Sewing product digital simulation method based on Internet of Things

Abstract

The invention particularly relates to a sewing product digital simulation method based on the Internet of Things. The method comprises the steps of collecting multi-source data; data transmission; carrying out digital modeling; calibrating model parameters; and performing digital simulation. According to the invention, by means of the Internet of Things technology, multi-source data in the sewing process can be comprehensively and accurately collected, including sewing machine operation parameters, cloth physical characteristic data, sewing environment data, sewing product identity and position information and the like; through deep analysis of the data, an accurate digital model can be established, and the situations of cloth deformation, stitch quality and the like can be effectively predicted; by analyzing factors such as presser foot pressure, sewing machine puncture times and environmental parameters, an evaluation coefficient is obtained to determine cloth deformation, so that problems possibly affecting product quality are found in advance, process parameters are adjusted in time, it is ensured that the quality of sewn products is stable and meets high standards, and the defective rate is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of sewing product production, and in particular to a digital simulation method for sewing products based on the Internet of Things. Background Art

[0002] In the traditional production process of sewing products, due to the lack of comprehensive and accurate control over the sewing process, problems such as low production efficiency and uneven product quality are more prominent.

[0003] In the past, it was difficult to accurately monitor sewing machine operating parameters in real time. Parameters like speed, stitch density, and thread tension were often manually adjusted based on worker experience. Without precise data feedback and automated monitoring, sewing quality was significantly affected by human factors, making it difficult to ensure consistency.

[0004] For example, unstable stitch density may lead to inconsistent firmness of sewn products, and inappropriate thread tension may easily cause problems such as thread breakage or fabric wrinkling.

[0005] Therefore, it is of practical significance to digitally simulate the sewing process to simulate the deformation of the fabric during sewing and adjust the sewing-related parameters accordingly to reduce the deformation of the fabric.

[0006] Therefore, a digital simulation method of sewing products based on the Internet of Things is proposed to address the above-mentioned problems. Summary of the Invention

[0007] The purpose of the present invention is to solve the above problems and to propose a digital simulation method for sewn products based on the Internet of Things.

[0008] In order to achieve the above object, the present invention adopts the following technical solutions:

[0009] The digital simulation method of sewing products based on the Internet of Things includes:

[0010] Multi-source data collection: obtain various parameter information during the sewing process;

[0011] Data transmission: Transmit the collected data from the sensor node to the gateway device;

[0012] Digital modeling: Establishing a 3D digital model of sewing products and a sewing process model; including analyzing fabric-related data to obtain evaluation coefficients, and predicting fabric deformation during the sewing process based on the evaluation coefficients;

[0013] Model parameter calibration: Compare the collected data with the simulation results of the digital model and adjust the parameters of the digital model;

[0014] Digital simulation: simulate the sewing process of different styles of sewing products.

[0015] Preferably, the multi-source data collection specifically includes:

[0016] The various parameter information during the sewing process includes sewing machine operating parameters, fabric physical property data, sewing environment data, and sewing product identity and location information;

[0017] Sewing machine operating parameters include:

[0018] Speed: The speed sensor installed on the sewing machine motor monitors the motor's rotation speed in real time;

[0019] Stitch density: Use optical sensors or encoders to monitor the up and down movement of the sewing machine needle and the feed speed of the fabric; obtain stitch density information by counting the number of stitches per unit length;

[0020] Thread tension, using a tension sensor installed on the thread path to measure the thread tension changes during the sewing process;

[0021] Cloth physical property data includes:

[0022] Thickness, using ultrasonic sensors or laser displacement sensors to measure the thickness of the fabric;

[0023] Hardness: A certain pressure is applied to the fabric through a pressure sensor to measure the deformation of the fabric, thereby evaluating the hardness of the fabric;

[0024] Material: Spectral sensors are used to analyze the spectral characteristics of fabrics and identify and classify them according to the spectral characteristics of different materials;

[0025] Sewing environment data includes:

[0026] Temperature and humidity: temperature and humidity sensors are installed in the sewing workshop to monitor the temperature and humidity changes of the environment in real time;

[0027] Sewn product identity and location information includes:

[0028] Electronic tags: RFID tags are attached to sewn products, and the tag information is read by RFID readers at different locations on the production line;

[0029] Position sensors use laser positioning systems or visual recognition systems to determine the specific position and posture of sewn products on the production line.

