A textile fabric research and development analysis system

Abstract

The present invention provides a textile fabric R&D and analysis system, relating to the field of textile production analysis technology. The system comprises an intelligent fabric comparison and processing system, a multimodal data integration and analysis system, an integrated artificial intelligence-assisted decision-making system, and an automated experimental design and adjustment system. The system includes a master database, comprising a fabric database, a fabric usage information database, and a storage database. The entire system architecture, in conjunction with the master database, enables automatic identification, processing, and allocation of R&D fabrics to complete screening. The intelligent fabric comparison and processing system automates the entire fabric processing process. The system can identify, classify, and allocate large quantities of fabric in a short period of time, significantly reducing the time and labor intensity of manual operations. The use of a robotic arm and an automated liquid handling system not only increases processing speed but also ensures operational consistency and reliability, significantly improving production efficiency and better meeting rapidly changing R&D needs.

Description

Technical Field

[0001] The present invention relates to the technical field of textile production analysis, and in particular to a textile fabric research and development analysis system. Background Art

[0002] According to Chinese Patent Publication No. CN117147808A, a big data-based spunlace nonwoven fabric technology development monitoring system, which relates to the field of spunlace nonwoven fabrics, aims to address the time-consuming trial and error and search for the impact of changes during the R&D process, which slows down overall R&D. The key technical solution includes a master control system that includes a raw material quality detection module for raw material quality testing, a finished product quality detection module for finished product quality testing, a quantity detection module for detecting the number of materials passing through, a cleanliness monitoring module for measuring the cleanliness of test materials, a quality detection module for detecting the quality of finished fabrics, a big data recording module for recording fabric test data, a data extraction module for extracting data, and a data processing module for processing the data. This system achieves multi-level detection and data acquisition, multi-dimensional data analysis, and facilitates trial and error.

[0003] The above patent documents and prior art have the following technical problems when used:

[0004] Traditional manual operations are time-consuming and prone to human errors, affecting production efficiency. Data silos: Even if some digital fabric R&D technologies are adopted, the various data sources cannot be effectively integrated, resulting in low information utilization, insufficient decision-making basis, and insufficient accuracy: manual classification and analysis often lead to errors, affecting the reliability of experimental results. Parameter adjustment is inflexible: traditional methods have poor adaptability to experimental conditions and are difficult to adjust quickly according to actual conditions, affecting experimental results. Summary of the Invention

[0005] Technical Solution

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a textile fabric R&D and analysis system, which consists of an intelligent fabric comparison and processing system, a multimodal data integration and analysis system, an integrated artificial intelligence auxiliary decision-making system, and an automated experimental design and adjustment system. The system is internally provided with a general database, which includes a fabric database, a fabric usage information database, and a storage database, wherein:

[0007] Intelligent fabric matching and processing system: uses machine learning algorithms to identify fabric characteristics and automatically adjusts processing parameters based on fabric characteristics, automatically identifying, processing, and distributing R&D fabrics;

[0008] Multimodal data integration and analysis system: Utilizing internal multimodal data fusion algorithms, we develop visualization analysis tools to process, analyze, and integrate chemical, biological, and bioinformatics data for fabric R&D;

[0009] Integrated artificial intelligence-assisted decision-making system: Based on the system's large amount of fabric comparison data and research experience data, combined with artificial intelligence technology and internal deep learning algorithms, it establishes a fabric-target relationship model, provides the output results of the fabric bioactivity prediction model, and generates decision-making suggestions for auxiliary experiments;

[0010] Automated experimental design adjustment system: The biological activity of fabrics is designed through experimental design algorithms, and the experimental design given by the experimental scheme optimization algorithm is further adjusted. Automated experiments are carried out through an automated experimental platform, and synchronous data analysis and feedback are performed. Experimental schemes are automatically generated according to experimental objectives and fabric characteristics, and the experimental design is continuously adjusted through a feedback mechanism.

[0011] Preferably, the overall database further includes the following contents:

[0012] Fabric database: used to store data on fabric categories required for production;

[0013] Fabric usage information database: stores target data corresponding to the fabrics being developed;

[0014] Storage database: stores the instructions, signals, experimental data, historical data and other information made by each subsystem in the system, and provides basic data for system cycle training.

[0015] Preferably, the intelligent fabric comparison and processing system includes a fabric identification and classification module, an automatic fabric processing module, a parameter adjustment module, a high-throughput fabric processing module and an intelligent fabric tracking and management module.

[0016] Preferably, the fabric recognition and classification module includes an image recognition unit, a feature extraction and selection unit, a model training unit, a machine algorithm unit and a real-time recognition and classification unit.

[0017] Preferably, the high-throughput fabric processing module includes microfluidics technology, a multi-channel liquid processing unit, a sample distribution algorithm, a mixing efficiency optimization algorithm and a real-time control system.

[0018] Preferably, the multimodal data integration and analysis system processes data in five steps: data acquisition and preprocessing, feature extraction and selection, data fusion and integration, and multimodal data analysis and model evaluation and adjustment.

[0019] Preferably, the integrated artificial intelligence assisted decision-making system includes processing content for five steps: data preprocessing, feature extraction and selection, model establishment and training, model evaluation and adjustment, and intelligent assisted decision-making module.

[0020] Preferably, the automated experiment design adjustment system includes five steps: an experiment design algorithm, an experiment plan optimization algorithm, an automated experiment platform, a data analysis and feedback mechanism module, and a remote monitoring and control module.

[0021] Preferably, the steps of the textile fabric research and development analysis system are:

[0022] Sp1: Intelligent fabric matching: Using deep learning algorithms, the system automatically identifies the characteristics of R&D fabrics and achieves rapid screening and classification of fabrics by adaptively adjusting processing parameters;

[0023] Sp2: Multimodal data fusion: Through internal multimodal data fusion algorithms, collect and process data from chemistry, biology and bioinformatics to generate comprehensive visual analysis reports;

[0024] Sp3: AI-assisted decision generation: Combined with historical fabric comparison data, deep learning models are used to establish fabric-target relationships and generate fabric bioactivity prediction models to provide decision recommendations for experiments;

[0025] Sp4: Automated Experimental Design: The system automatically generates experimental design plans based on fabric characteristics and historical data, adjusts experimental parameters, and improves experimental efficiency;

[0026] Sp5: Experimental result feedback and iteration: After the experiment is completed, the system will feed back the experimental results to the database, and use the feedback information to continuously adjust the model and experimental design to achieve dynamic iteration.

