Automatic control method and system for intelligent textile fabric production line

Abstract

The invention provides an automatic control method and system for an intelligent textile fabric production line. Belongs to the technical field of automation control. The method comprises the following steps: acquiring multi-dimensional sensing data of a textile fabric production line and textile fabric production whole-process data; on the basis of the whole-process data, a system modeling and simulation technology is applied to deeply analyze internal logics and interrelations of all links in the textile fabric production process, and an accurate textile fabric production topological structure model is constructed; through real-time data acquisition and intelligent scheduling optimization, the equipment operation state and production parameters can be adjusted in time according to market requirements and actual production conditions, the equipment idle time and the production waiting time are shortened, and the overall operation efficiency of a production line is improved.

Description

Technical Field

[0001] The invention provides an intelligent automatic control method and system for a textile fabric production line, belonging to the technical field of automatic control. Background Art

[0002] In the traditional textile fabric production model, production line control relies heavily on manual experience and established procedures. Adjustments to production parameters often lag behind, making it difficult to respond quickly to real-time production conditions and changing market demands. For example, in the fabric printing and dyeing process, errors in color matching and dyeing time control often lead to unstable product quality due to manual judgment. In terms of equipment scheduling, the lack of scientific planning can easily lead to idle or overused equipment, affecting overall production efficiency. Moreover, traditional methods make it difficult to effectively integrate and analyze the massive amounts of data in the production process, and are unable to provide accurate support for production decisions. As the textile industry develops towards intelligence and personalization, existing production control methods can no longer meet market demand, and an innovative intelligent textile fabric production line automation control method is urgently needed. Summary of the Invention

[0003] The present invention provides an intelligent automated control method and system for a textile fabric production line to solve the problems mentioned in the above background technology:

[0004] The present invention proposes an intelligent automatic control method for a textile fabric production line, the method comprising:

[0005] S1: Acquire multi-dimensional sensor data from the textile fabric production line and data from the entire textile fabric production process. Based on this data, apply system modeling and simulation technology to deeply analyze the inherent logic and interrelationships of each link in the textile fabric production process and construct an accurate textile fabric production topology model.

[0006] S2: Based on a data fusion algorithm, the structural information in the textile fabric production topology model is deeply integrated with multi-dimensional sensor data to build an intelligent sensor network covering the entire production line. Based on the intelligent sensor network, big data analysis and machine learning algorithms are used to combine multi-source information to build a market demand forecast model and generate a textile fabric market demand forecast dataset.

[0007] S3: Use neural network algorithms to dynamically optimize the production process parameters in the textile fabric production topology model. During the optimization process, through continuous iterative calculations, the optimal combination of production process parameters is found to form a process optimization data set.

[0008] S4: Continuously collects real-time production line sensor data; compares and analyzes real-time data with process optimization datasets in real time, and uses quality inspection models to monitor and evaluate product quality in real time throughout the production process. Once a quality issue is discovered, the link and cause of the issue are located, and a quality inspection dataset is generated.

[0009] S5: Taking into account the product quality reflected by the quality inspection dataset and the market demand changes reflected by the textile fabric market demand forecast dataset, an intelligent scheduling algorithm is used to dynamically schedule and optimize the production line equipment in the textile fabric production topology model.

[0010] The present invention proposes an intelligent automatic control system for a textile fabric production line, comprising:

[0011] one or more processors;

[0012] a memory for storing one or more programs,

[0013] When the one or more programs are executed by the one or more processors, the one or more processors implement any one of the methods described above.

[0014] The beneficial effects of the present invention are as follows: through real-time data collection and intelligent scheduling optimization, the equipment operating status and production parameters can be adjusted in time according to market demand and actual production conditions, equipment idleness and production waiting time can be reduced, and the overall operating efficiency of the production line can be improved. Quality inspection based on real-time sensor data and process optimization data sets can timely discover quality problems in the production process and make adjustments, ensure the stability and consistency of textile fabric product quality, and improve the market competitiveness of products. Optimization of production process parameters and equipment scheduling is carried out in combination with market demand forecasts, so that textile fabric production can better adapt to market changes, quickly respond to market demand, reduce inventory risks, and improve the economic benefits of the enterprise. The entire control process relies on advanced data analysis algorithms and intelligent control platforms to achieve automated decision-making and execution of the production process, reduce manual intervention, reduce labor intensity, and improve the level of intelligent production. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 A diagram showing the steps of the method of the present invention;

[0016] Figure 2 The present invention Figure 1 Detailed step diagram of S4. DETAILED DESCRIPTION

[0017] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0018] One embodiment of the present invention, as Figure 1 As shown, the method includes:

[0019] S1: Acquire multi-dimensional sensor data from the textile fabric production line and data from the entire textile fabric production process. Based on this data, apply system modeling and simulation technology to deeply analyze the inherent logic and interrelationships of each link in the textile fabric production process and construct an accurate textile fabric production topology model.

[0020] S2: Based on a data fusion algorithm, the structural information in the textile fabric production topology model is deeply integrated with multi-dimensional sensor data to build an intelligent sensor network covering the entire production line. Based on the intelligent sensor network, big data analysis and machine learning algorithms are used to combine multi-source information to build a market demand forecast model and generate a textile fabric market demand forecast dataset.

[0021] S3: Use neural network algorithms to dynamically optimize the production process parameters in the textile fabric production topology model. During the optimization process, through continuous iterative calculations, the optimal combination of production process parameters is found to form a process optimization data set.

[0022] S4: Continuously collects real-time production line sensor data; compares and analyzes real-time data with process optimization datasets in real time, and uses quality inspection models to monitor and evaluate product quality in real time throughout the production process. Once a quality issue is discovered, the link and cause of the issue are located, and a quality inspection dataset is generated.

[0023] S5: Taking into account the product quality reflected by the quality inspection dataset and the market demand changes reflected by the textile fabric market demand forecast dataset, an intelligent scheduling algorithm is used to dynamically schedule and optimize the production line equipment in the textile fabric production topology model.

[0024] The working principle of the above technical solution is as follows: it obtains multi-dimensional sensor data from the textile fabric production line and full-process data on textile fabric production, covering factors such as raw material characteristics, production processes, and finished product specifications. Based on this full-process data, it uses system modeling and simulation technology to deeply analyze the internal logic and interrelationships of each link in the textile fabric production process, and constructs an accurate textile fabric production topology model. This model can intuitively present the entire production path from raw material input to finished product output, as well as the data flow and influence mechanism between each node. For example, in the fiber processing stage, sensors collect fiber moisture, length, fineness and other data; in the spinning stage, they record yarn twist, strength, and evenness; in the weaving process, they obtain fabric parameters such as warp and weft density and tension; and in the printing and dyeing stage, they monitor dye concentration, temperature, time, and fabric color fastness. At the same time, the whole process data such as raw material characteristic reports provided by raw material suppliers, standard operating procedures for production processes, and customer requirements for finished product specifications are collected to build a comprehensive and accurate textile fabric production topology model.

[0025] Based on the data fusion algorithm, the structural information in the textile fabric production topology model is deeply integrated with the multi-dimensional sensor data to build an intelligent sensor network covering the entire production line; this network can perceive the operating status and environmental changes of the production line in real time and accurately; based on the intelligent sensor network, big data analysis and machine learning algorithms are used, combined with historical sales data, market trends, seasonal factors and other multi-source information, to build a market demand forecast model and generate a textile fabric market demand forecast data set; this data set can accurately predict the market demand for textile fabrics of different specifications and styles in the future, and provide forward-looking guidance for production decisions; for example, by analyzing the sales data of textile fabrics in different seasons and regions in the past few years, combined with current fashion trends and changes in consumer preferences, it is possible to predict the demand for a certain functional sports fabric in the East China region in the next quarter, as well as the sales trend of specific patterned curtain fabrics in the northern market.

[0026] With the textile fabric market demand prediction dataset as the guide, the neural network algorithm is used to dynamically optimize the production process link parameters in the textile fabric production topology structure model. In the optimization process, full consideration is given to production efficiency, product quality, energy consumption and other target factors, and through continuous iterative calculation, the optimal production process parameter combination is found to form the process optimization dataset. The dataset is transmitted to the textile fabric production control platform, and the control platform automatically adjusts the running state of the production equipment according to these parameters to realize precise parameter adjustment of the production process and ensure that the production process is always in the best state. For example, according to market demand prediction, the demand for a high-count pure cotton fabric will increase significantly. Through intelligent optimization algorithm, the speed of spinning equipment, twist, and the density of warp and weft of weaving equipment are optimized and adjusted to improve production efficiency and reduce energy consumption while ensuring product quality.