[0030] Preferably, the data transmission specifically includes:

[0031] Wireless transmission: Using wireless communication technology to transmit the collected data from the sensor node to the gateway device;

[0032] Cloud computing platform access: The gateway device uploads the collected data to the cloud computing platform via the Internet;

[0033] And remove noise and outliers in the collected data.

[0034] Preferably, the digital modeling includes:

[0035] 3D digital model of sewing products:

[0036] Geometric shape modeling: obtaining the shape data of sewn products and converting the scanned data into a three-dimensional geometric model;

[0037] Modeling of cloth deformation characteristics: building a cloth deformation model based on the elasticity, plasticity, and anisotropy of the cloth;

[0038] Suture mechanical properties modeling, studying the tensile, bending, and torsion mechanical properties of sutures, and establishing a constitutive model of sutures;

[0039] Sewing process model:

[0040] Modeling the motion trajectory of a sewing machine needle: establishing a kinematic model of the needle to describe its motion trajectory in space;

[0041] Modeling the stitch formation process, studying the path and interweaving pattern of the stitch in the fabric, and establishing a mathematical model of stitch formation.

[0042] Preferably, the sewing process model further includes modeling of the force and deformation of the fabric during the sewing process, including analyzing fabric-related data to obtain an evaluation coefficient, and determining the fabric deformation during the sewing process based on the evaluation coefficient, specifically including the following parts:

[0043] Select a fabric of preset thickness as the test fabric;

[0044] Obtain the presser foot pressure applied to the test fabric during the sewing process at preset time intervals to obtain the presser foot pressure at each time point;

[0045] The presser foot pressure at each time point is averaged to obtain a presser foot pressure reference value;

[0046] Extract the maximum presser foot pressure and the minimum presser foot pressure from the presser foot pressure, and calculate the difference to obtain the extreme pressure difference value;

[0047] Preset an allowable fluctuation range of the presser foot pressure reference value, match the presser foot pressure at each time point with the allowable fluctuation range of the preset presser foot pressure reference value, and record the presser foot pressure that is not within the allowable fluctuation range of the preset presser foot pressure reference value as abnormal pressure;

[0048] Obtain the number of abnormal pressures, and divide all the abnormal pressure numbers by the number of presser foot pressures to obtain the abnormal ratio;

[0049] The pressure deviation value is obtained by comprehensively analyzing the pressure extreme difference value and the abnormal ratio;

[0050] After analyzing the sewing speed of the sewing machine, the speed ratio is obtained;

[0051] After analyzing the environmental information, the environmental impact value is obtained;

[0052] The pressure deviation value, the speed ratio, and the environmental impact value are comprehensively processed to obtain the evaluation coefficient; a threshold value of the evaluation coefficient is preset, and the evaluation coefficient is compared with the threshold value of the evaluation coefficient. If the evaluation coefficient is greater than the threshold value of the evaluation coefficient, it is determined that the fabric corresponding to the evaluation coefficient will be deformed.

[0053] Preferably, the model parameter calibration specifically includes:

[0054] Comparative analysis: compare the actual collected information such as sewing machine operating parameters, fabric deformation data, and stitch quality with the simulation results of the digital model; calculate the error index between the actual data and the simulated data;

[0055] Parameter adjustment: adjust the parameters of the digital model according to the results of comparative analysis;

[0056] An optimization algorithm is used to globally optimize the model parameters to minimize the error between the actual data and the simulated data.

[0057] Preferably, the digital simulation specifically includes:

[0058] Sewing scene simulation:

[0059] Style simulation: according to different sewing product styles, adjust the geometric shape and sewing process parameters of the digital model to simulate the sewing process of different styles of sewing products;

[0060] Material simulation: change the material parameters of fabric and stitching in the model to simulate the sewing process of sewing products made of different materials.

