Textile dyeing method based on first principle and method for determining reaction conditions thereof

Abstract

The application discloses a textile dyeing method based on first principle and a reaction condition determination method thereof, and relates to the technical field of textile dyeing, and aims to solve the technical problem that in the current technology, there is a lack of in-depth basic research on the dyeing process and accurate model construction, which leads to the difficulty in accurately achieving the ideal state of the dyeing effect, and the method comprises the following steps: basic research, theoretical analysis is realized by numerical analysis of experiments, and standard reaction conditions of textile dyeing are found by simulation. A perfect basic library and experience library are established, and the simulation model is corrected and optimized by using experimental data, so that the standard reaction conditions are accurately found, and the problem of unstable dyeing quality caused by condition fluctuation and experience error in the traditional dyeing method is reduced. The application makes the color difference of dyeing controlled in a very small range, and the uniformity and brightness of the color are also significantly improved, so that the demand for high-quality textile production is met, and the market competitiveness of the product is improved.

Description

Technical Field

[0001] This invention relates to the field of textile dyeing technology, and more specifically, to a textile dyeing method based on first principles and a method for determining reaction conditions. Background Technology

[0002] Textile dyeing is a crucial process in the textile industry, its core lying in the interaction between dye molecules and fiber molecules. This process involves complex physical and chemical changes, which, by attaching dye molecules to fiber molecules, endow textiles with a rich variety of colors.

[0003] Several factors play a crucial role in the dyeing process. First is the choice of dye. Different types of dyes, such as reactive dyes, acid dyes, and disperse dyes, have different chemical structures and dyeing properties, making them suitable for different fiber materials. For example, reactive dyes are commonly used for dyeing cellulosic fibers such as cotton and linen, as they react chemically with the fibers to form covalent bonds, thus achieving good dyeing effects and colorfastness. Acid dyes, on the other hand, are more suitable for dyeing protein fibers such as wool and silk.

[0004] The type and properties of fibers also have a significant impact on the dyeing effect. Natural fibers such as cotton are hydrophilic, and the functional groups such as hydroxyl groups in their fiber structure can interact with dye molecules; while synthetic fibers such as polyester fibers have relatively stable chemical structures and require specific dyes and dyeing conditions to achieve good dyeing results.

[0005] The dyeing process conditions, including temperature, time, pH value, and dye liquor concentration, are also key factors. Temperature affects the diffusion rate and reactivity of dye molecules. Generally, appropriately increasing the temperature helps dye molecules penetrate the fiber more quickly, but excessively high temperatures may lead to dye decomposition or fiber damage. The duration of the dye-fiber reaction determines the degree of completeness of the reaction; too short a time may result in uneven dyeing or a light color, while too long a time may increase costs and energy consumption. pH value affects the ionization state of the dye and the surface charge of the fiber, thus affecting the binding force between the dye and the fiber. Proper control of the dye liquor concentration ensures that dyeing results are achieved while avoiding dye waste and environmental pollution. Since textile dyeing is essentially a physicochemical reaction between dye and fiber molecules, consistent conditions lead to consistent results; therefore, controlling the various dyeing conditions is crucial to achieving the desired dyeing effect.

[0006] However, current textile dyeing technologies primarily rely on past experience to determine dyeing processes and parameters, lacking in-depth fundamental research and precise model building. Traditional methods typically involve a first-dye test upon receiving an order; if successful, the formula and process are directly fixed for subsequent production. However, actual production scenarios are dynamic, with factors such as batch variations in raw materials, fluctuations in equipment operating conditions, and changes in environmental temperature and humidity all impacting dyeing results. The lack of a comprehensive foundational and empirical database for systematically analyzing these variables, and the inability to accurately simulate and correct the dyeing process using experimental data, makes it difficult to quickly adjust process parameters in the face of these changes. This results in highly unstable dyeing quality, frequent color differences, and difficulty in guaranteeing color uniformity and vibrancy. For special materials such as bamboo and hemp fibers, the lack of targeted and precise processes makes it even more difficult to achieve ideal dyeing results, failing to meet the growing demand in the high-quality textile market. This severely restricts the competitiveness of enterprises in the market and hinders the textile industry's progress towards high-end development. Therefore, we propose a first-principles-based textile dyeing method and a method for determining reaction conditions. Summary of the Invention

[0007] The purpose of this invention is to overcome the shortcomings of the prior art, adapt to practical needs, and provide a textile dyeing method based on first principles and a method for determining reaction conditions, so as to solve the technical problem that the lack of in-depth basic research and accurate model construction of the dyeing process in the current technology makes it difficult to accurately achieve the ideal state of dyeing effect.