[0027] Relying on the intelligent sensing network of the textile fabric production line, real-time production line sensing data is continuously collected; these real-time data are compared and analyzed with the process optimization dataset in real time, and quality detection models (such as appearance quality detection models based on image recognition and internal quality detection models based on sensor data) are used to monitor and evaluate the product quality in the production link in real time; once quality problems are found, the system can quickly locate the link and reason for the problem and generate a quality detection dataset; the dataset contains detailed information such as the type, location, and severity of the quality problem, providing a basis for subsequent quality improvement and production adjustment; for example, during the printing and dyeing process, the color of the fabric is monitored in real time by a color sensor, and when the color deviation exceeds the set range, the system immediately records the information of the location and analyzes the reasons such as dye ratio, dyeing time, or equipment failure, and the relevant information is included in the quality detection dataset; considering the product quality reflected by the quality detection dataset and the market demand changes embodied in the textile fabric market demand prediction dataset, intelligent scheduling algorithm is used to dynamically schedule and optimize the production line equipment in the textile fabric production topology structure model. In the optimization process, full consideration is given to factors such as the running state of the equipment, the maintenance cycle, and the production capacity, and the production tasks and operation sequence of the equipment are reasonably arranged to improve the utilization rate and production efficiency of the equipment. The optimized equipment scheduling scheme is arranged to form the equipment scheduling optimization dataset, which is transmitted to the textile fabric production control platform, and the control platform executes the equipment scheduling task according to the dataset to realize intelligent and automated scheduling of the production line equipment. For example, according to the quality detection dataset, it is found that a weaving equipment produces a high rate of defective products, and combined with the market demand prediction dataset, the production task of the equipment is adjusted to the production of products with relatively low quality requirements, and the production task of high-quality products is allocated to equipment with more stable performance to ensure that production orders are completed on time and with quality.

[0028] The benefits of this technical solution are: through intelligent sensor networks and data optimization algorithms, the production process can be dynamically adjusted based on real-time market demand and quality data. Intelligent scheduling and process optimization effectively reduce idle time in the production process, improve equipment utilization, and thus significantly enhance production efficiency. By optimizing equipment operating parameters (such as spinning equipment speed and weft and warp density of weaving equipment), energy consumption can be minimized while ensuring fabric quality. This not only reduces production costs but also has a positive impact on environmental protection.

[0029] Quality inspection models based on multidimensional sensor data and image recognition enable real-time monitoring of fabric quality, identifying and locating issues at specific stages. For example, real-time detection of fabric color changes during the printing and dyeing process ensures product color consistency, reducing rework and customer complaints caused by quality issues. Combined with market demand forecasting datasets, accurate predictions can be made of future market demand for textile fabrics of different specifications and styles, enabling adjustments to production strategies to ensure products remain relevant and enhance market adaptability.

[0030] By optimizing production processes, the system reduces unnecessary waste while maintaining high quality. Intelligent scheduling also increases equipment utilization, reducing labor and maintenance costs. The system responds in real time to quality issues or changes in market demand, allowing rapid adjustments to production strategies. Intelligent equipment scheduling rationally arranges production sequences based on specific production tasks, enhancing the flexibility and adaptability of the production line.

[0031] By accurately predicting market demand, we ensure that the fabrics we produce meet customer expectations for style, specifications, and quality. A precise quality monitoring system also ensures consistent and stable product quality, enhancing customer trust in the brand. The entire system combines data modeling, real-time monitoring, and machine learning algorithms to provide management with precise data support and decision-making foundations. Continuously iterating and optimizing production parameters ensures scientific and forward-looking decision-making.

[0032] In one embodiment of the present invention, the S1 includes:

[0033] S11. Arrange various types of sensors at each key link of the textile fabric production line to obtain multi-dimensional sensor data; collect data on the entire textile fabric production process;

[0034] S12. Use system modeling and simulation technology to analyze the inherent logic and interrelationships of each link in the textile fabric production process based on the collected full-process data; and construct a textile fabric production system model containing multiple subsystems;

[0035] S13. Use simulation software to simulate the model, observe the data flow and influence mechanism between subsystems, and analyze the impact of different parameter changes on the production process and product quality;

[0036] S14. Based on the results of system modeling and simulation analysis, a textile fabric production topology model is constructed; and corresponding attributes and parameters are assigned to each node and edge in the model.

[0037] The working principle of the above technical solution is as follows: various types of sensors are arranged in various key links of the textile fabric production line to obtain multi-dimensional sensor data; for example, temperature and humidity sensors are installed in the fiber preparation area to monitor the temperature and humidity of the fiber storage environment in real time to ensure that the fiber quality is not affected by the environment; yarn tension sensors and speed sensors are set in the spinning process to accurately grasp the tension changes and equipment speed during the yarn production process; warp and weft density detection sensors and fabric tension sensors are equipped in the weaving workshop to obtain the warp and weft density and tension of the fabric in a timely manner; temperature sensors, color detection sensors and pH sensors are installed in the printing and dyeing area to monitor the temperature, color and pH value in real time during the printing and dyeing process. Through these sensors, various types of data in the textile fabric production process are collected comprehensively and accurately. Data on the entire textile fabric production process covering factors such as raw material characteristics, production process, and finished product specifications are collected; a data sharing mechanism is established with raw material suppliers to obtain detailed raw material characteristic reports, including information such as fiber type, length, fineness, and strength; standard operating procedures for the production process are organized to clarify the process parameters and operating requirements of each process; and customer requirements for finished product specifications, such as fabric width, thickness, grammage, and color, are collected.

[0038] Using system modeling and simulation technology, based on the collected full-process data, we analyze the inherent logic and interrelationships of each link in the textile fabric production process; and build a textile fabric production system model that includes multiple subsystems such as raw material processing, spinning, weaving, printing and dyeing, and finishing;

[0039] By running the model using simulation software, we can observe the data flow and influence mechanism between subsystems and analyze the impact of different parameter changes on the production process and product quality. For example, we can simulate the impact of adjusting the speed of spinning equipment on yarn quality and production efficiency, providing a theoretical basis for optimizing the production process.

[0040] Based on the results of system modeling and simulation analysis, a topological structure model of textile fabric production was constructed; this model graphically and intuitively presents the entire production path from raw material input to finished product output, clearly showing the connection relationship and data flow between each production link; and assigning corresponding attributes and parameters to each node and edge in the model, such as equipment name, process parameters, material information, etc., so that the model can accurately reflect the characteristics and laws of the actual production process.

[0041] The effect of the above technical solution is that by arranging various sensors at key links of the production line, the real-time monitoring and accurate collection of various key data in the production process are ensured. The application of temperature and humidity sensors ensures the stability of the fiber storage environment, thereby reducing the quality fluctuations caused by environmental fluctuations.

[0042] By installing yarn tension sensors, speed sensors, fabric tension sensors, etc., accurate monitoring of each production link is achieved, ensuring the stability and consistency of textiles. Through the use of system modeling and simulation technology, the production process can be simulated and adjusted in real time, improving the intelligent level of the production process and reducing the need for manual intervention.

[0043] Simulation analysis helps enterprises identify potential problems and optimize solutions in advance, reducing the trial-and-error costs and time waste in traditional manual adjustment. By simulating and analyzing the impact of different process parameters on product quality and production efficiency, the process settings of each link are optimized, improving the overall production efficiency.

[0044] Through full-process data collection and analysis, the influence of each link on the quality of finished products is controllable, thereby improving the consistency of product quality. By precisely controlling the process parameters of each link, waste of raw materials and energy is effectively reduced, improving production efficiency and resource utilization.

[0045] By providing accurate finished product specifications and meeting customer demands, the market adaptability and customer satisfaction of products are improved, enhancing the market competitiveness of enterprises. By constructing a topological structure model, enterprises can more intuitively understand the relevance of each link, helping management make more scientific and efficient decisions.

[0046] One embodiment of the present application, the S12, comprises:

[0047] The collected full-process data is cleaned and preprocessed, and data mining and feature extraction algorithms are used to extract key features from massive production data;

[0048] Each production link in the textile fabric production process is analyzed to determine the input, output, and interaction between each link and other links, and a production process logic diagram is drawn;

[0049] Based on the results of the analysis of the logical relationship between the production links, the textile fabric production process is divided into multiple relatively independent subsystems, and the boundaries and functions of each subsystem are determined, and the main production process and key equipment within the subsystem are determined;

[0050] For each divided subsystem, a system modeling method is used to construct its internal model; combined with the characteristics of the subsystem and the production data, a modeling tool and algorithm are selected;

[0051] Analyze the coupling relationship between each subsystem, that is, how the output of one subsystem affects the input of other subsystems, and how this influence is transmitted and amplified throughout the production process; use system dynamics to establish a coupling relationship model between subsystems to describe the data flow and interaction mechanism between subsystems;

[0052] Integrate each subsystem model and its coupling relationship model to build a textile fabric production system model containing multiple subsystems.

