Efficient intelligent short-process water-saving printing and dyeing control method and system

Abstract

The invention provides an efficient intelligent short-process water-saving printing and dyeing control method and system. The method belongs to the technical field of textile printing and dyeing, and comprises the steps: carrying out the real-time collection of key parameters in the printing and dyeing process through a high-precision sensor, and transmitting the data collected by the sensor to a cloud data center in real time through the Internet of Things; mining historical printing and dyeing data through big data and a machine learning algorithm, and establishing a printing and dyeing process model; based on real-time sensor data and the printing and dyeing process model, the printing and dyeing effect is predicted in real time, and printing and dyeing parameters are automatically adjusted according to the prediction result; key parameters are collected in real time through a high-precision sensor, historical data are mined in combination with big data and a machine learning algorithm, and an accurate printing and dyeing process model is established, so that the printing and dyeing effect can be predicted in real time, the printing and dyeing parameters are automatically adjusted according to the prediction result, and the printing and dyeing efficiency and quality are improved.

Description

Technical Field

[0001] The invention proposes a high-efficiency, intelligent, short-process, water-saving printing and dyeing control method and system, belonging to the technical field of textile printing and dyeing. Background Art

[0002] Although there are many water-saving printing and dyeing processes in the existing technology, such as the use of integrated dyeing equipment and the optimization of auxiliary agent formula, most of these methods focus on the improvement of a single link and lack overall intelligent regulation. In addition, the problems of water waste, high energy consumption and environmental pollution in the traditional printing and dyeing process are still serious, and an innovative method that can comprehensively optimize the printing and dyeing process and achieve efficient water saving is urgently needed. Summary of the invention

[0003] The present invention provides a highly efficient, intelligent, short-process, water-saving printing and dyeing control method and system to solve the problems mentioned in the above background technology: The present invention proposes a highly efficient, intelligent, short-process, water-saving printing and dyeing control method, the method comprising: S1. Real-time collection of key parameters in the printing and dyeing process through high-precision sensors, and real-time transmission of the data collected by the sensors to the cloud data center through the Internet of Things; S2. Use big data and machine learning algorithms to mine historical printing and dyeing data and establish a printing and dyeing process model; based on real-time sensor data and the printing and dyeing process model, make real-time predictions on the printing and dyeing effects, and automatically adjust the printing and dyeing parameters according to the prediction results; S3. According to the results of intelligent analysis, the operating parameters of the printing and dyeing equipment are adjusted in real time through the automatic control system, and based on the adaptive control strategy, the control parameters are dynamically adjusted according to the actual changes of the fabric during the printing and dyeing process; S4. Adopt intelligent water-saving system to automatically adjust the amount of flushing water according to the type of fabric and the printing and dyeing stage, and introduce wastewater recycling and reuse technology to treat wastewater through physical, chemical or biological methods; S5. Based on the intelligent monitoring system, the operating status and energy consumption of printing and dyeing equipment are monitored in real time to detect and warn of potential faults.

[0004] The present invention proposes a highly efficient, intelligent, short-process, water-saving printing and dyeing control system, the system comprising: Data acquisition module: collects key parameters of the printing and dyeing process in real time through high-precision sensors, and transmits the data collected by the sensors to the cloud data center in real time through the Internet of Things; Model building module: Use big data and machine learning algorithms to mine historical printing and dyeing data and establish a printing and dyeing process model; based on real-time sensor data and the printing and dyeing process model, make real-time predictions on the printing and dyeing effects, and automatically adjust the printing and dyeing parameters according to the prediction results; Parameter adjustment module: Based on the results of intelligent analysis, the operating parameters of the printing and dyeing equipment are adjusted in real time through the automatic control system, and based on the adaptive control strategy, the control parameters are dynamically adjusted according to the actual changes of the fabric during the printing and dyeing process; Intelligent water-saving module: It adopts an intelligent water-saving system to automatically adjust the amount of flushing water according to the type of fabric and the printing and dyeing stage, and introduces wastewater recycling and reuse technology to treat wastewater through physical, chemical or biological methods; Fault warning module: Based on the intelligent monitoring system, it monitors the operating status and energy consumption of printing and dyeing equipment in real time, detects and warns of potential faults.

[0005] The beneficial effects of the present invention are as follows: by collecting key parameters in real time through high-precision sensors, and mining historical data in combination with big data and machine learning algorithms, an accurate printing and dyeing process model is established, which enables real-time prediction of printing and dyeing effects, and automatic adjustment of printing and dyeing parameters according to the prediction results, thereby improving the efficiency and quality of printing and dyeing; an intelligent water-saving system is adopted to automatically adjust the amount of flushing water according to the type of fabric and the printing and dyeing stage, and introduce wastewater recovery and reuse technology, which not only reduces the consumption of water resources, but also reduces production costs; an intelligent monitoring system is used to monitor the operating status and energy consumption of printing and dyeing equipment in real time, discover and warn of potential faults, and help to take timely measures to prevent equipment failures, thereby reducing downtime and maintenance costs and further reducing energy consumption; the automatic control system is integrated with the printing and dyeing equipment to realize real-time adjustment of operating parameters, and based on an adaptive control strategy Dynamically adjust control parameters to improve the degree of automation in the production process, reduce human intervention, and improve production efficiency and stability; use IoT technology to transmit data to the cloud data center in real time, adopt a parallel processing architecture to perform preliminary processing and real-time analysis on the received data, enhance data management, storage and analysis capabilities, compress the original data at the sensor end through a data compression algorithm, and transmit the data through the TCP protocol. At the same time, use the intelligent routing function of the IoT gateway to select the optimal transmission path and build a distributed data receiving system to ensure efficient and stable data transmission; introduce a redundant control system to switch to the backup system when the main control system fails; implement a fault detection mechanism to discover and locate network faults or node faults, and handle them based on the fault recovery strategy, thereby improving the overall reliability and fault tolerance of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0006] Figure 1 It is a step diagram of the method of the present invention; Figure 2 This is a system module diagram of the present invention. DETAILED DESCRIPTION

[0007] The preferred embodiments of the present invention are described below in conjunction with 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.

[0008] One embodiment of the present invention, as Figure 1 As shown, a highly efficient, intelligent, short-process, water-saving printing and dyeing control method comprises: S1. Real-time collection of key parameters in the dyeing and printing process using high-precision sensors, including fabric humidity, temperature, pH value, and dye concentration; and real-time transmission of the data collected by the sensors to a cloud data center via the Internet of Things; S2. Use big data and machine learning algorithms to mine historical printing and dyeing data and establish a printing and dyeing process model; based on real-time sensor data and the printing and dyeing process model, make real-time predictions on the printing and dyeing effects, and automatically adjust the printing and dyeing parameters according to the prediction results; S3. According to the results of intelligent analysis, the operating parameters of the printing and dyeing equipment are adjusted in real time through the automatic control system, and the operating parameters include heating temperature, stirring speed, and flushing water volume; and based on the adaptive control strategy, the control parameters are dynamically adjusted according to the actual changes of the fabric during the printing and dyeing process; S4. Adopt intelligent water-saving system to automatically adjust the amount of flushing water according to the type of fabric and the printing and dyeing stage, and introduce wastewater recycling and reuse technology to treat wastewater through physical, chemical or biological methods; S5. Based on the intelligent monitoring system, the operating status and energy consumption of printing and dyeing equipment are monitored in real time to detect and warn of potential faults.

[0009] The working principle of the above technical solution is as follows: during the printing and dyeing process, high-precision sensors are arranged to monitor key parameters such as humidity, temperature, pH value and dye concentration of the fabric in real time; the data collected by the sensors are transmitted to the cloud data center in real time through the Internet of Things technology to ensure the timeliness and accuracy of the data; big data technology is used to mine and analyze historical printing and dyeing data and establish a printing and dyeing process model; based on real-time sensor data and the printing and dyeing process model, the printing and dyeing effect is predicted in real time through machine learning algorithms; according to the prediction results, the printing and dyeing parameters, such as dye concentration, temperature, etc., are automatically adjusted to optimize the printing and dyeing effect; according to the results of intelligent analysis, the automatic control system adjusts the operating parameters of the printing and dyeing equipment in real time, such as heating temperature degree, stirring speed, flushing water volume, etc.; introduce adaptive control strategy to dynamically adjust control parameters according to the actual changes of fabrics in the printing and dyeing process (such as humidity, temperature, etc.) to ensure the stability and efficiency of the printing and dyeing process; adopt intelligent water-saving system to automatically adjust the flushing water volume according to the type of fabric and the printing and dyeing stage to reduce the waste of water resources; introduce wastewater recovery and reuse technology to treat wastewater through physical, chemical or biological methods to achieve the recovery and reuse of dyes and auxiliaries in wastewater, reduce production costs and reduce environmental pollution; based on intelligent monitoring system, monitor the operating status and energy consumption of printing and dyeing equipment in real time; through data analysis, discover and warn potential faults to ensure the continuity and stability of the printing and dyeing process.