[0008] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a textile dyeing method based on first principles, comprising the following steps: S1: Exploration of dyeing conditions: Basic research, through numerical analysis and simulation of experiments to achieve theoretical analysis, and to find the standard reaction conditions for dyeing textiles; Step S1 specifically includes the following steps: S101: Establish a basic library and an experience library; S102: Establish a basic model based on physical processes and existing fundamental knowledge; S103: Define boundary conditions for the basic model to generate a simulation model; S104: Use experimental data as a reference to perform data correction on the simulation model to achieve theoretical analysis; S105: Compare the theoretical analytical data obtained in step S104 with the experience base and correct the simulation model to form a standard reaction model; S106: Compare the standard reaction model with the base library. If it conforms to the base library, solidify the base library to form a standard model. If it does not conform to the base library, upgrade the base library and add the non-conforming model information to the base library to form a standard model. S2: Dyeing and Finishing Model Construction: Form a large-scale dyeing and finishing model, automatically generate processes, procedures and parameters based on the basic data of the reactants; construct a large-scale dyeing and finishing model based on the formed standard model and the actual needs and changing factors in the textile dyeing process. Step S2 specifically includes the following steps: S201. Compare the standard model with the large model; S202. The standard model conforms to the large model. The large model is directly modified, and then the process, flow and parameters are generated based on the production data of the large model to guide dyeing and finishing. S203. The standard model does not conform to the large model. Modify the large model and use the standard model as part of the large model to form a dyeing and finishing large model. Then, generate processes, procedures and parameters to guide dyeing and finishing by performing calculations on the dyeing and finishing large model based on the input data. S3: Practical guidance on process: Provide guidance on dyeing and finishing based on the described process, procedure and parameters.

[0009] Preferably, step S104 specifically includes: establishing the simulation model, simulating and calculating experimental results through the simulation model, comparing experimental data with simulation results, identifying the differences between simulation results and experimental results, adjusting theoretical parameters based on numerical analysis, and achieving theoretical analysis.

[0010] Preferably, step S203 specifically includes: inputting the standard model into the large model for calculation; if the process flow and parameters for textile raw materials cannot be formed in the large model, a new material module is added to form a new standard model and a large model; if the process flow and parameters can be formed, the dyeing and finishing are guided according to the formed process flow and parameters.

[0011] Preferably, the textile is one or a combination of cotton, bamboo, hemp, cotton-hemp blend, hemp / cotton blend, or polyester.

[0012] A method for determining reaction conditions for textile dyeing based on first principles includes the following steps: T1: First, conduct in-depth research on the characteristics of different fiber materials in the textile dyeing process, analyze the differences in their molecular structure, chemical composition and physical properties, and at the same time, conduct a detailed analysis of the chemical properties and reactivity of the dyes and auxiliaries used. T2: Next, based on the knowledge of intermolecular forces and chemical reaction kinetics, and combined with professional theories in the field of textile dyeing, a basic model was established; When constructing the model, the diffusion coefficient D of the dye in the fiber is considered, which conforms to the Arrhenius equation with respect to temperature T: ;in Here, Ea is the pre-exponential factor, R is the diffusion activation energy, and E is the ideal gas constant. This equation is used to derive the effect of temperature on the dye diffusion process, and then to analyze the physicochemical changes during the dyeing process. Meanwhile, the moisture absorption properties of fibers can be determined based on the moisture absorption isotherm equation: ;in The moisture regain of the fiber. The moisture regain rate is the equilibrium moisture content of the fiber. It is a constant. The relative humidity is used to calculate the moisture absorption of fibers under different humidity conditions using this equation. T3: Subsequently, boundary conditions are defined for the constructed basic model. Based on the temperature range, pressure conditions, and reaction time factors in the actual dyeing process, reasonable boundary conditions are set to generate a simulation model. T4: Next, the data obtained through theoretical analysis will be compared with the data in the experience base to further revise the simulation model and form a standard reaction model; T5: Finally, based on the established standard model, and combined with the actual needs and changing factors in the textile dyeing process, a large-scale dyeing and finishing model is constructed.

[0013] Preferably, the basic model in step T2 includes an energy equation model to describe energy changes and transfers during the dyeing process; a mass conservation equation model to ensure that the total amount of matter remains unchanged before and after the reaction; a momentum conservation equation model to analyze the material flow and dynamic changes during the reaction process; and a phase transformation equation model to calculate possible phase transitions during the dyeing process. When constructing the model, a multi-component multiphase flow and unsteady heat transfer coupled solver is used. By coupling the gas-liquid and gas-liquid-solid evaporation-absorption-desorption processes, the mass conservation equation, interphase momentum equation, interphase energy equation, component transfer equation, and the mass, energy, and component transfer equations within the liquid-solid phase are solved to comprehensively simulate the physicochemical reaction process of textile dyeing.

[0014] Preferably, in step T4, the standard reaction model is compared with the basic library. If the standard reaction model conforms to the existing architecture and data patterns of the basic library, the basic library is solidified, and the standard reaction model is determined as the final standard model. If the standard reaction model does not conform to the basic library, the basic library is upgraded, and the model information that does not conform is added to the basic library. After repeated adjustments and improvements, the standard model is finally formed.

[0015] Preferably, in step T5, the standard model is compared with the large model. If the standard model conforms to the framework and requirements of the large model, the large model is directly modified and improved. Then, based on the modified large model and the input reactant data, the process, flow and parameters applicable to the dyeing of different textiles are automatically generated to guide the actual dyeing and finishing work. If the standard model does not conform to the large model, the large model is comprehensively modified, and the standard model is integrated into it as an important part of the large model to form a new dyeing and finishing large model.

[0016] Compared with the prior art, the beneficial effects of the present invention are: 1. This invention, through in-depth fundamental research on the textile dyeing process, establishes a comprehensive basic and empirical database. Experimental data is used to calibrate and optimize the simulation model, thereby accurately identifying standard reaction conditions. In practical applications, the processes, procedures, and parameters generated based on these precise conditions and the constructed large-scale dyeing and finishing model significantly reduce the instability in dyeing quality caused by condition fluctuations and empirical errors in traditional dyeing methods. Precise dyeing can be achieved for both common fibers such as cotton and polyester, and special materials such as bamboo and hemp. This invention controls color difference within a very small range, and significantly improves color uniformity and vibrancy, meeting the demands of high-quality textile production and enhancing product market competitiveness.

[0017] 2. Traditional textile dyeing relies heavily on the experience and judgment of skilled workers. With industry development, this approach faces challenges due to inconsistent worker skill levels and rising labor costs. The method and model of this invention overcome this limitation. Through systematic theoretical analysis and automated model calculations, operators only need to provide basic data on the reactants, and the large-scale dyeing and finishing model can automatically generate the required processes and procedures. Even personnel without in-depth professional knowledge can operate smoothly. This invention not only reduces enterprises' reliance on highly skilled workers, decreasing human training costs and time, but also further improves production efficiency and product quality consistency by reducing human interference during the production process. In the long run, it will further contribute to the intelligent transformation of the textile dyeing and printing industry, enhancing the overall industry's production efficiency and sustainable development capabilities.

[0018] 3. In the textile industry, there are numerous types of fibers and new materials are constantly emerging, while the production environment is also constantly changing. This invention, through in-depth analysis of the characteristics of different fiber materials and comprehensive application of physicochemical principles, constructs a basic model and a large-scale dyeing and finishing model with strong adaptability. When faced with new fiber materials, adjustments and optimizations can be made quickly by adding new material modules, ensuring the feasibility and effectiveness of dyeing. In actual production, this invention can accurately determine reaction conditions and flexibly calculate appropriate process parameters based on changes in temperature, humidity, dye concentration, and production scale, ensuring the smooth progress of the dyeing process and the stability of product quality. This strong adaptability makes this invention widely applicable in the textile printing and dyeing field, meeting the diverse needs of different enterprises and markets, and promoting the innovative development of the textile industry. Attached Figure Description

[0019] Figure 1 This is a flowchart of the steps of the present invention. Detailed Implementation

[0020] Example 1, such as Figure 1 As shown, the present invention relates to a first-principles-based textile dyeing method and a method for determining reaction conditions, comprising the following steps: S1: Exploration of dyeing conditions: Basic research, through numerical analysis and simulation of experiments to achieve theoretical analysis, and to find the standard reaction conditions for dyeing textiles.

