Textile production process optimization method based on intelligent terminal

Abstract

The invention discloses a spinning production process optimization method based on an intelligent terminal, and relates to the technical field of spinning production. According to the spinning production process optimization method based on the intelligent terminal, a sensor is integrated through the intelligent terminal, process parameters and yarn surface image data are obtained in real time and input into a spinning production process monitoring model, the matching degree of the process parameters and the yarn surface state is analyzed, and a process adaptation index is obtained. According to the process adaptation index, the yarn tension and the spinning speed are dynamically adjusted through a fuzzy control algorithm, and therefore the production process is optimized. Whether the textile equipment is abnormal or not is judged through the equipment position information and the process adaptation index, the inspection path is optimized through the A star algorithm, and it is ensured that the equipment is inspected and maintained in time. Intelligent monitoring and optimization adjustment in the spinning production process are achieved, the production efficiency, the yarn quality and the equipment operation stability are improved, and manual intervention and fault risks are reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of textile production, and in particular to a textile production process optimization method based on an intelligent terminal. Background Art

[0002] With the continuous development of the global manufacturing industry, the textile industry, as a traditional basic industry, occupies an important position in the global economy. In recent years, with the rapid progress of technologies such as artificial intelligence, the Internet of Things, and big data, the textile industry is also transforming towards intelligence and automation, gradually promoting the upgrading of production models. Especially in the context of increasingly fierce global competition, rising labor costs, and increased environmental protection requirements, textile companies are in urgent need of responding to changes in market demand by improving production efficiency, reducing costs, and improving product quality. In this process, intelligent textile production process optimization methods provide companies with new development opportunities. By applying intelligent terminal technology and real-time data monitoring, companies can achieve precise control of the production process, efficient use of resources, and real-time management of equipment, thereby maintaining a competitive advantage in the industry.

[0003] Although some textile enterprises have begun to introduce intelligent technologies in production, the existing textile production process optimization methods still face many challenges. Traditional control methods cannot flexibly adjust core process parameters according to real-time changes, resulting in product quality fluctuations and low production efficiency. Secondly, in a production environment where multiple devices operate in coordination, the operating status of the equipment varies greatly. Traditional resource scheduling methods cannot effectively make dynamic adjustments based on the real-time status of the equipment, resulting in low resource scheduling efficiency and uneven distribution of monitoring paths, which in turn affects production efficiency and the accuracy of equipment management. In addition, the existing image acquisition technology is interfered by factors such as light and image blur during the production process, resulting in inaccurate recognition of abnormal equipment status, affecting the monitoring accuracy of the production process and the effect of abnormality detection. Therefore, the existing methods cannot fully meet the needs of real-time, intelligent and resource optimization, and new technical means are urgently needed to solve these problems. Summary of the invention

[0004] In view of the shortcomings of the prior art, the present invention provides a textile production process optimization method based on an intelligent terminal, which can accurately analyze the matching degree between yarn quality and production process by real-time collection of spinning process data and yarn surface image data, and combine advanced intelligent algorithms such as fuzzy control and A-star algorithm, so as to realize automatic adjustment of spinning parameters, thereby improving the stability of the textile production process and yarn quality, reducing equipment failure rate, and improving production efficiency. The above-mentioned background technology problems are solved.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: a textile production process optimization method based on an intelligent terminal, comprising the following steps: S1. By connecting the sensor integrated in the smart glasses with the interface module of the textile equipment, the process parameters are obtained in real time, and the image of the yarn surface state is collected to obtain the yarn surface image data; S2. The process parameters and the yarn surface image data are input into the MES system, a textile production process monitoring model is constructed, the matching degree between the yarn surface state and the process parameters is analyzed, and the process adaptation index is obtained; S3. According to the process adaptation index, the yarn tension and spinning speed are dynamically adjusted through the fuzzy control algorithm; S4. According to the textile equipment position information and the process adaptation index, the abnormal working state of the textile equipment is judged, and the inspection trajectory is dynamically planned through the A star algorithm to optimize the inspection path; the MES system has the functions of receiving information, displaying the textile equipment position information and reporting the processing results; the process adaptation index is used to evaluate the matching degree between the yarn quality and the current production process parameters.

