Textile fabric research and development analysis system

Abstract

The invention provides a textile fabric research and development analysis system, and relates to the technical field of textile production analysis, and the system is composed of an intelligent fabric comparison processing system, a multi-modal data integration analysis system, an integrated artificial intelligence auxiliary decision making system and an automatic experiment design adjustment system. The total database comprises a cloth database, a cloth use information database and a storage database, so that the whole system structure is matched with the total database to realize automatic identification, processing, distribution and screening of research and development cloth, and automation of the whole cloth processing process is realized through the intelligent cloth comparison processing system. The system can identify, classify and distribute a large amount of cloth in a short time, and time and labor intensity of manual operation are remarkably reduced. And the mechanical arm and the automatic liquid treatment system are used, so that the treatment speed is increased, the consistency and reliability of operation are ensured, the production efficiency is greatly improved, and the research and development requirements of rapid change can be better met.

Description

Technical Field

[0001] The 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 the spunlace nonwoven technology development direction monitoring system based on big data with Chinese patent publication number CN117147808A, it involves the technical field of spunlace nonwovens and aims to solve the problem that a lot of time is needed to conduct trial and error and find the influence of the change amount during the research and development process, which reduces the overall research and development speed. The key points of its technical solution are that it includes a master control system, and the master control system includes a raw material quality detection module for raw material quality detection, a finished product quality detection module for finished product quality detection, a quantity detection module for detecting the number of materials passing, a cleanliness monitoring module for measuring the cleanliness of the detection material, a quality detection module for detecting the quality of the finished fabric, a big data recording module for recording fabric detection data, a data extraction module for extracting data, and a data processing module for processing data. The effect of multi-level detection to obtain data, multi-dimensional data analysis and easy trial and error is achieved.

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

[0004] Traditional manual operation is time-consuming and prone to human errors, affecting production efficiency and data silos: Even if some digital fabric research and development 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, and inflexible parameter adjustment: 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 research and development analysis system, 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 adjustment system, the system is provided with a general database, the general database includes a fabric database, a fabric usage information database and a storage database, wherein:

[0007] Intelligent fabric comparison and processing system: It uses machine learning algorithms to identify fabric features and automatically adjust processing parameters based on fabric features to automatically identify, process and distribute R&D fabrics;

[0008] 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 R&D fabrics;

[0009] Integrated artificial intelligence decision-making system: Based on a large amount of fabric comparison data and research experience data in the system, combined with artificial intelligence technology, using 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 for auxiliary experiments;

[0010] Automated experimental design adjustment system: Design the biological activity of fabrics through experimental design algorithms, and further adjust the given experimental design through experimental scheme optimization algorithms. Perform automated experiments through automated experimental platforms, and conduct synchronous data analysis and feedback. Automatically generate experimental schemes based on experimental objectives and fabric characteristics, and continuously adjust the experimental design through feedback mechanisms.

[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 R&D fabrics;

[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 collection 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 contents of 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 scheme 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 comparison: 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 the internal multimodal data fusion algorithm, 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 research and development analysis system, which has the following beneficial effects:

[0029] 1. The present invention realizes automation of the entire fabric processing process through an intelligent fabric comparison and processing system. The system can identify, classify and distribute a large amount of fabric in a short time, significantly reducing the time and labor intensity of manual operation. The use of a robotic arm and an automatic liquid handling system not only improves the processing speed, but also ensures the consistency and reliability of the operation, greatly improves production efficiency, can better meet the rapidly changing research and development needs, and is particularly suitable for high-throughput experimental environments.

[0030] 2. The present invention extracts and classifies fabric features by using a deep learning algorithm. By learning the features of multiple fabric types (biological, chemical, 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 the occurrence of errors and repeated experiments.

