Digital twinning-based textile printing and dyeing auxiliary ratio optimization method and system

Abstract

The invention provides a digital twinning-based textile printing and dyeing auxiliary ratio optimization method and system, and relates to the technical field of textile printing and dyeing, and the method comprises the steps: carrying out the rendering of an initial cloth printing and dyeing twinning model through a cloth feature data set, and obtaining a cloth printing and dyeing twinning model; on the basis of a cloth printing and dyeing twin model, with the requirement for the expected printing and dyeing quality as the constraint, the scheme evaluation function is used for optimizing an auxiliary matching scheme, and an optimized matching scheme set is determined; and respectively carrying out sampling printing and dyeing tests according to the optimal proportioning scheme set, and evaluating and determining the optimal proportioning scheme to execute the printing and dyeing operation of the target cloth. The technical problems that according to an existing printing and dyeing auxiliary matching method, due to the fact that an adaptive auxiliary matching scheme cannot be set according to the actual state of the cloth after pretreatment, the fineness and accuracy of setting of the auxiliary matching scheme are poor, and the overall printing and dyeing quality of the cloth is uneven can be solved; the reliability and stability of cloth dyeing can be improved, and the effect of improving the overall printing and dyeing quality of cloth is achieved.

Description

Technical Field

[0001] This application relates to the field of textile printing and dyeing technology, and in particular to a method and system for optimizing the formulation of textile printing and dyeing auxiliaries based on digital twins. Background Technology

[0002] In the textile printing and dyeing industry, pretreatment is a crucial step in ensuring that fabric can absorb dye evenly and thoroughly. However, due to the influence of many factors such as pretreatment process parameters, personnel operation procedures, and working environment, the quality of the fabric after each pretreatment may fluctuate, resulting in inconsistent fabric condition. This fluctuation may negatively impact subsequent printing and dyeing processes, affecting product quality.

[0003] Traditional dyeing and printing processes typically use dyeing auxiliaries with uniform proportions. While this method is simple, the fluctuations in fabric quality can lead to inconsistent overall dyeing quality, affecting the overall quality of the dyeing process. For example, when fabric quality fluctuates significantly, using dyeing auxiliaries with uniform proportions may result in uneven dyeing and noticeable color differences in some fabrics, impacting the overall appearance and performance of the product.

[0004] In summary, existing methods for formulating textile printing and dyeing auxiliaries cannot set appropriate ratios based on the actual state of the fabric after pretreatment, resulting in poor precision and accuracy in the formulation of the auxiliary ratios and causing inconsistent overall printing and dyeing quality of the fabric. Summary of the Invention

[0005] The purpose of this application is to provide a method and system for optimizing the proportion of textile printing and dyeing auxiliaries based on digital twins, in order to solve the technical problem that existing methods for proportioning textile printing and dyeing auxiliaries cannot set an appropriate auxiliary proportion scheme according to the actual state of the fabric after pretreatment, resulting in poor precision and accuracy of the auxiliary proportion scheme setting and inconsistent overall printing and dyeing quality of the fabric.

[0006] In view of the above problems, this application provides a method and system for optimizing the formulation of textile printing and dyeing auxiliaries based on digital twins.

[0007] Firstly, this application provides a method for optimizing the proportion of textile dyeing and printing auxiliaries based on digital twins, implemented through a digital twin-based textile dyeing and printing auxiliaries proportion optimization system. The method includes: generating an initial fabric dyeing and printing twin model based on digital twins, using initial fabric attribute information, a predetermined dyeing and printing process, and predetermined dyeing and printing equipment for simulation modeling; performing fixed-point sampling and testing on the pre-treated target fabric according to predetermined detection indicators to obtain a fabric feature dataset; rendering the initial fabric dyeing and printing twin model using the fabric feature dataset to obtain a fabric dyeing and printing twin model; and, based on the fabric dyeing and printing twin model, using a scheme evaluation function to determine the proportion of auxiliaries while satisfying the expected dyeing and printing quality. The process involves optimizing a set of formulation schemes, where auxiliary agent ratio parameters include auxiliary agent type, concentration, proportion, and addition order. Based on this optimized formulation scheme set, sampling dyeing tests are conducted on the target fabric. The evaluation function is used to assess multiple test results to determine the optimal formulation scheme. Dyeing operations are then performed on the target fabric based on the optimal formulation scheme. The initial fabric attribute information, predetermined dyeing process, predetermined dyeing equipment, fabric feature dataset, and optimal formulation scheme are added to a sample optimization database. A scheme optimization model is trained using the sample optimization database, and subsequent auxiliary agent ratio optimization is performed after the model converges.

[0008] Secondly, this application also provides a digital twin-based textile printing and dyeing auxiliary ratio optimization system for executing the digital twin-based textile printing and dyeing auxiliary ratio optimization method as described in the first aspect, comprising: a twin model construction module, used to perform simulation modeling based on the digital twin, according to initial fabric attribute information, predetermined printing and dyeing process, and predetermined printing and dyeing equipment, to generate an initial fabric printing and dyeing twin model; a twin model rendering module, used to perform fixed-point sampling and detection on the pre-processed target fabric according to predetermined detection indicators, obtain a fabric feature dataset, and use the fabric feature dataset to render the initial fabric printing and dyeing twin model to obtain a fabric printing and dyeing twin model; and an auxiliary ratio scheme optimization module, used to optimize the auxiliary ratio scheme based on the fabric printing and dyeing twin model, with the expected printing and dyeing quality as a constraint, using a scheme evaluation function. The process includes: 1) Optimizing the auxiliary agent ratio scheme to determine an optimized set of ratio schemes, where auxiliary agent ratio parameters include auxiliary agent type, concentration, proportion, and addition order; 2) A sampling dyeing and printing test module, used to conduct sampling dyeing and printing tests on the target fabric according to the optimized ratio scheme set, evaluating multiple test results using the scheme evaluation function to determine the optimal ratio scheme; 3) An optimization database construction module, used to execute dyeing and printing operations on the target fabric based on the optimal ratio scheme, adding the initial fabric attribute information, predetermined dyeing and printing process, predetermined dyeing and printing equipment, fabric feature dataset, and optimal ratio scheme to the sample optimization database; and 4) A scheme optimization model training module, used to train a scheme optimization model using the sample optimization database, and executing subsequent auxiliary agent ratio scheme optimization after the scheme optimization model converges.

