletic performance adjustment factor to the result,measuring the relationship between the strength of a material and its deformability can help evaluate the material when it is subjected to a forcePerformance, especially when subjected to tensile forces. If this ratio is higher, it means that the material is able to withstand a greater tensile force under a given deformation condition, The material was evaluated for its properties in terms of friction and energy loss. A lower friction coefficient is generally beneficial for reducing energy losses, so this part can also be used to take into account the behaviour of the material under friction conditions, +.>The relationship between the maximum deformation length and width and the motion performance adjusting factors and the relationship between the humidity and the stretching speed are expressed, the deformation capacity and the motion performance related to the humidity of the material are taken into consideration, the factors such as strength, friction, energy loss and motion performance adjustment are taken into consideration by the formula, the comprehensive evaluation of the performance of the material under different stress and motion conditions is provided, and the motion performance of the material can be calculated and evaluated more accurately. />

In this embodiment, step S52 includes the steps of:

In this embodiment, the dynamic matching simulation data is analyzed by using image processing or simulation software to detect the deformation of the clothing material. The method can include detecting stretching, twisting and deformation of cloth, extracting and recording deformation data of clothing materials under different conditions, wherein the data describe the change condition of the materials under different dynamic conditions, and carrying out comfort analysis by using the deformation data of the materials so as to determine the contact and friction condition of the materials to skin under different dynamic conditions. This includes evaluating the degree of friction of the material against the skin, the air permeability, and the feel of the skin, and based on the results of the comfort analysis, generating comfort data describing the material under various dynamic scenarios. This may include a comfort score or describing the effect of the material on the skin, and stability analysis is performed using the generated material comfort data to determine the stability of the material under different dynamic scenarios. This includes generating stability data describing the material under various dynamic scenarios based on the results of the stability analysis, taking into account the wear resistance, tear strength, and long-term performance of the material. This may include stability scoring or describing the performance of the material over extended periods of use, defining adaptation range parameters that will measure the adaptation range of the material based on the material stability data. The adaptation range may include a particular type of motion, duration, and environmental conditions, with the adaptation range for each material identified based on the material stability data. This may involve determining which dynamic scenarios the material is suitable for and in which cases an alternative material is required, generating parameters describing the adaptation range of the material based on the results of the adaptation range identification. The parameters can be used for guiding the design and material selection of the clothing, the generated material adaptation range parameters are used for dynamically matching simulation data to determine which materials and modules are applied under specific dynamic situations, and the power demand analysis is performed according to the power demand data and the simulation data with the material adaptation range parameters applied to determine the required strength and dynamic demands of each module under different situations.

In this embodiment, a modular design system for a garment process with adjustable parameters is provided, including:

The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein.

It will be understood that, although the terms "first," "second," etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another element. For example, a first element could be termed a second element, and, similarly, a second element could be termed a first element, without departing from the scope of example embodiments. The term "and/or" as used herein includes any and all combinations of one or more of the associated listed items.

The foregoing is only a specific embodiment of the invention to enable those skilled in the art to understand or practice the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. The modular design method of the clothing technology with adjustable parameters is characterized by comprising the following steps of:

2. The modular design method for a garment process with adjustable parameters according to claim 1, wherein the specific steps of step S1 are as follows:

step S11: obtaining target client parameters;

3. The modular design method for a garment process with adjustable parameters according to claim 1, wherein the specific steps of step S2 are as follows:

the dynamic adaptive calculation formula of the joint movement in step S25 specifically includes:

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wherein,dynamic adaptation to joint movement>Is->Individual joints (I)>For total number of joints->Is->Weight of individual joints in motion, +.>Is->Mobility of individual joints, < >>Is->Maximum pivoting moment of the individual joints, < >>Dynamic shear stress of joint->Is->Damping force of individual joints and muscles, +.>Is->Anti-transpiration force of individual joints and muscles, +.>For the radius of twist of the joint +.>For the angular velocity of joint movement +.>Is the joint movement period.

4. The modular design method for a garment process with adjustable parameters according to claim 3, wherein the specific steps of step S23 are as follows:

5. The modular design method for a garment process with adjustable parameters according to claim 1, wherein the specific steps of step S3 are as follows:

6. The modular design method for a garment process with adjustable parameters according to claim 1, wherein the specific steps of step S4 are as follows:

7. The modular design method for a garment process with adjustable parameters according to claim 1, wherein the specific steps of step S5 are as follows:

step S54: and carrying out dynamic parameter adjustment on the clothing process module according to the athletic performance data, thereby generating a dynamic parameter module.

8. The modular design method for a clothing process with adjustable parameters according to claim 7, wherein the calculation formula of the motion performance of the module material in step S53 is specifically:

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for the exercise performance of bulk material->Is the elastic modulus of the material>Humidity is used optimally for the material, ">Optimal use temperature parameters for the material, +.>For air permeability->For glossiness, add>Is a stretchable speed of material->Is made of material with maximum bearing tension->Is the maximum deformation length of the material>Is the maximum deformation width of the material>Is of material density>Is the friction coefficient of the material>Is a motor performance adjusting factor.

9. The modular design method for a garment process with adjustable parameters according to claim 8, wherein the specific steps of step S52 are as follows:

10. A parameter-adjustable garment process modular design system for performing the parameter-adjustable garment process modular design method of claim 1, comprising: