Printing and dyeing intelligent control method and control system
By combining deep learning and digital twin technologies, intelligent control of the dyeing and printing process has been achieved, solving the problems of low efficiency and poor accuracy in traditional dyeing and printing methods, and realizing efficient and reliable formula optimization and improved production adaptability.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- ZHEJIANG JISHAN PRINTING&DYEING
- Filing Date
- 2025-12-23
- Publication Date
- 2026-04-10
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
Traditional dyeing and printing methods rely on manual experience, which is inefficient, costly, and difficult to meet the market demand for small-batch, multi-variety, and rapid response. Existing digital methods fail to fully consider fabric materials and environmental factors, and virtual simulation lacks realism and reliability, as well as an efficient closed-loop optimization mechanism.
A deep learning network is used to train an AI color prediction model, which is combined with digital twin technology for high-fidelity virtual prototyping and closed-loop optimization. The formula is optimized through intelligent iterative algorithms, realizing intelligent mapping and feedback learning from color features to production formula.
It significantly improves color matching efficiency and accuracy, reduces resource consumption and environmental pollution, enhances production adaptability and first-pass yield, and reduces reliance on human experience.
Smart Images
Figure CN121832268A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to a printing and dyeing method, more particularly, to a printing and dyeing intelligent control method and system. BACKGROUND
[0002] As an important link in the textile industry chain, the printing and dyeing industry has long relied on manual experience for color formula design and production adjustment. The traditional color matching method usually realizes through repeated trial and error and physical proofing, which not only has low efficiency and high cost, but also is limited by the technical level of the operator, and is prone to cause poor color accuracy, long production cycle and serious resource waste. In addition, with the increasing diversification of market demand and the improvement of environmental protection requirements, the traditional method is difficult to meet the production demand of small batch, multi-variety and fast response, which restricts the high quality and sustainable development of the printing and dyeing industry.
[0003] In recent years, with the development of artificial intelligence and digital twin technologies, the digitalization and intelligentization of the printing and dyeing process have gradually become an important direction for industry upgrading. The existing technology attempts to combine spectral data with formula prediction, or uses computer simulation means for preliminary color simulation, but there are still problems: the existing method usually fails to fully consider the influence of fabric material, process parameters and environmental factors on color development effect, and the virtual simulation lacks authenticity and reliability, and at the same time, there is a lack of efficient closed-loop optimization mechanism, which cannot realize rapid iteration and accurate correction of the formula before actual production. SUMMARY
[0004] In view of the deficiencies of the prior art, the purpose of the present application is to provide a printing and dyeing intelligent control method and system.
[0005] The above technical purpose of the present application is realized by the following technical scheme: a printing and dyeing intelligent control method, comprising the following steps: S1, model construction and data preparation: collect historical printing and dyeing data to construct a sample set, and train an AI color matching prediction model based on a deep learning network, so as to learn the mapping relationship between color features and production formula; S2, intelligent formula generation and virtual simulation: this step realizes accurate preliminary screening and virtual verification of the formula through the cooperation of artificial intelligence and digital twin technology, and specifically comprises the following steps: S2.1, intelligent formula prediction: obtain the color spectrum data of the customer sample through a high-precision color measurement device, extract the color feature vector after preprocessing, input the feature vector into the AI color matching prediction model, and calculate and output one or more initial predicted formulas by the model; S2.2, high-fidelity virtual proofing: input the initial prediction formula into a digital twin virtual proofing system based on a physical rendering engine, which is built by a physical rendering engine using a Monte Carlo ray tracing algorithm and a bidirectional reflectance distribution function model of material to simulate the real color development behavior of dyes on the base fabric; S2.3, environment and material parameter loading: load the accurate material parameters of the current base fabric to be produced into the virtual proofing system, including fiber type, fabric structure, pretreatment state, and current process parameters; S2.4, color development effect simulation: the virtual proofing system simulates the adsorption, diffusion and fixation process of dyes on a specific material based on physical mechanism, renders a high-fidelity virtual color sample, and calculates the predicted color difference value of the virtual color sample relative to the customer's sample; S3, formula iteration optimization: this step automatically optimizes the formula to the best state in the virtual environment through a closed-loop feedback mechanism; S4, physical execution and feedback learning: physical proofing or production is carried out according to the optimized production formula, and the successful production data is fed back to the sample set of S1 as a new sample for periodic incremental learning and updating of the AI color matching prediction model.