[0053] The working principle of the above technical solution is: clean and preprocess the collected full-process data, use data mining and feature extraction algorithms to extract key features from massive production data, such as raw material characteristic parameters, fluctuation range of process parameters of each process, product quality indicators, etc. These key features will be important inputs for subsequent modeling, which can more accurately reflect the internal laws of the production process. For example, by extracting the features of the raw fiber length data, analyzing its distribution law, and providing a basis for subsequent optimization of the spinning process;

[0054] Analyze each production link in the textile fabric production process, clarify the input, output and interaction relationship with other links of each link, and draw a production process logic diagram; for example, analyze the influence of the fiber preparation link on the spinning link, determine how factors such as fiber pretreatment method and moisture content affect yarn quality and spinning efficiency; study the connection between the spinning link and the weaving link, and clarify the mechanism of yarn quality indicators (such as yarn strength and twist) on fabric warp and weft density and fabric quality;

[0055] Based on the results of the analysis of the logical relationship of the production link, the textile fabric production process is divided into multiple relatively independent subsystems, such as raw material processing subsystem, spinning subsystem, weaving subsystem, printing and dyeing subsystem, finishing subsystem, etc., and the boundaries and functions of each subsystem are clarified, and the main production process and key equipment within the subsystem are determined; for example, the raw material processing subsystem is mainly responsible for the pretreatment of fiber cleaning, opening, carding, etc., to provide qualified fiber raw materials for subsequent spinning; the spinning subsystem includes processes such as cleaning, carding, drawing, roving, and spinning to process fibers into yarns that meet the requirements;

[0056] For each divided subsystem, a system modeling method is used to build its internal model; combined with the characteristics of the subsystem and production data, modeling tools and algorithms are selected, such as physical model-based modeling method, data-driven modeling method, etc. In the process of building the model, full consideration is given to the process parameters, equipment performance, material characteristics and other factors inside the subsystem to ensure that the model can accurately reflect the actual operation of the subsystem. For example, when building the spinning subsystem model, the influence of spinning equipment speed, flyer speed, roller pressure and other process parameters on yarn quality and production efficiency is considered, and a corresponding mathematical model is established to describe the relationship between these parameters;

[0057] The coupling relationship between each subsystem is analyzed, that is, how the output of one subsystem affects the input of other subsystems, and how this influence is transmitted and amplified in the entire production process; system dynamics is used to establish a coupling relationship model between subsystems to describe the data flow and interaction mechanism between subsystems; for example, the influence of the output of the spinning subsystem on the weaving subsystem is studied, and a correlation model between yarn quality indicators and fabric warp and weft density, fabric defect rate and other quality indicators is established to optimize the weaving process by adjusting the spinning process parameters.

[0058] The subsystem models and their coupling relationship models are integrated to build a textile fabric production system model that includes raw material processing, spinning, weaving, printing and dyeing, finishing and other subsystems.

[0059] The above technical solutions have the following effects: through data cleaning and feature extraction algorithms, key features can be extracted from massive production data, improving the analysis ability of the production process and providing more accurate basis for subsequent optimization. By clarifying the relationship between each production link, the operation and coordination of each link are optimized, reducing the variables and uncertainties in the production process.

[0060] By analyzing the input-output relationship of the production links, identifying key factors, optimizing each process parameter, and improving the efficiency and quality of the production process. By drawing a production flow logic diagram, the various links and data flow in the production process are visually displayed, enhancing the visualization of the production process and facilitating timely adjustments by management personnel.

[0061] System modeling and simulation can predict and analyze the mutual influence between each link in advance, reducing the trial-and-error cost and resource waste in production and reducing production costs. By establishing subsystem models and coupling relationship models, each production link can be better controlled to ensure that the output of each link can accurately meet the expectations, thereby improving the stability of the entire production process.

[0062] By optimizing process parameters and equipment operation at each stage, unnecessary intervention and adjustments in the production process are reduced, thereby improving overall production efficiency. Integrating various subsystem models can help management make more scientific and accurate decisions during the production optimization process, further improving the company's decision-making efficiency.

[0063] Through precise subsystem modeling and interaction analysis, quality fluctuations between each link can be reduced, improving the quality stability of the finished product. Through the application of system modeling and simulation technology, enterprises can achieve continuous innovation in production process and equipment optimization, enhancing their market competitiveness and technological leadership.

[0064] In one embodiment of the present invention, the S2 includes:

[0065] S21. Based on a data fusion algorithm, deeply fuse the structural information in the textile fabric production topology model with multidimensional sensor data; preprocess the deeply fused data; and use the data fusion algorithm to correlate and integrate the preprocessed sensor data with the structural information in the model to build an intelligent sensor network covering the entire production line.

[0066] S22. Collect multi-source information, including historical sales data, market trends, and seasonal factors, and integrate the multi-source data with data collected by the intelligent sensor network;

[0067] S23. Based on the integrated data, use big data analysis and machine learning algorithms to build a market demand forecasting model; use the built market demand forecasting model to predict the market demand for textile fabrics of different specifications and styles in the future; generate a textile fabric market demand forecasting dataset based on the forecast results.

[0068] The working principle of the above technical solution is as follows: Based on the data fusion algorithm, the structural information in the textile fabric production topology model is deeply integrated with the multi-dimensional sensor data; the deeply integrated data is preprocessed; and the data fusion algorithm is used to associate and integrate the preprocessed sensor data with the structural information in the model to build an intelligent sensor network covering the entire production line. This network can accurately perceive the production line's operating status and environmental changes in real time, providing a comprehensive and accurate data foundation for subsequent market demand forecasts.

[0069] Collect multi-source information, including historical sales data, market trends, and seasonal factors, and integrate them with the data collected by the intelligent sensing network; for example, obtain the sales data of textile fabrics in the past few years from the enterprise's sales system, including the sales volume and sales of different specifications and styles of fabrics in different regions and time periods; pay attention to industry dynamics and market research reports to understand the development trend and emerging demand of the textile fabric market; consider the impact of seasonal factors on textile fabric sales, such as increased demand for lightweight and breathable fabrics in summer and increased demand for warm fabrics in winter. Uniformly encode and store these multi-source information to establish a comprehensive information database;

[0070] Based on the integrated data, use big data analysis and machine learning algorithms to build a market demand prediction model; use the built market demand prediction model to predict the market demand for different specifications and styles of textile fabrics in the future; generate a textile fabric market demand prediction dataset based on the prediction results, which contains information such as predicted time range, fabric specification, style, and predicted market demand; this dataset can provide forward-looking guidance for production decisions, helping enterprises to reasonably arrange production plans and inventory management, and reduce market risks.

[0071] The effect of the above technical solution is: through the deep integration of data fusion algorithm and multi-dimensional sensing data, the running state and environmental changes of the production line can be accurately perceived in real time, providing a more comprehensive and accurate data basis for subsequent market demand prediction. Through real-time data monitoring of the intelligent sensing network, changes on the production line can be better handled, reducing decision-making errors caused by information lag, and thus reducing the risk in production.

[0072] Integrating multi-source information (such as historical sales data, market trends, and seasonal factors) and combining big data analysis and machine learning algorithms can help improve the accuracy of market demand prediction and provide forward-looking guidance for enterprise production decisions. Based on the demand prediction dataset, enterprises can more accurately plan production and manage inventory, thereby avoiding overproduction or inventory accumulation and improving overall production and supply chain efficiency.

[0073] By incorporating seasonal factors and market trends into the prediction model, enterprises can identify changes in market demand trends in advance, reducing the negative impact of market demand fluctuations. By uniformly encoding and storing multi-source data, a comprehensive information database is established, enhancing the efficiency of information integration and storage and providing a reliable foundation for subsequent data analysis and decision-making.

[0074] The construction of intelligent sensor networks and the application of data fusion technology have increased the automation level of production lines, enabling intelligent monitoring and optimization of the production process and reducing the need for human intervention. Accurate market demand forecasts enable companies to rationally arrange production and inventory, reducing unnecessary production and inventory backlogs, and minimizing resource waste.

[0075] By optimizing production planning and inventory management, and adjusting production strategies based on market demand forecasts, companies can better respond to market fluctuations, thereby strengthening their advantage in a highly competitive market. Combining historical data, market trends, seasonal factors, and other information, and using big data and machine learning models to predict market demand, the application value of big data analysis technology in actual production is enhanced.

[0076] In one embodiment of the present invention, the step S21 includes:

[0077] Analyze the structural information in the topological structure model of textile fabric production, extract key structural features, and perform feature extraction on multi-dimensional sensor data. Different feature extraction methods are used for different types of sensor data.

[0078] Perform spatiotemporal alignment on the extracted features; in the temporal dimension, interpolate or resample the sensor data collected at different times; in the spatial dimension, spatially map and correlate the sensor data based on the geographical location of the equipment in the production topology model and the spatial layout of the production links;

[0079] Convert and standardize heterogeneous data; for structural information, convert it into a unified data structure; for sensor data, convert it into a standard numerical format or classification format according to the data type and characteristics;

[0080] Perform preliminary fusion of pre-processed structural information and sensor data; adjust the algorithm parameters and weights according to the characteristics of the data and the fusion objectives to obtain the best fusion effect;

[0081] Based on the initial fusion, deep learning is used to deeply integrate structural information and sensor data; a deep neural network model is constructed, which takes structural features and sensor features as input, and through nonlinear transformation and feature extraction of multiple layers of neurons, the deep-level connections and inherent laws between data are mined;

[0082] Based on deeply integrated and correlated data, an intelligent sensor network covering the entire production line is built; and the functions and data transmission rules of each node in the network are clarified.