[0031] 3. The present invention integrates sensors and feedback mechanisms, so that the system can monitor the fabric processing process in real time and automatically adjust the processing parameters according to the fabric characteristics and experimental requirements. This adaptive capability enables each experiment to be carried out 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 the operation flow chart of the present invention;

[0033] Figure 2 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 be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work 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 is composed 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 provided with a general database, which includes a fabric database, a fabric usage information database and a storage database, wherein:

[0038] Intelligent fabric comparison and processing system: It uses machine learning algorithms to identify fabric features and automatically adjust processing parameters based on fabric features to automatically identify, process and distribute 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 , extract features through the convolutional neural network (CNN) model. Convolutional layer calculation formula:

[0040] ;

[0041] :No. Output features of the layer; :No. The weight parameters of the layer; Input features; Bias term; Activation function (ReLU);

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

[0043] ;

[0044] Current learning rate; Learning rate adjustment factor; Loss function; The gradient of the loss function with respect to the learning rate;

[0045] Multimodal data integration and analysis system: Utilize the internal multimodal data fusion algorithm to develop visualization analysis tools to process, analyze and integrate the chemical, biological and bioinformatics data of 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 fabrics. The data fusion model is used to calculate the vectors of different modal data. (Chemical Data), (Biological Data), (Informatics data), the multimodal fusion algorithm formula is:

[0046] ;

[0047] The fused data vector; 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] ;

[0049] Feature vector after dimensionality reduction; Projection matrix;

[0050] Integrated artificial intelligence decision-making system: Based on a large amount of fabric comparison data and research experience data in the system, combined with artificial intelligence technology, using 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 for auxiliary experiments; fabric-target refers to the specific biological performance indicators or comfort goals that fabrics are expected to achieve when they come into contact with the human body, biological tissues or specific biological simulation environments. Targets include the following parts:

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

[0052] Comfort and breathability targets: By testing volunteers under uniform test 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 being standardized and normalized by statistical methods, these data can be converted into feature vectors for subsequent model training.

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

[0056] Indicators including allergy level, breathability, biocompatibility, antibacterial performance, and humidity regulation performance are weighted and summarized or modeled to form a comprehensive score (a score between 0 and 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 physicochemical 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: stores the actual measured biological related parameters and experimental records (allergy test score data, microbial inhibition test data, volunteer comfort score data);

[0059] Fabric database: stores basic information about 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 the corresponding target data are mapped into feature vectors, and standardized and normalized.

[0063] Model training: (Fabric feature vector, target feature vector or bioactivity measurement value) is input into the deep learning model as training data. The model performs nonlinear transformation on the input features through weight matrix and activation function, and outputs the predicted fabric bioactivity value. Using known fabrics and their biological test data as training sets, the model parameters are optimized through back propagation algorithm and cross entropy loss or mean square error loss function.

[0064] Model output and decision suggestions: The trained model gives a predicted value of bioactivity for new or untested fabric input features. Based on the comparison of the predicted value with the development goals (including breathability, anti-allergy or comfort reaching a certain threshold), the system can automatically generate a decision suggestion. This decision suggestion is a digital value, a comprehensive score of 0-10, which results in a positive or negative suggestion. If it is positive, the designer can further optimize the formula of the input material to obtain better results. After repeated operations, the optimal value under a predetermined design state can be obtained, which is convenient for further trial production or experimental verification of the fabric.

[0065] The cloth feature vector is ;

[0066] The first Characteristic values; fabric features include: chemical properties (chemical composition of fibers, concentration of treatment agents), physical properties (fiber diameter, fabric density, thickness, ductility), image features (texture or structural features extracted based on convolutional neural networks), preliminary biological related features (known allergic reaction data, air permeability, preliminary biocompatibility measurements). After standardizing or normalizing these feature data, a fabric feature vector for model input can be formed;

[0067] The target feature vector is ;

[0068] For the Species / target feature data, It includes 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 value of skin irritation.