[0009] One or more technical solutions provided in this application have at least the following technical effects or advantages: An initial fabric dyeing twin model is constructed based on initial fabric attribute information, predetermined dyeing process, and predetermined dyeing equipment. Then, the pre-treated target fabric is sampled and tested at specific points to obtain a fabric feature dataset. This dataset is then used to render the initial fabric dyeing twin model, resulting in a fabric dyeing twin model. Based on this model, and constrained by the desired dyeing quality, an optimization scheme for auxiliary agent ratios is performed using a scheme evaluation function to determine an optimal ratio scheme set. Finally, sampling dyeing tests are conducted based on the optimized ratio scheme set to evaluate and determine the optimal solution. The optimal mixing scheme is then used to perform the dyeing and printing operation on the target fabric. The initial fabric attribute information, the predetermined dyeing and printing process, the predetermined dyeing and printing equipment, the fabric feature dataset, and the optimal mixing scheme are added to the sample optimization database to construct the sample optimization database. Finally, the sample optimization database is used to train the scheme optimization model, and after the scheme optimization model converges, the subsequent auxiliary agent mixing scheme optimization is performed. This can improve the adaptability of the auxiliary agent mixing scheme to the pretreated fabric state, thereby improving the reliability and stability of fabric dyeing and achieving the technical effect of improving the overall dyeing and printing quality of the fabric.

[0010] The above description is merely an overview of the technical solution of this application. To better understand the technical means of this application and to facilitate its implementation according to the description, and to make the above and other objects, features, and advantages of this application more apparent, specific embodiments of this application are described below. It should be understood that the content described in this section is not intended to identify key or important features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent through the following description. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely exemplary. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0012] Figure 1 This is a flowchart illustrating the method for optimizing the formulation of textile printing and dyeing auxiliaries based on digital twins, as described in this application. Figure 2 This is a flowchart illustrating the process of optimizing the formulation of textile printing and dyeing auxiliaries based on digital twins in this application. Figure 3 This is a schematic diagram of the structure of the textile printing and dyeing auxiliary ratio optimization system based on digital twins in this application.

[0013] Explanation of reference numerals in the attached figures: Twin model construction module 11, twin model rendering module 12, auxiliary agent ratio scheme optimization module 13, sampling dyeing test module 14, optimization database construction module 15, scheme optimization model training module 16. Detailed Implementation

[0014] This application provides a digital twin-based method and system for optimizing the proportion of textile dyeing auxiliaries. This solves the technical problem of existing methods for proportioning textile dyeing auxiliaries, which suffer from poor precision and accuracy in setting the proportions based on the actual state of the pretreated fabric. This results in inconsistent overall dyeing quality. The method improves the compatibility between the auxiliary proportions and the pretreated fabric state, thereby enhancing the reliability and stability of dyeing and ultimately improving the overall dyeing quality of the fabric.

[0015] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. It should be understood that this application is not limited to the exemplary embodiments described herein. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application. It should also be noted that, for ease of description, only the parts related to this application are shown in the accompanying drawings, not all of them.

[0016] Example 1, please refer to the appendix. Figure 1 This application provides a method for optimizing the formulation of textile printing and dyeing auxiliaries based on digital twins, which is applied to a system for optimizing the formulation of textile printing and dyeing auxiliaries based on digital twins. The method specifically includes the following steps: Step 1: Based on digital twins, perform simulation modeling based on the initial fabric attribute information, the predetermined printing and dyeing process, and the predetermined printing and dyeing equipment to generate an initial fabric printing and dyeing twin model.

[0017] Specifically, digital twin technology is a simulation technology based on virtual models. By creating a virtual copy corresponding to a physical entity, it enables the monitoring, prediction, and optimization of the physical entity. In the textile printing and dyeing industry, digital twin technology can be applied to the fabric printing and dyeing process. By establishing a digital twin model of the fabric printing and dyeing process, it is possible to predict the quality of the fabric printing and dyeing and optimize the printing and dyeing process.

[0018] First, initial fabric attribute information is collected, including fiber type, weave structure, dimensions, weight, and color. Then, based on the fabric's final use and customer requirements, the predetermined dyeing and finishing process and equipment are determined. The predetermined dyeing and finishing process includes dye selection and process flow, while the predetermined dyeing and finishing equipment includes equipment specifications, operating parameters, and performance indicators. Further, based on digital twin technology, simulation modeling is performed within a 3D simulation platform using the initial fabric attribute information, predetermined dyeing and finishing process, and predetermined dyeing and finishing equipment. This creates an initial fabric dyeing and finishing twin model, reflecting the fabric's behavior and state during the actual dyeing and finishing process. Constructing an initial fabric dyeing and finishing twin model based on digital twin technology improves the realism and accuracy of fabric dyeing and finishing simulation.

[0019] Step 2: Perform fixed-point sampling and detection on the preprocessed target fabric according to the predetermined detection indicators to obtain the fabric feature dataset. Use the fabric feature dataset to render the initial fabric printing twin model to obtain the fabric printing twin model.

[0020] Specifically, predetermined testing indicators are obtained, including at least pH value, moisture content, whiteness, fiber strength, surface smoothness, and thickness uniformity. Those skilled in the art may add additional testing indicators as needed. Next, at predetermined testing nodes, the pre-treated target fabric is sampled and tested at specific points according to the predetermined testing indicators. Multiple points can be selected on the fabric for sampling to ensure that the sampling points represent the overall state of the fabric. Then, professional testing equipment and methods are used to test the sampling points and collect characteristic data of the fabric, such as pH value, moisture content, whiteness, fiber strength, surface smoothness, and thickness uniformity, to obtain a fabric characteristic dataset. The initial fabric printing and dyeing twin model is further rendered using the fabric characteristic dataset to obtain a fabric printing and dyeing twin model, which reflects the actual state of the pre-treated fabric.

[0021] By constructing an initial fabric printing and dyeing twin model and rendering the initial fabric printing and dyeing twin model using the detected fabric feature dataset, an unnecessary simulation modeling time can be reduced, and the efficiency of obtaining the fabric printing and dyeing twin model can be improved.

[0022] Step 3: Based on the fabric printing and dyeing twin model, and with the expected printing and dyeing quality as a constraint, the auxiliary agent ratio scheme is optimized using the scheme evaluation function to determine the set of optimized ratio schemes. The auxiliary agent ratio parameters include auxiliary agent type, auxiliary agent concentration, auxiliary agent ratio, and auxiliary agent addition order.