[0010] The effects of the above technical solutions are as follows: key parameters in the printing and dyeing process, such as fabric humidity, temperature, pH value and dye concentration, are collected in real time through high-precision sensors, ensuring the accuracy and timeliness of the data; the printing and dyeing process model established based on big data and machine learning algorithms can predict the printing and dyeing effects in real time, and automatically adjust the printing and dyeing parameters according to the prediction results, thereby optimizing the printing and dyeing process and improving the printing and dyeing efficiency and quality; an intelligent water-saving system is used to automatically adjust the flushing water volume according to the fabric type and printing and dyeing stage, effectively reducing the waste of water resources; wastewater recovery and reuse technology is introduced to treat wastewater through physical, chemical or biological methods, realizing the recovery of dyes and auxiliaries in the wastewater. The recycling and reuse of wastewater reduces the impact of wastewater discharge on the environment; the intelligent monitoring system monitors the operating status and energy consumption of printing and dyeing equipment in real time, can promptly detect and warn of potential faults, and avoid production interruptions and increased energy consumption caused by equipment failure; the adaptive control strategy dynamically adjusts the control parameters according to the actual changes of the fabric during the printing and dyeing process, ensuring the stable operation and efficient use of the equipment, and further reducing energy consumption and production costs; this technical solution integrates advanced technologies such as the Internet of Things, big data, machine learning, and automated control to achieve intelligent control and management of the printing and dyeing process; the improvement of the level of intelligence not only improves production efficiency and quality, but also reduces manual intervention and error rates. By adjusting the printing and dyeing parameters and operating parameters, it can flexibly adapt to the needs of different fabric types and printing and dyeing stages, and improve production flexibility.

[0011] In one embodiment of the present invention, the S1 includes: S11, determining key parameters closely related to the printing and dyeing process, wherein the key parameters include fabric humidity, temperature, pH value and dye concentration, and selecting corresponding high-precision sensors for each parameter, such as temperature and humidity sensors, pH detectors and concentration detectors; S12. Deploy sensors at key locations of the printing and dyeing production line (such as dye tanks, fabric conveyor belts, dryers, etc.) and calibrate and maintain the sensors regularly; S13. Using the Internet of Things technology, the data collected by the sensor is transmitted to the cloud data center in real time. After receiving the data, the cloud data center performs preliminary data processing and storage; S14. Pre-process the stored data and analyze the changing trends of key parameters using statistical analysis methods.

[0012] The working principle of the above technical solution is as follows: First, according to the characteristics and requirements of the printing and dyeing process, determine the key parameters closely related to the printing and dyeing quality, efficiency and environmental protection, including the humidity, temperature, pH value and dye concentration of the fabric; select the corresponding high-precision sensor for each key parameter. For example, for humidity and temperature, you can choose a temperature and humidity sensor; for pH value, you can choose a pH detector; for dye concentration, you can choose a concentration detector. These sensors should have high precision, stability and reliability to ensure the accuracy of the collected data; deploy corresponding sensors at key locations of the printing and dyeing production line, such as dye tanks, fabric conveyor belts, dryers, etc. The selection of these locations should be based on the areas where the parameters change most significantly or have the greatest impact on the printing and dyeing quality during the printing and dyeing process; calibrate the sensors regularly to ensure the accuracy of their measurement results. At the same time, perform necessary maintenance on the sensors, such as cleaning and replacing damaged parts, to extend their service life and maintain their performance; use the Internet of Things technology to transmit the data collected by the sensors to the cloud data center in real time. This usually involves using wireless communication technologies (such as Wi-Fi, Bluetooth, LoRa, etc.) to send sensor data to a gateway or relay device, which then uploads the data to the cloud; after receiving the data, the cloud data center performs preliminary data processing and storage. Processing may include data cleaning, format conversion, etc. to ensure the accuracy and consistency of the data. Storage involves saving the data in a database or data warehouse for subsequent analysis and use; preprocessing the stored data, including removing outliers, filling missing values, data smoothing, etc., to improve the quality and reliability of the data; using statistical analysis methods to analyze the changing trends of key parameters. This can include calculating statistics such as mean, standard deviation, and changing trends, as well as drawing time series graphs, scatter plots, and other charts to intuitively display the changes in parameters.

[0013] The effect of the above technical solution is: by selecting high-precision sensors (such as temperature and humidity sensors, pH detectors, and concentration detectors) for each key parameter, the accuracy of the collected data is ensured. High-precision sensors can monitor key parameters such as fabric humidity, temperature, pH value, and dye concentration in real time, providing a reliable data basis for the precise control of the printing and dyeing process; sensors are deployed at key locations of the printing and dyeing production line so that key parameters in the production process can be monitored and recorded in real time. This not only enhances the visualization of the production process, but also improves production transparency, which helps managers to discover and solve problems in a timely manner; using the Internet of Things technology, the data collected by the sensors can be transmitted to the cloud data center in real time. After receiving the data, the cloud data center performs preliminary data processing and storage, providing a timely and accurate data source for subsequent data analysis and decision support; preprocessing the stored data, and using statistical analysis methods to analyze the changing trends of key parameters, which helps managers to have a deep understanding of the characteristics and laws of the printing and dyeing process. This can not only provide a scientific basis for optimizing the printing and dyeing process and improving production efficiency, but also provide strong support for preventing potential failures and reducing production costs; by real-time monitoring of key parameters, timely discovery and warning of potential production safety hazards can help ensure production safety. At the same time, real-time monitoring of emissions such as wastewater can also help reduce the risk of environmental pollution; the real-time data collection and analysis system can quickly respond to changes in market demand and customer orders, and adjust printing and dyeing parameters and operating parameters to meet the needs of different fabric types and printing and dyeing stages.

[0014] In one embodiment of the present invention, the S13 includes: S131, compressing the original data at the sensor end by using a data compression algorithm (such as LZW, Huffman coding, etc.); S132, transmitting data through the TCP protocol. Before data transmission, redundant backup of key data is performed. Once data loss or damage is detected, a retransmission mechanism is immediately triggered; S133, using the intelligent routing function of the IoT gateway, dynamically selecting the optimal transmission path according to the network conditions, building a distributed data receiving system, and using load balancing technology to evenly distribute the received data to multiple processing nodes; S134, using a parallel processing architecture in the cloud data center to perform parallel preliminary processing on the received data, wherein the preliminary processing includes data decompression, format conversion, and outlier detection; S135. Based on real-time data stream processing technologies (such as Apache Storm, Spark Streaming, etc.), analyze and process continuously arriving data streams in real time to discover and respond to abnormal changes in the data.

[0015] The working principle of the above technical solution is as follows: at the sensor end, the raw data will be processed by the data compression algorithm before transmission. These algorithms, such as LZW (Lempel-Ziv-Welch) coding or Huffman coding, can identify and eliminate redundant information in the data, thereby reducing the data volume and improving transmission efficiency; the compressed data is transmitted through the TCP (Transmission Control Protocol) protocol. The TCP protocol provides reliable data transmission services, including error detection, confirmation response, timeout retransmission and other mechanisms. Before data transmission, key data will be redundantly backed up. Once data loss or damage is detected, the retransmission mechanism will be triggered immediately to ensure the integrity and reliability of the data; the IoT gateway uses intelligent routing functions to dynamically select the optimal transmission path according to the current network conditions (such as bandwidth, delay, packet loss rate, etc.). At the same time, a distributed data receiving system is constructed, and the received data is evenly distributed to multiple processing nodes using load balancing technology to achieve efficient data processing and resource utilization; in the cloud data center, a parallel processing architecture is used to perform preliminary processing on the received data. These processes include data decompression, format conversion, and outlier detection. The parallel processing architecture can make full use of multi-core processors and distributed computing resources to improve processing speed; based on real-time data stream processing technologies (such as Apache Storm, Spark Streaming, etc.), the continuously arriving data streams can be analyzed and processed in real time. These technologies can process high-speed, large-scale data streams, and discover and respond to abnormal changes in data, such as sudden trend changes and abnormal peaks.

[0016] The effects of the above technical solutions are as follows: at the sensor end, by adopting efficient data compression algorithms such as LZW and Huffman coding, the volume of original data can be significantly reduced, thereby reducing the bandwidth and time required for data transmission; data compression can also reduce the storage space occupied and reduce storage costs; by using the intelligent routing function of the IoT gateway, the system can dynamically select the optimal transmission path according to the current network conditions, thereby avoiding network congestion and increasing data transmission speed; building a distributed data receiving system and using load balancing technology to evenly distribute the received data to multiple processing nodes can ensure the timeliness and efficiency of data processing; before data transmission, redundant backup of key data can ensure that in the event of data loss or damage, the original information can be restored through the backup data; once data loss or damage is detected, the retransmission mechanism is immediately triggered to To ensure the integrity and reliability of data; TCP protocol is used for data transmission, which has reliable transmission and error correction mechanisms to ensure the accuracy and integrity of data during transmission; encryption technology can also be used during data transmission and storage to prevent data from being illegally accessed or tampered with; a parallel processing architecture is used in cloud data centers to make full use of multi-core processors and distributed computing resources to perform parallel preliminary processing of received data; parallel processing can significantly improve the speed and efficiency of data processing and shorten data processing time; based on real-time data stream processing technology (such as Apache Storm, Spark Streaming, etc.), the system can perform real-time analysis and processing of continuously arriving data streams; real-time data analysis can promptly detect and respond to abnormal changes in data, providing timely and accurate information support for decision-making.