[0021] Step S1 specifically includes the following steps: S101: Establish a basic library and an experience library.

[0022] When establishing the basic database, we extensively collected physicochemical property data of different fiber types (such as cotton, linen, silk, wool, and synthetic fibers, as well as common and new fibers) under various environmental conditions. This included microstructural data such as fiber crystallinity, orientation, and surface morphology, as well as mechanical properties and chemical stability data under different temperatures, humidity levels, and pH conditions. For the experience database, we integrated years of actual production case data accumulated in the global textile printing and dyeing industry. This included dyeing process parameters, problems encountered, and solutions for different dye types (reactive dyes, acid dyes, disperse dyes, etc.) combined with fibers, providing comprehensive reference for subsequent model construction and revision.

[0023] S102: Establish a basic model based on physical effects and existing fundamental knowledge.

[0024] In addition to considering the physicochemical principles already mentioned, it is also necessary to delve into the specific interaction mechanisms between fibers and dye molecules. For example, certain fiber surfaces possess specific functional groups that may form different types of bonds with dye molecules, such as hydrogen bonds, van der Waals forces, or covalent bonds. The strength and stability of these bonds can change with reaction conditions, and these should be accurately described in the basic model by introducing appropriate parameters and functional relationships.

[0025] S103: Define boundary conditions for the basic model to generate a simulation model.

[0026] When defining boundary conditions, it is necessary to consider not only conventional factors such as temperature, pressure, and time, but also dynamic factors such as the flow rate of the dye liquor and the intensity of agitation. This is because, in actual dyeing processes, the flow state of the dye liquor significantly affects the diffusion rate and uniformity of dye molecules into the fiber. By accurately setting these boundary conditions, the simulation model can more closely resemble actual dyeing conditions.

[0027] S104: Use experimental data as a reference to perform data correction on the simulation model to achieve theoretical analysis.

[0028] Step S104 specifically includes: establishing a simulation model, simulating and calculating experimental results through the simulation model, comparing experimental data with simulation results, identifying the differences between simulation results and experimental results, adjusting theoretical parameters based on numerical analysis, and achieving theoretical analysis.

[0029] In this process, advanced data analysis algorithms, such as multiple linear regression analysis and neural network algorithms, are used to deeply mine and analyze a large amount of simulated and experimental data in order to more accurately determine the influence weight of each parameter on the staining results, thereby efficiently adjusting the theoretical parameters and improving the accuracy of the model.

[0030] S105: Compare the theoretical analytical data obtained in step S104 with the experience base and correct the simulation model to form a standard reaction model.

[0031] S106: Compare the standard reaction model with the base library. If it conforms to the base library, solidify the base library to form the standard model; if it does not conform to the base library, upgrade the base library and add the information of the non-conforming model to the base library to form the standard model.

[0032] During the comparison process, association rule analysis, a data mining technique, is used to find potential correlations and patterns between theoretical analysis data and experience base data. If differences are found between the theoretical analysis data and typical cases in the experience base, the reasons for the differences are further investigated. These differences may be due to certain special fiber microstructures or dye molecule characteristics not being fully reflected in the model. Based on this, the simulation model is modified accordingly to ensure that the resulting standard reaction model has broad applicability and reliability.

[0033] When the standard response model is compared with the base library, a similarity matching algorithm, such as cosine similarity or Euclidean distance, is used to calculate the degree of similarity between the two. If the similarity exceeds a set threshold, it is considered to conform to the base library and can be fixed in the base library; if it is below the threshold, the base library is upgraded by adding new model information according to certain classification and indexing rules for subsequent querying and use. At the same time, the data structure and management system of the base library are updated to improve data retrieval and processing efficiency.

[0034] During the comparison process, model comparison tools are used to conduct a comprehensive comparative analysis of the structure, parameters, variables, etc. of the standard model and the large model. It is necessary to consider not only the consistency of the mathematical model, but also to evaluate its adaptability and effectiveness in practical application scenarios.