[0069] The integrated artificial intelligence-assisted decision-making system establishes the cloth-target relationship through a deep learning model, generates an activity prediction model, and provides suggestions for decision-making;

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

[0071] ;

[0072] : Activity prediction value; Activation function (Sigmoid or Softmax); Weight matrix; Bias term; Concatenation of feature vectors;

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

[0074] ;

[0075] Loss function; Actual label; Predict labels; Quantity of fabric;

[0076] Automated experimental design adjustment system: Design the biological activity of fabrics through experimental design algorithms, and further adjust the given experimental design through experimental scheme optimization algorithms. Perform automated experiments through automated experimental platforms, and conduct synchronous data analysis and feedback. Automatically generate experimental schemes based on experimental objectives and fabric characteristics, and continuously adjust the experimental design through feedback mechanisms.

[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. Assume that the experimental factors are , the response value is , the response surface model is:

[0078] ;

[0079] Experimental response value; intercept term; - order coefficient; : 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] ;

[0082] Fitness value; : Target response value; Experimental response value;

[0083] The response value in RSM is the target index predicted by the fabric bioactivity prediction model (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, and finishing process parameters), and uses the bioactivity score as the output for modeling, so as to find the relationship between the factor 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 is used to give estimated bioactivity values ​​for fabrics under various potential experimental conditions. RSM's experimental design algorithm uses the predicted bioactivity values ​​output by the prediction model as the initial data for fitting the response surface. Through the experimental points designed by RSM, the system determines a set of experimental conditions (factor combinations) and obtains the corresponding predicted bioactivity scores through 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: According to 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 value of biological activity 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 the factor and the response, and find the optimization direction.

[0088] Experimental verification and feedback: Conduct actual experiments on the optimized parameter combinations and measure real biological activity data through an automated experimental platform. Feed the real data back to the database and use algorithms such as Bayesian optimization to update the prior distribution and model parameters, so that both the prediction model and RSM are improved.

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

[0090] Steps to implement the path:

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

[0092] a. Use the prediction model to give the biological activity prediction results as the response value 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. Use the automated experimental platform to carry out actual test experiments and feed back the experimental results 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 biological activity of the fabric or achieves the set target performance.

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

[0098] ;

[0099] : Conditional probability, that is, the probability of a response under given parameter conditions; : Likelihood function of experimental data; the prior distribution of the response; Marginal distribution ;

[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: Extract fabric features and classify them 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-making recommendations, the automated experimental design system generates experimental plans and adjusts experimental parameters in real time, continuously improving experimental design and optimization algorithms through feedback mechanisms. Specific embodiment 2:

[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 R&D fabrics;

[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 is used to store all fabric information required for production, covering different types of R&D fabric data. It records the basic properties of fabrics (such as type, source, chemical composition, etc.). It provides real-time status and availability information of fabrics, so that the system can quickly access and retrieve relevant data when performing fabric comparison and analysis.

[0111] Fabric usage information database: This database stores fabric usage information that corresponds to or may correspond to the fabric under development. The fabric usage information database includes biological information data such as whether the human body is allergic to the fabric, the degree of allergy, the allergy of other organisms, the comfort of the human body, the fit, the degree of sweat absorption and perspiration, and the degree of breathability. It provides target information for fabric bioactivity research and supports the matching and analysis between fabric and fabric usage information. It stores historical target research results to facilitate reference and decision-making for future experiments and promote 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 records all operation logs during system operation and provides transparency and traceability of system operation.

[0113] Provide basic training data for machine learning models, support the system's cyclic training and optimization, and ensure the system's continuous improvement and intelligent upgrade. In the entire system, the general database classifies and stores various information about the system's operation, and stores the type information of each fabric through the fabric database, including the storage of biological, material, chemical and other information, so that the system can analyze according to the characteristics of the fabric, which is convenient for matching the fabric usage information. At the same time, through the fabric characteristics, combined with the corresponding reaction principles or reaction technologies, corresponding automated experiments can be carried out to provide data basis and guarantee for the subsequent post-screening verification part. Through the fabric usage information database, a large amount of data that can be combined with fabrics is classified and stored, and its biological characteristics are recorded, which is convenient for rapid search and matching with the R&D fabrics in the fabric database according to the fabric usage information database, and realizes the screening of fabric biological activity. At the same time, the storage database classifies and stores the operating data of each subsystem, module, unit, etc. in the entire system, which is convenient for training and learning according to the stored data, optimizing the entire system, and improving the intelligent output results, thereby improving the accuracy of the entire system in screening fabric biological activity.

