Textile dyeing method based on first principle and reaction condition determination method thereof

Abstract

The invention discloses a textile dyeing method based on a first principle and a reaction condition determination method thereof, relates to the technical field of textile dyeing, and aims to solve the technical problem that the dyeing effect is difficult to accurately reach an ideal state due to the lack of deep basic research and accurate model construction on the dyeing process in the prior art. Comprising the following steps: basic research: carrying out numerical analysis on an experiment for simulation to realize theoretical analysis, and finding standard reaction conditions of textile dyeing; according to the method, a perfect basic library and an experience library are established, a simulation model is corrected and optimized by utilizing experimental data, standard reaction conditions are accurately found, and the problem of unstable dyeing quality caused by condition fluctuation and experience errors in a traditional dyeing method is reduced. According to the invention, the color difference of dyeing is controlled within an extremely small range, the uniformity and vividness of the color are also remarkably improved, the requirements of high-quality textile production are met, and the market competitiveness of the product is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of textile dyeing, and more specifically, to a textile dyeing method based on first principles and a method for determining its reaction conditions. Background Art

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

[0003] During the dyeing process, multiple factors play key roles. First is the choice of dye. Different types of dyes, such as reactive dyes, acid dyes, disperse dyes, etc., have different chemical structures and dyeing characteristics and are suitable for different fiber materials. For example, reactive dyes are commonly used for dyeing cellulose fibers such as cotton and linen, and they can react with the fibers to form covalent bonds, thus achieving good dyeing effects and color fastness; acid dyes are more suitable for dyeing protein fibers such as wool and silk.

[0004] The type and properties of the fiber also have an important impact on the dyeing effect. Natural fibers such as cotton are hydrophilic, and functional groups such as hydroxyl groups in their fiber structures 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 better dyeing effects.

[0005] The technological conditions for dyeing, including temperature, time, pH value, dye liquor concentration, etc., are also key factors. The temperature affects the diffusion rate and reaction activity of dye molecules. Generally speaking, appropriately increasing the temperature helps the dye molecules enter the fiber interior faster, but too high a temperature may cause dye decomposition or fiber damage. The length of time determines the degree of full reaction between the dye and the fiber. Too short a time may result in uneven dyeing or light color, and too long a time may increase costs and energy consumption. The 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. Reasonable control of the dye liquor concentration can ensure good dyeing effects while avoiding dye waste and environmental pollution. And the essence of textile dyeing is the physical and chemical reaction between dye molecules and fiber molecules. If the conditions are the same, the results will be the same. Therefore, controlling the various conditions of dyeing can achieve ideal dyeing effects.

[0006] However, in the existing textile dyeing technology, determining the dyeing process and parameters mainly relies on past experience, lacking in-depth basic research on the dyeing process and precise model construction. Traditional methods usually conduct a first-batch test after receiving an order. If the first batch is successful, the formula and process are directly fixed for subsequent production. However, the actual production scenario is dynamically changing. Factors such as differences in raw material batches, fluctuations in equipment operating status, and changes in environmental temperature and humidity will all affect the dyeing results. Since there is no established comprehensive basic database and experience database to systematically analyze these variables, and it is also impossible to use experimental data to accurately simulate and correct the dyeing process, it is difficult to quickly adjust process parameters when facing these changes. This results in extremely unstable dyeing quality, frequent occurrence of color difference problems, and difficulty in ensuring color uniformity and vividness. For special materials such as bamboo and hemp fibers, due to the lack of targeted precise processes, the dyeing effect is even more difficult to reach the ideal state, unable to meet the growing demand of the high-quality textile market, severely restricting the improvement of the enterprise's competitiveness in the market and limiting the pace of the textile industry's development towards high-end. In view of this, we propose a textile dyeing method based on first principles and a method for determining its reaction conditions. Summary of the Invention

[0007] The purpose of the present invention is to overcome the deficiencies of the existing technology, meet the actual needs, and provide a textile dyeing method based on first principles and a method for determining its reaction conditions to solve the technical problem in the current technology that there is a lack of in-depth basic research on the dyeing process and precise model construction, resulting in the difficulty of accurately achieving the ideal dyeing effect.