[0017] In one embodiment of the present invention, the S133 includes: Deploy network monitoring nodes at key locations of the printing and dyeing production line to monitor key indicators such as network bandwidth, latency, and packet loss rate in real time, obtain data from network monitoring nodes, and apply data analysis algorithms (such as time series analysis, machine learning models, etc.) to conduct real-time evaluation of network conditions and predict network change trends; Based on the results of real-time monitoring of network conditions, the optimal transmission path is selected based on the preset dynamic path selection strategy; the intelligent routing module is integrated in the IoT gateway to adjust the data transmission path in real time according to the dynamic path selection strategy; Deploy multiple data receiving nodes in the cloud data center to form a distributed data receiving system, and use load balancing algorithms (such as weighted polling, minimum number of connections, etc.) to dynamically allocate data transmission tasks based on the load of the receiving nodes; Monitor the load of receiving nodes in real time, including key indicators such as CPU usage, memory usage, disk I / O, etc., and dynamically adjust the load balancing strategy based on the results of real-time load monitoring, such as adding or reducing receiving nodes, adjusting weights, etc. During the data transmission and reception process, a fault detection mechanism is implemented to discover and locate network faults or node faults, and handle them based on fault recovery strategies, such as automatic retransmission, switching to alternative paths, restarting receiving nodes, etc.

[0018] The working principle of the above technical solution is as follows: deploy network monitoring nodes at key locations of the printing and dyeing production line, which are responsible for real-time monitoring of key indicators such as network bandwidth, delay, and packet loss rate; the data collected by the network monitoring nodes are transmitted to the analysis center, and data analysis algorithms (such as time series analysis, machine learning models, etc.) are applied to analyze these data to evaluate the network status in real time and predict network change trends; based on the results of real-time monitoring of network status, a preset dynamic path selection strategy is formulated. These strategies may include weight allocation based on different indicators such as network bandwidth, delay, and packet loss rate, as well as priority rules for path selection; integrate an intelligent routing module in the Internet of Things gateway, which adjusts the data transmission path in real time according to the dynamic path selection strategy. When the network status changes, the intelligent routing module can quickly identify and select the optimal transmission path; deploy multiple data receiving nodes in the cloud data center to form a distributed data receiving system. These nodes are responsible for receiving data transmitted from the Internet of Things gateway; load balancing algorithms (such as weighted polling, minimum number of connections, etc.) are used to dynamically allocate data transmission tasks. These algorithms decide which node should receive new data tasks based on the load of the receiving node (such as key indicators such as CPU usage, memory occupancy, disk I / O, etc.); monitor the load of the receiving node in real time, and dynamically adjust the load balancing strategy based on the results of real-time load monitoring. For example, when the load of a node is too high, new receiving nodes can be added or the weights of existing nodes can be adjusted to distribute the load; implement fault detection mechanisms during data transmission and reception, and discover and locate network or node failures by monitoring network status, node status, and data transmission integrity; and handle based on fault recovery strategies, such as automatically retransmitting lost data packets, switching to alternate transmission paths, and restarting failed receiving nodes. These measures are designed to ensure the continuity and integrity of data transmission.

[0019] The effects of the above technical solutions are as follows: by deploying network monitoring nodes at key locations of the printing and dyeing production line and monitoring key indicators such as network bandwidth, delay, and packet loss rate in real time, network bottleneck problems can be discovered and resolved in a timely manner; applying data analysis algorithms to conduct real-time evaluation of network conditions and predicting network change trends can help adjust network strategies in advance to avoid network congestion or interruptions; based on the results of real-time monitoring of network conditions, a preset dynamic path selection strategy can be used to select the optimal transmission path to ensure efficient and stable data transmission; the integration of intelligent routing modules in the Internet of Things gateway enables the data transmission path to be adjusted in real time according to network conditions, further improving transmission efficiency; deploying multiple data receiving nodes in the cloud data center to form a distributed data receiving system can disperse data receiving pressure and improve data receiving efficiency; using a load balancing algorithm to dynamically allocate data transmission tasks, so that the load of each receiving node is more balanced, avoiding the situation where some nodes are overloaded while other nodes are idle. ; Real-time monitoring of the load of the receiving node, including key indicators such as CPU usage, memory occupancy, disk I / O, etc., helps to promptly discover and solve load imbalance problems; according to the results of real-time load monitoring, dynamically adjust the load balancing strategy, such as adding or reducing receiving nodes, adjusting weights, etc., to further optimize data reception and processing capabilities; implement a fault detection mechanism during data transmission and reception, which can promptly discover and locate network failures or node failures, avoiding data loss or transmission interruption; based on fault recovery strategy processing, such as automatic retransmission of lost data packets, switching to backup transmission paths, restarting faulty receiving nodes, etc., it can quickly restore the normal operation of data transmission and reception; through the comprehensive use of real-time monitoring, dynamic adjustment, fault detection and recovery mechanisms, the reliability of the overall system has been significantly improved; this technical solution can flexibly adjust strategies according to different network conditions, load conditions and fault conditions, ensuring efficient and stable data transmission and reception.

[0020] In one embodiment of the present invention, the S135 includes: Dynamically split the data stream into multiple small data streams or data blocks according to its size and complexity, and clean and filter the data to remove noise, outliers, and redundant data before it enters the analysis system; Utilize distributed computing resources to build a parallel processing architecture, distribute the split data blocks to multiple processing nodes for parallel processing, and dynamically adjust the distribution of data blocks according to the real-time load of each processing node; Use streaming computing engines (such as Apache Storm, Spark Streaming, etc.) to quickly analyze and process real-time data streams. According to business needs, select appropriate real-time algorithms (such as sliding average algorithms, moving average algorithms, etc.) to calculate and analyze data streams and extract valuable information; Combine machine learning algorithms and deep learning models to analyze and predict real-time data, integrate real-time monitoring tools (such as Grafana, Kibana, etc.), and display data indicators and monitoring information in real time; Through visualization, complex analysis results can be presented to decision makers and business users in an intuitive way.

[0021] The working principle of the above technical solution is as follows: according to the size and complexity of the data stream, the system dynamically splits it into multiple small data streams or data blocks. This can optimize processing efficiency, reduce processing delays, and facilitate subsequent parallel processing; before the data enters the analysis system, the system cleans and filters the data to remove noise, outliers and redundant data. This ensures the data quality and accuracy of subsequent analysis; using distributed computing resources, the system builds a parallel processing architecture to allocate the split data blocks to multiple processing nodes for parallel processing. This can significantly improve the processing speed and meet real-time requirements; the system dynamically adjusts the allocation of data blocks according to the real-time load of each processing node to ensure that the load of each node is balanced and avoid processing bottlenecks. This further improves processing efficiency and system stability; the system uses advanced streaming computing engines (such as Apache Storm, Spark Streaming, etc.) to quickly analyze and process real-time data streams. These engines are capable of processing high-throughput, low-latency data streams to ensure real-time performance; according to business needs, the system selects appropriate real-time algorithms (such as sliding average algorithms, moving average algorithms, etc.) to calculate and analyze data streams and extract valuable information. These algorithms can help decision makers quickly understand data trends and anomalies; the system combines machine learning algorithms and deep learning models to analyze and predict real-time data. By training models, the system can identify patterns and trends in data and provide accurate prediction results; based on the results of machine learning and deep learning analysis, the system provides intelligent decision support for decision makers. This helps decision makers make correct decisions quickly and optimize business processes and strategies; the system integrates real-time monitoring tools (such as Grafana, Kibana, etc.) to display data indicators and monitoring information in real time. This helps managers to understand the system status and data changes in a timely manner and ensure the stability and reliability of the system; through charts, dashboards, etc., the system presents complex analysis results to decision makers and business users in an intuitive way. This helps them quickly understand and apply analysis results, improve work efficiency and decision accuracy.