[0061] Preferably, the virtual display and interaction are also included, and the specific contents are:

[0062] Virtual Showcase:

[0063] 3D model display: using 3D modeling software and rendering engine to present the digital simulation results to users in the form of 3D models;

[0064] Animation demonstration: create an animated video of the sewing process, showing the movement of the sewing machine, the deformation of the fabric, and the formation of the stitches;

[0065] Interactive features:

[0066] Model operation provides users with functions such as rotation, scaling, translation and sectioning of 3D models, allowing users to observe the details of sewing products from different angles and levels;

[0067] Parameter modification allows users to modify the parameters of the digital model and sewing process parameters and observe the changes in simulation results in real time.

[0068] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0069] 1. By leveraging Internet of Things technology, the present invention can comprehensively and accurately collect multi-source data from the sewing process, including sewing machine operating parameters, fabric physical properties, sewing environment data, and sewn product identity and location information. Through in-depth analysis of this data, an accurate digital model can be established to effectively predict fabric deformation, stitch quality, and other conditions. By analyzing factors such as presser foot pressure, sewing machine puncture times, and environmental parameters, an evaluation coefficient is derived to determine fabric deformation, thereby identifying problems that may affect product quality in advance and allowing timely adjustment of process parameters to ensure stable and high-standard quality of sewn products and reduce the defective rate.

[0070] 2. The present invention, on the one hand, uses 3D modeling software and a rendering engine to display digital simulation results in the form of realistic 3D models. Users can also intuitively understand the sewing machine movement, fabric deformation, and stitch formation process through animated videos. On the other hand, users can perform various operations on the 3D model, modify digital model parameters and sewing process parameters, and observe changes in simulation results in real time. This enables users, such as designers and process personnel, to explore sewing effects under different design and process schemes in a virtual environment, optimize the design and process in advance, and avoid problems caused by unreasonable design or process in actual production. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Further details, features and advantages of the present application are disclosed in the following description of exemplary embodiments in conjunction with the accompanying drawings, in which:

[0072] Figure 1 is a flow chart of the present invention; DETAILED DESCRIPTION

[0073] Several embodiments of the present application will be described in more detail below with reference to the accompanying drawings so that those skilled in the art can implement the present application. The present application can be embodied in many different forms and for many different purposes and should not be limited to the embodiments described herein. These embodiments are provided to make the present application comprehensive and complete and to fully convey the scope of the present application to those skilled in the art. The embodiments do not limit the present application.

[0074] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs. It will be further understood that terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the relevant art and / or the context of this specification, and will not be interpreted in an idealized or overly formal sense unless explicitly defined as such herein.

[0075] See also Figure 1 As shown, the present invention provides a technical solution:

[0076] The digital simulation method of sewing products based on the Internet of Things includes:

[0077] Multi-source data collection: obtain various parameter information during the sewing process;

[0078] Multi-source data collection, including:

[0079] The various parameter information during the sewing process includes sewing machine operating parameters, fabric physical property data, sewing environment data, and sewing product identity and location information;

[0080] Sewing machine operating parameters include:

[0081] The rotation speed of the motor is monitored in real time by a speed sensor (such as a Hall sensor) installed on the sewing machine motor. The sensor converts the pulse signal generated by the motor rotation into an electrical signal and transmits it to the data acquisition module. After processing, accurate speed data is obtained.

[0082] Stitch density: Use optical sensors or encoders to monitor the up and down movement of the sewing machine needle and the feed speed of the fabric; obtain stitch density information by counting the number of stitches per unit length;

[0083] Thread tension: A tension sensor is installed on the thread path to measure the change in thread tension during the sewing process. The tension sensor converts the force change into an electrical signal, which is amplified and converted to obtain the specific value of the thread tension.

[0084] Cloth physical property data includes:

[0085] Thickness: Use ultrasonic sensors or laser displacement sensors to measure the thickness of fabrics. Ultrasonic sensors emit ultrasonic waves to the fabric and calculate the thickness based on the time difference of the reflected waves. Laser displacement sensors determine the thickness of fabrics by emitting a laser beam and measuring the position change of the reflected light.