[0027] Beneficial effects

[0028] The present invention provides a textile fabric R&D and analysis system. It has the following beneficial effects:

[0029] 1. This invention automates the entire fabric processing process through an intelligent fabric matching and processing system. This system can identify, classify, and distribute large quantities of fabric in a short period of time, significantly reducing the time and labor required for manual operations. The use of a robotic arm and automated liquid handling system not only increases processing speed but also ensures operational consistency and reliability, significantly improving production efficiency and better meeting rapidly changing R&D needs, making it particularly suitable for high-throughput experimental environments.

[0030] 2. This invention uses a deep learning algorithm to extract and classify fabric features. By learning the characteristics of multiple fabric types (biological, chemical, and material), the system can effectively distinguish different fabrics and accurately classify them, improving the accuracy of fabric identification and ensuring the reliability of the basic data for subsequent data analysis and experimental design, thereby reducing errors and duplication of experiments.

[0031] 3. By integrating sensors and feedback mechanisms, the system can monitor the fabric processing process in real time and automatically adjust processing parameters based on the fabric's characteristics and experimental requirements. This adaptive capability ensures that each experiment is conducted under optimal conditions, ensuring the stability of the fabric processing process, reducing experimental errors caused by human factors, and improving the reliability and repeatability of experimental results. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 It is an operation flow chart of the present invention;

[0033] Figure 2 This is a system architecture diagram of the present invention;

[0034] Figure 3 It is a module composition diagram of the present invention. DETAILED DESCRIPTION

[0035] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention. Specific embodiment one:

[0037] like Figure 1-Figure 3 As shown, a textile fabric R&D and analysis system consists of an intelligent fabric comparison and processing system, a multimodal data integration and analysis system, an integrated artificial intelligence decision-making system, and an automated experimental design and adjustment system. The system has a general database, which includes a fabric database, a fabric usage information database, and a storage database, including:

[0038] Intelligent fabric matching and processing system: This system uses machine learning algorithms to identify fabric characteristics and automatically adjusts processing parameters based on these characteristics, enabling automatic identification, processing, and distribution of R&D fabrics.

[0039] The machine learning algorithm of the fabric feature extraction model uses a deep learning algorithm to perform fabric feature recognition, classification, and automatic parameter adjustment. The fabric feature vector is x = [x1, x2, ..., x n ], extract features through the convolutional neural network (CNN) model. The convolutional layer calculation formula is:

[0040] y i =f(∑ j w ij ·x j +b i );

[0041] y i : Output feature of layer i; w ij : weight parameter of the i-th layer; x j : Input features; b i : bias term; f: activation function (ReLU);

[0042] The automatic parameter adjustment algorithm uses an adaptive learning rate adjustment algorithm to optimize the processing parameters:

[0043]

[0044] α t : Current learning rate; η: Learning rate adjustment coefficient; Loss function; The gradient of the loss function with respect to the learning rate;

[0045] Multimodal data integration and analysis system: Using the internal multimodal data fusion algorithm, develop visualization analysis tools to process, analyze and integrate the chemical, biological and bioinformatics data of the R&D fabrics; The multimodal data integration and analysis system uses a multimodal data fusion algorithm to integrate and analyze the chemical data, biological data and informatics data of the fabrics. The data fusion model, different modal data vectors are x c (Chemical Data), x b (biological data), x i (Informatics data), the multimodal fusion algorithm formula is:

[0046] z=W c x c +W b x b +W i x i +b;

[0047] z: fused data vector; W c 、W b 、W i : Weight matrices of different modalities; b: Bias vector Multimodal data analysis and visualization Apply principal component analysis to the fused data to reduce the data dimension:

[0048] z ′ =P·z;

[0049] z ′: eigenvector after dimensionality reduction; P: PCA projection matrix;

[0050] Integrated AI-assisted decision-making system: Based on the system's extensive fabric comparison data and research experience, combined with AI technology and internal deep learning algorithms, a fabric-target relationship model is established, which provides outputs of the fabric bioactivity prediction model and generates decision-making recommendations for auxiliary experiments. Fabric-target refers to the specific biological performance indicators or comfort targets that fabrics are expected to achieve when in contact with the human body, biological tissues, or specific biological simulation environments. Targets include the following:

[0051] Allergic reaction sensitivity target: measured through skin patch test, in vitro cell stimulation test or erythema and pruritus quantitative indicators, quantified by numerical value or grade (allergy score of 0-10).

[0052] Comfort and breathability targets: By testing volunteers under uniform experimental conditions, the fitting feedback is quantified into specific values (0-10 scores) and recorded in the database.

[0053] Antibacterial and anti-allergic biocompatibility targets: Quantitative indicators (inhibition rate, cell activity change rate) are obtained through microbial culture inhibition tests, protein adsorption tests or cell compatibility experiments.

[0054] Each fabric and its specific process corresponds to a set of target data. After statistical standardization and normalization, these data can be converted into feature vectors for subsequent model training.

[0055] Fabric bioactivity refers to the comprehensive score or predicted value of the specific biological responses or comfort indicators exhibited by fabrics when interacting with biological systems (including human skin and microbial ecosystems). It reflects the potential biological response characteristics of fabrics in use scenarios.

[0056] Indicators including allergy level, breathability, biocompatibility, antibacterial performance, and humidity regulation performance are weighted and aggregated or model fused to form a comprehensive score (score between 0-100).

[0057] Through historical experimental data and multimodal data analysis (chemical composition, bioinformatics parameters, human test data), a deep learning model is used to associate the physical and chemical characteristics of the fabric with the biological response data, and a model is trained to predict the potential biological activity of unknown fabrics.

[0058] Data sources include: Fabric usage information database: which stores actual measured biological parameters and experimental records (allergy test scoring data, microbial inhibition test data, and volunteer comfort score data);

[0059] Fabric database: stores basic information on the physical and chemical properties of fabrics (raw materials, chemical treatment, fiber structure).

[0060] After feature extraction, dimensionality reduction and fusion of the above information, the fabric feature vector is associated with the target data to train a model for predicting biological activity.

[0061] Follow these steps to build a cloth-target relationship model:

[0062] Data Preparation: The chemical structure and physical parameter data of the fabric are obtained from the fabric database, and the corresponding biological indicators and comfort parameters are obtained from the fabric usage information database. Through feature engineering, the fabric data and corresponding target data are mapped into feature vectors and then standardized and normalized.

[0063] Model Training: Fabric feature vectors, target feature vectors, or bioactivity measurements are input as training data into the deep learning model. The model applies a nonlinear transformation to the input features using a weight matrix and activation function, outputting a predicted fabric bioactivity value. Using known fabrics and their biological test data as the training set, model parameters are optimized using backpropagation with either cross-entropy loss or mean squared error loss.