[0035] When modifying the large model, an incremental learning algorithm is used to fine-tune the relevant parameters and rules of the large model based on the new information provided by the standard model. This ensures that the modified large model can maintain its original advantages while adapting to new coloring requirements. At the same time, the modification process and reasons are recorded to facilitate subsequent tracking and optimization.

[0036] When adding new material modules, a modular design concept is used to encapsulate the relevant physicochemical properties and reaction characteristics of the new materials with dyes into independent modules. These modules are connected and interact with the large model through interfaces to ensure that the new modules can be seamlessly integrated into the large model system. At the same time, the overall architecture and algorithms of the large model are optimized and adjusted to improve its ability and accuracy in processing new materials.

[0037] When studying the properties of fiber materials, advanced material characterization techniques, such as scanning electron microscopy (SEM), atomic force microscopy (AFM), and Fourier transform infrared spectroscopy (FTIR), are used to accurately analyze the microstructure and chemical functional groups of the fibers. For dyes and auxiliaries, high-performance liquid chromatography (HPLC) and mass spectrometry (MS) are used to determine their chemical composition and purity. At the same time, thermal analysis techniques (such as DSC and TGA) are used to study their thermal stability and reactivity, providing accurate data support for subsequent model construction.

[0038] S2: Dyeing and Finishing Model Construction: Form a large-scale dyeing and finishing model, and automatically generate processes, procedures and parameters based on the basic data of the reactants.

[0039] Step S2 specifically includes the following steps: S201. Compare the standard model with the large model.

[0040] S202. The standard model conforms to the large model. The large model is directly modified, and then the process, flow and parameters are generated based on the production data of the large model to guide dyeing and finishing.

[0041] S203. If the standard model does not conform to the large model, modify the large model and use the standard model as part of the large model to form a dyeing and finishing large model. Then, generate processes, procedures and parameters to guide dyeing and finishing by performing calculations on the dyeing and finishing large model based on the input data.

[0042] Step S203 specifically includes: inputting the standard model into the large model for calculation; if the process flow and parameters for textile raw materials cannot be formed in the large model, a new material module is added to form a new standard model and a large model; if the process flow and parameters can be formed, the dyeing and finishing are guided according to the formed process flow and parameters.

[0043] S3: Practical guidance on process: providing guidance on dyeing and finishing based on processes, procedures, and parameters.

[0044] When constructing the large-scale dyeing and finishing model, a distributed computing architecture and cloud computing technology are adopted to improve the model's computational efficiency and data processing capabilities. This enables the model to quickly respond to different reactant base data input by users and generate accurate processes, procedures, and parameters. Simultaneously, a model evaluation index system is established to evaluate and optimize the large-scale dyeing and finishing model from multiple dimensions, including accuracy, stability, and versatility, continuously improving its performance.

[0045] During the model fusion process, model fusion algorithms (such as weighted average method, stacking method, etc.) are used to integrate the advantages of standard model and large model, thereby improving the overall performance of the new dyeing and finishing large model.

[0046] A method for determining reaction conditions for textile dyeing based on first principles includes the following steps: T1: First, we will conduct in-depth research on the characteristics of various reactants in the textile dyeing process, such as the different fiber materials, and analyze the differences in their molecular structure, chemical composition and physical properties. At the same time, we will conduct a detailed analysis of the chemical properties and reactivity of the dye auxiliaries used.

[0047] T2: Next, based on the basic principles of physical chemistry, such as intermolecular forces and chemical reaction kinetics, and combined with professional theories in the field of textile dyeing, a basic model was established. When constructing the model, the diffusion coefficient D of the dye in the fiber is considered, which conforms to the Arrhenius equation with respect to temperature T: ;in Here, Ea is the pre-exponential factor, R is the diffusion activation energy, and E is the ideal gas constant. This equation is used to derive the effect of temperature on the dye diffusion process, and then to analyze the physicochemical changes during the dyeing process. Meanwhile, the moisture absorption properties of fibers can be determined based on the moisture absorption isotherm equation: ;in The moisture regain of the fiber. The moisture regain rate is the equilibrium moisture content of the fiber. It is a constant. The relative humidity is used to calculate the moisture absorption of fibers under different humidity conditions using this equation. The basic model in step T2 includes an energy equation model to describe energy changes and transfers during the dyeing process; a mass conservation equation model to ensure that the total amount of matter remains constant before and after the reaction; a momentum conservation equation model to analyze the material flow and dynamic changes during the reaction process; and a phase transformation equation model to calculate possible phase transitions during the dyeing process. When constructing the model, a multi-component multiphase flow and unsteady heat transfer coupled solver is used. By coupling the gas-liquid and gas-liquid-solid evaporation-absorption-desorption processes, the mass conservation equation, interphase momentum equation, interphase energy equation, component transfer equation, and the mass, energy, and component transfer equations within the liquid-solid phase are solved to comprehensively simulate the physicochemical reaction process of textile dyeing.