[0086] Hardness: A certain pressure is applied to the fabric through a pressure sensor to measure the deformation of the fabric, thereby evaluating the hardness of the fabric;

[0087] Material: Spectral sensors are used to analyze the spectral characteristics of fabrics and identify and classify them according to the spectral characteristics of different materials;

[0088] Sewing environment data includes:

[0089] Temperature and humidity: temperature and humidity sensors are installed in the sewing workshop to monitor the temperature and humidity changes of the environment in real time;

[0090] Sewn product identity and location information includes:

[0091] Electronic tags: RFID (radio frequency identification) tags are attached to sewing products. RFID readers are used to read the tag information at different locations on the production line to obtain the sewing product's identity, production batch, order number and other data;

[0092] Position sensors use laser positioning systems or visual recognition systems to determine the specific position and posture of sewn products on the production line;

[0093] Data transmission: Transmit the collected data from the sensor node to the gateway device;

[0094] Data transmission, specifically including:

[0095] Wireless transmission: Using wireless communication technologies such as Wi-Fi, Bluetooth, and ZigBee to transmit collected data from sensor nodes to gateway devices;

[0096] Wired transmission: For scenarios with short distances and large data transmission volumes, wired communication methods such as Ethernet and RS-485 can be used to ensure the stability and reliability of data transmission.

[0097] Cloud computing platform access: The gateway device uploads the collected data to the cloud computing platform via the Internet for centralized processing and storage;

[0098] and remove noise and outliers from the collected data;

[0099] For example, for speed data, if a value clearly exceeds the normal range, it is considered an outlier and is removed or corrected;

[0100] Digital modeling: Establishing a 3D digital model of sewing products and a sewing process model; including analyzing fabric-related data to obtain evaluation coefficients, and predicting fabric deformation during the sewing process based on the evaluation coefficients;

[0101] Digital modeling includes:

[0102] 3D digital model of sewing products:

[0103] Geometric shape modeling: using 3D scanning technology (such as laser scanning, structured light scanning) to obtain the shape data of sewn products, and converting the scanning data into a 3D geometric model through reverse engineering software;

[0104] Fabric deformation characteristic modeling: Based on the elasticity, plasticity and anisotropy of fabric, the finite element analysis method is used to establish the fabric deformation model;

[0105] Suture mechanical properties modeling: studying the tensile, bending, and torsion mechanical properties of sutures, and establishing a constitutive model for sutures. Simulating the stress and deformation of sutures during sewing, taking into account factors such as suture material, diameter, and twist.

[0106] The sewing process model also includes modeling the force and deformation of the fabric during the sewing process. This involves analyzing fabric-related data to obtain evaluation coefficients, and determining fabric deformation during the sewing process based on the evaluation coefficients. Specifically, it includes the following parts:

[0107] Select a fabric of preset thickness as the test fabric;

[0108] Obtain the presser foot pressure applied to the test fabric during the sewing process at preset time intervals to obtain the presser foot pressure at each time point;

[0109] The presser foot pressure at each time point is averaged to obtain a presser foot pressure reference value;

[0110] Extract the maximum presser foot pressure and the minimum presser foot pressure from the presser foot pressure, and calculate the difference to obtain the extreme pressure difference value;

[0111] Preset an allowable fluctuation range of the presser foot pressure reference value, match the presser foot pressure at each time point with the allowable fluctuation range of the preset presser foot pressure reference value, and record the presser foot pressure that is not within the allowable fluctuation range of the preset presser foot pressure reference value as abnormal pressure;

[0112] Obtain the number of abnormal pressures, and divide all the abnormal pressure numbers by the number of presser foot pressures to obtain the abnormal ratio;

[0113] The pressure deviation value is obtained by comprehensively analyzing the pressure extreme difference value and the abnormal ratio;

[0114] After normalizing the extreme pressure difference and the abnormal ratio, the extreme pressure difference and the abnormal ratio are used as the two right-angled sides of a right triangle, and the remaining side is connected to obtain a complete right triangle. The area of the right triangle is calculated and recorded as the pressure deviation value.