[0023] Specifically, firstly, multiple auxiliary agent ratio parameters are obtained from the auxiliary agent formulation scheme. These parameters include auxiliary agent type, concentration, proportion, and addition order. For example, suitable auxiliary agent types are selected based on the requirements of the dyeing and printing process and the characteristics of the fabric. Auxiliary agent types include dyes, fixing agents, leveling agents, thickeners, emulsifiers, dispersants, etc. Next, with the desired dyeing and printing quality as the optimization constraint, an optimization algorithm is used to optimize the auxiliary agent ratio scheme based on the fabric dyeing and printing twin model and the scheme evaluation function, resulting in a set of optimized ratio schemes. The desired dyeing and printing quality can be set according to actual quality requirements, including color fastness indicators, color indicators, texture indicators, and dimensional deviation indicators. The scheme evaluation function is used to comprehensively evaluate multiple auxiliary agent ratio schemes during the optimization process, with evaluation indicators including dyeing and printing quality, auxiliary agent resource consumption, and dyeing and printing efficiency. The set of optimized ratio schemes includes several superior ratio schemes.

[0024] Step 4: Conduct sampling dyeing tests on the target fabric according to the optimized formula set, evaluate the multiple test results using the formula evaluation function, and determine the optimal formula.

[0025] Specifically, based on the optimized formulation scheme set, sampling dyeing tests are conducted on the target fabric. During the test, actual dyeing operations are performed according to the selected auxiliary agent formulation scheme. After the test, the dyeing results of the fabric are collected, including indicators such as color fastness, color, texture, and dimensional deviation. The collected test results are then evaluated using a scheme evaluation function, which comprehensively considers multiple factors such as dyeing quality, auxiliary agent resource consumption, and dyeing efficiency to determine the score of each formulation scheme. Finally, based on the evaluation results, the formulation scheme with the highest score is selected as the optimal formulation scheme.

[0026] Since the auxiliary agent formulation scheme is based on the simulation optimization of the fabric printing and dyeing twin model, it has a certain degree of deviation. By obtaining multiple better formulation schemes and conducting subsequent actual tests based on these schemes, the uncertainty caused by simulation deviations can be reduced. This allows for the improvement of the accuracy and reliability of the optimal formulation scheme setting without wasting testing resources.

[0027] Step 5: Perform the dyeing and printing operation on the target fabric based on the optimal ratio scheme, and add the initial fabric attribute information, the predetermined dyeing and printing process, the predetermined dyeing and printing equipment, the fabric feature dataset, and the optimal ratio scheme to the sample optimization database.

[0028] Specifically, following the optimal auxiliary agent ratio scheme, the target fabric is then dyed using a predetermined dyeing process and equipment to ensure that all process parameters and equipment settings meet the requirements of the optimal ratio scheme. Furthermore, the initial fabric attribute information, predetermined dyeing process, predetermined dyeing equipment, fabric feature dataset, and optimal ratio scheme are added as a set of sample data to a sample optimization database. This sample optimization database provides training data support for the subsequent construction of a scheme optimization model.

[0029] Step 6: Use the sample optimization database to train the scheme optimization model, and execute the subsequent auxiliary agent ratio scheme optimization after the scheme optimization model converges.

[0030] Specifically, a backpropagation (BP) neural network is a feedforward artificial neural network used in machine learning for iterative optimization. It mainly consists of an input layer, hidden layers, and an output layer, which are interconnected by weights. It can be used in predictive analytics and other fields. Next, a scheme optimization model is constructed based on the BP neural network. The input data for the input layer of this model includes initial fabric attribute information, a predetermined dyeing process, predetermined dyeing equipment, and a fabric feature dataset. The output data for the output layer is the optimal proportion scheme. Then, using the aforementioned sample optimization database as training data, the scheme optimization model is subjected to supervised training to obtain a scheme optimization model that meets the expected convergence constraints. These constraints represent the model's convergence accuracy and can be set according to actual needs. Finally, after the scheme optimization model converges, subsequent auxiliary agent proportion optimization is performed. By using a scheme optimization model based on a BP neural network to perform subsequent auxiliary agent proportion optimization, the model training data, being the optimized sample data, has high accuracy. Therefore, the optimization efficiency of subsequent auxiliary agent proportion schemes can be improved without affecting the accuracy of the initial optimization.

[0031] The proposed digital twin-based method for optimizing the proportion of textile dyeing auxiliaries is applied to a digital twin-based textile dyeing auxiliary proportion optimization system. This method addresses the problem that existing methods for proportioning textile dyeing auxiliaries fail to set appropriate proportions based on the actual state of the pre-treated fabric, resulting in poor precision and accuracy in the proportion settings and inconsistent overall dyeing quality. First, based on digital twins, a simulation model is generated using initial fabric attribute information, a predetermined dyeing process, and predetermined dyeing equipment. Then, the pre-treated target fabric is sampled and tested at predetermined detection indicators to obtain a fabric feature dataset. This dataset is then used to render the initial fabric dyeing twin model, resulting in a new fabric dyeing twin model. Next, based on the fabric dyeing twin model and constrained by the desired dyeing quality, an optimization scheme for the auxiliary proportions is performed using a scheme evaluation function to determine an optimized set of proportions. The auxiliary proportion parameters include the types of auxiliary agents. The process involves several steps: first, determining the type, concentration, ratio, and order of auxiliary agents; second, conducting sampling dyeing tests on the target fabric based on the optimized formulation scheme set; third, evaluating the test results using the scheme evaluation function to determine the optimal formulation scheme; fourth, performing dyeing operations on the target fabric based on the optimal formulation scheme, and adding the initial fabric attribute information, predetermined dyeing process, predetermined dyeing equipment, fabric feature dataset, and optimal formulation scheme to the sample optimization database; and finally, training a scheme optimization model using the sample optimization database, and performing subsequent auxiliary agent formulation scheme optimization after the model converges. This approach improves the compatibility between the auxiliary agent formulation scheme and the pretreated fabric state, thereby enhancing the reliability and stability of fabric dyeing and achieving the technical effect of improving the overall dyeing quality of the fabric.

[0032] Furthermore, step two of this application includes: The predetermined testing indicators include at least pH value, moisture content, whiteness, fiber strength, surface smoothness, and thickness uniformity.