[0022] The effect of the above technical solution is: by dynamically splitting the data stream according to its size and complexity, and using distributed computing resources for parallel processing, the speed and efficiency of data processing are significantly improved. This processing method ensures that even in the face of large-scale, highly complex data streams, the system can respond quickly and give analysis results; the use of a streaming computing engine to quickly analyze and process real-time data streams further shortens the processing time and meets real-time requirements. This is especially important for scenarios such as market analysis and real-time monitoring that require rapid response; cleaning and filtering the data before it enters the analysis system effectively removes noise, outliers, and redundant data, improving the quality and accuracy of the data. This helps reduce false positives and negative alerts and improves the reliability of analysis results. Selecting appropriate real-time algorithms to calculate and analyze data streams based on business needs can more accurately extract valuable information. Dynamically adjusting the allocation of data blocks based on the real-time load of each processing node avoids processing bottlenecks and overloads, and enhances the stability and reliability of the system. Using distributed computing resources to build a parallel processing architecture not only improves processing efficiency, but also enables the system to easily cope with the growth of data volume and has good scalability. Combining machine learning algorithms and deep learning models to analyze and predict real-time data provides decision makers and business users with smarter and more accurate decision support. Integrating real-time monitoring tools and visualization display methods enables decision makers and business users to understand system status and data changes in real time and make decisions quickly, which improves decision efficiency and reduces decision risks. Through in-depth analysis of real-time data, enterprises can discover new business opportunities, optimize existing processes, and improve operational efficiency.

[0023] In one embodiment of the present invention, the S2 includes: S21, acquiring historical printing and dyeing data, wherein the historical printing and dyeing data includes fabric type, dye type, printing and dyeing process parameters, and printing and dyeing effects, extracting key features from the historical data using a data mining algorithm, and performing dimensionality reduction processing on the features; S22. Based on the extracted features, a printing and dyeing process model is established using machine learning algorithms (such as support vector machines, neural networks, random forests, etc.), and the model is trained and optimized; S23, through the model update mechanism, the model is regularly updated and optimized according to the newly collected data, and the real-time sensor data is input into the printing and dyeing process model to make real-time predictions on the printing and dyeing effects; S24. According to the prediction results, the printing and dyeing parameters, such as dye concentration, pH value, heating temperature, etc., are automatically adjusted, and based on the feedback mechanism, the prediction model is fine-tuned according to the actual feedback of the printing and dyeing effect.

[0024] The working principle of the above technical solution is as follows: collect data from historical printing and dyeing records, including fabric type, dye type, printing and dyeing process parameters (such as dye concentration, pH value, heating temperature, etc.) and final printing and dyeing effect; use data mining algorithm to extract key features closely related to printing and dyeing effect from historical data; perform dimensionality reduction processing on the extracted features to reduce the complexity of the model and improve computational efficiency; based on the extracted features, select appropriate machine learning algorithms (such as support vector machines, neural networks, random forests, etc.) to establish a printing and dyeing process model; use historical data to train the model so that it can accurately predict the printing and dyeing effect; optimize the model through cross-validation, regularization and other methods to improve its generalization ability; establish a model update mechanism to regularly update and optimize the model according to newly collected printing and dyeing data; input real-time sensor data (such as fabric type, dye concentration, pH value, heating temperature, etc.) into the printing and dyeing process model; the model predicts the printing and dyeing effect in real time based on the input data, providing a basis for subsequent parameter adjustment; according to the prediction results of the model, automatically adjust the key parameters in the printing and dyeing process, such as dye concentration, pH value, heating temperature, etc. Through actual feedback from the printing and dyeing effects, the prediction model is fine-tuned to improve its prediction accuracy; this feedback mechanism can form a closed-loop control system to make the printing and dyeing process more stable and controllable.

[0025] The effects of the above technical solution are as follows: the printing and dyeing process model established through machine learning algorithms can realize real-time prediction of printing and dyeing effects and automatic adjustment of printing and dyeing parameters, thereby greatly improving the automation and intelligence level of the printing and dyeing process; this automatic adjustment reduces manual intervention, making the printing and dyeing process more stable and controllable, and helping to improve printing and dyeing efficiency and quality; the model can accurately predict the printing and dyeing effects based on historical data and real-time sensor data, and automatically adjust the printing and dyeing parameters such as dye concentration, pH value, heating temperature, etc. according to the prediction results; this accurate prediction and optimization can ensure that the printing and dyeing process is always kept in the best state, thereby improving the quality and consistency of printing and dyeing products; through model prediction and automatic adjustment of parameters, waste caused by improper parameter settings can be avoided, such as excessive use of dyes, energy waste, etc.; the ability to predict and adjust parameters in real time makes the production process more flexible and efficient, and can Quickly adjust production strategies according to different fabric types and dye types; this ability to optimize production processes helps improve production efficiency and reduce production costs; by optimizing printing and dyeing parameters and reducing waste, the discharge of wastewater, waste gas and waste residue in the printing and dyeing process can be reduced, thereby reducing pollution to the environment; the ability to predict and adjust parameters in real time enables companies to more accurately control energy consumption, such as electricity, steam, etc.; through energy-saving and consumption-reducing measures, companies can reduce production costs and reduce negative impacts on the environment; by improving printing and dyeing efficiency and quality, companies can produce high-quality printing and dyeing products that better meet market demand, thereby increasing product added value and market competitiveness; this technical solution supports customized production based on customer needs, such as selecting different fabric types, dye types and printing and dyeing process parameters; this customized production capability helps meet the diverse needs of the market and enhance the market competitiveness of companies.

[0026] In one embodiment of the present invention, the S23 includes: Preprocess the data collected by the sensor data and transmit the preprocessed data to the cloud data center using data compression and reliable transmission protocols; Real-time data stream processing engines (such as Apache Kafka Streams, Apache Flink, etc.) deployed in cloud data centers can be used to process and analyze continuously arriving data streams in real time. Based on the data caching mechanism, the newly collected data is temporarily stored and batch processed regularly (such as every hour or every day); incremental learning algorithms (such as online learning, incremental support vector machines, etc.) are used to adapt the model to new data without retraining the entire data set; After each model update, the model performance is evaluated using cross-validation, and the trained model is deployed as a real-time prediction service. When real-time sensor data is received, a prediction request is immediately triggered and the data is input into the prediction service for real-time prediction. Based on the prediction result caching mechanism, cached results are directly returned for prediction requests that appear repeatedly within a short period of time.

[0027] The working principle of the above technical solution is as follows: pre-process the raw data collected by the sensor, including data cleaning, format conversion, outlier processing, etc., to ensure the accuracy and consistency of the data; the pre-processed data will be used for subsequent analysis and prediction; use data compression technology, such as Huffman coding, LZW algorithm and other lossless compression methods to reduce data transmission volume and improve transmission efficiency; use reliable transmission protocols, such as TCP / IP protocol, to ensure the integrity and reliability of data during transmission; the pre-processed data is transmitted to the cloud data center through a reliable transmission protocol to provide a basis for subsequent processing and analysis; deploy real-time data stream processing engines in the cloud data center, such as Apache Kafka Streams, Apache Flink, etc.; these engines can process continuously arriving data streams and perform real-time analysis, feature extraction, anomaly detection and other operations; the results of real-time data processing will be used to update models, trigger prediction requests and other subsequent operations; use data caching Mechanism is used to temporarily store newly collected data; batch processing is performed on cached data regularly (such as every hour or every day), such as data aggregation and feature engineering, to improve processing efficiency; incremental learning algorithms are used, such as online learning and incremental support vector machines, so that the model can adapt to new data without retraining the entire data set; incremental learning algorithms can continuously update the model to improve the accuracy and generalization ability of the model; after each model update, the model performance is evaluated using methods such as cross-validation to ensure the accuracy and stability of the model; the trained model is deployed as a real-time prediction service, and a prediction request is triggered immediately when real-time sensor data is received; data is input into the prediction service for real-time prediction, and the prediction results will be used to guide subsequent operations, such as adjusting printing and dyeing parameters; based on the prediction result caching mechanism, cached results are directly returned for prediction requests that appear repeatedly within a short period of time; this can reduce the amount of calculation, improve response speed, and provide users with more timely and accurate prediction results.

[0028] The effects of the above technical solutions are as follows: pre-processing the data collected by the sensors can ensure the accuracy and consistency of the data and provide a reliable basis for subsequent analysis; data compression technology can significantly reduce the amount of data transmission, improve transmission efficiency and reduce network load; using reliable transmission protocols such as TCP / IP can ensure the integrity and reliability of data during transmission and avoid data loss or damage; real-time data stream processing engines (such as Apache Kafka Streams, Apache Flink, etc.) can process continuously arriving data streams and realize real-time data analysis, feature extraction and anomaly detection functions; this enables enterprises to obtain key information in the production process in a timely manner and provide real-time support for decision-making; the data caching mechanism can temporarily store newly collected data to avoid data loss and reduce the pressure on the real-time processing system; regular batch processing can further improve data processing efficiency and optimize resource utilization; incremental learning algorithms (such as online learning, incremental support vector machines, etc.) enable the model to be able to process data without retraining the entire system. The model can adapt to new data in the case of a data set, thereby improving the updating speed and accuracy of the model; this enables the model to continuously learn and optimize, and better adapt to changes in the production process; after each model update, the model performance is evaluated using cross-validation to ensure the stability and accuracy of the model; the trained model is deployed as a real-time prediction service, which can realize instant prediction of sensor data and provide rapid support for decision-making in the production process; based on the prediction result caching mechanism, the cached results are directly returned for prediction requests that appear repeatedly in a short period of time, which can significantly improve the response speed of the system; this enables enterprises to obtain prediction results faster and make production adjustments in time; prediction result caching can also reduce the amount of calculation, optimize system resource utilization, and reduce operating costs; through real-time data processing, model updates and prediction services, enterprises can promptly discover problems in the production process and make adjustments, thereby improving production efficiency; technologies such as data compression, reliable transmission, and prediction result caching can reduce network load and reduce the amount of calculation, thereby reducing operating costs.