[0008] To solve the above technical problems, the present invention provides the following technical solutions: A textile dyeing method based on first principles, including the following steps:

[0009] S1: Exploration of dyeing conditions: Basic research, through numerical analysis of experiments to conduct simulations for theoretical analysis, and find the standard reaction conditions for textile dyeing;

[0010] S2: Construction of dyeing and finishing model: Form a large dyeing and finishing model, and automatically generate processes, procedures, and parameters based on the basic data of the reactants;

[0011] S3: Practical operation guidance for the process: Guide dyeing and finishing according to the said processes, procedures, and parameters.

[0012] Preferably, the step S1 specifically includes the following steps:

[0013] S101: Establish a basic database and an experience database;

[0014] S102: Establish a basic model based on physical effects and existing basic knowledge;

[0015] S103: Define boundary conditions for the basic model to generate a simulation model;

[0016] S104: Use experimental data as a reference to correct the data of the simulation model to achieve theoretical analysis;

[0017] S105: Compare the theoretical analysis data obtained in step S15 with the empirical library and correct the simulation model to form a standard reaction model;

[0018] S106: Compare the standard reaction model with the basic library. If it meets the basic library, solidify the basic library to form a standard model; if it does not meet the basic library, upgrade the basic library, add the non - conforming model information to the basic library, and form a standard model.

[0019] Preferably, step S104 specifically includes: establishing the simulation model, obtaining experimental results through simulation and calculation of the simulation model, comparing the experimental data with the simulation results, finding the difference between the simulation results and the experimental results, and adjusting the theoretical parameters according to numerical analysis to achieve theoretical analysis.

[0020] Preferably, step S2 specifically includes the following steps:

[0021] S201: Compare the standard model with the large model;

[0022] S202: If the standard model meets the large model, directly modify the large model, and then generate processes, flows, and parameters based on the data of the large model to guide dyeing and finishing;

[0023] S203: If the standard model does not meet the large model, modify the large model, use the standard model as a part of the large model to form a dyeing and finishing large model, and generate processes, flows, and parameters for guiding dyeing and finishing by performing operations on the dyeing and finishing large model according to input data.

[0024] Preferably, step S203 specifically includes: inputting the standard model into the large model for operation. If the process flow and parameters for this material cannot be formed in the large model, add a new material module to form a new standard model and large model. If the process flow and parameters can be formed, guide dyeing and finishing based on the formed process flow and parameters.

[0025] Preferably, the textile is one or any combination of cotton, bamboo, hemp, cotton - linen blend, hemp / cotton blend, or polyester

[0026] A method for determining reaction conditions for textile dyeing based on the first - principles includes the following steps:

[0027] T1: First, conduct in-depth research on various reactants in the textile dyeing process, such as the characteristics of different fiber materials, analyze the differences in their molecular structures, chemical compositions, and physical properties. At the same time, conduct a detailed analysis of the chemical properties and reaction activities of the dyeing auxiliaries used.

[0028] T2: Then, based on the basic principles of physical chemistry, such as intermolecular forces and chemical reaction kinetics knowledge, combined with the professional theory in the field of textile dyeing, establish a basic model.

[0029] When constructing the model, consider the diffusion coefficient D of the dye in the fiber, which conforms to the Arrhenius equation with temperature T: where D 0 is the pre-exponential factor, Ea is the activation energy of diffusion, and R is the ideal gas constant. Through this equation, the influence of temperature on the dye diffusion process is obtained, and then the physical and chemical changes in the dyeing process are analyzed.

[0030] At the same time, for the moisture absorption performance of the fiber, according to the moisture absorption isotherm equation: where W is the moisture regain of the fiber, W m is the equilibrium moisture regain of the fiber, C is a constant, and RH is the relative humidity. Through this equation, the moisture absorption of the fiber in different humidity environments is obtained.

[0031] T3: Subsequently, define the boundary conditions for the constructed basic model. According to the temperature range, pressure conditions, and reaction time factors in the actual dyeing process, set reasonable boundary conditions, and then generate a simulation model.