[0083] The working principle of the above technical solution is: to analyze the structural information in the topological structure model of textile fabric production and extract key structural features, such as the topological order of production links, the connection weights between each link, the position and function of equipment in the production process, etc. These structural features can reflect the overall architecture and operation logic of the production system, and perform feature extraction on multi-dimensional sensor data. Different feature extraction methods are used for different types of sensor data; for example, for temperature and humidity sensor data, statistical features such as mean, variance, maximum, and minimum are extracted to reflect changes in ambient temperature and humidity; for yarn tension sensor data, features such as tension fluctuation frequency and peak value are extracted to analyze tension stability during yarn production. Through data feature analysis and extraction, more representative and discriminative feature vectors are provided for subsequent data fusion;

[0084] The extracted features are aligned in time and space. In the time dimension, the sensor data collected at different times are interpolated or resampled so that they have a unified timestamp to ensure the synchronization of the data in time. For example, if the sampling frequency of some sensors is low and that of other sensors is high, the low-frequency data is converted to the same time resolution as the high-frequency data through interpolation methods for comprehensive analysis. In the spatial dimension, the sensor data are spatially mapped and associated based on the geographical location of the equipment in the production topology model and the spatial layout of the production links. For example, the data of the warp and weft density detection sensors and fabric tension sensors distributed in different locations of the weaving workshop are aligned with the position of the weaving link in the production topology structure to clarify the specific production location and area to which the data corresponds.

[0085] Transform and standardize heterogeneous data. For structural information, convert it into a unified data structure, such as using a graph database to store production topology for efficient query and analysis. For sensor data, convert it into a standard numerical format or classification format based on the data type and characteristics. For example, convert the RGB value of the color detection sensor into a standard color code, and convert the data of the pH sensor into a numerical value that conforms to chemical measurement standards. At the same time, normalize the data to map data of different dimensions to the same numerical range, eliminate the impact of data dimensions on the fusion results, and improve the accuracy and stability of data fusion.

[0086] Perform a preliminary fusion of preprocessed structural information and sensor data. Based on the data characteristics and fusion objectives, adjust the algorithm parameters and weights to achieve the best fusion effect. For example, when using the weighted average method, assign different weights to the structural information and sensor data based on their importance and reliability, and then perform a weighted sum to obtain the fusion result. During the preliminary fusion process, evaluate the fusion performance of different algorithms through experiments and comparative analysis, and select the data fusion algorithm that best suits this textile fabric production scenario.

[0087] Based on the initial fusion, deep learning is used to deeply fuse structural information and sensor data. A deep neural network model is constructed, taking structural and sensor features as input. Through nonlinear transformation and feature extraction of multiple layers of neurons, the deep connections and inherent patterns between the data are explored. For example, a convolutional neural network (CNN) is used to fuse and analyze image data (such as images of equipment operating status) and sensor data in the production topology structure, automatically extracting key features and patterns in the data. Alternatively, a recurrent neural network (RNN) and its variants (such as LSTM and GRU) are used to process time series sensor data and structural information, capturing the dynamic changes and correlations of the data in the temporal dimension. Through deep fusion, more comprehensive, accurate, and intelligently perceived fused data is generated. Simultaneously, the fused data is correlated and integrated with other relevant information in the production topology model, such as equipment maintenance records and historical process parameter adjustment information, further improving the data foundation of the intelligent sensor network, enabling it to more comprehensively and deeply reflect the operating status of the production line and environmental changes.

[0088] Based on deeply integrated and correlated data, an intelligent sensor network covering the entire production line is constructed. The functions and data transmission rules of each node in the network are clearly defined to ensure efficient and accurate data flow within the network. For example, the integrated data is distributed to the corresponding production process nodes, and their status information is updated in real time. This information is then transmitted to relevant upstream and downstream nodes via the network, enabling real-time monitoring and coordinated control of the production process. Simultaneously, the intelligent sensor network is optimized, employing technologies such as data compression and encryption to reduce data transmission volume and ensure data security. Network topology optimization algorithms are employed to improve network reliability and stability, ensuring that the network continues to operate normally even when some nodes fail. By continuously optimizing the intelligent sensor network, it is able to accurately perceive the production line's operating status and environmental changes in real time, providing a comprehensive and accurate data foundation for subsequent market demand forecasts.

[0089] The effect of the above technical solution is: through deep learning and multi-dimensional data feature extraction, the scheme improves the fusion efficiency of data at each link, and enhances the comprehensive analysis capability of different types of sensor data. Through alignment processing, data standardization and conversion in the time and space dimensions, the processing differences between heterogeneous data types are reduced, and the subsequent data analysis and model training process is simplified. Using a unified data structure (such as a graph database to store structure information) and standardized processing, the computational difficulty caused by different data formats and scales can be effectively reduced.

[0090] By constructing an intelligent sensing network and optimizing the network topology, the scheme strengthens the real-time monitoring and collaborative control of each link in the production process. Especially when a fault occurs, the high reliability and stability of the network ensure the smooth flow of data, which helps to quickly respond to abnormal situations in production and ensures the stability of production efficiency and quality.

[0091] By real-time acquisition and fusion of various sensing data in the production process, abnormal changes in key parameters such as yarn tension, temperature and humidity can be identified in a timely manner, providing real-time feedback for adjustments in the production process. This timely adjustment can effectively avoid instability in the production process, improve production efficiency and ensure product quality. By extracting key structural features (such as the topological order of production links and the connection weights of each link), the overall architecture and operation logic of the production system can be more clearly understood. This is of great significance for optimizing production processes, improving the coordination of production lines, and improving equipment utilization.

[0092] By applying data compression and encryption techniques, the scheme not only ensures data security, but also effectively reduces data transmission volume. This can ensure efficient and secure transmission of a large amount of sensing data in the production process without affecting real-time performance. On the basis of deep fusion, the intelligent sensing network constructed can provide more accurate data basis for future market demand forecasting. By analyzing historical data and production status, combined with deep learning models, more rapid and accurate predictions of market demand changes can be made.

[0093] One embodiment of the present application, the S3, comprises:

[0094] S31, guided by the textile fabric market demand prediction data set, determine the target of production process link parameter optimization; according to the market demand prediction result, analyze the quality requirements and yield demand of different specifications and styles of textile fabrics;

[0095] S32, use neural network algorithm to dynamically optimize the production process link parameters in the textile fabric production topology structure model; the process parameters in the production topology structure model are used as the input variables of the neural network, and the optimization objective function is used as the output variable;

[0096] S33, through the self-learning and adaptive capabilities of the neural network, the values ​​of the input variables are continuously adjusted. During the optimization process, the optimal combination of production process parameters is found through continuous iterative calculations;

[0097] S34. Arrange the optimized production process parameters to form a process optimization data set; and record and classify the process optimization data set in detail;

[0098] S35. Transmit the process optimization data set to the textile fabric production control platform; the control platform automatically adjusts the operating status of the production equipment according to the parameters.

[0099] The working principle of the above technical solution is as follows: guided by the textile fabric market demand forecast data set, the production process parameter optimization target is determined; based on the market demand forecast results, the quality requirements and production requirements of textile fabrics of different specifications and styles are analyzed; for example, if the market demand forecast shows that the demand for a certain high-count pure cotton fabric will increase significantly, the optimization goal can be set to improve the production efficiency of the fabric, ensure its quality stability, and reduce energy consumption. Multiple optimization objectives are quantified and weighted to form a comprehensive optimization objective function;

[0100] The neural network algorithm is used to dynamically optimize the production process parameters in the textile fabric production topology model; the process parameters in the production topology model are used as the input variables of the neural network, and the optimization objective function is used as the output variable;

[0101] Through the self-learning and adaptive capabilities of the neural network, the values ​​of the input variables are continuously adjusted to achieve the optimal value of the output variables. During the optimization process, multiple target factors such as production efficiency, product quality, and energy consumption are fully considered, and the optimal combination of production process parameters is found through continuous iterative calculations. For example, the neural network algorithm optimizes the speed and twist of spinning equipment and the warp and weft density and tension of weaving equipment to achieve the optimization of the production process.

[0102] The optimized production process parameters are organized into a process optimization data set; this data set contains the optimal process parameters for each production link, such as the optimal speed and twist of spinning equipment, the optimal warp and weft density and tension of weaving equipment, and the optimal temperature and time of printing and dyeing equipment. The process optimization data set is recorded in detail and stored in a classified manner;

[0103] The process optimization dataset is transmitted to a textile fabric production control platform; the control platform automatically adjusts the operating state of the production equipment according to the parameters to achieve precise parameter adjustment of the production process. For example, the control platform automatically adjusts the motor speed of the spinning equipment and the twist adjustment device according to the parameters in the process optimization dataset, so that the production parameters of the yarn reach the optimal value; adjust the warp and weft density control device and tension adjustment device of the weaving equipment to ensure that the quality of the fabric meets the requirements. Through the precise parameter adjustment of the control platform, it ensures that the production process is always in the best state, improves the production efficiency and product quality;

[0104] The effect of the above technical solution is that by applying neural network algorithm to the optimization of production process parameters, the scheme realizes the automation and intelligentization of production process. Neural network can dynamically adjust production process parameters according to market demand prediction data, thereby improving the intelligent decision-making level of production, reducing manual intervention, and improving overall production efficiency.