[0006] The application further provides that the formula iteration optimization specifically includes the following steps: S3.1, color difference compliance judgment: compare the predicted color difference calculated in step S2.4 with the preset target tolerance range; S3.2, intelligent iteration optimization: if the predicted color difference does not fall within the target tolerance range, start the built-in optimization algorithm, automatically generate the adjustment amount of the current formula components based on the direction and size of the predicted color difference, and form a new corrected formula; S3.3, cycle simulation verification: load the corrected new formula into the digital twin virtual proofing system again, repeat the sub-steps of S2.2 to S2.4, and perform a new round of virtual proofing and color difference prediction; S3.4, result output: repeat the iteration process of S3.2 to S3.3 until the predicted color difference of the virtual color sample falls within the target tolerance range, and lock the corresponding formula at this time as the final optimized production formula.
[0007] The application further provides that in step S1, the historical printing and dyeing data includes color spectrum data, dye formula, base fabric material parameter, process parameter and actual color difference data, and the sample set is processed by data cleaning and normalization to eliminate noise and dimension difference.
[0008] The application is further configured that in step S2.1, the preprocessing includes filtering and denoising, baseline correction and feature enhancement on the color spectrum data to extract more robust color feature vectors.
[0009] The application is further configured that in step S2.3, the material parameters further include porosity, surface roughness and hydrophilicity index of the base cloth, and the process parameters include dye vat temperature, pH value and auxiliary agent concentration.
[0010] The application is further configured that in step S2.4, the simulation of the dye adsorption, diffusion and fixation process is based on Fick's diffusion law and Langmuir adsorption model, and considers factors such as dye concentration gradient, temperature and time to calculate the spectral reflectance of the virtual color sample.
[0011] The application is further configured that in step S3.2, the optimization algorithm is one of gradient descent method, genetic algorithm or particle swarm optimization algorithm.
[0012] The application is further configured that in step S4, the feedback learning includes associating the actual color difference data after physical proofing or production with the optimized production formula, and updating the AI color matching prediction model in an online learning or batch learning manner to improve the prediction accuracy of the model.
[0013] An intelligent control system for printing and dyeing, comprising: A data acquisition module configured with a high-precision spectral colorimeter for acquiring color spectrum data of incoming samples and extracting color feature vectors; A central processing module in communication connection with the data acquisition module, comprising: A formula prediction unit with an AI color matching prediction model for generating an initial predicted formula based on the color feature vectors; A virtual proofing unit with a digital twin simulation model for loading the initial predicted formula, base cloth material parameters and process parameters, simulating the color development process and outputting a predicted color difference; An optimization decision unit for comparing the predicted color difference with a preset tolerance and adjusting the formula and re-simulating through an optimization algorithm when the color difference exceeds the limit; A physical execution module connected with the central processing module, comprising an automated material system for executing the optimized formula; A feedback learning module connected with the data acquisition module, the central processing module and the physical execution module for collecting production data and updating the AI color matching prediction model; The formula prediction unit, the virtual proofing unit and the optimization decision unit are connected in sequence to form an intelligent iterative optimization loop, which can complete automatic prediction, simulation verification and iterative optimization of the formula in a virtual environment, and finally output a production formula meeting the color difference requirements.