[0032] T4: After that, compare the data obtained through theoretical analysis with the data in the empirical database, and further correct the simulation model to form a standard reaction model.

[0033] T5: Finally, according to the formed standard model, combined with the actual requirements and changing factors in the textile dyeing process, construct a large-scale dyeing and finishing model.

[0034] Preferably, the basic model in step T2 covers an energy equation model for describing the energy change and transfer in the dyeing process; a mass conservation equation model to ensure that the total amount of substances remains unchanged before and after the reaction; a momentum conservation equation model to analyze the material flow and dynamic changes during the reaction; a phase transformation equation model to calculate the possible phase state changes in the dyeing process. When constructing the model, a multi-component, multi-phase, multi-phase flow and unsteady heat transfer coupling solver is used. By coupling the evaporation-absorption-desorption processes of gas-liquid and gas-liquid-solid, the mass conservation equation, the interphase momentum equation, the interphase energy equation, the component transfer equation, and the internal mass, energy, and component transfer equations of the liquid and solid phases are solved to comprehensively simulate the physical and chemical reaction processes of textile dyeing.

[0035] Preferably, in step T4, the standard reaction model is compared with the basic library. If the quasi-reaction model conforms to the existing architecture and data rules 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 non-conforming model information is added to the basic library. After repeated adjustments and improvements, a standard model is finally formed.

[0036] 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 in a targeted manner, and then, based on the modified large model and the input basic data of the reactants, the processes, procedures and parameters suitable for 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.

[0037] Compared with the prior art, the present invention has the following beneficial effects:

[0038] 1. The present invention conducts in-depth basic research on the textile dyeing process, establishes a complete basic library and experience library, and uses experimental data to calibrate and optimize the simulation model, so that the standard reaction conditions can be accurately found. In practical applications, the process, process and parameters generated based on these precise conditions and the constructed dyeing and finishing large model greatly reduce the problem of unstable dyeing quality caused by condition fluctuations and empirical errors in traditional dyeing methods. Whether it is for common fibers such as cotton and polyester, or special materials such as bamboo and linen, accurate dyeing can be achieved. The present invention controls the color difference of dyeing within a very small range, and the uniformity and brightness of the color are also significantly improved, which meets the needs of high-quality textile production and improves the market competitiveness of the product.

[0039] 2. Traditional textile dyeing is highly dependent on the experience and judgment of skilled technicians. With the development of the industry, this method faces the dilemma of uneven personnel quality and rising labor costs. The method and model of the present invention break this limitation. Through systematic theoretical analysis and automated model calculation, the operator only needs to provide the basic data of the reactants, and the dyeing and finishing large model can automatically generate the required processes and procedures, which can be smoothly operated even by personnel without deep professional knowledge. The present invention not only reduces the company's dependence on senior technicians and reduces the cost and time of human training, but also in the production process, due to the reduction of interference from human factors, further improves production efficiency and product quality consistency. In the long run, it will help promote the intelligent transformation of the textile printing and dyeing industry and enhance the production efficiency and sustainable development capabilities of the entire industry.

[0040] 3. In the textile industry, there is a wide variety of fiber types and new materials are constantly emerging. At the same time, the production environment is also in dynamic change. Through in-depth analysis of the characteristics of different fiber materials and comprehensive application of physical and chemical principles, the basic model and the dyeing and finishing large model constructed by the present invention have strong adaptability. When facing new fiber materials, it can quickly adjust and optimize by adding new material modules, etc., to ensure the feasibility and effect of dyeing them. In actual production of the present invention, whether it is the change of conditions such as temperature, humidity, dye concentration, or the adjustment of production scale, it can generate appropriate process parameters in a timely manner based on accurate determination of reaction conditions and flexible model operations, ensuring the smooth progress of the dyeing process and the stability of product quality. This strong adaptability makes the present invention have a broad application prospect in the field of textile printing and dyeing, can meet the diverse needs of different enterprises and markets, and promote the innovative development of the textile industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 It is a flowchart of the steps of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0042] Example 1, as Figure 1 shown, a method for dyeing textiles based on first principles and a method for determining reaction conditions thereof according to the present invention include the following steps:

[0043] S1: Exploration of dyeing conditions: Basic research, through numerical analysis of experiments for simulation to achieve theoretical analysis, and find the standard reaction conditions for textile dyeing.