[0029] In one embodiment of the present invention, the S24 includes: Analyze the prediction results of the printing and dyeing process model to extract key prediction indicators, such as printing and dyeing uniformity, color brightness, and fabric strength; compare the prediction results with the preset printing and dyeing quality standards to formulate adjustment strategies for printing and dyeing parameters; for example, if the prediction results show that the printing and dyeing uniformity is insufficient, it may be necessary to increase the dye concentration or adjust the fabric transfer speed; Design the interface between printing and dyeing equipment and control system to remotely and automatically adjust printing and dyeing parameters, and the interface supports real-time data transmission and command issuance; Through the control system, according to the formulated adjustment strategy, the parameters of the printing and dyeing equipment, such as dye concentration, pH value, heating temperature, etc., are automatically adjusted. During the adjustment process, the parameter changes are monitored in real time; By deploying monitoring equipment such as cameras and spectrometers at key locations of the printing and dyeing production line, the printing and dyeing effects can be monitored in real time, data from the monitoring equipment can be collected, and the printing and dyeing effects can be evaluated in real time through data analysis algorithms; Compare the evaluation results with the prediction results, analyze the prediction accuracy, and store the real-time monitoring printing and dyeing effect data and the corresponding process parameters, prediction results and other information into the feedback database; Through the model fine-tuning algorithm, the prediction model is fine-tuned using the data in the feedback database, and after each fine-tuning, the model is verified using the cross-validation method.

[0030] The working principle of the above technical solution is as follows: analyze the prediction results of the printing and dyeing process model and extract key prediction indicators, such as printing and dyeing uniformity, color brightness and fabric strength; these indicators are important parameters for measuring printing and dyeing effects and are instructive for the formulation of subsequent adjustment strategies; compare the prediction results with the preset printing and dyeing quality standards to determine whether the current printing and dyeing effect meets the standards; if the prediction results show that a certain indicator does not meet the standards, such as insufficient printing and dyeing uniformity, formulate corresponding adjustment strategies, such as increasing the dye concentration or adjusting the fabric transmission speed; design the interface between the printing and dyeing equipment and the control system to ensure that the printing and dyeing parameters can be adjusted remotely and automatically; the interface supports real-time data transmission and instruction issuance to ensure that the adjustment instructions can be transmitted to the printing and dyeing equipment in a timely and accurate manner; through the control system, according to the formulated adjustment strategy, automatically adjust the parameters of the printing and dyeing equipment, such as dye concentration, pH value, heating temperature, etc.; during the adjustment process, monitor the parameter changes in real time to ensure that the adjustment process is smooth and orderly; deploy monitoring equipment at key locations of the printing and dyeing production line, such as Cameras, spectrometers, etc. are used to monitor the printing and dyeing effects in real time; these devices can collect key data in the printing and dyeing process, providing a basis for subsequent evaluation; the collected monitoring equipment data is processed through data analysis algorithms to evaluate the printing and dyeing effects in real time; the evaluation results can reflect the quality of the current printing and dyeing effects and provide a basis for subsequent model fine-tuning; the evaluation results are compared with the prediction results to analyze the prediction accuracy; through comparison, the performance of the prediction model in practical applications can be understood, providing direction for subsequent fine-tuning; the real-time monitored printing and dyeing effect data and the corresponding process parameters, prediction results and other information are stored in the feedback database; the feedback database is an important data source for model fine-tuning, which can provide rich historical data and real-time data support; the model fine-tuning algorithm is used to fine-tune the prediction model according to the data in the feedback database; the fine-tuned model needs to be verified by the cross-validation method to ensure its accuracy and stability; through continuous fine-tuning and verification, the prediction model can gradually adapt to changes in the production process and improve prediction accuracy.

[0031] The effects of the above technical scheme are as follows: by analyzing the prediction results of the printing and dyeing process model, extracting key prediction indicators, and comparing them with the preset printing and dyeing quality standards, the adjustment strategy of printing and dyeing parameters can be accurately formulated; this enables the printing and dyeing process to be adjusted in a targeted manner according to the prediction results, thereby improving the printing and dyeing quality and uniformity, and ensuring that key indicators such as color brightness and fabric strength meet the standards; designing the interface between the printing and dyeing equipment and the control system to realize remote automatic adjustment of printing and dyeing parameters, and support real-time data transmission and instruction issuance; automatically adjusting equipment parameters such as dye concentration, pH value, heating temperature, etc. through the control system, and monitoring parameter changes in real time during the adjustment process to ensure that the adjustment process is smooth and effective; real-time monitoring of printing and dyeing effects, timely detection of problems and adjustments, can reduce fabric waste and dye loss caused by unqualified printing and dyeing; and help reduce Low production costs and improved resource utilization efficiency; automated adjustment and real-time monitoring can reduce manual intervention and improve production efficiency; at the same time, real-time evaluation of printing and dyeing effects through data analysis algorithms can quickly feedback production status and provide a basis for production scheduling and optimization; by deploying monitoring equipment at key locations of the printing and dyeing production line and collecting data for analysis, intelligent monitoring and management of the printing and dyeing process are realized; it helps companies to promptly discover problems in the production process and take corresponding measures to improve them; using the data in the feedback database to fine-tune the prediction model and verify it through cross-validation methods, the accuracy and stability of the prediction model can be continuously optimized; the prediction model can be better adapted to changes in the production process and improve the effect of intelligent management; through accurate prediction and automated adjustment, the quality of printing and dyeing products can be significantly improved.

[0032] In one embodiment of the present invention, S3 includes: S31, integrating the automatic control system with the printing and dyeing equipment, adjusting the operating parameters of the printing and dyeing equipment in real time, and introducing a redundant control system to switch to the backup system when the main control system fails; S32, based on the adaptive control strategy, dynamically adjust the control parameters according to the actual changes of the fabric during the printing and dyeing process (such as humidity, temperature, etc.); based on the neural network control algorithm, regularly evaluate and optimize the adaptive control strategy; S33. Monitor the operating status and energy consumption of printing and dyeing equipment in real time, and use machine learning algorithms to warn of potential faults. Once the warning is triggered, an alarm message is immediately sent to the operator, and the emergency response mechanism is automatically activated, such as shutting down for inspection, switching to backup equipment, etc.

[0033] The working principle of the above technical solution is: the automatic control system is tightly integrated with the printing and dyeing equipment to realize the real-time adjustment of the operating parameters of the printing and dyeing equipment. This ensures that the printing and dyeing process can be carried out according to the preset process requirements and can be flexibly adjusted according to the actual situation; a redundant control system is introduced as a backup for the main control system. When the main control system fails, the redundant control system can quickly switch to the working state and take over the control task of the printing and dyeing equipment to ensure the continuity and stability of the production process; based on the adaptive control strategy, the control parameters are dynamically adjusted according to the actual changes of the fabric during the printing and dyeing process (such as humidity, temperature, etc.). This ensures that the printing and dyeing process can adapt to the changes of different materials and different process requirements, and improve the quality and consistency of printing and dyeing; the adaptive control strategy is regularly evaluated and optimized using the neural network control algorithm. The neural network can learn the complex relationships in the printing and dyeing process, and adjust the control strategy according to the learning results to make it more accurate and efficient; real-time monitoring of the operating status and energy consumption of the printing and dyeing equipment. Through sensors and data analysis technology, the operation data of the equipment is collected in real time, processed and analyzed; and machine learning algorithms are used to warn of potential faults. Machine learning algorithms can learn the normal operation and failure modes of equipment, and predict and warn potential failures based on the learning results; once the warning is triggered, an alarm message is immediately sent to the operator, and the emergency response mechanism is automatically activated. This includes shutdown inspection, switching to backup equipment and other measures to ensure production safety and continuity of the production process.