[0014] In summary, the present application has the following beneficial effects: By deep integration of artificial intelligence and digital twin technology, the present application realizes intelligent prediction of formula, high-fidelity virtual simulation and closed-loop optimization decision, significantly improves color matching efficiency and accuracy, greatly reduces dependence on artificial experience and physical sampling, integrates multiple parameters of base cloth material and process conditions, simulates dye color development process based on physical mechanism, enhances the authenticity and reliability of virtual sampling, relies on intelligent iterative optimization algorithm and feedback learning mechanism to automatically correct formula and continuously evolve in virtual environment, thereby improving one-time compliance rate and production adaptability, and effectively reducing resource consumption and environmental pollution. BRIEF DESCRIPTION OF DRAWINGS
[0015] Fig. 1 is a method flowchart of the present application; Fig. 2 is a specific flowchart of the intelligent formula generation and virtual simulation steps in the present application; Fig. 3 is a specific flowchart of the formula iterative optimization steps in the present application. DETAILED DESCRIPTION
[0016] The technical solutions in the embodiments of the present application will be described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.
[0017] Referring to Figs. 1-3 As shown in the figure, an intelligent printing and dyeing control method comprises the following steps: S1, model construction and data preparation: collect historical printing and dyeing data to construct a sample set, and train an AI color matching prediction model based on a deep learning network to make it learn the mapping relationship between color features and production formula; The AI color matching prediction model used in the embodiment is a hybrid deep learning model combining convolutional neural network and fully connected layer. The input layer receives color spectrum reflectivity data with a dimension of 1x361, with a wavelength range of 360nm-780nm and an interval of 1nm. Then, two one-dimensional convolution layers are connected. The first convolution layer uses 64 convolution kernels with a size of 1x5, and the activation function is ReLU. The second convolution layer uses 128 convolution kernels with a size of 1x3, and the activation function is ReLU. Then, a global average pooling layer is connected, and the fabric material type and process parameters are input as auxiliary inputs. The combined feature vector is input into three fully connected layers with decreasing number of units, including 512 units, 256 units, and 128 units, all using ReLU activation. The final output layer uses a linear activation function to output the predicted concentration formula of N dyes. The training samples of the AI color matching prediction model are composed of: The training sample set comes from the collected historical printing and dyeing data. Each sample is a four-tuple, including X, M, P, and Y. X is the input feature, which is color spectrum reflectivity data with a dimension of 1x361, representing the reflectivity value of a known color in the wavelength range of 360nm to 780nm with an interval of 1nm. This is the core input for the model to learn color features. M is the auxiliary input, which is the fabric material type and related physical parameters. P is the auxiliary input, which includes dyeing temperature, pH value, and auxiliary agent concentration, which need to be normalized. Y is the training target, which is the actual concentration formula of N dyes. This is the proven successful and real formula for producing the color X on the specific fabric M and process P. N depends on the number of commonly used dye types in the production line. Before training, the sample set needs to be preprocessed, including the following steps: Data cleaning: remove abnormal samples with obvious measurement errors, incomplete records, or production failures; Normalization: map data with different dimensions and ranges to the same scale. X is usually in the range of [0, 1] and does not need additional processing. The numerical parameters in M and P need to be normalized. Y is the key object of normalization. The concentration value of each dye is divided by its maximum available concentration, which is compressed to the interval [0, 1]. This not only accelerates the model convergence, but also keeps the concentration constraint condition in step S3 consistent, i.e. 0 to maximum concentration. The training of the model is a multi-output regression problem, which aims to make the predicted formula direction vector Ŷ as close as possible to the true formula direction vector Y. Therefore, the loss function used is the mean square error, which is calculated as: Loss = (1 / N) *∑ (Y_i - Ŷ_i)², where N is the number of output dyes, Y_i is the true normalized concentration of the i-th dye, and Ŷ_i is the normalized concentration of the i-th dye predicted by the model. The function strongly penalizes large deviations between predicted and true values, driving the