[0044] The specific steps in step S1 include the following steps:

[0045] S101: Establish a basic library and an experience library.

[0046] When establishing the basic library, widely collect the physical and chemical property data of different fiber types (such as common and new fibers such as cotton, hemp, silk, wool, chemical fibers, etc.) under various environmental conditions, including microscopic structure data such as the crystallinity, orientation degree, and surface morphology of the fibers, as well as the mechanical properties and chemical stability data under different conditions such as temperature, humidity, and pH value. For the experience library, integrate the actual production case data accumulated by the global textile printing and dyeing industry over the years, covering information such as the dyeing process parameters, problems and solutions of the combination of different dye types (reactive dyes, acid dyes, disperse dyes, etc.) and fibers, providing a comprehensive reference basis for subsequent model construction and correction.

[0047] S102: Establish a basic model according to physical actions and existing basic knowledge.

[0048] In addition to considering the physical and chemical principles mentioned above, it is also necessary to further study the special interaction mechanisms between fibers and dye molecules. For example, some fibers have specific functional groups on their surfaces that may form different types of binding modes with dye molecules, such as hydrogen bonds, van der Waals forces, or covalent bonds. The strength and stability of these binding modes will change with the reaction conditions, and should be accurately described in the basic model by introducing corresponding parameters and functional relationships.

[0049] S103: Define boundary conditions for the basic model to generate a simulation model.

[0050] When defining boundary conditions, not only conventional factors such as temperature, pressure, and time should be considered, but also dynamic factors such as the flow rate and stirring intensity of the dye solution. Because in the actual dyeing process, the flow state of the dye solution will significantly affect the diffusion rate and uniformity of the dye molecules into the fiber. By accurately setting these boundary conditions, the simulation model can be closer to the actual dyeing conditions.

[0051] S104: Using the experimental data as a reference to perform data correction on the simulation model to achieve theoretical analysis.

[0052] Step S104 specifically includes: establishing a simulation model, simulating and calculating the experimental results through the simulation model, comparing the experimental data with the simulation results, finding the difference between the simulation results and the experimental results, adjusting the theoretical parameters according to the numerical analysis, and realizing the theoretical analysis.

[0053] During this process, advanced data analysis algorithms, such as multivariate linear regression analysis and neural network algorithms, are used to deeply mine and analyze a large amount of simulation data and experimental data in order to more accurately determine the influence weight of each parameter on the dyeing results, thereby efficiently adjusting the theoretical parameters and improving the accuracy of the model.

[0054] S105: Compare the theoretical analytical data obtained in step S15 with the experience database and correct the simulation model to form a standard reaction model.

[0055] S106: Compare the standard reaction model with the basic library. If it is consistent with the basic library, solidify the basic library to form a standard model. If it is not consistent with the basic library, upgrade the basic library and add the inconsistent model information to the basic library to form a standard model.

[0056] During the comparison process, the association rule analysis method in data mining technology is used to find the potential associations and rules between the theoretical analysis data and the empirical library data. If differences are found between the theoretical analysis data and the typical cases in the empirical library, the reasons for the differences are further traced. It may be that some special fiber microstructures or dye molecular characteristics are not fully reflected in the model. Based on this, the simulation model is modified in a targeted manner to ensure that the formed standard reaction model has wide applicability and reliability.

[0057] When comparing the standard reaction model with the basic library, a similarity matching algorithm, such as the cosine similarity algorithm or the Euclidean distance algorithm, etc., is used to calculate the similarity between the two. If the similarity exceeds the set threshold, it is considered to conform to the basic library and the basic library can be solidified; if it is lower than the threshold, the basic library is upgraded, and the new model information is added to the basic library according to certain classification and indexing rules for subsequent query and use. At the same time, the data structure and management system of the basic library are updated to improve the data retrieval and processing efficiency.