[0033] Specifically, the predetermined testing indicators include at least pH value, moisture content, whiteness, fiber strength, surface smoothness, and thickness uniformity. Those skilled in the art may also add additional testing indicators according to actual conditions. For example, the pH of the fabric is crucial for the use of dyes and auxiliaries. Different dyes and auxiliaries have different pH requirements. Too high or too low a pH value may affect the adsorption of dyes and color fastness. The moisture content of the fabric affects the absorption and coloring effect of dyes. Too high or too low a moisture content may lead to uneven dyeing.

[0034] Further optimization of the additive formulation scheme is carried out, as shown in the attached document. Figure 2 As shown, step three of this application includes: A threshold for auxiliary agent ratio parameters is obtained. Any parameter is randomly selected without replacement from this threshold and combined to generate a first initial auxiliary agent ratio scheme. This process is iterated multiple times to obtain multiple initial auxiliary agent ratio schemes. These multiple initial auxiliary agent ratio schemes are then input into the fabric printing and dyeing twin model for simulated printing and dyeing, resulting in multiple simulated printing and dyeing results. Based on the expected printing and dyeing quality, the multiple simulated printing and dyeing results are filtered to determine a set of qualified initial auxiliary agent ratio schemes. The expected printing and dyeing quality includes color fastness indicators, color indicators, texture indicators, and size deviation indicators. Texture indicators include texture clarity, texture alignment accuracy, texture symmetry, and texture uniformity. Based on the scheme evaluation function and the set of qualified initial auxiliary agent ratio schemes, the auxiliary agent ratio schemes are optimized, and an optimized ratio scheme set is output.

[0035] Specifically, firstly, threshold values ​​for auxiliary agent proportioning parameters are obtained. These threshold values ​​refer to the adjustment range of auxiliary agent proportioning parameters, including threshold values ​​for auxiliary agent type, concentration, proportion, and addition order. Next, any parameter from these threshold values ​​is randomly selected without replacement and combined to generate a first initial auxiliary agent proportioning scheme. This process is then iteratively repeated multiple times using the same method to obtain multiple initial auxiliary agent proportioning schemes that satisfy predetermined quantity constraints.

[0036] Next, the multiple initial auxiliary agent formulation schemes are input into the fabric dyeing twin model for simulated dyeing. The fabric dyeing twin model simulates the physical and chemical changes of the fabric during the dyeing process, including dye adsorption, diffusion, and fixation, resulting in multiple simulated dyeing results. Further, the multiple simulated dyeing results are screened according to the expected dyeing quality, which includes color fastness, color, texture, and dimensional deviation indicators. The texture indicators include texture clarity, texture alignment accuracy, texture symmetry, and texture uniformity. The initial auxiliary agent formulation schemes corresponding to the simulated dyeing results that meet the expected dyeing quality are extracted and set as qualified initial auxiliary agent formulation schemes, resulting in a set of qualified initial auxiliary agent formulation schemes. Finally, based on the scheme evaluation function and the set of qualified initial auxiliary agent formulation schemes, an optimization algorithm is used to optimize the auxiliary agent formulation schemes, resulting in a set of optimized formulation schemes.

[0037] Furthermore, based on the aforementioned scheme evaluation function and the set of qualified initial additive ratio schemes, the additive ratio scheme is optimized. This application also includes the following steps: The qualified initial adjuvant formulation scheme set is evaluated using the aforementioned scheme evaluation function, outputting multiple fitness values. Each qualified initial adjuvant formulation scheme is considered an initial solution. These initial solutions are arranged in descending order of fitness, and the first N initial solutions are designated as the first solutions, and the last M initial solutions as the last solutions. Clustering is performed on the M last solutions centered on the N first solutions, resulting in N neighborhoods, where M is an integer multiple of N. Within each of the N neighborhoods, using the first solutions as the optimization direction, a mutation is performed on the last solutions within the neighborhood according to a predetermined variation length, resulting in N more... In the new domain, if the mutated tail solution does not meet the threshold of the auxiliary agent ratio parameter and / or the expected dyeing quality, then this mutation will not be performed; N update domains are identified, and if in the same update domain, the fitness of a tail solution is greater than that of the first solution, then the tail solution is used to replace the first solution; iterative optimization is performed until the predetermined number of optimizations is met, and N current domains are output. The N current domains are evaluated based on the scheme evaluation function, and the domain with the largest sum of fitness is selected as the optimal domain, and the optimal domain is set as the set of optimized ratio schemes.

[0038] Specifically, firstly, the scheme evaluation function is used to evaluate multiple qualified initial adjuvant ratio schemes in the qualified initial adjuvant ratio scheme set to obtain multiple fitness values, where the higher the fitness value, the better the overall effect of the scheme. Next, the qualified initial adjuvant ratio schemes are regarded as initial solutions, and multiple initial solutions are arranged in descending order of fitness to generate an initial solution sequence. Further, the first N initial solutions in the initial solution sequence are set as first solutions, and the last M initial solutions are set as tail solutions, where M is an integer multiple of N. Then, with the N first solutions as the center, the M tail solutions are randomly clustered equally to obtain N neighborhoods, where the number of tail solutions in each neighborhood is the same.

[0039] Then, within N domains, taking the first solution as the optimization direction, the tail solutions within each domain are mutated once according to a predetermined variation length. The predetermined variation length includes the adjustment step size for auxiliary agent type, auxiliary agent concentration, auxiliary agent ratio, and auxiliary agent addition order. During the mutation process, if the mutated tail solution does not meet the auxiliary agent ratio parameter threshold and / or the expected dyeing quality, then the tail solution is not mutated, resulting in N updated domains. Next, the N updated domains are identified. If, within the same updated domain, a tail solution has a fitness greater than the first solution, then the tail solution replaces the first solution. This iterative optimization continues until a predetermined number of optimization attempts is reached. This predetermined number of attempts can be set according to the expected optimization accuracy, outputting N current domains. Then, the N current domains are evaluated based on the scheme evaluation function. The domain with the largest sum of fitness is selected as the optimal domain, and this optimal domain is set as the optimized ratio scheme set.

[0040] By using the above-mentioned optimization algorithm to find the optimal ratio scheme, the algorithm has strong global convergence ability, thus avoiding getting trapped in local optima and improving the globality and rationality of the optimization. At the same time, the algorithm can retain multiple better results, which provides support for subsequent actual testing.