[0006] Furthermore, the process parameters include yarn tension, spinning speed, spinning temperature, and air flow; the yarn surface image data includes yarn thickness change rate, yarn color difference value, and yarn roughness.

[0007] Furthermore, the specific process of constructing a textile production process monitoring model is as follows: data cleaning, denoising and standardization of process parameters and yarn surface image data; extracting surface quality features from yarn surface image data, combining process parameters, constructing a data set, training the data set through a neural network, establishing a textile production process monitoring model, and analyzing the relationship between process parameters and yarn quality.

[0008] Furthermore, the specific process of analyzing the matching degree between the yarn surface state and the process parameters is as follows: the yarn surface image data and the process parameters are input into the established textile production process monitoring model; the correlation between the characteristics of the yarn surface image data and the process parameter characteristics is extracted, and the various features in the process parameters and the surface image data are compared. Based on the correlation function that has been trained in the textile production process monitoring model, the matching degree between the process parameters and the yarn surface quality is calculated, and by analyzing the matching index, it is identified whether the process parameters match the yarn surface state.

[0009] Furthermore, the specific process of obtaining the process adaptation index is as follows: according to the trained correlation function in the textile production process monitoring model, the relationship between the yarn surface state characteristics and the process parameters is analyzed, the matching degree between the process parameters and the yarn surface quality is identified, and the process adaptation index is obtained.

[0010] Furthermore, according to the process adaptation index, the specific process of dynamically adjusting the yarn tension and spinning speed through the fuzzy control algorithm is as follows: inputting the process adaptation index into the fuzzy control system; performing fuzzy reasoning according to the preset fuzzy rule library to analyze the relationship between the process adaptation index and the yarn tension and spinning speed; performing defuzzification processing to obtain the adjustment values ​​of the yarn tension and spinning speed; adjusting the yarn tension and spinning speed according to the defuzzification results, and monitoring the adjusted yarn tension and spinning speed in real time.

[0011] Furthermore, based on the location information and process adaptation index of the textile equipment, the specific process of judging the abnormal working state of the textile equipment is as follows: obtaining the location information and process adaptation index of the textile equipment; comparing the process adaptation index of the equipment with a preset threshold value based on the threshold range of the process adaptation index; when the process adaptation index exceeds the threshold range of the process adaptation index, it indicates that there is an abnormality in the textile equipment and triggers an alarm message and marks the abnormal position of the equipment.

[0012] Furthermore, the specific process of optimizing the inspection path by dynamically planning the inspection trajectory through the A-star algorithm is as follows: determine the textile equipment that needs to be inspected based on the alarm information, and obtain the process adaptation index of the textile equipment; construct a map model of the location of the textile equipment and define the connection relationship between the equipment; according to the process adaptation index of the equipment, analyze the process adaptation index and path cost of the equipment through the A-star algorithm, calculate the shortest path from the current equipment to the target equipment, and generate the inspection trajectory; monitor the process adaptation index in real time and dynamically adjust the inspection path.

[0013] The present invention has the following beneficial effects: (1) This textile production process optimization method based on smart terminals connects the sensor integrated in smart glasses with the interface module of textile equipment to obtain process parameters in real time and collect yarn surface images, so as to accurately monitor the quality changes of yarns during the textile production process. This process can capture the surface state of the yarn in detail by obtaining yarn surface image data, providing high-quality data support for subsequent analysis. After inputting these data into the MES system, combined with the textile production process monitoring model, the matching degree between the yarn surface state and the process parameters can be comprehensively analyzed, and then the process adaptation index can be generated. This real-time data collection and analysis method not only improves the transparency of the production process, but also provides a scientific basis for process optimization, which helps to achieve refined management of the production process and improve product quality and production efficiency.

[0014] (2) The textile production process optimization method based on the intelligent terminal can effectively realize the precise control of textile process parameters by dynamically adjusting the yarn tension and spinning speed according to the process adaptation index through the fuzzy control algorithm. The fuzzy control algorithm adjusts the yarn tension and spinning speed in real time based on the change of the process adaptation index, thereby reducing the quality fluctuation in the textile process and improving the consistency and stability of the yarn. This intelligent adjustment method not only reduces the errors caused by human intervention, but also improves the automation and intelligence level of the system and optimizes the production efficiency. Combining the location information and process adaptation index of the textile equipment, the inspection trajectory is dynamically planned through the A-star algorithm, and the inspection path is optimized, which can improve the efficiency and accuracy of equipment inspection. When the process adaptation index of the equipment exceeds the preset threshold, it can be timely judged whether the equipment is in an abnormal state, and the inspection path can be optimized to reduce invalid inspections and waiting time. By optimizing the inspection path, it is ensured that the equipment failure can be discovered and handled in time, reducing the downtime caused by equipment failure in the production process, and improving the overall production efficiency and equipment reliability.