[0115] After analyzing the sewing speed of the sewing machine, the speed ratio is obtained;

[0116] Obtain the number of punctures per unit time of the sewing machine; extract the standard number of punctures corresponding to the thickness of the fabric according to the thickness of the fabric, and divide the number of punctures per unit time of the sewing machine by the corresponding standard number of punctures to obtain the speed ratio;

[0117] After analyzing the environmental information, the environmental impact value is obtained;

[0118] Obtaining suitable sewing environment parameter information and marking it as standard environment parameter information, the environment parameter information including temperature and humidity;

[0119] Obtaining environmental parameter information at preset time intervals after the sewing machine is started;

[0120] The standard deviation value is obtained by calculating the difference between the environmental parameter information obtained at each time interval and the standard environmental parameter information;

[0121] Extract the maximum standard deviation value and the minimum deviation value from the acquired environmental parameter information, and calculate the difference between the maximum deviation value and the minimum deviation value to obtain the deviation extreme value;

[0122] The durations corresponding to the maximum deviation value and the minimum deviation value are obtained, and the durations corresponding to the maximum deviation value and the minimum deviation value are summed to obtain the deviation period;

[0123] After normalizing the standard deviation, extreme deviation, and deviation period, the standard deviation and extreme deviation are used as the long side and short side of the rectangle, respectively, to construct a complete rectangle. The deviation period is used as the height of the rectangle to build a rectangular model, calculate the volume of the rectangle, and record it as the influence value.

[0124] Calculate the impact value of each environmental parameter separately, and preset the weight factor of each environmental parameter. After multiplying each environmental parameter with its corresponding weight factor, the sum is calculated to obtain the environmental impact value;

[0125] The evaluation coefficient is obtained by comprehensively processing the pressure deviation value, speed ratio and environmental impact value;

[0126] Preset weight factors for pressure deviation value, speed ratio, and environmental impact value. Multiply the pressure deviation value, speed ratio, environmental impact value, and their corresponding weight factors and then sum them to obtain an evaluation coefficient. Preset an evaluation coefficient threshold. Compare the evaluation coefficient with the evaluation coefficient threshold. If the evaluation coefficient is greater than the evaluation coefficient threshold, it is determined that the fabric corresponding to the evaluation coefficient will be deformed.

[0127] Sewing process model:

[0128] Sewing machine needle motion trajectory modeling: Based on the mechanical structure and motion principle of the sewing machine, a kinematic model of the needle is established to describe the needle's motion trajectory in space. Factors such as the sewing machine's speed, stitch length, and the movement of the thread take-up mechanism are taken into account to simulate the needle's up and down motion and back and forth swing.

[0129] Modeling the stitch formation process: studying the path and interweaving pattern of the stitch in the fabric, and establishing a mathematical model of stitch formation. Simulating the formation process of different stitch types by taking into account factors such as thread tension, fabric friction, and stitch density.

[0130] Model parameter calibration: Compare the collected data with the simulation results of the digital model and adjust the parameters of the digital model;

[0131] Model parameter calibration, including:

[0132] Comparative analysis: compare the actual data collected, such as sewing machine operating parameters, fabric deformation data, and stitch quality, with the simulation results of the digital model. By plotting the curves of the actual data and the simulated data, the differences between the two can be visually observed. Statistical analysis methods are used to calculate error indicators between the actual data and the simulated data, such as mean square error (MSE) and mean absolute error (MAE), to evaluate the accuracy of the model.

[0133] Parameter adjustment: adjust the parameters of the digital model according to the results of comparative analysis;

[0134] For example, if the simulated deformation of the fabric is smaller than the actual situation, the elastic modulus parameter of the fabric can be appropriately increased; if the tightness of the stitches is inconsistent with the actual situation, parameters such as thread tension and stitch density can be adjusted;

[0135] Use optimization algorithms (such as genetic algorithm, particle swarm optimization) to globally optimize model parameters to minimize the error between actual data and simulated data

[0136] Digital simulation: simulate the sewing process of different styles of sewing products;

[0137] Digital simulation, including:

[0138] Sewing scene simulation:

[0139] Style simulation: Adjust the geometric shape and sewing process parameters of the digital model according to the different sewing product designs to simulate the sewing process of different sewing product styles. For example, simulate the sewing of different styles such as shirts, pants, and skirts to observe the deformation of the fabric, the distribution of the stitches, and the sewing quality.