model to learn the exact formula mapping relationship; The model is trained using the backpropagation algorithm and an optimizer, which includes the following steps: Data set division: randomly divide the total sample set into training set, validation set and test set according to a certain proportion; Iterative training: the training set is used to update the model weights, and the validation set is used to monitor the training process and prevent overfitting; Convergence judgment: training will not go on indefinitely. When the loss value on the training set falls below a pre-set very small threshold, the model is considered to have converged, and training is terminated; After training is completed, an AI color matching prediction model is finally obtained. This model has strong feature extraction capability, and its CNN part has learned how to extract deep color features most relevant to the formula from 361-dimensional spectral data. It also understands the relationship between multi-modal inputs. The model not only looks at color, but also learns how to adjust the output of the formula by considering fabric material and process parameters. It is a reliable formula generator that can quickly generate an initial predicted formula very close to the true demand when encountering a new color spectrum input. The higher the accuracy of the initial formula, the better the starting point for subsequent S2 and S3 virtual optimization steps, which can greatly reduce the number of iterations required for optimization and improve the efficiency of the entire system; S2, intelligent formula generation and virtual simulation: through the cooperation of artificial intelligence and digital twin technology, this step realizes the accurate preliminary screening and virtual verification of the formula, which includes the following steps: S2.1, intelligent formula prediction: obtain the color spectrum data of the customer's sample through high-precision color measurement equipment. After preprocessing, the color feature vector is extracted and input into the AI color matching prediction model. The model calculates and outputs one or more initial predicted formulas; S2.2, high-fidelity virtual proofing: input the initial predicted formula into a digital twin virtual proofing system based on a physical rendering engine; S2.3, load environmental and material parameters: load the accurate material parameters of the current production base cloth into the virtual proofing system, including fiber type, fabric structure, pretreatment state, and current process parameters; S2.4, color effect simulation: the virtual proofing system is based on physical mechanism, simulates the adsorption, diffusion and fixation process of dyes on specific materials, renders high-fidelity virtual color samples, and calculates the predicted color difference value of the virtual color sample relative to the customer sample; Wherein, the core of the virtual proofing system is a color rendering calculation model based on physical mechanism. For the dye adsorption process, Langmuir adsorption isotherm formula Γ=(Γ_max* K*C) / (1+ K*C) is used for calculation, wherein Γ is the adsorption amount, Γ_max is the maximum adsorption amount, which is determined by experiment in advance, for example, about 20 mg / g for reactive dye X on cotton fabric, K is the adsorption equilibrium constant, about 0.8 L / mg for reactive dye X, and C is the dye concentration. For the dye diffusion process, based on Fick's second law, the diffusion equation ∂C / ∂t=D * ∂²C / ∂x² is solved by finite element method, wherein the diffusion coefficient D is a function of temperature and material, D=D0 * exp(-Ea / RT), D0 is the pre-exponential factor, about 5.0×10⁻ 5 m² / s for cotton fiber, and activation energy Ea is about 45 kJ / mol. The calculated concentration distribution of dyes inside the fiber is converted into absorption coefficient and scattering coefficient in light ray tracing, R is the universal gas constant, T is the thermodynamic temperature, and RT is the product of the universal gas constant and the thermodynamic temperature; S3, formula iteration optimization: this step automatically optimizes the formula to the best state in the virtual environment through closed-loop feedback mechanism, which includes the following steps: S3.1, color difference compliance judgment: compare the predicted color difference calculated in step S2.4 with the preset target tolerance range; S3.2, intelligent iteration optimization: if the predicted color difference does not fall within the target tolerance range, start the built-in optimization algorithm, automatically generate the adjustment amount of the current formula components based on the direction and size of the predicted color difference, and form a new corrected formula; S3.3, cycle simulation verification: load the corrected new formula into the digital twin virtual proofing system again, repeat the sub-steps of S2.2 to S2.4, and perform a new round of virtual proofing and color difference prediction; S3.4, result output: repeat the iteration process of S3.2 to S3.3 until the predicted color difference of the virtual color sample