[0041] Furthermore, in constructing the scheme evaluation function, this application also includes the following steps: The expression for the scheme evaluation function is: ;in, The fitness of the i-th scheme is evaluated. For the quality weight of printing and dyeing, As a weight for the consumption of auxiliary agent resources, Weighted by dyeing and printing efficiency. As the weight for color fastness, For color weights, For texture weights, As the weight for size deviation, Let be the colorfastness evaluation coefficient for scheme i. The standard color fastness factor is... Let i be the grayscale value of the i-th scheme. Standard color grayscale value, For the texture features of the i-th scheme, For standard texture features, For the size data of the i-th scheme, For standard size data, Let be the total amount of auxiliary resources consumed in the i-th scheme. Let be the dyeing duration for scheme i.

[0042] Specifically, in the scheme evaluation function, Let be the evaluation fitness of the i-th scheme. The higher the evaluation fitness, the better the scheme's performance. For the quality weight of printing and dyeing, As a weight for the consumption of auxiliary agent resources, Weighted by dyeing and printing efficiency. As the weight for color fastness, For color weights, For texture weights, As the weight for size deviation, Let be the colorfastness evaluation coefficient for scheme i. The standard color fastness factor is... Let i be the grayscale value of the i-th scheme. Standard color grayscale value, For the texture features of the i-th scheme, For standard texture features, For the size data of the i-th scheme, For standard size data, Let be the total amount of auxiliary resources consumed in the i-th scheme. Let represent the dyeing and printing time for the i-th scheme. A higher colorfastness evaluation coefficient indicates better fabric dyeing and printing quality; a smaller color deviation indicates better fabric dyeing and printing quality; and a smaller texture feature deviation indicates better fabric dyeing and printing quality. Texture features include texture clarity, texture alignment accuracy, texture symmetry, and texture uniformity. A smaller size deviation indicates better fabric dyeing and printing quality. Weights can be set according to the overall influence of the indicators; the higher the influence, the greater the weight. By constructing a scheme evaluation function, a basis for evaluating scheme effectiveness is provided, while simultaneously improving the accuracy and reliability of scheme evaluation.

[0043] Furthermore, multiple test results were obtained. Step four of this application includes: According to the optimized blending scheme set, sampling dyeing tests are performed on the target fabrics to obtain multiple fabric dyeing results; a first fabric dyeing result is randomly selected from the multiple fabric dyeing results, and image acquisition and grayscale processing are performed on the first fabric dyeing result. Based on the grayscale of the first fabric and the grayscale of the expected color, a deviation comparison is performed to obtain a first color test result; features are extracted from the grayscale image of the first fabric through a texture feature extraction channel, and a deviation comparison is performed between the first texture feature extraction result and the expected texture feature to obtain a first texture test result, wherein the texture feature extraction channel is constructed based on a convolutional neural network; a first test result is constructed based on the first color test result and the first texture test result and added to the multiple test results, wherein the first test result also includes a first color fastness test result and a first size deviation test result.

[0044] Specifically, the target fabric is further sampled and dyed according to the optimized proportioning scheme set to obtain multiple fabric dyeing results. Then, a first fabric dyeing result is randomly selected from the multiple fabric dyeing results, and the first fabric dyeing result is any one of the multiple fabric dyeing results. Then, the first fabric dyeing result is subjected to image acquisition and grayscale processing. For example, a high-resolution camera or scanning device is used to acquire images of the fabric dyeing result, and the acquired color image is converted into a grayscale image. Grayscale processing can eliminate color information and retain only brightness information, which helps to more accurately compare color deviations and improve the accuracy of color difference analysis. Furthermore, the deviation is compared based on the grayscale image of the first fabric and the grayscale image of the expected color. This is achieved by calculating the difference between the two grayscale images. The smaller the difference, the smaller the color deviation, thus obtaining the first color test result.

[0045] A texture feature extraction channel is constructed based on a convolutional neural network (CNN), a deep learning model used to extract texture features from grayscale images of fabric. The input data of the texture feature extraction channel is the grayscale image of the fabric, and the output data is the fabric texture features. The texture feature extraction channel is trained to convergence using sample data. By constructing a texture feature extraction channel based on a CNN for texture feature analysis, the accuracy and efficiency of fabric texture feature analysis can be improved.

[0046] Then, the first fabric grayscale image is used to extract features through the texture feature extraction channel to obtain the first texture feature extraction result; further, the deviation between the first texture feature extraction result and the expected texture feature is compared to obtain the first texture test result, where the smaller the texture feature deviation, the better the fabric printing and dyeing quality.

[0047] Color fastness and dimensional deviation are tested on the first fabric printing and dyeing results to obtain the first color fastness test result and the first dimensional deviation test result. Finally, a first test result is constructed based on the first color test result, the first texture test result, the first color fastness test result, and the first dimensional deviation test result, and added to the plurality of test results.

[0048] Furthermore, the training scheme optimizes the model. Step five of this application includes: After the number of optimized sample data in the sample optimization database meets the predetermined constraints, the sample optimization database is divided into Q equal parts, and the first sample set is constructed by selecting Q times with replacement. This process is repeated Q times to obtain Q sample sets. Using the initial fabric attribute information, dyeing process, dyeing equipment, and fabric feature data as inputs, and the optimal ratio scheme as supervision, the BP neural network is trained using the Q sample sets to obtain Q convergent scheme optimization units. Based on the Q convergent scheme optimization units, the scheme optimization model is constructed, where the output of the scheme optimization model is the mode of the output results of the Q convergent scheme optimization units.

[0049] Specifically, a predetermined number constraint is configured for the sample data. After the number of sample optimization data in the sample optimization database meets the predetermined number constraint, the sample optimization database is divided into Q equal parts. The first sample set is constructed by selecting Q times with replacement from the Q datasets. The same method is used to iteratively select Q times to obtain Q sample sets.