[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 , the cloth feature vector to be found is ,Define the distance between the fabric feature and the fabric in the database as:

[0115] ;

[0116] : Fabric Features and the cloth in the database The Euclidean distance of , : Fabric Features and the cloth in the database In the The eigenvalues ​​of the dimensions are When the value is the smallest, the closest matching fabric is found, achieving efficient retrieval.

[0117] Fabric feature analysis and fabric usage information matching model When analyzing fabric characteristics and matching fabric usage information, principal component analysis (PCA) is used to reduce data dimension in order to find the best matching vector between fabric and target point in fabric feature space. The fabric feature matrix is , calculate the covariance matrix for:

[0118] ;

[0119] C: covariance matrix; : Fabric quantity; No. The feature vector of the cloth; : cloth mean vector;

[0120] Through Perform eigenvalue decomposition to obtain the main eigenvectors for dimensionality reduction so as to match the fabric usage information more efficiently.

[0121] In the activity analysis of cloth and target, a deep learning model is used to construct the cloth-target relationship. The cloth feature vector is , the target feature vector is , the model outputs the predicted value of fabric bioactivity

[0122] ;

[0123] : Prediction value of fabric bioactivity; Activation function (such as Sigmoid or Softmax, for nonlinear transformation) Weight matrix; Bias term; Concatenation of cloth 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 , the response value is , then the experimental model is:

[0125] ;

[0126] Response value (experimental result); intercept; : first-order effect coefficient; 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 through the Bayesian optimization algorithm. The probability update formula of Bayesian optimization is:

[0128] ;

[0129] :In the condition Next response The probability of : Given Response The cloth characteristics of the prior probability of the response; Marginal probability of fabric features; In order to improve the intelligent output of the system, the system cyclic learning optimization model uses the gradient descent algorithm to perform cyclic learning optimization on the model parameters. Assume the loss function is the sum of squares of the system output errors, and the gradient descent update rule is:

[0130] ;

[0131] System parameters; Learning rate; The gradient of the loss function with respect to the parameters;

[0132] Fabric entry: store fabric chemical, biological and material information according to the fabric database content of the total database. Data integration analysis: use PCA to reduce the dimension of fabric features and match them with the data in the fabric usage information database. Experimental design and feedback optimization: design experiments through RSM, and feedback experimental data to the database in real time, and continuously update the experimental plan through Bayesian optimization. Intelligent loop learning: use loop learning optimization model to optimize system parameters and improve the accuracy of fabric comparison. Specific embodiment three:

[0134] like Figure 1-Figure 3 As shown, when the entire device screens the biological activity of fabrics, it first performs an initial fabric comparison through the intelligent fabric comparison processing system. The specific structure is that the intelligent fabric comparison 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 through image recognition technology and machine learning algorithms to ensure that different types of fabrics can be accurately identified and classified;

[0136] The fabric recognition and classification module uses image recognition technology and machine learning algorithms to classify fabrics. The core algorithm is a convolutional neural network. Model and formula:

[0137] Convolutional layer formula:

[0138] ;

[0139] : Convolutional layer output features; : weight parameters of the convolution kernel; Features of the input image; Bias; Activation function (ReLU); classification layer formula (Softmax):

[0140] ;

[0141] : Input features Belongs to category The probability of :No. The weight of the class; 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] Automatic fabric processing module: It can automatically process fabrics according to the needs of fabric comparison test, including fabric distribution, dilution, mixing and other operations, which can be realized through robotic arms, automatic liquid handling systems, etc., to improve 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 flow according to the characteristics of different fabrics and the requirements of the screening experiment. Through the integrated sensors and feedback mechanism, 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] Control volume; Error (the difference between the expected value and the actual value); , , : Controller gain parameter method: Proportional-integral-differential control (PID control) method is adopted to optimize parameters through real-time feedback to ensure the stability and accuracy of the fabric handling process.