falls within the target tolerance range, and lock the corresponding formula at this time as the final optimized production formula; The optimization decision unit of the embodiment selects a particle swarm optimization algorithm with constraints, the target function is the total color difference DE calculated by the CIEDE2000 color difference formula, the optimization variable is the concentration of each dye in the formula, the change range is constrained to be from 0 to the maximum available concentration of the dye, and the algorithm parameters are: the particle swarm size is set to 30, the maximum number of iterations is 100, the inertia weight is linearly decreased from 0.9 to 0.4, the individual learning factor and the social learning factor are both set to 2.0, the optimization algorithm automatically searches in the concentration constraint space of the formula according to the DE, AL, a, and b values of the current virtual color sample, to find a new formula that minimizes DE, wherein DE is the total color difference, AL is the lightness difference, a is the red-green difference, and b is the yellow-blue difference; S4, physical execution and feedback learning: physical sampling or production is performed according to the optimized production formula, and successful production data is fed back to the sample set of S1 as a new sample for periodic incremental learning and updating of the AI color matching prediction model, wherein the successful production data refers to production data in which the CIEDE2000 color difference value DE between the actual color sample obtained by measuring the color sample after physical sampling or large-scale production according to the optimized production formula and the customer sample is within a preset target tolerance range; In step S1, the historical printing and dyeing data includes color spectrum data, dye formula, base cloth material parameters, process parameters, and actual color difference data, and the sample set is processed by data cleaning and normalization to eliminate noise and dimension differences. In step S2.1, the preprocessing includes filtering and denoising, baseline correction, and feature enhancement of the color spectrum data to extract more robust color feature vectors. In step S2.2, the digital twin virtual sampling system is constructed by a physical rendering engine, which uses a Monte Carlo ray tracing algorithm and a material bidirectional reflectance distribution function model to simulate the real color development behavior of dyes on the base cloth. In step S2.3, the material parameters also include the porosity, surface roughness, and hydrophilicity of the base cloth. In step S2.3, the process parameters include dye vat temperature, pH value, and auxiliary agent concentration. In step S2.4, the simulation of the dye adsorption, diffusion, and fixation process is based on the Fick's diffusion law and the Langmuir adsorption model, and takes into account the dye concentration gradient, temperature, and time factors to calculate the spectral reflectance of the virtual color sample. In step S3.2, the optimization algorithm is one of gradient descent, genetic algorithm, or particle swarm optimization algorithm. In step S4, feedback learning includes associating the actual color difference data after physical sampling or production with the optimized production formula, and updating the AI color matching prediction model in an online learning or batch learning manner to improve the prediction accuracy of the model.
[0018] The present application builds an AI prediction, virtual simulation, closed-loop optimization, feedback learning whole-process intelligent control framework, which works collaboratively through four main steps: Model building and data preparation: use historical production data to train an AI color matching prediction model, learn the complex nonlinear mapping relationship from color features to production formula, and lay the foundation for intelligent formula generation; Intelligent formula generation and virtual simulation: combining AI prediction and digital twin technology, quickly screening and high-fidelity virtual proofing customer samples, simulating color development under actual base cloth and process conditions, and predicting color difference; Formula iteration optimization: build a closed-loop feedback optimization loop in the digital twin environment, automatically analyze and predict color difference through intelligent algorithms, and iteratively adjust the formula until the predicted color difference of the virtual color sample meets the preset standard, ensuring the optimality of the formula in the virtual world; Physical execution and feedback learning: use the optimized formula for actual production, and use the production result data as new knowledge feedback to the AI model, so that it can continuously learn and evolve, and continuously improve the prediction accuracy and adaptability; Through the above scheme, the printing and dyeing experience can be deposited into an iteratively optimized digital model and algorithm, forming an intelligent system that can self-optimize and continuously improve.