[0050] An initial scheme optimization unit is constructed based on a backpropagation (BP) neural network. Then, using initial fabric attribute information, dyeing process, dyeing equipment, and fabric feature data as input data, and the optimal ratio scheme as supervisory data, the BP neural network is trained using Q sample sets. During supervised training, the output result is calculated through forward propagation, and then the connection weights and thresholds are adjusted through backpropagation. This forward and backpropagation process is repeated until the deviation between the network's output and the optimal ratio scheme reaches a preset threshold or the number of iterations reaches a preset upper limit, resulting in Q convergent scheme optimization units. Finally, the scheme optimization model is constructed based on these Q convergent scheme optimization units, where the output of the scheme optimization model is the mode of the output results of the Q convergent scheme optimization units. By constructing the scheme optimization model based on Q convergent scheme optimization units, the prediction error of a single convergent scheme optimization unit can be reduced, improving the accuracy and reliability of the scheme optimization model's output.

[0051] In summary, the textile printing and dyeing auxiliary ratio optimization method based on digital twins provided in this application has the following technical effects: 1. An initial fabric printing and dyeing twin model is constructed based on the initial fabric attribute information, the predetermined printing and dyeing process, and the predetermined printing and dyeing equipment. Then, the pre-treated target fabric is sampled and tested at specific points to obtain a fabric feature dataset. This dataset is then used to render the initial fabric printing and dyeing twin model, resulting in the final fabric printing and dyeing twin model. Based on this model, and constrained by the desired printing and dyeing quality, an optimization scheme for auxiliary agent ratios is performed using a scheme evaluation function to determine the optimal ratio scheme set. Further, sampling printing and dyeing tests are conducted based on the optimized ratio scheme set to evaluate and determine the best... The optimal mixing ratio scheme is then used to perform the dyeing and printing operation on the target fabric. The initial fabric attribute information, the predetermined dyeing and printing process, the predetermined dyeing and printing equipment, the fabric feature dataset, and the optimal mixing ratio scheme are added to the sample optimization database to construct the sample optimization database. Finally, the sample optimization database is used to train the scheme optimization model, and after the scheme optimization model converges, the subsequent auxiliary agent mixing ratio scheme optimization is performed. This can improve the adaptability of the auxiliary agent mixing ratio scheme to the pre-treated fabric state, thereby improving the reliability and stability of fabric dyeing and achieving the technical effect of improving the overall dyeing and printing quality of the fabric.

[0052] 2. Since the auxiliary agent formulation scheme is based on the simulation optimization of the fabric printing and dyeing twin model, it has a certain degree of deviation. By obtaining multiple better formulation schemes and conducting subsequent actual tests based on these schemes, the uncertainty caused by simulation deviations can be reduced. This allows for the improvement of the accuracy and reliability of the optimal formulation scheme setting without wasting testing resources.

[0053] 3. By constructing a scheme optimization model based on BP neural network to perform subsequent auxiliary agent ratio scheme optimization, since the model training data is the sample data obtained from optimization, the accuracy is high. Therefore, the optimization efficiency of subsequent auxiliary agent ratio scheme can be improved without affecting the accuracy of auxiliary agent ratio scheme optimization.

[0054] 4. By using the above optimization algorithm to optimize the matching scheme, the algorithm has strong global convergence ability, thus avoiding getting trapped in local optima and improving the globality and rationality of the optimization. At the same time, the algorithm can retain multiple better results, which provides support for subsequent actual testing.

[0055] Example 2: Based on the same inventive concept as the digital twin-based textile printing and dyeing auxiliary ratio optimization method described in the previous examples, this application also provides a digital twin-based textile printing and dyeing auxiliary ratio optimization system. Please refer to the appendix. Figure 3 ,include: The twin model construction module 11 is used to perform simulation modeling based on digital twins, according to the initial fabric attribute information, the predetermined printing and dyeing process and the predetermined printing and dyeing equipment, to generate the initial fabric printing and dyeing twin model.

[0056] The twin model rendering module 12 is used to perform fixed-point sampling and detection on the preprocessed target fabric according to predetermined detection indicators, obtain fabric feature dataset, and use the fabric feature dataset to render the initial fabric printing twin model to obtain the fabric printing twin model.

[0057] The auxiliary agent ratio optimization module 13 is used to optimize the auxiliary agent ratio scheme based on the fabric printing and dyeing twin model, with the expected printing and dyeing quality as a constraint, and to determine the set of optimized ratio schemes using the scheme evaluation function. The auxiliary agent ratio parameters include auxiliary agent type, auxiliary agent concentration, auxiliary agent ratio, and auxiliary agent addition order.

[0058] The sampling dyeing test module 14 is used to conduct sampling dyeing tests on the target fabric according to the optimized ratio scheme set, evaluate the multiple test results using the scheme evaluation function, and determine the optimal ratio scheme.

[0059] The database construction module 15 is used to perform the dyeing and printing operation of the target fabric based on the optimal ratio scheme, and to add the initial fabric attribute information, the predetermined dyeing and printing process, the predetermined dyeing and printing equipment, the fabric feature dataset and the optimal ratio scheme to the sample optimization database.

[0060] The scheme optimization model training module 16 is used to train the scheme optimization model using the sample optimization database, and to perform subsequent auxiliary agent ratio scheme optimization after the scheme optimization model converges.

[0061] Furthermore, the twin model rendering module 12 in the digital twin-based textile printing and dyeing auxiliary ratio optimization system is also used for: the predetermined detection indicators include at least pH value, moisture content, whiteness, fiber strength, surface smoothness and thickness uniformity.

[0062] Furthermore, the auxiliary ratio optimization module 13 in the digital twin-based textile printing and dyeing auxiliary ratio optimization system is also used for: obtaining auxiliary ratio parameter thresholds; randomly selecting any parameter from the auxiliary ratio parameter thresholds without replacement to generate a first initial auxiliary ratio scheme; iterating multiple times to obtain multiple initial auxiliary ratio schemes; inputting the multiple initial auxiliary ratio schemes into the fabric printing and dyeing twin model for simulated printing and dyeing to obtain multiple simulated printing and dyeing results; filtering the multiple simulated printing and dyeing results according to the expected printing and dyeing quality to determine a qualified initial auxiliary ratio scheme set, wherein the expected printing and dyeing quality includes color fastness index, color index, texture index and size deviation index, and the texture index includes texture clarity, texture alignment accuracy, texture symmetry and texture uniformity; and optimizing the auxiliary ratio scheme based on the scheme evaluation function and the qualified initial auxiliary ratio scheme set to output an optimized ratio scheme set.