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

[0148] The high-throughput fabric processing module uses 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] Flow rate; Pressure difference; : Pipe radius; Liquid viscosity; Pipeline length;

[0152] By adjusting the pressure difference and pipe size, control the liquid flow rate, 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 according to 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 operations, and provides reliable support for fabric research and development. 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, which specifically include the following contents:

[0157] Image recognition unit: Using computer vision technology and convolutional neural network deep learning models, the image of the R&D fabric is extracted and classified. By training the deep learning model, the system can automatically learn the characteristics of the R&D fabric and identify different types of fabrics.

[0158] Feature extraction and selection unit: In the image recognition process, it is necessary to extract and select features from the image of the R&D fabric. Image processing technology, including edge detection, color extraction and other methods, is used to extract the visual features of the R&D fabric. At the same time, deep learning technology is used to automatically learn image features through models such as convolutional neural networks.

[0159] Machine Learning Algorithm Unit: Use machine learning algorithms, including 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 the image recognition system, model training and optimization are required to improve the accuracy and generalization ability of classification, which includes selecting appropriate training data sets, adjusting model structure and hyperparameters, etc. Through repeated training and optimization of the model, the system can better adapt to different types of R&D fabrics and improve the accuracy of recognition and classification;

[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 the principle of microfluidics, which can achieve precise control and efficient processing of tiny liquid samples, including microchannel design, sample distribution, mixing and analysis. It is controlled and used as a whole through microfluidics chips. When using it, the corresponding type of chip model is selected according to the disclosed technology;

[0165] Multi-channel liquid handling system: The multi-channel liquid handling system can process multiple samples at the same time, improve the throughput and efficiency of sample processing, and use multi-channel infusion pumps, microvalves and other components to achieve its liquid distribution, mixing, separation and other functions;

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

[0167] ;

[0168] in, For sample allocation, is the total sample size, is the number of channels;

[0169] Mixing efficiency optimization algorithm: For samples that need to be mixed, an optimization algorithm is used to ensure uniform mixing of 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 , and the target concentration for ideal uniform mixing is , mixing efficiency E:

[0171] ;

[0172] is the concentration value of the i-th point 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 means that the mixing is insufficient.

[0173] When using a rotating 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 structure parameters or vortex generation frequency (v). The control parameter vector is ,but:

[0174] ;

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

[0176] Adjust according to the mixing efficiency and sample properties; the mixing efficiency optimization algorithm uses iterative optimization to Optimization: Initial parameter setting: According to the sample viscosity, diffusion coefficient, and target mixing efficiency threshold, the initial parameters are given. .

[0177] Hybrid Experimentation and Evaluation: Using Execute the mixing process, measure the output cross-sectional concentration distribution and calculate the value of the mixing efficiency E. The value of the mixing efficiency E is between 60-100% depending on the material conditions.

[0178] Update parameters: According to the mixing efficiency E relative to the target improvement space, Adjustment: If the mixing efficiency E is too low, increase the rotation speed , 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] in is the learning rate or adjustment step size, The mixing efficiency E is The sensitivity direction of the optimization process is determined (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: Realize real-time monitoring and regulation of the sample processing process, collect sample processing parameters in real time through sensors, and adjust the control system according to real-time data.