[0058] During the comparison process, a model comparison tool is used to conduct a comprehensive comparative analysis of the structure, parameters, variables, etc. of the standard model and the large model. Not only the consistency of the mathematical model should be considered, but also its adaptability and effectiveness in the actual application scenario should be evaluated.

[0059] When modifying the large model, an incremental learning algorithm is adopted. According to the new information provided by the standard model, the relevant parameters and rules of the large model are fine-tuned to ensure that the modified large model can not only maintain its original advantages but also adapt to the new dyeing requirements. At the same time, the modification process and reasons are recorded for subsequent traceability and optimization.

[0060] When modifying the large model, an incremental learning algorithm is adopted. According to the new information provided by the standard model, the relevant parameters and rules of the large model are fine-tuned to ensure that the modified large model can not only maintain its original advantages but also adapt to the new dyeing requirements. At the same time, the modification process and reasons are recorded for subsequent traceability and optimization.

[0061] When adding a new material module, the modular design concept is utilized. The information such as the relevant physical and chemical properties of the new material and its reaction characteristics with dyes is encapsulated into an independent module, which is connected and interacted with the large model through an interface to ensure that the new module can seamlessly integrate into the large model system. At the same time, the overall architecture and algorithm of the large model are optimized and adjusted to improve its processing ability and accuracy for new materials.

[0062] When studying the characteristics of fiber materials, advanced material characterization techniques, such as scanning electron microscopy (SEM), atomic force microscopy (AFM), Fourier transform infrared spectroscopy (FTIR), etc., are used to accurately analyze the microstructure and chemical functional groups of the fiber; for dye auxiliaries, analytical methods such as high performance liquid chromatography (HPLC) and mass spectrometry (MS) are adopted to determine their chemical composition and purity. At the same time, thermal analysis techniques (such as DSC, TGA) are used to study their thermal stability and reactivity, providing accurate data support for subsequent model construction.

[0063] S2: Dyeing and finishing model construction: Form a large dyeing and finishing model, and automatically generate processes, procedures, and parameters according to the basic data of the reactants.

[0064] Step S2 specifically includes the following steps:

[0065] S201. Compare the standard model with the large model.

[0066] S202. If the standard model conforms to the large model, directly modify the large model, and then generate processes, flows, and parameters based on the data produced by the large model to guide dyeing and finishing.

[0067] S203. If the standard model does not conform to the large model, modify the large model, form a dyeing and finishing large model with the standard model as a part of the large model, and generate processes, flows, and parameters to guide dyeing and finishing by performing operations on the dyeing and finishing large model according to the input data.

[0068] Step S203 specifically includes: Input the standard model into the large model for operation. If the process flow and parameters for this material cannot be formed in the large model, add a new material module to form a new standard model and large model. If the process flow and parameters can be formed, guide dyeing and finishing according to the formed process flow and parameters.

[0069] S3: Practical operation of process guidance: Guide dyeing and finishing according to processes, flows, and parameters.

[0070] When constructing the dyeing and finishing large model, a distributed computing architecture and cloud computing technology are adopted to improve the operation efficiency of the model and data processing ability, and can quickly respond to different reactant basic data input by users to generate accurate processes, flows, and parameters. At the same time, establish a model evaluation index system to evaluate and optimize the dyeing and finishing large model from multiple dimensions such as accuracy, stability, and generality, and continuously improve its performance.

[0071] In the process of model fusion, use model fusion algorithms (such as weighted average method, Stacking method, etc.) to integrate the advantages of the standard model and the large model to improve the overall performance of the new dyeing and finishing large model.

[0072] A method for determining reaction conditions for textile dyeing based on the first principles includes the following steps:

[0073] T1: First, conduct in-depth research on various reactants in the textile dyeing process, such as the characteristics of different fiber materials, analyze the differences in their molecular structures, chemical compositions, and physical properties, and at the same time conduct a detailed analysis of the chemical properties and reaction activities of the dyeing auxiliaries used.