[0063] Furthermore, the auxiliary ratio optimization module 13 in the digital twin-based textile printing and dyeing auxiliary ratio optimization system is also used for: evaluating the qualified initial auxiliary ratio scheme set using the scheme evaluation function, and outputting multiple fitness values; considering the qualified initial auxiliary ratio scheme as the initial solution, arranging the multiple initial solutions according to fitness values ​​from largest to smallest, and selecting the first N initial solutions of the initial solution sequence as the first solution and the last M initial solutions as the last solutions; clustering the M last solutions with the N first solutions as the center to obtain N neighborhoods, where M is an integer multiple of N; within the N neighborhoods, using the first solutions as the optimization direction, according to a predetermined... The variable-asynchronous-length algorithm mutates the tail solutions within a domain once, resulting in N updated domains. If the mutated tail solutions do not meet the threshold of the auxiliary agent ratio parameter and / or the expected dyeing quality, the mutation is not performed. The N updated domains are identified. If, within the same updated domain, the fitness of a tail solution is greater than that of the first solution, the tail solution replaces the first solution. Iterative optimization is performed until a predetermined number of optimization attempts is met, outputting N current domains. Based on the scheme evaluation function, the N current domains are evaluated, and the domain with the largest sum of fitness is selected as the optimal domain. The optimal domain is then set as the set of optimized ratio schemes.

[0064] Furthermore, the auxiliary ratio optimization module 13 in the digital twin-based textile printing and dyeing auxiliary ratio optimization system is also used for: the expression of the scheme evaluation function is: ;in, The fitness of the i-th scheme is evaluated. For the quality weight of printing and dyeing, As a weight for the consumption of auxiliary agent resources, Weighted by dyeing and printing efficiency. As the weight for color fastness, For color weights, For texture weights, As the weight for size deviation, Let be the colorfastness evaluation coefficient for scheme i. Here, is the standard colorfastness factor, and is the grayscale value of the i-th scheme. Standard color grayscale value, For the texture features of the i-th scheme, For standard texture features, For the size data of the i-th scheme, For standard size data, Let be the total amount of auxiliary resources consumed in the i-th scheme. Let be the dyeing duration for scheme i.

[0065] Furthermore, the sampling dyeing test module 14 in the digital twin-based textile dyeing auxiliary ratio optimization system is also used for: performing sampling dyeing tests on the target fabrics according to the optimized ratio scheme set to obtain multiple fabric dyeing results; randomly selecting a first fabric dyeing result from the multiple fabric dyeing results, performing image acquisition and grayscale processing on the first fabric dyeing result, comparing the deviation between the first fabric grayscale and the expected color grayscale to obtain a first color test result; extracting features from the first fabric grayscale image through a texture feature extraction channel, comparing the deviation between the first texture feature extraction result and the expected texture feature to obtain a first texture test result, wherein the texture feature extraction channel is constructed based on a convolutional neural network; constructing a first test result based on the first color test result and the first texture test result, and adding it to the multiple test results, wherein the first test result also includes a first color fastness test result and a first size deviation test result.

[0066] Furthermore, the scheme optimization model training module 16 in the digital twin-based textile printing and dyeing auxiliary ratio optimization system is also used for: after the number of sample optimization data in the sample optimization database meets a predetermined constraint, dividing the sample optimization database into Q equal parts, and selecting Q times with replacement to construct a first sample set, iterating and selecting Q times to obtain Q sample sets; using the initial fabric attribute information, sample printing and dyeing process, sample printing and dyeing equipment, and sample fabric feature data as inputs, and using the optimal ratio scheme of the samples as supervision, supervising the training of the BP neural network using the Q sample sets to obtain Q convergent scheme optimization units; constructing the scheme optimization model based on the Q convergent scheme optimization units, wherein the output of the scheme optimization model is the mode of the output results of the Q convergent scheme optimization units.

[0067] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. The method and specific examples of optimizing the proportion of textile printing and dyeing auxiliaries based on digital twins in Embodiment 1 are also applicable to the system of optimizing the proportion of textile printing and dyeing auxiliaries based on digital twins in this embodiment. Through the foregoing detailed description of the method of optimizing the proportion of textile printing and dyeing auxiliaries based on digital twins, those skilled in the art can clearly understand the system of optimizing the proportion of textile printing and dyeing auxiliaries based on digital twins in this embodiment. Therefore, for the sake of brevity, it will not be described in detail here. As for the system disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple, and relevant parts can be referred to in the method section.

[0068] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. 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 this application. Therefore, this application is not 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.

[0069] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of this application and its equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for optimizing the formulation of textile printing and dyeing auxiliaries based on digital twins, characterized in that, include: Based on digital twins, simulation modeling is performed according to the initial fabric attribute information, the predetermined dyeing process and the predetermined dyeing equipment to generate the initial fabric dyeing twin model; The pre-processed target fabric is sampled and tested at fixed points according to the predetermined detection indicators to obtain the fabric feature dataset. The fabric feature dataset is then used to render the initial fabric printing and dyeing twin model to obtain the fabric printing and dyeing twin model. Based on the fabric printing and dyeing twin model, and with the expectation of printing and dyeing quality as a constraint, the scheme evaluation function is used to optimize the auxiliary agent ratio scheme and determine the set of optimized ratio schemes. The auxiliary agent ratio parameters include auxiliary agent type, auxiliary agent concentration, auxiliary agent ratio and auxiliary agent addition order. Based on the set of optimized proportioning schemes, sampling dyeing tests were conducted on the target fabrics. The scheme evaluation function was used to evaluate the multiple test results and determine the optimal proportioning scheme. The target fabric is dyed and printed based on the optimal ratio scheme, and the initial fabric attribute information, the predetermined dyeing and printing process, the predetermined dyeing and printing equipment, the fabric feature dataset and the optimal ratio scheme are added to the sample optimization database. The sample optimization database is used to train a scheme optimization model, and after the scheme optimization model converges, subsequent adjuvant ratio scheme optimization is performed.

2. The method for optimizing the formulation of textile printing and dyeing auxiliaries based on digital twins according to claim 1, characterized in that, The predetermined testing indicators include at least pH value, moisture content, whiteness, fiber strength, surface smoothness, and thickness uniformity.