[0105] By optimizing the quality requirements and yield demand of different specifications and styles of textile fabrics, especially in reducing energy consumption, the scheme can effectively reduce unnecessary energy consumption. For example, by optimizing the speed, twist and other parameters of the spinning equipment, energy waste is reduced while ensuring product quality, reducing energy consumption in the production process.

[0106] Through the self-learning and self-adaptive ability of neural network algorithm, the scheme can continuously adjust the production process parameters to achieve the optimal production efficiency and product quality. The optimized process parameters accurately adjust the operating state of the equipment, ensuring the optimal operating state of each link, thereby ensuring the high efficiency and high quality of textile fabric production.

[0107] By quantifying and weighting multiple optimization objectives, a comprehensive optimization objective function is generated, which not only optimizes the production process but also significantly improves production efficiency. For example, the optimal process parameters of spinning, weaving, printing and dyeing and other production links ensure the efficient operation of the equipment, thereby improving the overall efficiency of the production process. Through detailed recording and classified storage of the process optimization dataset, the scheme strengthens the standardization of data management. The optimization parameters of each link are systematically archived for subsequent analysis and reuse, which also helps to standardize the process and improve the transparency and traceability of production management.

[0108] By precisely adjusting production process parameters, the solution ensures consistent quality compliance at every stage of the production process. Each optimized parameter adjustment effectively avoids fluctuations in production, thereby improving product quality stability and ensuring that the quality of each batch of fabric meets market demand. The neural network's self-learning and adaptive capabilities enable the production process to flexibly respond to market demand fluctuations. As market demand changes, optimization targets and parameters are adjusted accordingly, allowing the production process to adapt promptly to changing demand, enhancing the production system's adaptability and flexibility.

[0109] By transmitting optimized production process parameters to the production control platform, the solution enables precise control of the production process. The control platform adjusts the equipment's operating status based on the optimized parameters, ensuring optimal operation at every stage of the production process and improving production controllability and stability. Automated production parameter adjustment reduces the need for human intervention and the risk of operational errors. Through precise parameter adjustment on the control platform, the production equipment's adjustment process becomes more accurate and reliable, reducing the risk of quality issues or reduced production efficiency due to improper operation.

[0110] One embodiment of the present invention, as Figure 2 As shown, the S4 includes:

[0111] S41, based on the intelligent sensor network of the textile fabric production line, continuously collects real-time production line sensor data; and transmits the data to the data acquisition system; the data acquisition system performs preliminary processing and storage on the collected data.

[0112] S42, performing real-time comparative analysis on the real-time data and the process optimization data set; calculating the deviation value between the real-time data and the process optimization data using a data comparison algorithm;

[0113] S43. Use the quality inspection model to monitor and evaluate the product quality in the production process in real time; if quality problems are found, the system will locate the links and causes of the problems and generate a quality inspection data set.

[0114] The working principle of the above technical solution is as follows: Based on the intelligent sensor network of the textile fabric production line, real-time production line sensor data is continuously collected; each sensor in the intelligent sensor network collects data in real time according to the set sampling frequency and transmits the data to the data acquisition system; the data acquisition system performs preliminary processing and storage on the collected data to ensure the integrity and accuracy of the data. For example, the yarn tension sensor collects real-time data on the tension changes of the yarn during the production process and transmits it to the data acquisition system; the fabric tension sensor monitors the fabric tension in real time and promptly provides feedback to the data acquisition system;

[0115] Perform real-time comparative analysis of real-time data with process optimization datasets. Using a data comparison algorithm, calculate the deviation between real-time data and process optimization data. If the deviation is within a preset range, the production process is considered normal. If the deviation exceeds a set threshold, it indicates a possible quality issue. For example, compare the yarn tension data collected in real time with the optimal tension value in the process optimization dataset. If the deviation exceeds a certain percentage, it indicates a possible problem in the yarn production process.

[0116] Quality inspection models are used to monitor and evaluate product quality in real time throughout the production process. These quality inspection models include appearance quality inspection models based on image recognition and intrinsic quality inspection models based on sensor data. The appearance quality inspection model based on image recognition uses a camera to capture images of the textile fabric's exterior and uses image processing techniques and pattern recognition algorithms to detect surface defects, stains, and color variations. The intrinsic quality inspection model based on sensor data uses real-time sensor data, such as yarn tension, fabric tension, and printing and dyeing temperature, to assess the fabric's intrinsic quality indicators, such as strength, elasticity, and color fastness. If a quality issue is detected, the system locates the issue's origin and cause and generates a quality inspection dataset. This dataset contains detailed information, including the type, location, and severity of the quality issue. For example, if image recognition detects a defect on the fabric surface, the system accurately records the defect's location, size, and shape, and analyzes possible causes, such as equipment failure, inappropriate process parameters, or raw material quality issues. The quality inspection dataset is fed back to the production management department and quality improvement team, providing a basis for subsequent quality improvements and production adjustments.

[0117] The benefits of this technical solution are: the application of neural network algorithms makes production process adjustments more automated and intelligent. The system can dynamically adjust process parameters based on real-time data, reducing manual intervention and improving intelligent decision-making in the production process. By precisely optimizing textile equipment operating parameters such as yarn tension, fabric tension, and equipment speed, the system can reduce unnecessary energy consumption, particularly without compromising product quality, thereby optimizing production efficiency.

[0118] The neural network's self-learning and adaptive capabilities enable precise adjustment of various process parameters in the production process based on real-time data. This ensures efficient production and improves product quality consistency. By optimizing parameters across multiple production links, the system ensures optimal equipment operation and improves production efficiency. In particular, comprehensive optimization of spinning, weaving, printing, and dyeing further enhances the efficiency of the entire production process.

[0119] The system records all process parameters and quality inspection data during the production process in detail, providing a reliable basis for subsequent analysis, improvement, and quality traceability. This not only improves the transparency of production management but also enhances the ability to monitor potential problems during the production process. By adjusting process parameters in real time, the system can consistently guarantee the quality of each batch of products throughout the production process, avoiding quality fluctuations and ensuring that the fabric quality meets market demand and customer expectations.

[0120] When market demand changes, production process parameters are adjusted accordingly, enhancing the system's adaptability. This enables the production process to quickly respond to market changes and flexibly address varying production needs. The automated production process reduces manual intervention, lowering the risk of quality issues and inefficiencies caused by human error. The system's precise control ensures the stability of the production process.

[0121] By integrating image recognition and sensor data into its quality inspection model, the system monitors and evaluates product quality in real time, promptly identifying and locating problems. This enables more precise quality control, helping to identify and resolve issues promptly and prevent defective products from entering the market. Leveraging an intelligent control platform, the system precisely adjusts equipment operating conditions based on optimized process parameters, ensuring optimal operation of every link, thereby improving the controllability and stability of the production process.

[0122] In one embodiment of the present invention, the step S41 includes:

[0123] S411, analyzing the temporal correlation and spatial correlation between the sensor data, and determining the sampling time sequence and frequency of different sensors based on the analysis results;

[0124] S412. Based on the set sampling strategy, each sensor collects data in real time. During the collection process, the sensor converts the sensed physical quantity into an electrical signal and the analog signal into a digital signal. The sensor transmits the digital signal to the data collection system through a combination of multiple communication technologies. During the data transmission process, the transmitted data is encrypted using data encryption technology.

[0125] S413: After receiving the data transmitted by the sensor, the data acquisition system performs preliminary processing;

[0126] S414. Store the preliminarily processed data in a data storage system; adopt a distributed storage architecture to disperse and store the data on multiple storage nodes, and classify and store the data according to the characteristics and usage frequency of the data.

[0127] The working principle of the above technical solution is: analyze the time correlation and spatial correlation between the data of each sensor, and determine the sampling time sequence and frequency of different sensors based on the analysis results. For example, in the spinning process, the data of the yarn tension sensor and the speed sensor are closely related. In order to accurately analyze the relationship between yarn tension and equipment speed, these two sensors are set to adopt synchronous sampling, that is, data is collected at the same time point; in the weaving workshop, the sampling frequencies of the warp and weft density detection sensor and the fabric tension sensor can be differentiated according to the production speed and quality control requirements of the fabric. The warp and weft density detection sensor can appropriately reduce the sampling frequency to reduce the amount of data processing, while the fabric tension sensor requires a higher sampling frequency to monitor the tension changes of the fabric in real time.

[0128] Based on the set sampling strategy, each sensor collects data in real time. During the collection process, the sensor converts the sensed physical quantity into an electrical signal and the analog signal into a digital signal. The sensor transmits the digital signal to the data acquisition system through a combination of multiple communication technologies. For example, for sensors that are close to each other and have large data volumes, Ethernet wired communication is used to ensure high-speed and stable data transmission. For sensors on some mobile devices or in areas where wiring is difficult, wireless communication technologies such as Wi-Fi, ZigBee, or industrial Internet of Things-specific wireless protocols are used to achieve real-time data transmission. In addition, data encryption technology is used to encrypt the transmitted data during the data transmission process.