[0064] Model Output and Decision Recommendations: The trained model predicts bioactivity for new or untested fabric input features. Based on a comparison of the predicted value with development goals (including breathability, anti-allergy, or comfort reaching a certain threshold), the system automatically generates a decision recommendation. This recommendation is a numerical value, a comprehensive score on a scale of 0-10, resulting in a positive or negative recommendation. A positive recommendation allows designers to further optimize the input material formula for better results. Repeated operations yield an optimal value for a predetermined design state, facilitating further fabric trial production or experimental verification.

[0065] The cloth feature vector is X F =[x f1 ,x f2 ,…,x fm ];

[0066] x fm Represents the mth eigenvalue of the fabric; fabric features include: chemical properties (chemical composition of the fiber, concentration of the treatment agent), physical properties (fiber diameter, fabric density, thickness, ductility), image features (texture or structural features extracted based on convolutional neural networks), and preliminary biological-related features (known allergic reaction data, air permeability, preliminary biocompatibility measurements). After standardization or normalization of these feature data, a fabric feature vector for model input can be formed;

[0067] The target feature vector is X T =[x t1 ,x t2 ,…,x tn ];

[0068] x tn is the characteristic data of the nth type / target, X T These include characteristic parameters such as the concentration threshold of specific allergens, the expected reduction in immune response scores, the expected inhibition rate of target strains, temperature and humidity control targets, and the maximum allowable skin irritation level.

[0069] The integrated artificial intelligence-assisted decision-making system establishes the fabric-target relationship through deep learning models, generates activity prediction models, and provides advice for decision-making;

[0070] The fabric feature vector of the fabric-target relationship model is x, the target feature vector is y, and the model output is the predicted value of fabric bioactivity

[0071]

[0072] Activity prediction value; σ: activation function (Sigmoid or Softmax); W: weight matrix; b: bias term; [x; y]: concatenation of feature vectors;

[0073] Model training and optimization use cross entropy loss function for model optimization:

[0074]

[0075] Loss function; y i :actual label; Prediction label; N: number of fabrics;

[0076] Automated experimental design adjustment system: The biological activity of fabrics is designed through experimental design algorithms, and the experimental design given by the experimental scheme optimization algorithm is further adjusted. Automated experiments are carried out through an automated experimental platform, and synchronous data analysis and feedback are performed. Experimental schemes are automatically generated according to experimental objectives and fabric characteristics, and the experimental design is continuously adjusted through a feedback mechanism.

[0077] The automated experimental design adjustment system automatically generates experimental plans and adjusts experimental parameters through experimental design algorithms and experimental optimization algorithms to obtain the best experimental results. The experimental design model uses an experimental design algorithm based on the response surface method (RSM) to maximize the experimental response value. Assuming that the experimental factors are x1, x2, ..., x k , the response value is y, and the response surface model is:

[0078]

[0079] y: experimental response value; β0: intercept term; β i :-order coefficient; β ij : interaction term coefficient;∈: error term;

[0080] The experimental optimization algorithm uses a genetic algorithm (GA) to adjust the experimental parameters and iterates the solution through the fitness function (objective function). The fitness function is defined as:

[0081] f(x)=-(y target -y(x)) 2 ;

[0082] f(x): fitness value; y target : target response value; y(x): experimental response value;

[0083] The response value in RSM is the target indicator predicted by the fabric bioactivity prediction model (a comprehensive score of comfort score, antibacterial performance score, and allergic reaction score). RSM requires the input of several controllable experimental factors (including fabric raw material ratio, processing temperature, pressure, chemical reagent addition amount, and finishing process parameters), and uses the bioactivity score as the output to model the relationship between the factors and the response value. The output of the prediction model is used as the response value of RSM:

[0084] Before conducting actual experiments, the fabric bioactivity prediction model generates estimated bioactivity values for the fabric under various potential experimental conditions. The RSM experimental design algorithm uses the predicted bioactivity values output by the prediction model as the initial data for fitting the response surface. Using the experimental points designed by RSM, the system determines a set of experimental conditions (factor combinations) and generates the corresponding predicted bioactivity scores using the prediction model.

[0085] Once the initial distribution of predicted bioactivity values is obtained through the prediction model, RSM can be used to iteratively optimize the experimental factors:

[0086] Preliminary design: Based on the design matrix generated by RSM, several groups of factor combinations (including raw material A / B ratio, processing temperature, immersion time and other parameters) are selected, and the predicted bioactivity value corresponding to each group of factors is calculated through the prediction model.

[0087] Fitting response surface: Substitute the predicted value into the RSM model for fitting, obtain the approximate relationship between factors and responses, and find the optimization direction.

[0088] Experimental Verification and Feedback: Actual experiments are conducted on the optimized parameter combinations, and real bioactivity data is measured using an automated experimental platform. This real data is fed back into the database, and algorithms such as Bayesian optimization are used to update the prior distribution and model parameters, thereby improving both the predictive model and the RSM.

[0089] Dynamic Iteration: In subsequent iterations, RSM will utilize updated prediction models and real experimental data to perform more precise factor optimization, thereby gradually approaching the optimal bioactive response region and achieving synergistic optimization between experimental design and prediction models.

[0090] Steps to implement the path:

[0091] Input: basic properties of fabric (physical and chemical data), historical experimental data (actual values of biological activity), prediction model parameters and RSM test factors.

[0092] a. Use the prediction model to give the predicted bioactivity results as response values for different factor combinations;

[0093] b. Use RSM to fit and optimize the response values and determine the factor combination to be tested in the next round of experiments;

[0094] c. Conduct actual experiments using the automated experimental platform and feed the experimental results back to the database;

[0095] d. Use real experimental results to update and optimize the prediction model and RSM model parameters (using Bayesian optimization);

[0096] e. Repeat the above process to gradually improve the prediction accuracy and experimental efficiency, narrow the factor search space, and finally find the optimal parameter combination that maximizes the bioactivity of the fabric or achieves the set target performance.

[0097] The automated experimental platform with real-time feedback mechanism feeds experimental data back to the system after each experiment is completed, and continuously improves the experimental design using the Bayesian optimization method. The update formula of Bayesian optimization is:

[0098]

[0099] p(y|x): conditional probability, i.e., probability of response under given parameters; p(x|y): likelihood function of experimental data; p(y): prior distribution of response; p(x): marginal distribution (x=0);

[0100] The system's operation plan is based on the mutual coordination of each subsystem. The specific steps are as follows:

[0101] 1. Fabric comparison: Fabric features are extracted and classified through an intelligent fabric comparison processing system.

[0102] 2. Data integration and analysis: Chemical data, biological data and bioinformatics data are processed and integrated through a multimodal data integration and analysis system.

[0103] 3. Decision support: In the decision-making system, historical data and prediction models are combined to generate fabric bioactivity predictions and decision-making recommendations.