3. The method for optimizing the formulation of textile printing and dyeing auxiliaries based on digital twins according to claim 1, characterized in that, Optimizing the formulation of auxiliary agents includes: Obtain the threshold of the auxiliary agent ratio parameter, and randomly select any parameter from the auxiliary agent ratio parameter threshold without replacement to generate the first initial auxiliary agent ratio scheme. Iterate and select multiple times to obtain multiple initial auxiliary agent ratio schemes. The multiple initial auxiliary agent ratio schemes are input into the fabric printing and dyeing twin model for simulated printing and dyeing, and multiple simulated printing and dyeing results are obtained. Based on the expected dyeing quality, the multiple simulated dyeing results are screened to determine a set of qualified initial auxiliary agent ratio schemes. The expected dyeing quality includes color fastness index, color index, texture index and size deviation index. The texture index includes texture clarity, texture alignment accuracy, texture symmetry and texture uniformity. Based on the evaluation function of the proposed scheme and the set of qualified initial adjuvant ratio schemes, the adjuvant ratio scheme is optimized, and the set of optimized ratio schemes is output.

4. The method for optimizing the formulation of textile printing and dyeing auxiliaries based on digital twins according to claim 3, characterized in that, Based on the aforementioned scheme evaluation function and the set of qualified initial adjuvant ratio schemes, the adjuvant ratio scheme optimization is performed, including: The qualified initial adjuvant formulation scheme set is evaluated using the scheme evaluation function, and multiple fitness scores are output. The qualified initial additive ratio scheme is regarded as the initial solution. Multiple initial solutions are arranged in descending order of fitness. The first N initial solutions of the initial solution sequence are set as the first solution and the last M initial solutions are set as the last solutions. The M last solutions are clustered with the N first solutions as the center to obtain N neighborhoods, where M is an integer multiple of N. Within N domains, taking the first solution as the optimization direction, the tail solutions within the domain are mutated once according to the predetermined variable asynchronous length to obtain N updated domains. If the mutated tail solutions do not meet the threshold of the auxiliary agent ratio parameter and / or the expected printing and dyeing quality, then this mutation is not performed. Identify N update neighborhoods. If, within the same update neighborhood, the fitness of the tail solution is greater than that of the first solution, then the tail solution is used to replace the first solution. Perform iterative optimization until the predetermined number of optimization attempts is met, output N current domains, evaluate the N current domains based on the scheme evaluation function, select the domain with the largest sum of fitness as the optimal domain, and set the optimal domain as the set of optimized allocation schemes.

5. The method for optimizing the formulation of textile printing and dyeing auxiliaries based on digital twins according to claim 3, characterized in that, Construct a solution evaluation function, including: The expression for the scheme evaluation function is: ； in, The fitness of the i-th scheme is evaluated. For the quality weight of printing and dyeing, As a weight for the consumption of auxiliary agent resources, Weighted by dyeing and printing efficiency. As the weight for color fastness, For color weights, For texture weights, As the weight for size deviation, Let be the colorfastness evaluation coefficient for scheme i. The standard color fastness factor is... Let i be the grayscale value of the i-th scheme. Standard color grayscale value, For the texture features of the i-th scheme, For standard texture features, For the size data of the i-th scheme, For standard size data, Let be the total amount of auxiliary resources consumed in the i-th scheme. Let be the dyeing duration for the i-th scheme.

6. The method for optimizing the formulation of textile printing and dyeing auxiliaries based on digital twins according to claim 3, characterized in that, Multiple test results were obtained, including: Based on the optimized formula set, sampling dyeing tests were conducted on the target fabrics to obtain multiple fabric dyeing results. Randomly select the first fabric printing result from the multiple fabric printing results, perform image acquisition and grayscale processing on the first fabric printing result, and compare the deviation between the first fabric grayscale and the expected color grayscale to obtain the first color test result. The texture feature extraction channel is used to extract features from the grayscale image of the first fabric. The deviation between the first texture feature extraction result and the expected texture feature is compared to obtain the first texture test result. The texture feature extraction channel is constructed based on a convolutional neural network. A first test result is constructed based on the first color test result and the first texture test result, and added to the plurality of test results. The first test result also includes a first color fastness test result and a first size deviation test result.

7. The method for optimizing the formulation of textile printing and dyeing auxiliaries based on digital twins according to claim 1, characterized in that, The training scheme optimizes the model, including: After the number of sample optimization data in the sample optimization database meets the predetermined constraints, the sample optimization database is divided into Q equal parts, and the first sample set is constructed by selecting Q times with replacement. The first sample set is constructed by iteratively selecting Q times to obtain Q sample sets. Using the initial fabric attribute information, dyeing process, dyeing equipment and fabric feature data as inputs, and the optimal ratio scheme as supervision, the BP neural network is trained by Q sample sets to obtain Q convergent scheme optimization units. The scheme optimization model is constructed based on the Q convergent scheme optimization units, wherein the output of the scheme optimization model is the mode of the output results of the Q convergent scheme optimization units.

8. A textile printing and dyeing auxiliary ratio optimization system based on digital twins, characterized in that, The steps for implementing the textile printing and dyeing auxiliary ratio optimization method based on digital twins as described in any one of claims 1 to 7 include: The twin model construction module is used to perform simulation modeling based on digital twins, according to the initial fabric attribute information, the predetermined dyeing process and the predetermined dyeing equipment, to generate the initial fabric dyeing twin model. The twin model rendering module is used to perform fixed-point sampling and detection on the preprocessed target fabric according to predetermined detection indicators, obtain fabric feature dataset, and use the fabric feature dataset to render the initial fabric printing and dyeing twin model to obtain the fabric printing and dyeing twin model. The auxiliary agent ratio optimization module is used to optimize the auxiliary agent ratio scheme based on the fabric printing and dyeing twin model, with the expected printing and dyeing quality as a constraint, and to determine the set of optimized ratio schemes using the scheme evaluation function. The auxiliary agent ratio parameters include auxiliary agent type, auxiliary agent concentration, auxiliary agent ratio and auxiliary agent addition order. The sampling dyeing test module is used to conduct sampling dyeing tests on the target fabric according to the set of optimized ratio schemes, evaluate the multiple test results using the scheme evaluation function, and determine the optimal ratio scheme. An optimized database construction module is used to perform dyeing and printing operations on the target fabric based on the optimal ratio scheme, and to add the initial fabric attribute information, the predetermined dyeing and printing process, the predetermined dyeing and printing equipment, the fabric feature dataset, and the optimal ratio scheme to the sample optimization database. The scheme optimization model training module is used to train the scheme optimization model using the sample optimization database, and to perform subsequent auxiliary agent ratio scheme optimization after the scheme optimization model converges.

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