[0048] T3: Subsequently, boundary conditions are defined for the constructed basic model. Based on the temperature range, pressure conditions, and reaction time factors in the actual dyeing process, reasonable boundary conditions are set to generate a simulation model.

[0049] T4: Next, the data obtained through theoretical analysis will be compared with the data in the experience base to further revise the simulation model and form a standard reaction model.

[0050] In step T4, the standard reaction model is compared with the base library. If the standard reaction model conforms to the existing architecture and data patterns of the base library, the base library is solidified and the standard reaction model is determined as the final standard model. If the standard reaction model does not conform to the base library, the base library is upgraded and the incompatible model information is added to the base library. After repeated adjustments and improvements, the standard model is finally formed.

[0051] T5: Finally, based on the established standard model, and combined with the actual needs and changing factors in the textile dyeing process, a large-scale dyeing and finishing model is constructed.

[0052] In step T5, the standard model is compared with the large model. If the standard model conforms to the framework and requirements of the large model, the large model is directly modified and improved. Then, based on the modified large model and the input reactant data, the process, flow and parameters applicable to the dyeing of different textiles are automatically generated to guide the actual dyeing and finishing work. If the standard model does not conform to the large model, the large model is comprehensively modified, and the standard model is integrated into it as an important part of the large model to form a new dyeing and finishing large model.

[0053] The embodiments disclosed in this invention are preferred embodiments, but are not limited thereto. Those skilled in the art can easily understand the spirit of this invention based on the above embodiments and make different extensions and variations, but as long as they do not depart from the spirit of this invention, they are all within the protection scope of this invention.

Claims

1. A textile dyeing method based on first principles, characterized in that, Includes the following steps: S1: Exploration of dyeing conditions: Basic research, through numerical analysis and simulation of experiments to achieve theoretical analysis, and to find the standard reaction conditions for dyeing textiles; Step S1 specifically includes the following steps: S101: Establish a basic library and an experience library; S102: Establish a basic model based on physical processes and existing fundamental knowledge; S103: Define boundary conditions for the basic model to generate a simulation model; S104: Use experimental data as a reference to perform data correction on the simulation model to achieve theoretical analysis; S105: Compare the theoretical analytical data obtained in step S104 with the experience base and correct the simulation model to form a standard reaction model; S106: Compare the standard reaction model with the base library. If it conforms to the base library, solidify the base library to form a standard model. If it does not conform to the base library, upgrade the base library and add the non-conforming model information to the base library to form a standard model. S2: Dyeing and Finishing Model Construction: Form a large-scale dyeing and finishing model, automatically generate processes, procedures and parameters based on the basic data of the reactants; construct a large-scale dyeing and finishing model based on the formed standard model and the actual needs and changing factors in the textile dyeing process. Step S2 specifically includes the following steps: S201. Compare the standard model with the large model; S202. The standard model conforms to the large model. The large model is directly modified, and then the process, flow and parameters are generated based on the production data of the large model to guide dyeing and finishing. S203. The standard model does not conform to the large model. Modify the large model and use the standard model as part of the large model to form a dyeing and finishing large model. Then, generate processes, procedures and parameters to guide dyeing and finishing by performing calculations on the dyeing and finishing large model based on the input data. S3: Practical guidance on process: Provide guidance on dyeing and finishing based on the described process, procedure and parameters.

2. The textile dyeing method based on first principles according to claim 1, characterized in that, Step S104 specifically includes: establishing the simulation model, simulating and calculating experimental results through the simulation model, comparing experimental data with simulation results, identifying the differences between simulation results and experimental results, adjusting theoretical parameters based on numerical analysis, and achieving theoretical analysis.