[0074] T2: Then, based on the basic principles of physical chemistry, such as intermolecular forces and chemical reaction kinetics knowledge, combined with the professional theory in the field of textile dyeing, establish a basic model;

[0075] When constructing the model, consider the diffusion coefficient D of the dye in the fiber, which conforms to the Arrhenius equation with the temperature T: where D 0 is the pre-exponential factor, Ea is the activation energy of diffusion, R is the ideal gas constant. The influence of temperature on the dye diffusion process is obtained through this equation, and then the physical and chemical changes during the dyeing process are analyzed;

[0076] Meanwhile, for the moisture absorption performance of the fiber, according to the moisture absorption isotherm equation: where W is the moisture regain of the fiber, W m is the equilibrium moisture regain of the fiber, C is a constant, and RH is the relative humidity. The moisture absorption of the fiber under different humidity environments is obtained through this equation.

[0077] The basic model in step T2 covers the energy equation model, which is used to describe the energy change and transfer during the dyeing process; the mass conservation equation model, which ensures that the total amount of substances remains unchanged before and after the reaction; the momentum conservation equation model, which analyzes the material flow and dynamic changes during the reaction process; the phase transformation equation model, which calculates the possible phase state changes during the dyeing process. When constructing the model, a multi-component, multi-phase, multi-phase flow and unsteady heat transfer coupling solver is used. By coupling the evaporation-absorption-desorption processes of gas-liquid and gas-liquid-solid, the mass conservation equation, the inter-phase momentum equation, the inter-phase energy equation, the component transfer equation, and the internal mass, energy, and component transfer equations of the liquid and solid phases are solved to comprehensively simulate the physical and chemical reaction processes of textile dyeing.

[0078] T3: Subsequently, boundary conditions are defined for the constructed basic model. According to the temperature range, pressure conditions, and reaction time factors in the actual dyeing process, reasonable boundary conditions are set, and then a simulation model is generated.

[0079] T4: After that, the data obtained through theoretical analysis is compared with the data in the empirical library to further correct the simulation model and form a standard reaction model.

[0080] 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 rules 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 non-conforming model information is added to the basic library. After repeated adjustment and improvement, a standard model is finally formed.

[0081] T5: Finally, according to the formed standard model, combined with the actual requirements and changing factors in the textile dyeing process, a large dyeing and finishing model is constructed.

[0082] 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 accordingly. Then, based on the modified large model and the input basic data of the reactants, the processes, procedures, and parameters applicable to different textile dyeing are automatically generated to guide the actual dyeing and finishing work. If the standard model does not meet the large model, the large model is comprehensively modified, and the standard model is incorporated as an important part of the large model to form a new large dyeing and finishing model.

[0083] The embodiments disclosed in the present invention are preferred embodiments, but are not limited thereto. Those of ordinary skill in the art can easily understand the spirit of the present invention based on the above embodiments and make different extensions and changes. However, as long as they do not depart from the spirit of the present invention, they are within the protection scope of the present invention.

Claims

1. A textile dyeing method based on first principles, characterized in that: The following steps are involved: S1: Exploration of dyeing conditions: basic research, through numerical analysis of experiments to simulate theoretical analysis, to find the standard reaction conditions for textile dyeing; S2: Dyeing and finishing model construction: forming a large dyeing and finishing model, automatically generating processes, procedures and parameters based on the basic data of reactants; S3: Process Guidance Practice: Guiding dyeing and finishing according to the described processes, procedures and parameters.

2. A textile dyeing method based on first principles according to claim 1, characterized in that: The step S1 specifically includes the following steps: S101: Establish basic database and experience database; S102: Establish a basic model based on physical effects and existing basic knowledge; S103: defining boundary conditions for the basic model to generate a simulation model; S104: using the experimental data as a reference to perform data correction on the simulation model to achieve theoretical analysis; S105: comparing the theoretical analytical data obtained in step S15 with the empirical database and correcting the simulation model to form a standard reaction model; S106: Compare the standard reaction model with the basic library. If it is consistent with the basic library, solidify the basic library to form a standard model. If it is not consistent with the basic library, upgrade the basic library and add the inconsistent model information to the basic library to form a standard model.