[0104] 4. Experimental design and optimization: Based on decision recommendations, the automated experimental design system generates experimental plans and adjusts experimental parameters in real time, continuously improving experimental design and optimization algorithms through a feedback mechanism. Specific embodiment two:

[0106] like Figure 1-Figure 3 As shown, the overall database further includes the following:

[0107] Fabric database: used to store data on fabric categories required for production;

[0108] Fabric usage information database: stores target data corresponding to the fabrics being developed;

[0109] Storage database: stores the instructions, signals, experimental data, historical data and other information made by each subsystem in the system, and provides basic data for system cycle training.

[0110] Fabric Database: This database stores all fabric information required for production, including data on various types of R&D fabrics. It records basic fabric properties (such as type, source, and chemical composition). It provides real-time status and availability information for fabrics, enabling the system to quickly access and retrieve relevant data for fabric comparison and analysis.

[0111] Fabric Usage Information Database: This database stores fabric usage information that corresponds or may correspond to fabrics under development. This database includes bioinformatics data such as human allergies to the fabric, the degree of allergy, other biological allergies, human comfort, fit, sweat absorption and wicking, and breathability. This database provides target information for fabric bioactivity research and supports the matching and analysis of fabrics and fabric usage information. It also stores historical target research results for reference and decision-making in future experiments, promoting the development of new fabrics.

[0112] Storage database: This database is responsible for storing the instructions, signals, experimental data, and historical data generated by each subsystem in the system. It also records all operation logs during system operation, providing transparency and traceability of system operation.

[0113] It provides basic training data for machine learning models, supports the system's cyclic training and optimization, and ensures continuous improvement and intelligent upgrades. Throughout the system, the master database categorizes and stores various information related to system operation. The fabric database stores information on the types of fabrics, including biological, material, and chemical information. This allows the system to analyze fabric characteristics and facilitate matching of fabric usage information. At the same time, based on fabric characteristics and corresponding reaction principles or reaction technologies, corresponding automated experiments can be conducted, providing a data foundation and guarantee for subsequent post-screening verification. The fabric usage information database categorizes and stores a large amount of data that can be combined with fabrics, and records their biological characteristics. This facilitates rapid search and matching of R&D fabrics in the fabric database based on the fabric usage information database, realizing fabric bioactivity screening. At the same time, the storage database categorizes and stores data on the operation of each subsystem, module, and unit in the entire system, facilitating training and learning based on the stored data, optimizing the entire system, and improving intelligent output results, thereby increasing the accuracy of the entire system's fabric bioactivity screening.

[0114] Data classification storage and retrieval algorithm: The total database realizes classification storage and fast retrieval functions, and uses the "K-nearest neighbor algorithm (KNN)" to quickly search and match data. The cloth vector in the cloth database is X=[x1,x2,…,x n ], the fabric feature vector to be found is q, and the distance between the fabric feature and the fabric in the database is defined as:

[0115]

[0116] d(q,X i ): Fabric Features and cloth X in the database i The Euclidean distance of q j 、x ij : Fabric feature q and fabric X in the database i When the d value of the feature value in the jth dimension is the smallest, the closest matching fabric is found, achieving efficient retrieval.

[0117] The fabric feature analysis and fabric usage information matching model uses principal component analysis (PCA) to reduce data dimensionality when analyzing fabric characteristics and matching fabric usage information, so as to find the best matching vector between the fabric and the target point in the fabric feature space. The fabric feature matrix is X, and the covariance matrix C is calculated as:

[0118]

[0119] C: covariance matrix; n: number of fabrics; X i : The feature vector of the i-th cloth; cloth mean vector;

[0120] By performing eigenvalue decomposition on C, the main eigenvectors are obtained to reduce the dimension so as to match the fabric usage information more efficiently.

[0121] Fabric-target relationship model In the activity analysis of fabrics and targets, a deep learning model is used to build the fabric-target relationship. The fabric feature vector is x, the target feature vector is y, and the model outputs the fabric bioactivity prediction value

[0122]

[0123] Fabric bioactivity prediction value; σ: activation function (such as Sigmoid or Softmax, for nonlinear transformation); W: weight matrix; b: bias term; [x; y]: concatenation of fabric and target feature vectors;

[0124] In the automated experimental design and feedback optimization system, the response surface method (RSM) is used to optimize the experimental scheme. The experimental factors are x1, x2, ..., x k , the response value is y, then the experimental model is:

[0125]

[0126] y: response value (experimental result); β0: intercept; β i : first-order effect coefficient; β ij : interaction effect coefficient;∈: random error term;

[0127] After the experimental design is completed, the experimental results are fed back to the database and the experimental design is updated using the Bayesian optimization algorithm. The probability update formula of Bayesian optimization is:

[0128]

[0129] p(y|x): the probability of response y under condition x; p(x|y): the likelihood of the fabric feature given the response y; p(y): the prior probability of the response; p(x): the marginal probability of the fabric feature; the system cyclic learning optimization model uses the gradient descent algorithm to perform cyclic learning optimization on the model parameters to improve the intelligent output of the system. Assuming the loss function is the sum of squares of the system output errors, and the gradient descent update rule is:

[0130]

[0131] θ: system parameters; η: learning rate; The gradient of the loss function with respect to the parameters;

[0132] Fabric Entry: Based on the fabric database content in the master database, fabric chemical, biological, and material information is stored. Data Integration and Analysis: Fabric features are reduced in dimension using PCA and then matched with data in the fabric usage information database. Experimental Design and Feedback Optimization: Experiments are designed using RSM, and experimental data is fed back to the database in real time. Experimental plans are continuously updated using Bayesian optimization. Intelligent Circular Learning: A cyclic learning optimization model is used to optimize system parameters and improve fabric matching accuracy. Specific embodiment three:

[0134] like Figure 1-Figure 3 As shown in the figure, when the entire device screens the bioactivity of fabrics, it first performs an initial fabric comparison through the intelligent fabric comparison and processing system. The specific structure is that the intelligent fabric comparison and processing system includes a fabric identification and classification module, an automatic fabric processing module, a parameter adjustment module, a high-throughput fabric processing module and an intelligent fabric tracking and management module. The specific contents include the following:

[0135] Fabric Identification and Classification Module: It can automatically identify and classify different types of R&D fabrics. This is achieved through image recognition technology and machine learning algorithms to ensure that different types of fabrics can be accurately identified and classified.