[0019] An intelligent control system for printing and dyeing, comprising: A data acquisition module configured with a high-precision spectral colorimeter for obtaining color spectrum data of samples and extracting color feature vectors. The module has built-in data processing circuit or software that can perform preliminary filtering and feature extraction on the original spectral data to generate color feature vectors, which are uploaded to the central processing module through a communication interface; A central processing module, usually carried by an industrial computer or a high-performance server, is in communication connection with the data acquisition module through Ethernet communication for transmitting color feature vectors, including: A formula prediction unit with an AI color matching prediction model for generating an initial predicted formula based on the color feature vector; A virtual proofing unit with a digital twin simulation model for loading initial predicted formula, base cloth material parameters and process parameters, simulating color development process and outputting predicted color difference; An optimization decision unit for comparing predicted color difference with preset tolerance and adjusting formula and re-simulating through optimization algorithm when color difference exceeds the limit; A physical execution module connected with the central processing module through an industrial bus, including an automatic material system that can receive the final optimized formula instruction, automatically control the execution mechanism such as material tank, weighing device and conveying pump, complete precise preparation and delivery of dyes, and realize automation of production process; The feedback learning module is connected with the data acquisition module, the central processing module and the physical execution module, is used for monitoring the result after physical execution, acquires the color measurement data in actual production through a data interface, associates the color measurement data with corresponding formulas, materials and process parameters, forms new training samples, and stores the new training samples in a central database. The feedback learning module adopts an online incremental learning mode. After each successful physical production, the system stores the sample, including color characteristics, formulas, materials, processes and actual color difference, into a dynamic database. When new samples accumulate to 100, the system triggers an incremental update training of the model once. The update training uses all historical data, but uses a smaller learning rate for fine-tuning. The training period is 50 epochs. The formula prediction unit, the virtual proofing unit and the optimization decision unit are sequentially connected to form an intelligent iterative optimization loop, which can complete automatic prediction, simulation verification and iterative optimization of formulas in a virtual environment, and finally output a production formula meeting color difference requirements.
[0020] The above is only a preferred embodiment of the present application, and the protection scope of the present application is not limited to the above-mentioned embodiments. Any technical solution falling within the concept of the present application shall be considered as falling within the protection scope of the present application. It should be noted that, for ordinary skilled persons in the art, some improvements and refinements without departing from the principles of the present application shall also be considered as falling within the protection scope of the present application.
Claims
1. A method for intelligent control of dyeing and printing, characterized in that: Comprising the following steps: S1, model construction and data preparation: collect historical printing and dyeing data to construct a sample set, and train an AI color matching prediction model based on a deep learning network to learn the mapping relationship between color features and production formula; S2, intelligent formula generation and virtual simulation: this step realizes accurate preliminary screening and virtual verification of the formula through the cooperation of artificial intelligence and digital twin technology, specifically comprising the following steps: S2.1, intelligent formula prediction: obtain the color spectrum data of the customer's sample through high-precision color measurement equipment, extract the color feature vector after preprocessing, input the feature vector into the AI color matching prediction model, and output one or more initial prediction formulas by the model; S2.2, high-fidelity virtual proofing: input the initial prediction formula into a digital twin virtual proofing system based on a physical rendering engine, wherein the digital twin virtual proofing system is constructed by a physical rendering engine, and the physical rendering engine adopts a Monte Carlo ray tracing algorithm and a bidirectional reflectance distribution function model to simulate the real color development behavior of dyes on the base fabric; S2.3, environment and material parameter loading: load the accurate material parameters of the current base fabric to be produced into the virtual proofing system, including fiber type, fabric structure, pretreatment state, and current process parameters; S2.4, color development effect simulation: the virtual proofing system simulates the adsorption, diffusion and fixation process of dyes on specific materials based on physical mechanism, renders a high-fidelity virtual color sample, and calculates the predicted color difference value of the virtual color sample relative to the customer's sample; S3, formula iterative optimization: this step automatically optimizes the formula to the best state in the virtual environment through a closed-loop feedback mechanism; S4, physical execution and feedback learning: perform physical proofing or production according to the optimized production formula, and feed back the successful production data this time to the sample set of S1 for periodic incremental learning and updating of the AI color matching prediction model.