[0129] After the data acquisition system receives the data transmitted by the sensor, it performs preliminary processing, including checking the integrity of the data to see if each data packet contains complete information, such as sensor number, timestamp, collected data, etc. If the data packet is found to be incomplete, a retransmission request is sent to the sensor in a timely manner to ensure that complete data is obtained. The data is then verified for validity, and the rationality of the collected data is determined based on the pre-set data range and logical rules. For example, for data collected by the temperature sensor, check whether it is within a reasonable temperature range; for yarn tension data, determine whether it conforms to the normal tension change pattern during the yarn production process. If abnormal data is found, it will be marked and processed, and data smoothing, interpolation and other methods can be used to correct the abnormal data, or the abnormal data can be stored separately for subsequent analysis and processing;

[0130] The preliminarily processed data is stored in a data storage system; a distributed storage architecture is adopted to store the data on multiple storage nodes, and the data is stored in categories according to its characteristics and frequency of use; for example, sensor data with higher real-time requirements are stored in a cache for quick access and analysis; historical data and backup data are stored in a disk array or cloud storage to ensure data security and long-term preservation. At the same time, a perfect data management system is established to index and label the stored data, facilitating subsequent data query and retrieval. A unique identifier is set for each sensor data to record the collection time, source, type and other information of the data, realizing fine-grained management of the data. Through the data management system, the required data can be quickly located and obtained, providing convenience for subsequent data analysis and processing.

[0131] The effect of the above technical solution is that by applying the neural network algorithm, the adjustment process of the production process is more intelligent and automated. The system can dynamically adjust the process parameters according to real-time data, reducing manual intervention, thereby improving the efficiency and accuracy of production decisions. Precise optimization of equipment operating parameters, especially yarn tension, fabric tension and equipment speed, can significantly reduce energy consumption without affecting product quality. This optimization effectively reduces unnecessary energy consumption and improves energy use efficiency.

[0132] The self-learning ability of the neural network enables real-time and accurate adjustment of production process parameters based on data, ensuring stable operation of the production process and improving the consistency of product quality. Comprehensive optimization of multiple production links improves the efficiency of the equipment, ensuring that each link in the production process is in the best state, thereby improving overall production efficiency.

[0133] The system enhances the transparency of production management by recording all production processes and quality data in detail, ensuring the reliability of quality traceability. This provides data support for subsequent analysis and improvement, improving the level of quality control. By adjusting the process parameters in real time, the system ensures the stability of the quality of each batch of products, avoiding quality fluctuations and ensuring the quality level required by market demand and customers.

[0134] When market demand changes, production process parameters can be quickly adjusted to adapt to new production requirements, making the production process more flexible and self-adapting. Automated production processes reduce human intervention, thereby reducing the risk of low production efficiency and quality problems caused by human operation errors.

[0135] By integrating image recognition and sensor data, the system monitors product quality in real time, identifying and locating problems promptly. This enables more precise quality control and helps resolve potential production issues promptly. The intelligent control platform precisely regulates equipment operating status, ensuring efficient and stable operation at every stage of the production process, thereby improving the controllability and stability of the entire production process.

[0136] In one embodiment of the present invention, the step S414 includes:

[0137] Build a distributed storage architecture and a distributed storage cluster, connecting multiple storage nodes through a network to form a unified storage resource pool; and configure a redundant backup mechanism for the distributed storage system;

[0138] Develop a data classification strategy based on the characteristics and usage frequency of different sensor data in the textile fabric production process; allocate the initially processed data to the corresponding storage location based on the developed data classification strategy;

[0139] Set a unique identifier for each sensor data, establish a comprehensive data indexing system, and create multi-level indexes based on the different attributes and characteristics of the data.

[0140] The working principle of the above technical solution is as follows: construct a distributed storage architecture and a distributed storage cluster, connecting multiple storage nodes through the network to form a unified storage resource pool; during the construction process, the storage nodes are reasonably configured and optimized to ensure load balancing between nodes and avoid single points of failure and performance bottlenecks. For example, based on the hot areas of data access, hot data is distributed on storage nodes with higher performance to improve data access speed, and a redundant backup mechanism is configured for the distributed storage system, such as using multiple copies or erasure coding technology, to ensure that data is still fully available when some storage nodes fail, thereby improving data reliability and availability;

[0141] A data classification strategy is developed based on the characteristics and frequency of use of different sensor data in the textile fabric production process. For example, based on data real-time requirements, sensor data with extremely high real-time requirements, such as yarn tension and fabric tension, which require real-time monitoring and feedback during the production process, is classified into one category. Data with relatively low real-time requirements but requiring long-term preservation for historical analysis and quality traceability, such as production equipment operation logs and historical ambient temperature and humidity data, is classified into another category. Based on the data's business relevance, sensor data closely related to the same production process or process is grouped together. For example, sensor data related to yarn quality testing in the spinning process, including yarn thickness and evenness, is grouped together. This classification strategy can more accurately meet the storage, access, and management requirements of different data types. Preliminary processed data is allocated to appropriate storage locations based on the established data classification strategy. Data with extremely high real-time requirements is stored in a cache, which can utilize an in-memory database or a high-performance solid-state drive (SSD) as storage media to enable fast data read and write and real-time access. For example, during the weaving process, the fabric tension data obtained in real time needs to be immediately fed back to the control system for adjustment. Storing this type of data in a high-speed cache can ensure that the control system can obtain the latest data in a very short time, thereby ensuring production stability and product quality. For data that has relatively low real-time requirements but needs to be stored for a long time, it is stored in a disk array or cloud storage. Disk arrays have large storage capacity and relatively high cost-effectiveness, making them suitable for storing large amounts of historical data; cloud storage provides flexible scalability and convenient remote access capabilities, making it easier for different departments and branches of the enterprise to share and utilize data. For example, by storing the operation log data of production equipment in the past few years in cloud storage, enterprise managers can query and analyze the operating status of the equipment anytime and anywhere through the network, providing a decision-making basis for equipment maintenance and upgrades;

[0142] Set a unique identifier for each sensor data, which can contain key information such as sensor number, collection time, data type, etc. to ensure the uniqueness and identifiability of the data. For example, for the data collected by a yarn tension sensor at a specific time, its identifier can be designed as "sensor ID_collection time_yarn tension". Through this identification method, the specific data can be located quickly and accurately. And establish a complete data indexing system to create multi-level indexes according to the different attributes and characteristics of the data. Indexes can be created according to dimensions such as sensor type, collection time range, and production link to facilitate subsequent data query and retrieval. For example, when it is necessary to query all sensor data of a specific production link within a certain time period, the indexing system can quickly filter out the data that meets the conditions, greatly improving the efficiency of data query;

[0143] The benefits of this technical solution are: by building a distributed storage architecture and redundant backup mechanisms, data availability is ensured even when some storage nodes fail, improving the system's fault tolerance and data reliability. By rationally configuring and optimizing storage nodes, hotspot data is stored on high-performance nodes, speeding up data access and optimizing system performance, ensuring rapid feedback for data with extremely high real-time requirements.

[0144] Through load balancing and distributed storage cluster design, single points of failure and performance bottlenecks are avoided, enhancing system stability and reliability. Data classification strategies are implemented to appropriately store data based on real-time requirements and business relevance, ensuring that the storage, access, and management needs of different data types are accurately met.

[0145] Cloud storage provides flexible scalability and convenient remote access for data that requires less real-time storage but is stored over a long period of time, enabling convenient data sharing and utilization across departments and branches within the enterprise. By establishing a multi-level indexing system that indexes data based on sensor type, acquisition time, and other dimensions, the efficiency of data query and retrieval is improved, reducing the time required to find specific data.

[0146] Storing real-time data in high-speed cache enables timely access and feedback of critical data during the production process, ensuring efficient production and stable product quality. By rationally selecting storage media, such as combining SSDs with disk arrays, storage device configuration is optimized, reducing the cost of large-scale data storage and improving the overall storage system's cost-effectiveness.

[0147] Each sensor data is assigned a unique identifier, and a data indexing system is established to ensure data identifiability and efficient location, enhance data traceability, and improve the accuracy of data management. Through a comprehensive storage architecture and data indexing, the system can clearly present the storage status and access paths of all data, ensuring transparency in data management and efficient control.

[0148] In one embodiment of the present invention, the S5 includes:

[0149] S51. Determine factors for optimizing production line equipment scheduling based on the product quality status reflected by the comprehensive quality inspection dataset and the market demand changes reflected by the textile fabric market demand forecast dataset. Obtain the product quality status of each piece of equipment based on the quality inspection dataset.

[0150] S52. Use an intelligent scheduling algorithm to dynamically optimize the scheduling of production line equipment in the textile fabric production topology model; organize the optimized equipment scheduling plan into an equipment scheduling optimization data set;

[0151] S53. Transmitting the equipment scheduling optimization data set to the textile fabric production control platform; the control platform executes the equipment scheduling task according to the data set.