[0136] The fabric identification and classification module uses image recognition technology and machine learning algorithms to classify fabrics. The core algorithm is the convolutional neural network (CNN) model and formula:

[0137] Convolutional layer formula:

[0138] y i =f(∑ j w ij ·x j +b i );

[0139] y i : The i-th output feature of the convolutional layer; w ij : weight parameter of convolution kernel; x j : Features of the input image; b i : bias; f: activation function (ReLU); classification layer formula (Softmax):

[0140]

[0141] p(y=k|x): the probability that the input feature x belongs to category k; w k : The weight of the kth class; e: The base of the natural logarithm; Use the labeled fabric data to train the CNN model and optimize the classification accuracy through the loss function (cross entropy loss);

[0142] Automated fabric processing module: It can automatically process fabrics according to the needs of fabric comparison tests, including fabric distribution, dilution, mixing and other operations. This is achieved through robotic arms and automatic liquid handling systems, improving the efficiency and accuracy of fabric processing.

[0143] Parameter adjustment module: Through the intelligent system, it can automatically adjust the processing parameters and optimize the processing process according to the characteristics of different fabrics and the requirements of the screening experiment. Through the integration of sensors and feedback mechanisms, it can realize real-time monitoring and adjustment of the fabric processing process to ensure the accuracy and consistency of fabric processing;

[0144] The parameter adjustment module uses an adaptive algorithm based on feedback control, collects data in real time through sensors, and dynamically adjusts the processing parameter model and formula: Feedback control formula:

[0145]

[0146] u(t): controlled quantity; e(t): error (difference between expected value and actual value); K p ,K i ,K d :Controller gain parameter method: Proportional-integral-derivative control (PID control) method is adopted to optimize parameters through real-time feedback to ensure the stability and accuracy of the fabric processing process.

[0147] High-throughput fabric processing module: capable of processing multiple fabrics simultaneously, and achieving rapid processing and efficient distribution of fabrics, achieved through microfluidics technology, multi-channel liquid handling systems, etc., to improve the throughput and efficiency of fabric processing;

[0148] The high-throughput fabric processing module utilizes microfluidics technology and a multi-channel liquid handling system to achieve high-throughput fabric processing. The flow rate control of the microfluidics system uses fluid dynamics equations.

[0149] Flow rate formula (Hagen-Poiseuille equation):

[0150]

[0151] Q: flow rate; ΔP: pressure difference; r: pipe radius; η: liquid viscosity; L: pipe length;

[0152] By adjusting the pressure difference ΔP and the pipe size, the liquid flow rate is controlled to achieve simultaneous processing of multiple fabrics and improve processing efficiency.

[0153] Intelligent fabric tracking and management module: It can track the fabric processing process in real time and record relevant information through barcode, RFID and other technologies to ensure the accuracy and reliability of fabric tracking and management.

[0154] The intelligent fabric comparison and processing system processes R&D fabrics, can automatically identify the characteristics of R&D fabrics, and automatically adjust processing parameters based on these fabric characteristics. This function greatly improves the efficiency and accuracy of fabric processing, reduces human intervention and errors, and thus accelerates the process of product development. At the same time, automatic fabric allocation also ensures the rational use of experimental resources, improves overall work efficiency, improves the efficiency and accuracy of fabric comparison experiments, reduces the need for manual operation, and provides reliable support for fabric R&D. Specific embodiment four:

[0156] like Figure 1-Figure 3 As shown, the fabric recognition and classification module includes an image recognition unit, a feature extraction and selection unit, a model training unit, a machine algorithm unit, and a real-time recognition and classification unit, specifically including the following:

[0157] Image recognition unit: Using computer vision technology and a convolutional neural network deep learning model, it extracts and classifies features from images of R&D fabrics. By training the deep learning model, the system can automatically learn the features of R&D fabrics and identify different types of fabrics.

[0158] Feature extraction and selection unit: During the image recognition process, it is necessary to extract and select features from images of R&D fabrics. Image processing techniques, including edge detection and color extraction, are used to extract the visual features of the R&D fabrics. Deep learning techniques are also used to automatically learn image features through models such as convolutional neural networks.

[0159] Machine Learning Algorithm Unit: Uses machine learning algorithms, including one or more of convolutional neural networks, support vector machines, and random forests, to perform classification tasks. These algorithms can accurately classify R&D fabrics based on extracted image features.

[0160] Model training unit: In image recognition systems, model training and optimization are required to improve classification accuracy and generalization capabilities. This includes selecting appropriate training datasets, adjusting model structure and hyperparameters, etc. By repeatedly training and optimizing the model, the system can better adapt to different types of R&D fabrics and improve recognition and classification accuracy.

[0161] Real-time recognition and classification unit: By optimizing algorithms and parallel computing technology, the system's processing speed and real-time performance are improved. At the same time, hardware accelerators including GPUs are used to accelerate image processing and model inference processes in actual use, further improving system performance and efficiency.

[0162] Establish an efficient and accurate fabric identification and classification system to provide reliable technical support for intelligent fabric processing.

[0163] The high-throughput fabric processing module includes microfluidics technology, a multi-channel liquid handling unit, a sample distribution algorithm, a mixing efficiency optimization algorithm, and a real-time control system. Specifically, it includes the following:

[0164] Microfluidics: Microfluidics is a fabric processing technology based on microfluidics principles that enables precise control and efficient processing of tiny liquid samples. It includes microchannel design, sample distribution, mixing, and analysis. Overall control is achieved through microfluidic chips. When using microfluidics, select the corresponding chip model based on publicly available technology.

[0165] Multi-channel liquid handling system: The multi-channel liquid handling system can process multiple samples simultaneously, improving the throughput and efficiency of sample processing. It uses multi-channel infusion pumps, microvalves and other components to achieve its liquid distribution, mixing, separation and other functions;

[0166] Sample allocation algorithm: realizes fast and accurate allocation of samples. Allocation algorithms include random allocation, uniform allocation, priority allocation, etc. Its formula can be expressed as:

[0167]

[0168] Where A is the sample allocation, M is the total sample size, and N is the number of channels;

[0169] Mixing efficiency optimization algorithm: For samples that require mixing, an optimization algorithm is used to ensure uniform mixing of the samples, including rotation mixing and vortex mixing;

[0170] The mixing efficiency E is an indicator of the uniformity of the component concentration distribution in the output sample. The concentration of the mixed component A at each point is C i , and the target concentration for ideal uniform mixing is C target , mixing efficiency E:

[0171]

[0172] C initial,i is the concentration value of point i before mixing. When the actual concentration distribution is close to the ideal value, E approaches 1, indicating high mixing efficiency. When the E value is low, it indicates insufficient mixing.

[0173] When using a rotary mixing or vortex mixing device, the mixing process can be affected by changing factors such as the rotation frequency (ω), rotation time (t), flow channel structural parameters, or vortex generation frequency (v). The control parameter vector is P = [ω, t, v, ...], then:

[0174] E=f(P;μ,D,C target );

[0175] μ is the sample viscosity (the viscosity of the fabric treatment fluid affects the shear and diffusion rates within the fluid); D is the diffusion coefficient (the ability of a chemical component to diffuse in a solution); G target is the desired uniform concentration.