[0140] Material simulation: Replace the material parameters of fabrics and stitches in the model to simulate the sewing process of sewing products made of different materials; study the impact of different materials on sewing technology and sewing product quality, such as the differences in the sewing process between cotton and silk fabrics;

[0141] Virtual display and interaction:

[0142] The specific contents are:

[0143] Virtual Showcase:

[0144] 3D model display: using 3D modeling software and rendering engines, the digital simulation results are presented to users in the form of realistic 3D models; users can view the appearance, internal structure and dynamic demonstration of the sewing process of the sewing products through computer screens or virtual reality devices;

[0145] Animation demonstration: Create an animated video of the sewing process to show the movement of the sewing machine, the deformation of the fabric, and the formation of the stitches. Animation demonstration can help users understand the sewing process and simulation results more intuitively.

[0146] Interactive features:

[0147] Model manipulation allows users to rotate, scale, translate, and slice 3D models, enabling them to observe details of sewing products from different angles and levels. For example, users can control the rotation of the model using a mouse or gestures to view the back and internal structure of the sewing product.

[0148] Parameter modification allows users to modify the parameters of the digital model and sewing process parameters, and observe the changes in simulation results in real time. Users can adjust parameters such as stitch density and thread tension according to their needs and design requirements, and explore the sewing effects under different parameter combinations.

[0149] The above formulas are obtained by collecting a large amount of data and performing software simulation, and a formula close to the actual value is selected. The influencing weight factors and specific coefficient values in the formula are set by technical personnel in this field according to actual conditions, and can be adjusted and modified later.

[0150] The above description of the embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A digital simulation method for sewing products based on the Internet of Things, characterized in that: include: Multi-source data collection: obtain various parameter information during the sewing process; Data transmission: Transmit the collected data from the sensor node to the gateway device; Digital modeling: Establish a three-dimensional digital model of sewing products and a sewing process model; including analyzing fabric-related data to obtain evaluation coefficients, and predicting fabric deformation during the sewing process based on the evaluation coefficients; Model parameter calibration: Compare the collected data with the simulation results of the digital model and adjust the parameters of the digital model; Digital simulation: simulate the sewing process of different styles of sewing products.

2. The method for digital simulation of sewn products based on the Internet of Things according to claim 1, characterized in that: Multi-source data collection, including: The various parameter information during the sewing process includes sewing machine operating parameters, fabric physical property data, sewing environment data, and sewing product identity and location information; Sewing machine operating parameters include: Speed: The speed sensor installed on the sewing machine motor monitors the motor's rotation speed in real time; Stitch density: Use optical sensors or encoders to monitor the up and down movement of the sewing machine needle and the feed speed of the fabric; obtain stitch density information by counting the number of stitches per unit length; Thread tension, using a tension sensor installed on the thread path to measure the thread tension changes during the sewing process; Cloth physical property data includes: Thickness, using ultrasonic sensors or laser displacement sensors to measure the thickness of the fabric; Hardness: A certain pressure is applied to the fabric through a pressure sensor to measure the deformation of the fabric, thereby evaluating the hardness of the fabric; Material: Spectral sensors are used to analyze the spectral characteristics of fabrics and identify and classify them according to the spectral characteristics of different materials; Sewing environment data includes: Temperature and humidity: temperature and humidity sensors are installed in the sewing workshop to monitor the temperature and humidity changes of the environment in real time; Sewn product identity and location information includes: Electronic tags: RFID tags are attached to sewn products, and the tag information is read by RFID readers at different locations on the production line; Position sensors use laser positioning systems or visual recognition systems to determine the specific position and posture of sewn products on the production line.

3. The method for digital simulation of sewn products based on the Internet of Things according to claim 1, characterized in that: Data transmission, specifically including: Wireless transmission: Using wireless communication technology to transmit the collected data from the sensor node to the gateway device; Cloud computing platform access: The gateway device uploads the collected data to the cloud computing platform via the Internet; And remove noise and outliers in the collected data.