[0182] The high-throughput sample processing platform enables efficient operation and rapid and precise allocation 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 collection and preprocessing, feature extraction and selection, data fusion and integration, multimodal data analysis and model evaluation and adjustment, which specifically include the following contents:

[0185] Data collection and preprocessing: First, it is necessary to collect multimodal data from various sensors or data sources, including image data, sound data, text data, etc., and then preprocess these data, including denoising, standardization, normalization and other operations to ensure data quality and reliability;

[0186] Feature extraction and selection: For different types of data, the raw data is converted into feature vectors with information content, including the use of local binary pattern (LBP) feature extraction technology, and the use of 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] Center pixel value; Surrounding pixel values; ; Deep learning method: Combine recurrent neural network (RNN) to extract features of time series data. The basic formula of RNN:

[0190] ;

[0191] Current hidden state; Weight matrix; Current input; Bias term;

[0192] Data fusion and integration: Fusion of multimodal data from different sensors or data sources, mainly achieved through decision-level fusion and other methods, integrating the 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] Final output result; :weight; : 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, association rule mining and other tasks. According to the specific application scenario and task requirements, select the analysis method, train and test the data to 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, and optimize the model according to the evaluation results to further improve its performance and effect. Model optimization uses the cross entropy loss function for optimization:

[0198] ;

[0199] loss; :True label; Prediction probability;

[0200] Through the above content, the integration and analysis of multimodal data can be realized. The multimodal data integration and analysis system can process, analyze and integrate the chemical, biological and bioinformatics data of R&D fabrics. 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 applications. 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 building and training, model evaluation and adjustment, and intelligent assisted decision-making module.

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

[0204] Feature extraction and selection: AI technology helps extract valuable features from large amounts of data. Through machine learning algorithms and deep learning models, feature representation can be automatically learned from raw data, or the most representative features can be selected through feature selection methods to improve the performance and generalization ability of the model.

[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 other relevant information, including the chemical structure of the fabric, it can provide a reference for decision-making, help 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 and give a prediction of fabric bioactivity based on a large amount of fabric comparison data and research experience data, combined with artificial intelligence technology. This provides strong decision-making support for experimental design and reduces blindness and uncertainty.

[0209] The automated experiment design and 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.

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

[0211] Input data: cloth feature vector : 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 : Indicates the expected biological indicators (including the inhibition rate of specific bacteria 90%, or reduction in allergic reaction score . Experimental factor selection: Experimental factor To control the process parameters, including: chemical treatment agent concentration , Processing temperature , processing time ( The key factors that affect the final bioactivity index of the fabric are as follows. Experimental design method: The preliminary experimental plan is generated using the response surface method (RSM) or other statistical designs (including orthogonal design, full factorial design or fractional factorial design). Taking the quadratic polynomial model as an example, the response surface model is expressed as:

[0212] ;

[0213] is the bioactivity index of the fabric (including antibacterial rate), which is determined by the bioactivity prediction model ( ) gives a preliminary prediction value; Representative Experimental factors (including treatment agent concentration and temperature); is the parameter to be fitted, obtained by regression of future experimental data; is the experimental error term, with a value between 0 and 1.

[0214] Experimental scheme optimization algorithm: The experimental design algorithm is further optimized using an optimization algorithm. The optimization algorithm selects one of the genetic algorithm, particle swarm optimization algorithm, and simulated annealing algorithm. According to the algorithm, the optimal experimental scheme can be found through iteration under given experimental conditions to maximize the biological activity 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 real measurement data (actual biological activity results) obtained in the previous round of experiments;

[0218] Updated model for predicting bioactivity of fabrics Parameters , 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 factors are combined ( ) is encoded as chromosomes and outputs values ​​through biological activity prediction models Assess its fitness and set the goal to maximize the bioactivity value , the fitness function is:

[0219] ;

[0220] The larger the value, the better the solution. The genetic algorithm iteratively searches for the optimal factor combination through selection, crossover, and mutation operations, and verifies the distribution after continuous updating, so that the algorithm can more accurately locate the optimal parameter area after each new data is added. By optimizing the algorithm, the system can continuously reduce invalid experiments, quickly approach the optimal process parameters, and achieve efficient iteration of experimental solutions.