[0176] Adjust according to mixing efficiency and sample properties; the mixing efficiency optimization algorithm uses iterative optimization to optimize P: Initial parameter setting: According to the viscosity, diffusion coefficient and target mixing efficiency threshold of the sample, the initial parameter P is given (0) .

[0177] Hybrid experiment and evaluation: Using P (0) The mixing process is performed, the output cross-sectional concentration distribution is measured and the value of the mixing efficiency E is calculated. The value of the mixing efficiency E is between 60-100% according to the material conditions.

[0178] Update parameters: Adjust P based on the mixing efficiency E relative to the target improvement space. If the mixing efficiency E is too low, increase the rotation speed ω and extend the mixing time t. If the mixing efficiency E is close to 1 but the energy consumption is too high, reduce the parameters while maintaining the mixing efficiency E at a reasonable level.

[0179]

[0180] Where α is the learning rate or adjustment step size, It represents the sensitivity direction of the mixing efficiency E to P (estimated based on empirical data or numerical models); the measurement and update are repeated until the mixing efficiency reaches a preset threshold or the optimization process converges.

[0181] Real-time control system: Real-time monitoring and regulation of the sample processing process is achieved. Sample processing parameters are collected in real time through sensors, and the control system is adjusted according to real-time data.

[0182] The high-throughput sample processing platform enables efficient operation and rapid and precise distribution of sample processing, providing reliable support for applications such as fabric comparison. Specific embodiment five:

[0184] like Figure 1-Figure 3 As shown in the figure, the multimodal data integration and analysis system processes data in five steps: data acquisition and preprocessing, feature extraction and selection, data fusion and integration, and multimodal data analysis and model evaluation and adjustment. Specifically, it includes the following contents:

[0185] Data collection and preprocessing: First, multimodal data, including image data, sound data, and text data, must be collected from various sensors or data sources. This data must then be preprocessed, including denoising, standardization, and normalization, to ensure data quality and reliability.

[0186] Feature extraction and selection: For different types of data, raw data is converted into informative feature vectors. This includes using local binary pattern (LBP) feature extraction techniques and combining them with recurrent neural networks (RNNs) using deep learning algorithms to learn feature representations from the raw data.

[0187] Feature extraction: Use appropriate feature extraction techniques for different types of data. For image data, use local binary patterns (LBP):

[0188]

[0189] g c : Center pixel value; g i : surrounding pixel values; Deep learning method: Combined with recurrent neural network (RNN) to extract features of time series data. The basic formula of RNN is:

[0190] h t =f(W h h t-1 +W x x t +b);

[0191] h t : Current hidden state; W h ,W x : weight matrix; x t : Current input; b: Bias term;

[0192] Data fusion and integration: Fusing multimodal data from different sensors or data sources, mainly achieved through methods such as decision-level fusion, integrating decision or prediction results from different modalities to obtain the final classification or regression results;

[0193] Decision-level fusion: Integrate the decision or prediction results from different modalities. The fusion method uses weighted voting average, and the weighted average formula is:

[0194]

[0195] y: final output result; w i :weight; y i : Output of each mode

[0196] Multimodal data analysis: Analyze the integrated and fused multimodal data using various machine learning and data mining techniques, including classification, clustering, regression, and association rule mining. Based on the specific application scenario and task requirements, select the analysis method, train and test the data, and obtain the final analysis results.

[0197] Model evaluation and adjustment: including model performance evaluation, parameter tuning, cross-validation and other operations to ensure the generalization ability and stability of the model. Based on the evaluation results, the model is optimized to further improve its performance and effect. Model optimization uses the cross entropy loss function for optimization:

[0198]

[0199] L: loss; y i : real label; Prediction probability;

[0200] Through the above content, the integration and analysis of multimodal data can be achieved. The multimodal data integration and analysis system can process, analyze and integrate the chemical, biological and bioinformatics data of fabric research and development. This fusion analysis of multimodal data enables researchers to have a more comprehensive understanding of the mechanism and effect of the fabric, so as to make more accurate decisions. In addition, the use of visual analysis tools also makes the data more intuitive and easy to understand, which facilitates researchers to conduct in-depth exploration and analysis, thereby making full use of information from different data sources and providing reliable support for data-driven decision-making and application. Specific embodiment six:

[0202] like Figure 1-Figure 3 As shown, the integrated artificial intelligence-assisted decision-making system includes five steps of processing: data preprocessing, feature extraction and selection, model establishment and training, model evaluation and adjustment, and intelligent decision-making assistance module.

[0203] Data preprocessing: Before the experiment begins, raw data is preprocessed, including data cleaning, missing value processing, and outlier detection and processing. Artificial intelligence technology is used to automate these preprocessing steps to improve data quality and usability;

[0204] Feature extraction and selection: AI technologies help extract valuable features from large amounts of data. Machine learning algorithms and deep learning models can automatically learn feature representations from raw data, or select the most representative features through feature selection methods to improve model performance and generalization capabilities.

[0205] Model building and training: Through deep learning models including convolutional neural networks, combined with a large amount of experimental data and fabric bioactivity information, high-performance prediction models can be trained;

[0206] Model evaluation and adjustment: After the model is established, it needs to be evaluated and optimized. Artificial intelligence technology can help design effective evaluation indicators and evaluation methods, objectively evaluate the performance of the model, and optimize the model based on the evaluation results to improve its prediction accuracy and stability.

[0207] Intelligent decision-making support system: Based on the output results of the fabric bioactivity prediction model and combined with other relevant information, including the chemical structure of the fabric, it can provide a reference for decision-making, helping researchers make decisions quickly and accurately and select appropriate fabric candidate compounds for further research and development.

[0208] The integrated artificial intelligence-assisted decision-making system can establish a fabric-target relationship model based on a large amount of fabric comparison data and research experience data, combined with artificial intelligence technology, and provide predictions of fabric bioactivity, which provides strong decision-making support for experimental design and reduces blindness and uncertainty.

[0209] The automated experiment design and adjustment system includes five steps: experimental design algorithm, experimental scheme optimization algorithm, automated experiment platform, data analysis and feedback mechanism module, and remote monitoring and control module.

[0210] Experimental design algorithm: Factorial design and response surface methodology can be used to design fabric bioactivity experiments. Factorial design can help identify the key factors affecting fabric bioactivity and determine their level combinations. Response surface methodology can be used to design experiments for multiple factors and fit mathematical models based on experimental results to predict optimal conditions. In actual use, this algorithm can be combined with automation technology to quickly generate experimental design plans.