[0152] The working principle of the above technical solution is as follows: the product quality status reflected by the comprehensive quality inspection data set and the market demand changes reflected by the textile fabric market demand forecast data set are used to determine the factors for optimizing production line equipment scheduling; based on the quality inspection data set, the product quality status of each piece of equipment is obtained, and equipment with more quality problems is focused on; based on the market demand forecast data set, the market demand trends of textile fabrics of different specifications and styles are grasped, and the production tasks of the equipment are reasonably arranged. At the same time, factors such as the equipment's operating status, maintenance cycle, and production capacity are fully considered to ensure the scientific and reasonable nature of equipment scheduling;

[0153] Intelligent scheduling algorithms are used to dynamically schedule and optimize the production line equipment in the textile fabric production topology model. Intelligent scheduling algorithms can adopt scheduling algorithms based on multi-objective optimization, scheduling algorithms based on reinforcement learning, etc. Scheduling algorithms based on multi-objective optimization comprehensively consider multiple target factors such as equipment utilization, production efficiency, and product quality. By establishing a multi-objective optimization model, the optimization algorithm is used to solve the optimal equipment scheduling plan. Scheduling algorithms based on reinforcement learning achieve adaptive scheduling of equipment by allowing the intelligent agent to continuously learn and optimize scheduling strategies in the interaction with the environment. For example, a scheduling algorithm based on reinforcement learning is used to allow the intelligent agent to continuously adjust the production tasks and operating sequence of the equipment according to information such as equipment status, production tasks, and market demand to improve equipment utilization and production efficiency. The optimized equipment scheduling plan is organized into an equipment scheduling optimization data set. The data set contains information such as the equipment name, production task, operating sequence, start time, and end time. The equipment scheduling optimization data set is recorded and stored in detail for subsequent execution and monitoring.

[0154] The equipment scheduling optimization dataset is transmitted to the textile fabric production control platform; the control platform executes equipment scheduling tasks according to this dataset, controlling parameters such as equipment start, stop, and operating speed to achieve intelligent and automated scheduling of production line equipment. For example, based on the instructions in the equipment scheduling optimization dataset, the control platform assigns production tasks to the corresponding equipment and adjusts the equipment's operating sequence and timing to ensure efficient and orderly production. Simultaneously, the platform monitors the equipment's operating status in real time, promptly identifying and resolving any issues that arise during the scheduling process, ensuring smooth implementation of equipment scheduling.

[0155] The effect of the above technical solution is that: through the intelligent scheduling algorithm, combined with quality detection data and market demand prediction data, the scheduling of production line equipment is optimized, ensuring that the equipment runs on time and efficiently, improving production efficiency. Through dynamic scheduling and real-time monitoring of the running state of the equipment, potential equipment failures are discovered in time, and the running state and maintenance cycle of the equipment are optimized for optimal scheduling, reducing downtime caused by equipment failure.

[0156] Through the intelligent scheduling algorithm, the production tasks and operation sequence of the equipment are reasonably arranged to avoid idle or excessive load operation of the equipment, maximizing the utilization rate of the equipment and improving the production capacity. According to the quality detection data set, the equipment with less production quality problems is preferentially scheduled, thereby effectively reducing the product defect rate and improving the overall product quality.

[0157] Through intelligent scheduling, equipment resources are reasonably allocated, unnecessary energy consumption and equipment idling are reduced, resource waste in the production process is reduced, thereby reducing the overall production cost. According to the market demand prediction data, the priority of the production task and the equipment scheduling are adjusted to ensure quick response to changes in market demand, improving the adaptability of the production line to market changes.

[0158] Combining the multi-objective optimization model and the reinforcement learning algorithm makes the equipment scheduling more scientific and reasonable, which can comprehensively consider multiple factors such as production efficiency, equipment maintenance, market demand, etc., and optimizes the overall production scheduling scheme. Through real-time monitoring and recording of equipment scheduling data, every link in the production process can be tracked and managed, improving the transparency and controllability of the production process, ensuring that the production task is executed according to the plan.

[0159] Through the reinforcement learning-based scheduling algorithm, the system can dynamically adjust the scheduling of the production line equipment according to the actual production situation and changes in market demand, improving the flexibility and adaptability of the production line. Through dynamic scheduling optimization and real-time monitoring mechanism, the stability of the equipment operation in the production process is ensured, reducing the production interruption or equipment failure caused by improper scheduling, thereby improving the overall reliability of the production system.

[0160] One embodiment of the present application, the S52, comprises:

[0161] Through various sensors deployed on the textile fabric production line, multi-dimensional data related to equipment scheduling is collected in real time; and the collected multi-dimensional data is preprocessed;

[0162] The key parameters in the intelligent scheduling algorithm are initialized and set; the preprocessed production environment data is input into the preset intelligent scheduling algorithm, and the algorithm starts running according to the initialized parameters to generate a preliminary equipment scheduling scheme;

[0163] Evaluate the initially generated equipment scheduling plan based on the evaluation index system; optimize the initial scheduling plan based on the evaluation results;

[0164] The final equipment scheduling plan that has been evaluated and optimized is organized into an equipment scheduling optimization data set; and a structured database is used to store the equipment scheduling optimization data set.

[0165] The working principle of the above technical solution is: through the various sensors deployed on the textile fabric production line, multi-dimensional data related to equipment scheduling is collected in real time; including but not limited to equipment operating status data, such as equipment speed, temperature, pressure, vibration frequency, etc., and the collected multi-dimensional data is pre-processed;

[0166] Initialize key parameters in the intelligent scheduling algorithm. Preprocessed production environment data is fed into the pre-set intelligent scheduling algorithm, which then runs according to the initialized parameters, generating a preliminary equipment scheduling plan. During the algorithm's execution, the agent (for reinforcement learning algorithms) or individuals in the population (for genetic algorithms) continuously tries different equipment scheduling strategies based on current state information and algorithmic rules. These strategies are evaluated and improved through interaction with the environment (for reinforcement learning) or evolutionary operations (for genetic algorithms). For example, in reinforcement learning-based algorithms, the agent selects an action based on the current equipment status and production task requirements, such as assigning an idle piece of equipment to a rush order. After executing the action, the environment provides a reward signal, which the agent uses to update its policy network parameters and adjust the probability of future action selections. After repeated interactions and learning, the agent gradually generates a preliminary equipment scheduling plan. In genetic algorithms, the initial population evolves through operations such as selection, crossover, and mutation, generating new generations of individuals, each representing a new equipment scheduling plan. After a certain number of iterative evolutions, the individual with the highest fitness is selected as the preliminary equipment scheduling plan;

[0167] Based on the evaluation index system, the preliminary generated equipment scheduling plan is evaluated; the evaluation indicators include equipment utilization rate, that is, the ratio of the actual working time of the equipment in unit time to the total available time, reflecting the utilization efficiency of the equipment; production efficiency, which takes the number of qualified textile fabrics produced in unit time as an indicator to measure the speed and output of the production process; product quality pass rate, which calculates the ratio of the number of qualified products to the total number of products based on quality inspection data, reflecting the production process's ability to control product quality; energy consumption, which counts the power consumption and gas consumption of the equipment in the production process to evaluate the energy utilization efficiency of the production process; and production cost, which covers raw material cost, equipment maintenance cost, labor cost, etc., reflecting the economy of the production plan; according to the evaluation results, the preliminary scheduling is evaluated. The plan is optimized. If equipment utilization is low, analyze whether there is idle equipment or uneven load, and improve equipment utilization by adjusting the equipment's production task allocation or operating sequence. If production efficiency does not meet expectations, check for bottlenecks in the production process and optimize the production process or increase corresponding resource inputs. If the product quality pass rate is unsatisfactory, identify factors affecting product quality, such as equipment parameter settings and raw material quality, and adjust and improve relevant factors. If energy consumption is too high, analyze the main energy consumption links and implement energy-saving measures, such as optimizing equipment operating parameters and adopting energy-saving equipment. If production costs exceed the budget, find ways to reduce costs, such as optimizing raw material procurement strategies and reducing equipment maintenance expenses. Through multiple evaluations and optimizations, the equipment scheduling plan is optimized in all evaluation indicators or meets production requirements.

[0168] The final equipment scheduling plan that has been evaluated and optimized will be organized into an equipment scheduling optimization data set; this data set should contain detailed and accurate information, including the name and model of the equipment, specific requirements of the production task (such as textile fabric specifications, styles, order quantity, delivery period, etc.), the equipment's operating sequence and time schedule, key parameter settings during equipment operation (such as speed, temperature, pressure, etc.), as well as possible risk points and countermeasures, etc.; and a structured database will be used to store the equipment scheduling optimization data set, and the data will be classified and stored according to dimensions such as production date, production batch, and equipment type to facilitate subsequent query, analysis, and execution.

[0169] The benefits of this technical solution include: by collecting multi-dimensional data related to equipment scheduling in real time and incorporating intelligent scheduling algorithms, we optimize equipment scheduling, ensuring efficient equipment operation and reducing equipment idle time and production delays. By monitoring and analyzing equipment utilization in real time, we can effectively adjust the allocation and operation sequence of equipment production tasks, thereby reducing equipment idleness and load imbalances and improving production efficiency.