4. The method for digital simulation of sewn products based on the Internet of Things according to claim 1, characterized in that: Digital modeling includes: 3D digital model of sewing products: Geometric shape modeling: obtaining the shape data of sewn products and converting the scanned data into a three-dimensional geometric model; Modeling of cloth deformation characteristics: building a cloth deformation model based on the elasticity, plasticity, and anisotropy of the cloth; Suture mechanical properties modeling, studying the tensile, bending, and torsion mechanical properties of sutures, and establishing a constitutive model of sutures; Sewing process model: Modeling the motion trajectory of a sewing machine needle: establishing a kinematic model of the needle to describe its motion trajectory in space; Modeling the stitch formation process, studying the path and interweaving pattern of the stitch in the fabric, and establishing a mathematical model of stitch formation.

5. The method for digital simulation of sewn products based on the Internet of Things according to claim 4, characterized in that: The sewing process model also includes modeling the force and deformation of the fabric during the sewing process. This involves analyzing fabric-related data to obtain evaluation coefficients, and determining fabric deformation during the sewing process based on the evaluation coefficients. Specifically, it includes the following parts: Select a fabric of preset thickness as the test fabric; Obtain the presser foot pressure applied to the test fabric during the sewing process at preset time intervals to obtain the presser foot pressure at each time point; The presser foot pressure at each time point is averaged to obtain a presser foot pressure reference value; Extract the maximum presser foot pressure and the minimum presser foot pressure from the presser foot pressure, and calculate the difference to obtain the extreme pressure difference value; Preset an allowable fluctuation range of the presser foot pressure reference value, match the presser foot pressure at each time point with the allowable fluctuation range of the preset presser foot pressure reference value, and record the presser foot pressure that is not within the allowable fluctuation range of the preset presser foot pressure reference value as abnormal pressure; Obtain the number of abnormal pressures, and divide all the abnormal pressure numbers by the number of presser foot pressures to obtain the abnormal ratio; The pressure deviation value is obtained by comprehensively analyzing the pressure extreme difference value and the abnormal ratio; After analyzing the sewing speed of the sewing machine, the speed ratio is obtained; After analyzing the environmental information, the environmental impact value is obtained; The evaluation coefficient is obtained by comprehensively processing the pressure deviation value, speed ratio and environmental impact value; An evaluation coefficient threshold is preset, and the evaluation coefficient is compared with the evaluation coefficient threshold. If the evaluation coefficient is greater than the evaluation coefficient threshold, it is determined that the fabric corresponding to the evaluation coefficient will be deformed.

6. The method for digital simulation of sewn products based on the Internet of Things according to claim 1, characterized in that: Model parameter calibration, including: Comparative analysis: compare the actual collected information such as sewing machine operating parameters, fabric deformation data, and stitch quality with the simulation results of the digital model; calculate the error index between the actual data and the simulated data; Parameter adjustment: adjust the parameters of the digital model according to the results of comparative analysis; An optimization algorithm is used to globally optimize the model parameters to minimize the error between the actual data and the simulated data.

7. The method for digital simulation of sewn products based on the Internet of Things according to claim 1, characterized in that: Digital simulation, including: Sewing scene simulation: Style simulation: according to different sewing product styles, adjust the geometric shape and sewing process parameters of the digital model to simulate the sewing process of different styles of sewing products; Material simulation: change the material parameters of fabric and stitching in the model to simulate the sewing process of sewing products made of different materials.

8. The method for digital simulation of sewn products based on the Internet of Things according to claim 1, characterized in that: It also includes virtual displays and interactions, including: Virtual Showcase: 3D model display: using 3D modeling software and rendering engine to present the digital simulation results to users in the form of 3D models; Animation demonstration: create an animated video of the sewing process, showing the movement of the sewing machine, the deformation of the fabric, and the formation of the stitches; Interactive features: Model operation provides users with functions such as rotation, scaling, translation and sectioning of 3D models, allowing users to observe the details of sewing products from different angles and levels; Parameter modification allows users to modify the parameters of the digital model and sewing process parameters and observe the changes in simulation results in real time.

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