3. A textile dyeing method based on first principles according to claim 2, characterized in that: The step S104 specifically includes: establishing the simulation model, simulating and calculating the experimental results through the simulation model, comparing the experimental data with the simulation results, finding the difference between the simulation results and the experimental results, adjusting the theoretical parameters according to the numerical analysis, and realizing the theoretical analysis.

4. A textile dyeing method based on first principles and a method for determining reaction conditions thereof according to claim 3, characterized in that: The step S2 specifically includes the following steps: S201, comparing the standard model with the large model; S202, the standard model is consistent with the large model, the large model is directly modified, and then the process, process and parameters are generated according to the production data of the large model to guide dyeing and finishing; S203, if the standard model does not conform to the large model, the large model is modified, and the standard model is used as a part of the large model to form a dyeing and finishing large model, and the dyeing and finishing large model is operated according to the input data to generate processes, processes and parameters to guide dyeing and finishing.

5. A textile dyeing method based on first principles and a method for determining reaction conditions thereof according to claim 4, characterized in that: The step S203 specifically includes: inputting the standard model into the large model for calculation; if the process flow and parameters for the material cannot be formed in the large model, adding a new material module to form a new standard model and a large model; if the process flow and parameters can be formed, guiding dyeing and finishing according to the formed process flow and parameters.

6. A textile dyeing method based on first principles and a method for determining reaction conditions thereof according to claim 1, characterized in that: The textile is one of cotton, bamboo, linen, cotton and linen blended, linen / cotton blended or polyester or a combination of any of the above.

7. A method for determining reaction conditions for textile dyeing based on first principles, which is applicable to the textile dyeing method based on first principles according to claim 6, characterized in that: The steps include: T1: First, we conduct in-depth research on various reactants in the textile dyeing process, such as the characteristics of different fiber materials, analyze the differences in their molecular structure, chemical composition and physical properties, and analyze the chemical properties and reaction activity of the dye auxiliaries used in detail; T2: Then, based on the basic principles of physical chemistry, such as intermolecular forces and chemical reaction kinetics, 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 temperature T: Among them, D0 is 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 physical and chemical changes in the dyeing process are analyzed; At the same time, the hygroscopic properties of the fiber can be calculated based on the hygroscopic isotherm equation: Where W is the moisture regain of the fiber, W m is the moisture balance regain of the fiber, C is a constant, RH is the relative humidity. The equation can be used to determine the moisture absorption of the fiber under different humidity environments. T3: Then, the boundary conditions of the constructed basic model are defined. According to 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: After that, the data obtained through theoretical analysis are compared with the data in the experience database to further correct the simulation model and form a standard reaction model; T5: Finally, based on the formed standard model, combined with the actual needs and changing factors in the textile dyeing process, a large dyeing and finishing model is constructed.

8. A textile dyeing method based on first principles and a method for determining reaction conditions thereof according to claim 7, characterized in that: The basic model in step T2 includes an energy equation model, which is used to describe the energy change and transfer during the dyeing process; The mass conservation equation model ensures that the total amount of material remains unchanged before and after the reaction; the momentum conservation equation model analyzes the material flow and dynamic changes during the reaction; the phase transformation equation model calculates the phase transitions that may occur during the dyeing process; when constructing the model, a multi-component, multi-phase, multi-phase flow and unsteady heat transfer coupling solver is used to couple the gas-liquid and gas-liquid-solid evaporation-absorption-desorption processes to solve the mass conservation equation, the phase momentum equation, the phase energy equation, the component transfer equation, and the mass, energy, and component transfer equations within the liquid and solid phases, thereby fully simulating the physical and chemical reaction process of textile dyeing.

9. A textile dyeing method based on first principles and a method for determining reaction conditions thereof according to claim 8, characterized in that: In step T4, the standard reaction model is compared with the basic library. If the quasi-reaction model conforms to the existing architecture and data rules 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 non-conforming model information is added to the basic library. After repeated adjustments and improvements, a standard model is finally formed.

10. A textile dyeing method based on first principles and a method for determining reaction conditions thereof according to claim 9, 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 in a targeted manner. Then, based on the modified large model and the input basic data of the reactants, the process, flow and parameters suitable for 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 will be comprehensively 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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