[0211] Input data: cloth feature vector X F : Including fiber diameter, fabric density, initial setting of chemical finishing agent concentration, dyeing and finishing process parameters, and texture feature data after fabric image recognition. Target feature vector X T : Indicates the expected biological indicators (including the inhibition rate of specific bacteria ≥ 90%, or the reduction of allergic reaction score ≥ threshold). Experimental factor selection: Experimental factor (x i ) are the controlled process parameters, including chemical treatment agent concentration (x1), treatment temperature (x2), treatment time (x3), and other key factors that affect the final bioactivity index of the fabric. Experimental Design Method: A preliminary experimental plan was generated using the response surface methodology (RSM) or other statistical designs (including orthogonal, full factorial, or fractional factorial designs). Taking the quadratic polynomial model as an example, the response surface model is expressed as:

[0212]

[0213] y is the bioactivity index of the fabric (including the antibacterial rate), which is determined by the bioactivity prediction model (f([X F ;X T ]; θ)) gives the initial prediction value; x i represents the i-th experimental factor (including treatment agent concentration and temperature); β0, β i ,βij ,β ii is the parameter to be fitted, which is obtained by regression of future experimental data; ε is the experimental error term, with a value of 0-1.

[0214] Experimental plan optimization algorithm: The experimental design algorithm is further optimized using an optimization algorithm. The optimization algorithm is selected from the genetic algorithm, particle swarm optimization algorithm, and simulated annealing algorithm. According to the algorithm, under given experimental conditions, the optimal experimental plan can be found through iteration to maximize the bioactivity of the fabric or minimize side effects.

[0215] After obtaining preliminary experimental data and using the model to predict the results, the experimental plan needs to be further optimized to gradually approach the target bioactivity level.

[0216] enter:

[0217] The actual measurement data (actual biological activity results) obtained in the previous round of experiments;

[0218] The updated fabric bioactivity prediction model f([X F ;X T ]; θ) with a value of 0-1. The optimization goal is to maximize the biological activity of the fabric (maximize the antibacterial rate) or to achieve a specific target value (reduce the allergy score to a specified value). Genetic algorithm is used: experimental factor combination (x=[x1,x2,…,x k ]) is encoded as a chromosome, and the output value f([X F ;X T ]; θ) to evaluate its fitness, setting the goal to maximize the biological activity value y, and the fitness function is:

[0219] Fitness=f([X F ;X F ];θ);

[0220] Larger values indicate a better solution. Genetic algorithms iteratively search for the optimal factor combination through selection, crossover, and mutation operations. After continuous updates, the distribution is verified, allowing the algorithm to more accurately locate the optimal parameter region with each new data addition. By optimizing the algorithm, the system can continuously reduce invalid experiments, quickly approach the optimal process parameters, and achieve efficient experimental solution iteration.

[0221] Automated experimental platform: used to automatically perform fabric preparation, sample processing, experimental operations, etc., and the automated experimental platform integrates various instruments and equipment, including liquid handling systems, high-throughput screening instruments, real-time monitoring equipment, etc., to achieve automation and efficiency of the experimental process. It includes a robotic arm, automatic liquid handler, microfluidics technology, multi-channel fabric handling unit, temperature and humidity control system and online monitoring sensor. The multi-channel liquid handling system and microfluidics chip are used to automatically add specific concentrations of treatment agents according to the optimized plan. The control system sets the fabric treatment temperature and time parameters. Real-time sensors (optical detection equipment, colony counting device, allergen sensor) collect biological activity data (colony reduction rate, skin irritation).

[0222] Data analysis and feedback mechanism: During the experiment, it is necessary to monitor and analyze experimental data in real time, and adjust the experimental plan based on the experimental results. This is achieved through a real-time data acquisition and analysis system, including data visualization, statistical analysis, machine learning and other technologies, as well as an automated feedback mechanism, including automatic adjustment of experimental parameters or experimental conditions;

[0223] Analyze the data output by the automated experimental platform, compare the predicted values with the actual values, and update the model parameters to form a closed-loop feedback loop, thereby improving the accuracy of the next round of experimental design and optimization.

[0224] Data processing: cleaning, normalization, and statistical analysis of the biological activity data obtained from the experiment;

[0225] Model training and updating:

[0226] Use the loss function to update the model parameters θ:

[0227]

[0228] is the actual measured bioactivity value (actually measured antibacterial rate) of the ith experimental point; The model predicts the value; θ is continuously updated iteratively to improve the prediction accuracy and reliability, and the updated model parameters and experimental results are summarized and returned to the experimental scheme optimization algorithm to further adjust the factor level of the next experiment to achieve better experimental efficiency and optimization effect.

[0229] Feedback mechanism: The updated model parameters and experimental results are summarized and returned to the experimental plan optimization algorithm to further adjust the factor levels of the next experiment to achieve better experimental efficiency and optimization effect.

[0230] Remote monitoring and control: In order to improve the flexibility and efficiency of the experiment and realize remote monitoring and control functions, through network connection and remote control software, experimenters can monitor the progress and results of the experiment anytime and anywhere, and perform remote operations and adjustments, thereby improving the operability and manageability of the experimental platform. Real-time experimental data (current processing parameters, environmental conditions, real-time measurement values of biological activity) are transmitted to the remote terminal interface through the network to realize visual monitoring. With the help of the remote login system, researchers can modify experimental parameters, pause or restart the experiment, or make emergency adjustments to model parameters remotely.

[0231] Through the above implementation content, the automated experimental design adjustment system can automatically generate experimental plans based on experimental objectives and fabric characteristics, and continuously adjust the experimental design through a feedback mechanism. It can effectively improve the efficiency and accuracy of automatic screening of fabric biological activity, accelerate the process of product research and development, and provide strong support for the discovery and development of new products. Specific embodiment seven:

[0233] Steps of a textile fabric research and development analysis system:

[0234] Sp1: Intelligent fabric matching: Using deep learning algorithms, the system automatically identifies the characteristics of R&D fabrics and achieves rapid screening and classification of fabrics by adaptively adjusting processing parameters;

[0235] Sp2: Multimodal data fusion: Through internal multimodal data fusion algorithms, collect and process data from chemistry, biology and bioinformatics to generate comprehensive visual analysis reports;

[0236] Sp3: AI-assisted decision generation: Combined with historical fabric comparison data, deep learning models are used to establish fabric-target relationships and generate fabric bioactivity prediction models to provide decision recommendations for experiments;

[0237] Sp4: Automated Experimental Design: The system automatically generates experimental design plans based on fabric characteristics and historical data, adjusts experimental parameters, and improves experimental efficiency;

[0238] Sp5: Experimental result feedback and iteration: After the experiment is completed, the system will feed back the experimental results to the database, and use the feedback information to continuously adjust the model and experimental design to achieve dynamic iteration.