[0221] Automated experimental platform: used for automatic fabric preparation, sample processing, experimental operation, 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; including robotic arms, automatic liquid handlers, microfluidics technology, multi-channel fabric handling units, temperature and humidity control systems and online monitoring sensors, using multi-channel liquid handling systems and microfluidics chips to automatically add specific concentrations of treatment agents according to the optimization plan, control systems to set fabric processing temperature and time parameters, and real-time sensors (optical detection equipment, colony counting devices, allergen sensors) to 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 the 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 loss function to adjust model parameters To update:

[0227] ;

[0228] For the The actual measured biological activity value of each experimental point (actual measured antibacterial rate); Predict values ​​for the model; continuously iterate and update Improve prediction accuracy and reliability, return the updated model parameters and experimental results to the experimental scheme optimization algorithm, and 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 scheme 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, remote monitoring and control functions are realized. 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 achieve 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 according to experimental objectives and fabric characteristics, and continuously adjust the experimental design through a feedback mechanism, which 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 development and analysis system:

[0234] Sp1: Intelligent fabric comparison: 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 the internal multimodal data fusion algorithm, 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 article, relational terms such as including first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "including a reference structure" do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0240] Although 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 the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A textile fabric research and development 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 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 comparison and processing system: It uses machine learning algorithms to identify fabric features and automatically adjusts processing parameters based on fabric features to automatically identify, process and distribute R&D fabrics. The fabric features include chemical properties, physical properties and image features of the fabric. 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 R&D fabrics; The formula of the multimodal data fusion algorithm is: ； The fused data vector; 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. For chemical data, :Biological data, : Informatics data; Integrated artificial intelligence decision-making system: Based on a large amount of fabric comparison data and research experience data in the system, combined with artificial intelligence technology, using 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 for auxiliary experiments; Automated experimental design adjustment system: Design the biological activity of fabrics through experimental design algorithms, and further adjust the given experimental design through experimental scheme optimization algorithms. Perform automated experiments through automated experimental platforms, and conduct synchronous data analysis and feedback. Automatically generate experimental schemes based on experimental objectives and fabric characteristics, and continuously adjust the experimental design through feedback mechanisms.

2. A textile fabric research and development 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 R&D fabrics; 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.

3. A textile fabric research and development 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. A textile fabric research and development analysis system according to claim 3, characterized in that: The cloth 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. A textile fabric research and development analysis system according to claim 3, characterized in that: The high-throughput fabric processing module includes microfluidics, a multi-channel liquid processing unit, a sample distribution algorithm, a mixing efficiency optimization algorithm, and a real-time control system; Sample allocation algorithm: realize fast and accurate allocation of samples. Allocation algorithms include random allocation, uniform allocation, priority allocation, etc. The formula can be expressed as: ； in, Assign samples, is the total sample size, is the number of channels; The mixing efficiency optimization algorithm is: 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 , and the target concentration for ideal uniform mixing is , mixing efficiency E: ； is the concentration value at the i-th point before mixing. When the actual concentration distribution is close to the ideal value, the mixing efficiency E approaches 1, indicating high mixing efficiency; When using a rotating 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 structure parameters or vortex generation frequency (v). The control parameter vector is ,but: ； is the sample viscosity (the viscosity of the fabric treatment fluid affects the shear and diffusion rates within the fluid); is the diffusion coefficient (the ability of a chemical component to diffuse in a solution); is the desired uniform concentration; Adjust according to the mixing efficiency and sample properties; the mixing efficiency optimization algorithm uses iterative optimization to To optimize, according to the mixed efficiency E relative to the target improvement space, Make adjustments, ； in is the learning rate or adjustment step size, The mixing efficiency E is The sensitivity direction of the optimization process is determined (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.

6. A textile fabric research and development analysis system according to claim 1, characterized in that: The multimodal data integration and analysis system processes data in five steps: data collection and preprocessing, feature extraction and selection, data fusion and integration, and multimodal data analysis and model evaluation and adjustment.

7. A textile fabric research and development 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 assisted decision-making module.

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

9. A textile fabric research and development analysis system according to any one of claims 1 to 8, characterized in that: The research and development analysis system comprises the following steps: Sp1: Intelligent fabric comparison: 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 the internal multimodal data fusion algorithm, 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.

Images