3. The textile dyeing method based on first principles according to claim 1, characterized in that, Step S203 specifically includes: inputting the standard model into the large model for calculation; if the process flow and parameters for textile raw materials cannot be formed in the large model, a new material module is added to form a new standard model and a large model; if the process flow and parameters can be formed, the dyeing and finishing are guided according to the formed process flow and parameters.

4. The textile dyeing method based on first principles according to claim 1, characterized in that, The textiles are cotton, bamboo, hemp, cotton-hemp blends, hemp / cotton blends, or polyester, or any combination thereof.

5. A method for determining reaction conditions for textile dyeing based on first principles, applicable to the textile dyeing method based on first principles as described in any one of claims 1-4, characterized in that, Includes the following steps: T1: First, conduct in-depth research on the characteristics of different fiber materials in the textile dyeing process, analyze the differences in their molecular structure, chemical composition and physical properties, and at the same time, conduct a detailed analysis of the chemical properties and reactivity of the dyes and auxiliaries used. T2: Next, based on the knowledge of intermolecular forces and chemical reaction kinetics, and combined with professional theories in the field of textile dyeing, a basic model was established; When constructing the model, the diffusion coefficient D of the dye in the fiber is considered, which conforms to the Arrhenius equation with respect to temperature T: ;in The equation is given by the pre-exponential factor, Ea is the diffusion activation energy, and R is the ideal gas constant. The effect of temperature on the dye diffusion process is obtained through this equation, and then the physicochemical changes in the dyeing process are analyzed. Meanwhile, the moisture absorption properties of fibers can be determined based on the moisture absorption isotherm equation: ;in The moisture regain of the fiber. The moisture regain rate is the equilibrium moisture content of the fiber. It is a constant. The relative humidity is used to calculate the moisture absorption of fibers under different humidity conditions using this equation. T3: Subsequently, boundary conditions are defined for the constructed basic model. Based on the temperature range, pressure conditions, and reaction time factors in the actual dyeing process, reasonable boundary conditions are set to generate a simulation model. T4: Next, the data obtained through theoretical analysis will be compared with the data in the experience base to further revise the simulation model and form a standard reaction model; T5: Finally, based on the established standard model, and combined with the actual needs and changing factors in the textile dyeing process, a large-scale dyeing and finishing model is constructed.

6. The method for determining reaction conditions for textile dyeing based on first principles, as described in claim 5, is characterized in that... The basic model in step T2 includes an energy equation model, which is used to describe the energy changes and transfers during the staining process; The mass conservation equation model ensures that the total amount of matter remains unchanged before and after the reaction; the momentum conservation equation model analyzes the material flow and dynamic changes during the reaction process; the phase transformation equation model calculates the possible phase transitions during the dyeing process; when constructing the model, a multi-component multiphase flow and unsteady heat transfer coupled solver is used. By coupling the gas-liquid and gas-liquid-solid evaporation-absorption-desorption processes, the mass conservation equation, interphase momentum equation, interphase energy equation, component transfer equation, and the mass, energy, and component transfer equations within the liquid-solid phase are solved, comprehensively simulating the physicochemical reaction process of textile dyeing.

7. The method for determining reaction conditions for textile dyeing based on first principles, as described in claim 6, is characterized in that... In step T4, the standard reaction model is compared with the basic library. If the standard reaction model conforms to the existing architecture and data patterns of the basic library, the basic library is solidified and the standard reaction model is determined as the final standard model. If the standard reaction model does not conform to the basic library, the basic library is upgraded and the information of the non-conforming model is added to the basic library. After repeated adjustments and improvements, the standard model is finally formed.

8. The method for determining reaction conditions for textile dyeing based on first principles, as described in claim 7, is characterized in that: In step T5, the standard model is compared with the large model. If the standard model meets the framework and requirements of the large model, the large model is directly modified and improved. Then, based on the modified large model and the input basic data of the reactants, the process, flow and parameters suitable for dyeing different textiles are automatically generated to guide the actual dyeing and finishing work. If the standard model does not conform to the large model, the large model will be completely modified, and the standard model will be integrated into it as an important part of the large model to form a new dyeing and finishing large model.

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