2. The intelligent control method for printing and dyeing according to claim 1, characterized in that: The formula iterative optimization specifically comprises the following steps: S3.1, color difference compliance judgment: compare the predicted color difference calculated in step S2.4 with the preset target tolerance range; S3.2, intelligent iterative optimization: if the predicted color difference does not fall within the target tolerance range, start the built-in optimization algorithm, automatically generate the adjustment amount of the current formula components based on the direction and size of the predicted color difference, and form a corrected new formula; S3.3, cycle simulation verification: load the corrected new formula into the digital twin virtual proofing system again, and repeat the sub-steps of S2.2 to S2.4 to perform a new round of virtual proofing and color difference prediction; S3.4, result output: repeat the iteration process of S3.2 to S3.3 until the predicted color difference of the virtual color sample falls within the target tolerance range, and lock the corresponding formula at this time as the final optimized production formula.
3. The intelligent control method for printing and dyeing according to claim 1, characterized in that: In step S1, the historical printing and dyeing data includes color spectrum data, dye formula, base fabric material parameter, process parameter and actual color difference data, and the sample set is processed by data cleaning and normalization to eliminate noise and dimension difference.
4. The intelligent control method for printing and dyeing according to claim 1, characterized in that: In step S2.1, the preprocessing includes filtering and denoising, baseline correction and feature enhancement of the color spectrum data to extract more robust color feature vectors.
5. The intelligent control method for printing and dyeing according to claim 1, characterized in that: In step S2.3, the material parameters further include the porosity, surface roughness and hydrophilicity of the base cloth, and the process parameters include the dyeing vat temperature, pH value and auxiliary agent concentration.
6. The intelligent control method for printing and dyeing according to claim 1, characterized in that: In step S2.4, the simulation of the dye adsorption, diffusion and fixation process is based on the Fick's diffusion law and the Langmuir adsorption model, and considers the dye concentration gradient, temperature and time factors to calculate the spectral reflectance of the virtual color sample.
7. The intelligent control method for printing and dyeing according to claim 1, characterized in that: In step S3.2, the optimization algorithm is one of gradient descent method, genetic algorithm or particle swarm optimization algorithm.
8. The intelligent control method for printing and dyeing according to claim 1, characterized in that: In step S4, the feedback learning includes associating the actual color difference data after physical proofing or production with the optimized production formula, and updating the AI color matching prediction model in an online learning or batch learning manner to improve the prediction accuracy of the model.
9. A printing and dyeing intelligent control system for implementing the printing and dyeing intelligent control method according to any one of claims 1-8, characterized in that: Comprise: a data acquisition module configured with a high-precision spectral colorimeter for acquiring color spectrum data of samples and extracting color feature vectors; a central processing module in communication connection with the data acquisition module, comprising: a formula prediction unit with an AI color matching prediction model built-in for generating an initial predicted formula according to the color feature vectors; a virtual proofing unit with a digital twin simulation model built-in for loading the initial predicted formula, base cloth material parameters and process parameters, simulating the color development process and outputting the predicted color difference; an optimization decision unit for comparing the predicted color difference with the preset tolerance and adjusting the formula and re-simulating through the optimization algorithm when the color difference is out of limit; a physical execution module connected with the central processing module, comprising an automated material system for executing the optimized formula; a feedback learning module connected with the data acquisition module, the central processing module and the physical execution module for collecting production data and updating the AI color matching prediction model; the formula prediction unit, the virtual proofing unit and the optimization decision unit are connected in sequence to form an intelligent iterative optimization loop, which can complete the automatic prediction, simulation verification and iterative optimization of the formula in a virtual environment, and finally output a production formula meeting the color difference requirements.