[0034] The effects of the above technical solution are as follows: by integrating the automatic control system with the printing and dyeing equipment, the real-time adjustment of the operating parameters of the printing and dyeing equipment is realized, ensuring the continuity and stability of the production process; by introducing a redundant control system, when the main control system fails, it can quickly switch to the backup system, avoiding production interruptions caused by control system failures, and further improving production reliability; based on the adaptive control strategy, the control parameters are dynamically adjusted according to the actual changes of the fabric during the printing and dyeing process, ensuring the stability and consistency of the printing and dyeing quality; the adaptive control strategy is regularly evaluated and optimized using a neural network control algorithm, so that the control strategy can continuously adapt to changes in the production process, improving the accuracy and efficiency of the control; by real-time monitoring of fabric changes during the printing and dyeing process and dynamically adjusting control parameters, the printing and dyeing equipment can operate in the best state, thereby improving production efficiency; at the same time, dynamic adjustment can also avoid unnecessary energy consumption and achieve the goal of energy saving and consumption reduction; real-time monitoring of printing and dyeing equipment The system can predict the operating status and energy consumption of the equipment and use machine learning algorithms to warn of potential faults, so that timely measures can be taken before the fault occurs, avoiding production stagnation and increased energy consumption caused by the fault. Once the warning is triggered, the emergency response mechanism is immediately activated, such as shutdown inspection, switching to backup equipment, etc., to ensure the continuity and stability of production, while also reducing the losses caused by the fault. The automation control system is integrated with the printing and dyeing equipment to realize the automated management of the printing and dyeing process, reduce manual intervention, and improve management efficiency. The introduction of redundant control systems further enhances the stability and reliability of the system and provides a strong guarantee for intelligent management. Based on the application of neural network control algorithms and machine learning algorithms, data-driven management of the printing and dyeing process is realized. The intelligence level of the printing and dyeing process is improved by continuous learning and optimization of control strategies. The high quality and consistency of printing and dyeing products are ensured through precise control and monitoring. The production cost is reduced through measures such as energy saving and consumption reduction and fault warning.

[0035] In one embodiment of the present invention, the S32 includes: Based on control theory, we designed an adaptive control strategy to dynamically adjust the operating parameters of the printing and dyeing equipment according to the actual changes in the fabric; and integrated the adaptive control strategy into the automatic control system for field debugging; Based on neural network architectures, such as multi-layer perceptron (MLP) and recurrent neural network (RNN), neural network models for dyeing and printing process control are constructed; Preprocess the historical data of the printing and dyeing process, including fabric type, dye type, process parameters, environmental parameters, and printing and dyeing quality, and perform preprocessing such as cleaning and normalization. Use the preprocessed data to train the neural network model. Deploy the trained neural network model into the automated control system and combine it with the adaptive control strategy; The effect of the adaptive control strategy is measured through the set evaluation indicators, such as the stability of printing and dyeing quality, the degree of reduction in energy consumption, the response speed of the control strategy, etc. According to the predetermined period, the collected data is used to evaluate the adaptive control strategy and analyze the performance of the strategy in actual application; According to the evaluation results, the neural network model is further optimized, such as adjusting the network structure, increasing training data, etc. Combined with the optimization results of the neural network model, the adaptive control strategy is iteratively updated.

[0036] The working principle of the above technical solution is as follows: First, based on control theory, an adaptive control strategy is designed. Here, the adaptive adjustment algorithm of the PID (proportional-integral-differential) controller is taken as an example. The algorithm can dynamically adjust the operating parameters of the printing and dyeing equipment, such as dye injection amount, heating temperature, cloth transmission speed, etc., according to the actual changes of the cloth in the printing and dyeing process (such as humidity, temperature, etc.); integrate the designed adaptive control strategy into the automatic control system. This step includes combining the control algorithm with the control logic of the system to ensure that the algorithm can operate normally in the actual production environment. Subsequently, field debugging is carried out to verify the effectiveness and stability of the control strategy; based on neural network architectures, such as multi-layer perceptron (MLP), recurrent neural network (RNN), etc., a neural network model for printing and dyeing process control is constructed. These models can learn complex relationships in the printing and dyeing process, such as the relationship between fabric type, dye type and printing and dyeing quality; preprocess the historical data in the printing and dyeing process, including data cleaning (removing outliers, missing values, etc.), normalization (scaling the data to the same scale) and other steps. The preprocessed data is used to train the neural network model; the neural network model is trained using the preprocessed data. During the training process, the model continuously adjusts its internal parameters to minimize the prediction error, thereby improving the prediction and control capabilities of the printing and dyeing process; the trained neural network model is deployed in the automatic control system and combined with the adaptive control strategy. In this way, the system can realize intelligent control of the printing and dyeing process based on the prediction results of the neural network and the adjustment rules of the adaptive control strategy; a series of evaluation indicators are set, such as the stability of printing and dyeing quality, the degree of energy consumption reduction, the response speed of the control strategy, etc., to measure the effect of the adaptive control strategy; according to the predetermined cycle, the adaptive control strategy is evaluated using the collected data. The performance of the strategy in actual application is analyzed, including the performance in terms of control accuracy, stability, energy consumption, etc. According to the evaluation results, the neural network model is further optimized, such as adjusting the network structure and increasing the training data. Combined with the optimization results of the neural network model, the adaptive control strategy is iteratively updated to improve its control performance and adaptability.

[0037] The effect of the above technical solution is: by designing an adaptive control strategy, such as the adaptive adjustment algorithm of the PID controller, the operating parameters of the printing and dyeing equipment can be dynamically adjusted according to the actual changes of the fabric. This dynamic adjustment mechanism ensures that the printing and dyeing process can maintain high control accuracy and stability under different conditions; the model built based on the neural network architecture can learn the complex relationships in the printing and dyeing process, such as the relationship between fabric type, dye type and printing and dyeing quality. This learning ability enables the model to more accurately predict and control the printing and dyeing process, further improving the control accuracy; combining the adaptive control strategy with the neural network model realizes the intelligent control of the printing and dyeing process. This intelligent control can flexibly adjust the equipment operating parameters according to the actual situation, avoid unnecessary energy consumption, and improve production efficiency; by periodically evaluating the effect of the adaptive control strategy and optimizing the neural network model according to the evaluation results, the control strategy can be continuously improved to make it more adaptable to actual production needs. This optimization mechanism helps to further reduce energy consumption and improve production efficiency; the neural network model can learn the influence of different fabrics and dyes on the printing and dyeing process, so that the system can adapt to the production needs of a variety of fabrics and dyes. This adaptability enhances the flexibility and market competitiveness of the system; while S32 focuses on the optimization of control strategies, the robustness of the system can be further enhanced by combining real-time monitoring and fault warning systems (as described in S33). When potential faults occur, the system can take prompt measures to avoid production interruptions and losses; it combines advanced technologies such as control theory, neural networks, and machine learning to promote the intelligent development of the printing and dyeing industry. By implementing the above technical solutions, enterprises can achieve refined management of the printing and dyeing process and improve product quality and production efficiency.

[0038] In one embodiment of the present invention, the S4 includes: S41. Deploy an intelligent water-saving system in the flushing process of the printing and dyeing production line, wherein the intelligent water-saving system automatically adjusts the amount of flushing water according to the type of fabric and the printing and dyeing stage; and monitors the use of flushing water in real time through water-saving equipment; S42. Treat wastewater by physical, chemical or biological methods and regularly evaluate and optimize wastewater treatment processes; S43. Based on intelligent monitoring and control system, real-time monitoring of key parameters in wastewater treatment process (such as pH value, turbidity, etc.) and real-time monitoring of treated water quality; S44. Make feedback adjustments to the wastewater treatment process based on water quality monitoring results.

[0039] The working principle of the above technical solution is as follows: deploy an intelligent water-saving system in the flushing link of the printing and dyeing production line. The system can automatically adjust the amount of flushing water according to the type of fabric and the stage of printing and dyeing. For example, for different types of fabrics, the amount of flushing water required will vary due to their different water absorption and adsorption of dyes. The intelligent water-saving system can identify these differences and adjust the water volume accordingly; at the same time, the water-saving equipment will monitor the use of flushing water in real time to ensure that the water volume meets production needs without causing waste; printing and dyeing wastewater usually contains a large amount of pollutants such as organic matter, pigments and inorganic salts, which need to be treated before discharge or reuse; physical, chemical or biological methods are used to treat wastewater. Physical methods may include grid filtration, sedimentation, etc.; chemical methods may involve steps such as coagulation, precipitation, oxidation, etc.; biological methods may use the metabolic action of anaerobic or aerobic microorganisms to remove pollutants; regular evaluation and optimization of wastewater treatment processes to ensure that their treatment effects are stable and efficient. The evaluation may include the comparison of water quality before and after treatment, treatment efficiency, operating costs, etc. Based on the intelligent monitoring and control system, the key parameters in the wastewater treatment process, such as pH value, turbidity, etc., are monitored in real time. These parameters can reflect the effect and progress of wastewater treatment; at the same time, the quality of the treated water is monitored in real time to ensure that it meets the discharge standards or reuse requirements; according to the water quality monitoring results, the wastewater treatment process is adjusted. If the monitoring finds that the treated water quality does not meet the standards or the treatment efficiency decreases, the process needs to be adjusted to improve the situation; the adjustment may include changing the type or amount of treatment agent, adjusting the treatment time or temperature and other parameters.