[0170] Dynamic scheduling strategies based on reinforcement learning or genetic algorithms enable production systems to flexibly adjust equipment operations based on real-time feedback and changes in production tasks, improving the production line's ability to respond to emergencies. By continuously optimizing production processes and resource allocation, production bottlenecks are eliminated, production efficiency is improved, and the number of qualified textile fabrics produced per unit time is increased.

[0171] By analyzing key energy consumption areas and implementing energy-saving measures, such as optimizing equipment operating parameters and adopting energy-saving equipment, energy waste is reduced, thereby lowering energy consumption during the production process. Intelligent scheduling algorithms can assess equipment status and make adjustments in real time, reducing equipment failures or downtime, ensuring stable equipment operation during production, and improving production line reliability.

[0172] By optimizing equipment operating parameters, adjusting production processes, and adjusting equipment scheduling strategies based on quality inspection data, we ensured the stability and consistency of product quality and improved the product quality pass rate. By optimizing raw material procurement strategies, reducing equipment maintenance costs, and improving equipment utilization, we reduced production costs and improved production economics.

[0173] By building a structured database that records key information for each equipment dispatch, the scheduling process becomes more transparent and controllable, facilitating subsequent data analysis, query, and execution. Through multiple optimizations based on evaluation metrics, the intelligent scheduling algorithm can scientifically and rationally adjust production tasks, ensuring efficient execution of production plans and avoiding unnecessary production fluctuations.

[0174] One embodiment of the present invention is an intelligent automated control system for a textile fabric production line, comprising:

[0175] one or more processors;

[0176] a memory for storing one or more programs,

[0177] When the one or more programs are executed by the one or more processors, the one or more processors implement any one of the methods described above.

[0178] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. An intelligent automatic control method for a textile fabric production line, characterized in that: The method comprises: S1: Acquire multi-dimensional sensor data from the textile fabric production line and data from the entire textile fabric production process. Based on this data, apply system modeling and simulation technology to deeply analyze the inherent logic and interrelationships of each link in the textile fabric production process and construct an accurate textile fabric production topology model. S2: Based on a data fusion algorithm, the structural information in the textile fabric production topology model is deeply integrated with multi-dimensional sensor data to build an intelligent sensor network covering the entire production line. Based on the intelligent sensor network, big data analysis and machine learning algorithms are used to combine multi-source information to build a market demand forecast model and generate a textile fabric market demand forecast dataset. S3: Use neural network algorithms to dynamically optimize the production process parameters in the textile fabric production topology model. During the optimization process, through continuous iterative calculations, the optimal combination of production process parameters is found to form a process optimization data set. S4: Continuously collects real-time production line sensor data; compares and analyzes real-time data with process optimization datasets in real time, and uses quality inspection models to monitor and evaluate product quality in real time throughout the production process. Once a quality issue is discovered, the link and cause of the issue are located, and a quality inspection dataset is generated. S5: Taking into account the product quality reflected by the quality inspection dataset and the market demand changes reflected by the textile fabric market demand forecast dataset, an intelligent scheduling algorithm is used to dynamically schedule and optimize the production line equipment in the textile fabric production topology model.

2. The intelligent automatic control method for textile fabric production line according to claim 1, characterized in that: Said S1 comprises: S11. Arrange various types of sensors at each key link of the textile fabric production line to obtain multi-dimensional sensor data; collect data on the entire textile fabric production process; S12. Use system modeling and simulation technology to analyze the inherent logic and interrelationships of each link in the textile fabric production process based on the collected full-process data; and construct a textile fabric production system model containing multiple subsystems; S13. Use simulation software to simulate the model, observe the data flow and influence mechanism between subsystems, and analyze the impact of different parameter changes on the production process and product quality; S14. Based on the results of system modeling and simulation analysis, a textile fabric production topology model is constructed; and corresponding attributes and parameters are assigned to each node and edge in the model.

3. The intelligent automatic control method for textile fabric production line according to claim 1, characterized in that: Said S2 comprises: S21. Based on a data fusion algorithm, deeply fuse the structural information in the textile fabric production topology model with multidimensional sensor data; preprocess the deeply fused data; and use the data fusion algorithm to correlate and integrate the preprocessed sensor data with the structural information in the model to build an intelligent sensor network covering the entire production line. S22. Collect multi-source information, including historical sales data, market trends, and seasonal factors, and integrate the multi-source data with data collected by the intelligent sensor network; S23. Based on the integrated data, use big data analysis and machine learning algorithms to build a market demand forecasting model; use the built market demand forecasting model to predict the market demand for textile fabrics of different specifications and styles in the future; generate a textile fabric market demand forecasting dataset based on the forecast results.

4. The intelligent automatic control method for textile fabric production line according to claim 1, characterized in that: The S3 includes: S31. Guided by the textile fabric market demand forecast dataset, determine the target for optimizing production process parameters; analyze the quality requirements and production demands for textile fabrics of different specifications and styles based on the market demand forecast results; S32. Dynamically optimize the production process parameters in the textile fabric production topology model using a neural network algorithm; use the process parameters in the production topology model as input variables of the neural network and use the optimization objective function as the output variable; S33, through the self-learning and adaptive capabilities of the neural network, the values ​​of the input variables are continuously adjusted. During the optimization process, the optimal combination of production process parameters is found through continuous iterative calculations; S34. Arrange the optimized production process parameters to form a process optimization data set; and record and classify the process optimization data set in detail; S35. Transmit the process optimization data set to the textile fabric production control platform; the control platform automatically adjusts the operating status of the production equipment according to the parameters.

5. The intelligent automatic control method for textile fabric production line according to claim 1, characterized in that: Said S4 comprises: S41. An intelligent sensor network based on a textile fabric production line continuously collects real-time production line sensor data and transmits the data to a data acquisition system; the data acquisition system performs preliminary processing and storage on the collected data. S42, performing real-time comparative analysis on the real-time data and the process optimization data set; calculating the deviation value between the real-time data and the process optimization data using a data comparison algorithm; S43. Use the quality inspection model to monitor and evaluate the product quality in the production process in real time; if quality problems are found, the system will locate the links and causes of the problems and generate a quality inspection data set.

6. The intelligent automatic control method for textile fabric production line according to claim 5, characterized in that: The S41 includes: S411, analyzing the temporal correlation and spatial correlation between the sensor data, and determining the sampling time sequence and frequency of different sensors based on the analysis results; S412. Based on the set sampling strategy, each sensor collects data in real time. During the collection process, the sensor converts the sensed physical quantity into an electrical signal and the analog signal into a digital signal. The sensor transmits the digital signal to the data collection system through a combination of multiple communication technologies. During the data transmission process, the transmitted data is encrypted using data encryption technology. S413: After receiving the data transmitted by the sensor, the data acquisition system performs preliminary processing; S414. Store the preliminarily processed data in a data storage system; adopt a distributed storage architecture to disperse and store the data on multiple storage nodes, and classify and store the data according to the characteristics and usage frequency of the data.

7. The intelligent automatic control method for textile fabric production line according to claim 6, characterized in that: The S414 includes: Build a distributed storage architecture and a distributed storage cluster, connecting multiple storage nodes through a network to form a unified storage resource pool; and configure a redundant backup mechanism for the distributed storage system; Develop a data classification strategy based on the characteristics and usage frequency of different sensor data in the textile fabric production process; allocate the initially processed data to the corresponding storage location based on the developed data classification strategy; Set a unique identifier for each sensor data, establish a comprehensive data indexing system, and create multi-level indexes based on the different attributes and characteristics of the data.

8. The intelligent automatic control method for textile fabric production line according to claim 1, characterized in that: Said S5 comprises: S51. Determine factors for optimizing production line equipment scheduling based on the product quality status reflected by the comprehensive quality inspection dataset and the market demand changes reflected by the textile fabric market demand forecast dataset. Obtain the product quality status of each piece of equipment based on the quality inspection dataset. S52. Use an intelligent scheduling algorithm to dynamically optimize the scheduling of production line equipment in the textile fabric production topology model; organize the optimized equipment scheduling plan into an equipment scheduling optimization data set; S53. Transmitting the equipment scheduling optimization data set to the textile fabric production control platform; the control platform executes the equipment scheduling task according to the data set.

9. The intelligent automatic control method for textile fabric production line according to claim 8, characterized in that: The S52 includes: Through the various sensors deployed on the textile fabric production line, multi-dimensional data related to equipment scheduling is collected in real time; and the collected multi-dimensional data is pre-processed; Initialize the key parameters of the intelligent scheduling algorithm; input the pre-processed production environment data into the preset intelligent scheduling algorithm, and the algorithm starts running according to the initialized parameters to generate a preliminary equipment scheduling plan; Evaluate the initially generated equipment scheduling plan based on the evaluation index system; optimize the initial scheduling plan based on the evaluation results; The final equipment scheduling plan that has been evaluated and optimized is organized into an equipment scheduling optimization data set; and a structured database is used to store the equipment scheduling optimization data set.

10. An intelligent automated control system for a textile fabric production line, comprising: one or more processors; a memory for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors are enabled to implement the method according to any one of claims 1 to 9.

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