[0239] It should be noted that, in this document, relational terms such as "first and second" and the like are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variants thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a reference structure" does not exclude the presence of additional identical elements in the process, method, article, or device comprising the element.

[0240] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A textile fabric R&D and analysis system, characterized by: The system consists of an intelligent fabric comparison and processing system, a multimodal data integration and analysis system, an integrated artificial intelligence decision-making system, and an automated experimental design and adjustment system. The system is internally provided with a general database, which includes a fabric database, a fabric usage information database, and a storage database, wherein: Intelligent fabric matching and processing system: This system uses machine learning algorithms to identify fabric characteristics, including chemical, physical, and image features, and automatically adjusts processing parameters based on these characteristics to automatically identify, process, and distribute R&D fabrics. Multimodal data integration and analysis system: Utilizing internal multimodal data fusion algorithms, we develop visualization analysis tools to process, analyze, and integrate chemical, biological, and bioinformatics data for fabric R&D; The formula of the multimodal data fusion algorithm is: z=W c x c +W b x b +W i x i +b; z: fused data vector; W c 、W b 、W i : Weight matrices of different modalities; b: Bias vector, Multimodal data analysis and visualization Apply principal component analysis to the fused data to reduce the data dimension, x c :Chemical data, x b :Biological data, x i : Informatics data; Integrated artificial intelligence-assisted decision-making system: Based on the system's large amount of fabric comparison data and research experience data, combined with artificial intelligence technology and internal deep learning algorithms, a fabric-target relationship model is established, the output results of the fabric bioactivity prediction model are given, and decision-making suggestions are generated to assist experiments; the targets of the fabric-target relationship model include the following parts: Allergic reaction sensitivity targets: quantified by numerical values or grades through skin patch tests, in vitro cell stimulation tests, or quantitative indicators of erythema and pruritus; Comfort and breathability targets: By testing volunteers under uniform experimental conditions, the feedback from the trial is quantified into numerical values and recorded in the database; Antibacterial and anti-allergic biocompatibility targets: quantitative indicators can be obtained through microbial culture inhibition tests, protein adsorption tests, or cell compatibility experiments; Automated experimental design adjustment system: The biological activity of fabrics is designed through experimental design algorithms, and the experimental design given by the experimental scheme optimization algorithm is further adjusted. Automated experiments are carried out through an automated experimental platform, and synchronous data analysis and feedback are performed. Experimental schemes are automatically generated according to experimental objectives and fabric characteristics, and the experimental design is continuously adjusted through a feedback mechanism.

2. A textile fabric R&D and analysis system according to claim 1, characterized in that: The general database further includes the following: Fabric database: used to store data on fabric categories required for production; Fabric usage information database: stores target data corresponding to the fabrics being developed; Storage database: stores the instructions, signals, experimental data, and historical data information of each subsystem in the system, and provides basic data for system cycle training.

3. The textile fabric R&D and analysis system according to claim 1, characterized in that: The intelligent fabric comparison and processing system includes a fabric identification and classification module, an automatic fabric processing module, a parameter adjustment module, a high-throughput fabric processing module and an intelligent fabric tracking and management module.

4. The textile fabric R&D and analysis system according to claim 3, characterized in that: The fabric recognition and classification module includes an image recognition unit, a feature extraction and selection unit, a model training unit, a machine algorithm unit and a real-time recognition and classification unit.

5. The textile fabric R&D and analysis system according to claim 3, characterized in that: The high-throughput fabric processing module includes microfluidics technology, a multi-channel liquid handling unit, a sample distribution algorithm, a mixing efficiency optimization algorithm, and a real-time control system; Sample allocation algorithm: Achieve fast and accurate allocation of samples. The allocation algorithm includes random allocation, uniform allocation, and priority allocation. The formula is expressed as: Where A is the sample allocation, M is the total sample size, and N is the number of channels; The mixing efficiency optimization algorithm is: Mixing efficiency E target It is an indicator of the uniformity of the concentration distribution of the components in the output sample; the concentration of the mixed component T at each point is C i , and the target concentration for ideal uniform mixing is C target , mixing efficiency E target : C initial,i is the concentration value of the ith channel before mixing. When the actual concentration distribution is close to the ideal value, the mixing efficiency E target Approaching 1 indicates high mixing efficiency; When using a rotary mixing or vortex mixing device, the mixing process is affected by changing the rotation frequency ω, the rotation time t, the flow channel structure parameters or the vortex generation frequency v factor. The control parameter vector is P = [ω, t, v, ...], then: E=f(P;μ,D,C target ); μ is the sample viscosity; D is the diffusion coefficient; Adjust according to the mixing efficiency and sample properties; the mixing efficiency optimization algorithm uses iterative optimization to optimize P, and adjust P according to the improvement space of the mixing efficiency E relative to the target. Where α is the learning rate or adjustment step size, Represents the sensitivity direction of the mixing efficiency E to P; the measurement and update are repeated until the mixing efficiency reaches the preset threshold or the optimization process converges.

6. The textile fabric R&D and analysis system according to claim 1, characterized in that: The multimodal data integration and analysis system processes data in five steps: data acquisition and preprocessing, feature extraction and selection, data fusion and integration, and multimodal data analysis and model evaluation and adjustment.

7. The textile fabric R&D and analysis system according to claim 1, characterized in that: The integrated artificial intelligence-assisted decision-making system includes processing content for five steps: data preprocessing, feature extraction and selection, model establishment and training, model evaluation and adjustment, and intelligent decision-making assistance module.

8. The textile fabric R&D and analysis system according to claim 1, characterized in that: The automated experiment design adjustment system includes five steps: experiment design algorithm, experiment scheme optimization algorithm, automated experiment platform, data analysis and feedback mechanism module, and remote monitoring and control module.

9. A textile fabric R&D and analysis system according to any one of claims 1 to 8, characterized in that: The R&D analysis system comprises the following steps: Sp1: Intelligent fabric matching: Using deep learning algorithms, the system automatically identifies the characteristics of R&D fabrics and achieves rapid screening and classification of fabrics by adaptively adjusting processing parameters; Sp2: Multimodal data fusion: Through internal multimodal data fusion algorithms, collect and process data from chemistry, biology and bioinformatics to generate comprehensive visual analysis reports; Sp3: AI-assisted decision generation: Combined with historical fabric comparison data, deep learning models are used to establish fabric-target relationships and generate fabric bioactivity prediction models to provide decision recommendations for experiments; Sp4: Automated Experimental Design: The system automatically generates experimental design plans based on fabric characteristics and historical data, adjusts experimental parameters, and improves experimental efficiency; Sp5: Experimental result feedback and iteration: After the experiment is completed, the system will feed back the experimental results to the database, and use the feedback information to continuously adjust the model and experimental design to achieve dynamic iteration.

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