[0040] The effects of the above technical solutions are as follows: deploying an intelligent water-saving system in the flushing process of the printing and dyeing production line can automatically adjust the flushing water volume according to the type of fabric and the printing and dyeing stage, avoiding the waste of water resources in the traditional flushing method; real-time monitoring of the use of flushing water by water-saving equipment can further ensure the rational use of water resources and reduce unnecessary losses; after the wastewater is treated, the wastewater can be reused, thereby reducing dependence on fresh water resources; optimization and regular evaluation of the wastewater treatment process will help improve the efficiency of wastewater treatment and increase the amount of wastewater reuse; treating wastewater through physical, chemical or biological methods can effectively remove pollutants in the wastewater, such as organic matter, heavy metals, etc., and reduce pollution to the environment; the intelligent monitoring and control system monitors the key parameters in the wastewater treatment process in real time to ensure that the wastewater treatment effect meets the standards and further reduce the risk of pollutant emissions; Real-time monitoring of water quality can promptly identify water quality problems and take measures to adjust them, ensuring that the discharged water quality meets environmental protection requirements; the application of intelligent water-saving systems and intelligent monitoring and control systems has realized the automation and intelligent management of printing and dyeing production lines and improved production efficiency; automated management reduces manual operations and labor costs, while improving the stability and reliability of production lines; feedback and adjustment of wastewater treatment processes based on water quality monitoring results can continuously optimize production processes and improve production efficiency and quality; process optimization helps to reduce production costs and improve product competitiveness; regular evaluation and optimization of wastewater treatment processes can ensure process stability and efficiency; the optimized process helps to reduce energy consumption, reduce waste generation, and improve resource utilization efficiency; based on data feedback from intelligent monitoring and control systems, a continuous improvement mechanism can be established to continuously optimize production processes and water-saving measures.

[0041] In one embodiment of the present invention, S5 includes: S51. Based on the intelligent monitoring system, the operating status, energy consumption and changes in key parameters of printing and dyeing equipment are monitored in real time, and the monitoring data are deeply mined and analyzed through big data analysis and artificial intelligence algorithms; S52. Use machine learning algorithms to warn of potential faults. Once the warning is triggered, an alarm message is immediately sent to operators at different levels based on the severity and urgency of the fault based on a multi-level alarm mechanism.

[0042] The working principle of the above technical solution is as follows: Based on the intelligent monitoring system, the operating status, energy consumption and changes in key parameters (such as temperature, pressure, flow, etc.) of the printing and dyeing equipment can be monitored in real time. These monitoring data are the basis for subsequent analysis and early warning; the intelligent monitoring system uploads a large amount of collected monitoring data to the data center. After cleaning, integration and preprocessing, these data provide input for big data analysis and artificial intelligence algorithms; big data analysis and artificial intelligence algorithms are used to deeply mine and analyze the monitoring data. These algorithms can identify patterns, trends and anomalies in the data, thereby predicting potential failures of the equipment; through analysis, the system can provide data support for potential failure early warning. These data supports include fault type, possible causes, scope of impact, and recommended preventive measures; machine learning algorithms are used to warn of potential faults. These algorithms can identify new fault patterns and predict their development trends by training and learning historical fault data; once the early warning is triggered, the system immediately sends alarm information to operators at different levels based on the multi-level alarm mechanism. These alarm information includes fault type, severity, urgency, and recommended countermeasures; the multi-level alarm mechanism sends alarm information to operators at different levels according to the severity and urgency of the fault. For example, a minor fault may only trigger an alarm from a grassroots operator, while a serious fault may trigger an alarm from a senior manager. The alarm information should be concise and clear, and be able to quickly convey the key information of the fault. The delivery method may include SMS, email, phone notification, etc., to ensure that the operator can receive the alarm information in time.

[0043] The effect of the above technical solution is: through the intelligent monitoring system, the operating status, energy consumption and key parameters of the printing and dyeing equipment are monitored in real time, and combined with big data analysis and artificial intelligence algorithms, potential faults of the equipment can be identified in advance. This helps to take preventive measures before the failure occurs to avoid production interruptions and losses caused by the failure; early warning of potential faults allows maintenance work to be carried out before the failure occurs, avoiding emergency repairs and high maintenance costs caused by sudden failures. At the same time, regular preventive maintenance can also extend the service life of the equipment; timely fault warning and rapid response can significantly reduce the downtime of the equipment and ensure the continuous and stable operation of the production line. This helps to improve production efficiency and increase output; through intelligent monitoring systems and data analysis, enterprises can more accurately understand the operating status and production capacity of equipment, thereby optimizing production scheduling, reasonably arranging production tasks, and improving production efficiency and resource utilization; the potential fault warning system can timely discover safety hazards of equipment and avoid safety accidents caused by equipment failure. The multi-level alarm mechanism can send alarm information to operators at different levels according to the severity and urgency of different levels of faults. This helps enhance the company's emergency response capabilities and ensures that quick measures can be taken to respond when a failure occurs; through intelligent monitoring systems and data analysis, companies can obtain more accurate and comprehensive equipment operation data and production data.

[0044] One embodiment of the present invention, as Figure 2 As shown, a highly efficient, intelligent, short-process, water-saving printing and dyeing control system comprises: Data acquisition module: The key parameters of the printing and dyeing process are collected in real time through high-precision sensors, including the humidity, temperature, pH value and dye concentration of the fabric; and the data collected by the sensors are transmitted to the cloud data center in real time through the Internet of Things; Model building module: Use big data and machine learning algorithms to mine historical printing and dyeing data and establish a printing and dyeing process model; based on real-time sensor data and the printing and dyeing process model, make real-time predictions on the printing and dyeing effects, and automatically adjust the printing and dyeing parameters according to the prediction results; Parameter adjustment module: Based on the results of intelligent analysis, the operating parameters of the printing and dyeing equipment are adjusted in real time through the automatic control system, including heating temperature, stirring speed, and flushing water volume; and based on the adaptive control strategy, the control parameters are dynamically adjusted according to the actual changes of the fabric during the printing and dyeing process; Intelligent water-saving module: It adopts an intelligent water-saving system to automatically adjust the amount of flushing water according to the type of fabric and the printing and dyeing stage, and introduces wastewater recycling and reuse technology to treat wastewater through physical, chemical or biological methods; Fault warning module: Based on the intelligent monitoring system, it monitors the operating status and energy consumption of printing and dyeing equipment in real time, detects and warns of potential faults.

[0045] The working principle of the above technical solution is as follows: during the printing and dyeing process, high-precision sensors are arranged to monitor key parameters such as humidity, temperature, pH value and dye concentration of the fabric in real time; the data collected by the sensors are transmitted to the cloud data center in real time through the Internet of Things technology to ensure the timeliness and accuracy of the data; big data technology is used to mine and analyze historical printing and dyeing data and establish a printing and dyeing process model; based on real-time sensor data and the printing and dyeing process model, the printing and dyeing effect is predicted in real time through machine learning algorithms; according to the prediction results, the printing and dyeing parameters, such as dye concentration, temperature, etc., are automatically adjusted to optimize the printing and dyeing effect; according to the results of intelligent analysis, the automatic control system adjusts the operating parameters of the printing and dyeing equipment in real time, such as heating temperature degree, stirring speed, flushing water volume, etc.; introduce adaptive control strategy to dynamically adjust control parameters according to the actual changes of fabrics in the printing and dyeing process (such as humidity, temperature, etc.) to ensure the stability and efficiency of the printing and dyeing process; adopt intelligent water-saving system to automatically adjust the flushing water volume according to the type of fabric and the printing and dyeing stage to reduce the waste of water resources; introduce wastewater recovery and reuse technology to treat wastewater through physical, chemical or biological methods to achieve the recovery and reuse of dyes and auxiliaries in wastewater, reduce production costs and reduce environmental pollution; based on intelligent monitoring system, monitor the operating status and energy consumption of printing and dyeing equipment in real time; through data analysis, discover and warn potential faults to ensure the continuity and stability of the printing and dyeing process.

[0046] The effects of the above technical solutions are as follows: key parameters in the printing and dyeing process, such as fabric humidity, temperature, pH value and dye concentration, are collected in real time through high-precision sensors, ensuring the accuracy and timeliness of the data; the printing and dyeing process model established based on big data and machine learning algorithms can make real-time predictions on the printing and dyeing effects, and automatically adjust the printing and dyeing parameters according to the prediction results, thereby optimizing the printing and dyeing process and improving the printing and dyeing efficiency and quality; an intelligent water-saving system is used to automatically adjust the amount of flushing water according to the type of fabric and the printing and dyeing stage, effectively reducing the waste of water resources; wastewater recovery and reuse technology is introduced to treat wastewater through physical, chemical or biological methods, thereby realizing the recovery and reuse of dyes and auxiliaries in wastewater, reducing the impact of wastewater discharge on the environment, and meeting the requirements of green production and sustainable development; the operating status and energy consumption of printing and dyeing equipment are monitored in real time through an intelligent monitoring system. , which can timely detect and warn potential faults, avoiding production interruptions and increased energy consumption caused by equipment failures; the adaptive control strategy dynamically adjusts the control parameters according to the actual changes of the fabric during the printing and dyeing process, ensuring the stable operation and efficient utilization of the equipment, and further reducing energy consumption and production costs; this technical solution integrates advanced technologies such as the Internet of Things, big data, machine learning and automated control to achieve intelligent control and management of the printing and dyeing process; the improvement in the level of intelligence not only improves production efficiency and quality, but also reduces manual intervention and error rates, providing strong support for the transformation and upgrading of the printing and dyeing industry; due to the use of real-time data collection and intelligent analysis technology, this technical solution can quickly respond to changes in market demand and customer orders; by adjusting the printing and dyeing parameters and operating parameters, it can flexibly adapt to the needs of different fabric types and printing and dyeing stages, improving production flexibility and market competitiveness.

[0047] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. An efficient, intelligent, short-process, water-saving printing and dyeing control method, characterized in that: The method comprises: S1. Real-time collection of key parameters in the printing and dyeing process through high-precision sensors, and real-time transmission of the data collected by the sensors to the cloud data center through the Internet of Things; S2. Use big data and machine learning algorithms to mine historical printing and dyeing data and establish a printing and dyeing process model; based on real-time sensor data and the printing and dyeing process model, make real-time predictions on the printing and dyeing effects, and automatically adjust the printing and dyeing parameters according to the prediction results; S3. According to the results of intelligent analysis, the operating parameters of the printing and dyeing equipment are adjusted in real time through the automatic control system, and based on the adaptive control strategy, the control parameters are dynamically adjusted according to the actual changes of the fabric during the printing and dyeing process; S4. Adopt intelligent water-saving system to automatically adjust the amount of flushing water according to the type of fabric and the printing and dyeing stage, and introduce wastewater recycling and reuse technology to treat wastewater through physical, chemical or biological methods; S5. Based on the intelligent monitoring system, the operating status and energy consumption of printing and dyeing equipment are monitored in real time to detect and warn of potential faults.

2. According to claim 1, a highly efficient, intelligent, short-process, water-saving printing and dyeing control method is characterized in that: Said S1 comprises: S11. Determine the key parameters closely related to the printing and dyeing process, and select corresponding high-precision sensors for each parameter; S12. Deploy sensors at key locations on the printing and dyeing production line and calibrate and maintain the sensors regularly; S13. Using the Internet of Things technology, the data collected by the sensor is transmitted to the cloud data center in real time. After receiving the data, the cloud data center performs preliminary data processing and storage; S14. Pre-process the stored data and analyze the changing trends of key parameters using statistical analysis methods.

3. According to claim 2, a highly efficient, intelligent, short-process, water-saving printing and dyeing control method is characterized in that: The S13 comprises: S131, compressing the original data by using a data compression algorithm at the sensor end; S132, transmitting data through the TCP protocol. Before data transmission, redundant backup of key data is performed. Once data loss or damage is detected, a retransmission mechanism is immediately triggered; S133, using the intelligent routing function of the IoT gateway, dynamically selecting the optimal transmission path according to the network conditions, building a distributed data receiving system, and using load balancing technology to evenly distribute the received data to multiple processing nodes; S134, using a parallel processing architecture in the cloud data center to perform parallel preliminary processing on the received data, wherein the preliminary processing includes data decompression, format conversion, and outlier detection; S135. Based on real-time data stream processing technology, the continuously arriving data stream is analyzed and processed in real time to detect and respond to abnormal changes in the data.

4. According to claim 3, a highly efficient, intelligent, short-process, water-saving printing and dyeing control method is characterized in that: The S133 includes: Deploy network monitoring nodes at key locations of the printing and dyeing production line to monitor key network indicators in real time, obtain data from network monitoring nodes, and apply data analysis algorithms to conduct real-time evaluation of network conditions and predict network change trends; Based on the results of real-time monitoring of network conditions, the optimal transmission path is selected based on the preset dynamic path selection strategy; the intelligent routing module is integrated in the IoT gateway to adjust the data transmission path in real time according to the dynamic path selection strategy; Deploy multiple data receiving nodes in the cloud data center to form a distributed data receiving system, and use a load balancing algorithm to dynamically allocate data transmission tasks based on the load conditions of the receiving nodes; Monitor the load of the receiving node in real time and dynamically adjust the load balancing strategy based on the results of real-time load monitoring; During the data transmission and reception process, a fault detection mechanism is implemented to discover and locate network faults or node faults, and handle them based on the fault recovery strategy.

5. According to claim 1, a highly efficient, intelligent, short-process, water-saving printing and dyeing control method is characterized in that: The S2 comprises: S21, obtaining historical printing and dyeing data, using data mining algorithms to extract key features from the historical data, and performing dimensionality reduction processing on the features; S22. Based on the extracted features, a printing and dyeing process model is established using a machine learning algorithm, and the model is trained and optimized; S23, through the model update mechanism, the model is regularly updated and optimized according to the newly collected data, and the real-time sensor data is input into the printing and dyeing process model to make real-time predictions on the printing and dyeing effects; S24. Automatically adjust the printing and dyeing parameters according to the prediction results, and fine-tune the prediction model according to the actual feedback of the printing and dyeing effect based on the feedback mechanism.

6. According to claim 5, a highly efficient, intelligent, short-process, water-saving printing and dyeing control method is characterized in that: The S23 comprises: Preprocess the data collected by the sensor data and transmit the preprocessed data to the cloud data center using data compression and reliable transmission protocols; Through the real-time data stream processing engine deployed in the cloud data center, the continuously arriving data stream is processed and analyzed in real time; Based on the data caching mechanism, newly collected data is temporarily stored and batch processed regularly; the incremental learning algorithm is used to enable the model to adapt to new data without retraining the entire data set; After each model update, the model performance is evaluated using cross-validation, and the trained model is deployed as a real-time prediction service. When real-time sensor data is received, a prediction request is immediately triggered and the data is input into the prediction service for real-time prediction. Based on the prediction result caching mechanism, cached results are directly returned for prediction requests that appear repeatedly within a short period of time.

7. According to claim 1, a highly efficient, intelligent, short-process, water-saving printing and dyeing control method is characterized in that: The S3 includes: S31, integrating the automatic control system with the printing and dyeing equipment, adjusting the operating parameters of the printing and dyeing equipment in real time, and introducing a redundant control system to switch to the backup system when the main control system fails; S32, based on the adaptive control strategy, dynamically adjust the control parameters according to the actual changes of the fabric during the printing and dyeing process; based on the neural network control algorithm, regularly evaluate and optimize the adaptive control strategy; S33. Monitor the operating status and energy consumption of printing and dyeing equipment in real time, and use machine learning algorithms to warn of potential failures. Once the warning is triggered, an alarm message is immediately sent to the operator and the emergency response mechanism is automatically activated.

8. According to claim 1, a highly efficient, intelligent, short-process, water-saving printing and dyeing control method is characterized in that: The S4 comprises: S41. Deploy an intelligent water-saving system in the flushing process of the printing and dyeing production line, wherein the intelligent water-saving system automatically adjusts the amount of flushing water according to the type of fabric and the printing and dyeing stage; and monitors the use of flushing water in real time through water-saving equipment; S42. Treat wastewater by physical, chemical or biological methods and regularly evaluate and optimize wastewater treatment processes; S43, based on intelligent monitoring and control system, real-time monitoring of key parameters in wastewater treatment process and real-time monitoring of treated water quality; S44. Make feedback adjustments to the wastewater treatment process based on water quality monitoring results.

9. According to claim 1, a highly efficient, intelligent, short-process, water-saving printing and dyeing control method is characterized in that: The S5 comprises: S51. Based on the intelligent monitoring system, the operating status, energy consumption and changes in key parameters of printing and dyeing equipment are monitored in real time, and the monitoring data are deeply mined and analyzed through big data analysis and artificial intelligence algorithms; S52. Use machine learning algorithms to warn of potential faults. Once the warning is triggered, an alarm message is immediately sent to operators at different levels based on the severity and urgency of the fault based on a multi-level alarm mechanism.

10. An efficient, intelligent, short-process, water-saving printing and dyeing control system, characterized in that: The system comprises: Data acquisition module: collects key parameters of the printing and dyeing process in real time through high-precision sensors, and transmits the data collected by the sensors to the cloud data center in real time through the Internet of Things; Model building module: Use big data and machine learning algorithms to mine historical printing and dyeing data and establish a printing and dyeing process model; based on real-time sensor data and the printing and dyeing process model, make real-time predictions on the printing and dyeing effects, and automatically adjust the printing and dyeing parameters according to the prediction results; Parameter adjustment module: Based on the results of intelligent analysis, the operating parameters of the printing and dyeing equipment are adjusted in real time through the automatic control system, and based on the adaptive control strategy, the control parameters are dynamically adjusted according to the actual changes of the fabric during the printing and dyeing process; Intelligent water-saving module: It adopts an intelligent water-saving system to automatically adjust the amount of flushing water according to the type of fabric and the printing and dyeing stage, and introduces wastewater recycling and reuse technology to treat wastewater through physical, chemical or biological methods; Fault warning module: Based on the intelligent monitoring system, it monitors the operating status and energy consumption of printing and dyeing equipment in real time, detects and warns of potential faults.

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