[0015] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a flow chart of the textile production process optimization method based on intelligent terminals of the present invention. DETAILED DESCRIPTION

[0017] The embodiment of the present application solves the problems of unstable yarn quality, mismatched process parameters, and difficulty in timely detection of equipment failures in the textile production process through a textile production process optimization method based on an intelligent terminal.

[0018] The overall idea of ​​the problem in the embodiment of this application is as follows: By connecting the sensor integrated in the smart glasses with the interface module of the textile equipment, the process parameters can be obtained in real time, and the image of the yarn surface state can be collected to obtain the yarn surface image data.

[0019] The process parameters and yarn surface image data are input into the MES system, a textile production process monitoring model is constructed, the matching degree between the yarn surface state and the process parameters is analyzed, and the process adaptation index is obtained.

[0020] According to the process adaptation index, the yarn tension and spinning speed are dynamically adjusted through the fuzzy control algorithm.

[0021] According to the location information of textile equipment and the process adaptation index, the abnormal working status of the textile equipment is judged, and the inspection trajectory is dynamically planned through the A-star algorithm to optimize the inspection path.

[0022] See also Figure 1 , an embodiment of the present invention provides a technical solution: a textile production process optimization method based on a smart terminal, comprising the following steps: S1. By connecting the sensor integrated in the smart glasses with the interface module of the textile equipment, the process parameters are obtained in real time, and the image of the yarn surface state is collected to obtain the yarn surface image data; S2. The process parameters and the yarn surface image data are input into the MES system, a textile production process monitoring model is constructed, the matching degree between the yarn surface state and the process parameters is analyzed, and the process adaptation index is obtained; S3. According to the process adaptation index, the yarn tension and the spinning speed are dynamically adjusted through the fuzzy control algorithm; S4. According to the textile equipment position information and the process adaptation index, the abnormal working state of the textile equipment is judged, and the inspection trajectory is dynamically planned through the A star algorithm to optimize the inspection path; the MES system has the functions of receiving information, displaying the textile equipment position information and reporting the processing results; the process adaptation index is used to evaluate the matching degree between the yarn quality and the current production process parameters.

[0023] In this embodiment, step S1: smart glasses: a smart device with sensors that can collect environmental data in real time and display feedback information. In the present invention, smart glasses are connected to textile equipment through integrated sensors (such as temperature sensors, pressure sensors, optical sensors, etc.) to obtain real-time process parameters (such as yarn tension, rotation speed, etc.) and collect image data of yarn surface state through cameras. The use of smart glasses can improve the operator's work efficiency and provide real-time feedback of important process data in the production process. Process parameters: refers to key parameters that need to be controlled and optimized in the textile production process. Yarn surface state image data: The yarn surface is imaged through the camera on the smart glasses, and these image data can be used for subsequent quality analysis after processing. Step S2: MES system: Manufacturing Execution System is a system for real-time management of production site operations. The MES system can integrate data from various links (such as process parameters, equipment status, production tasks, etc.) and display, analyze and process them in real time. The role of this system is to connect production equipment with management to ensure smooth and efficient production process. Textile production process monitoring model: This model integrates spinning process data and yarn surface image data to establish a data analysis model for monitoring each link in the production process. The main function of this model is to analyze the relationship between process parameters and yarn quality, evaluate whether the production process has achieved the expected goals, and provide optimization suggestions. Process adaptation index: The process adaptation index is a value calculated based on the matching degree between the yarn surface quality and the process parameters (such as tension, speed, etc.) collected during the production process. This index is used to measure the adaptability of the current process parameters to the yarn quality. The higher the process adaptation index, the better the matching degree between the process parameters and the yarn surface quality, and the more stable the yarn quality. Step S3: Fuzzy control algorithm: Fuzzy control is a control method based on fuzzy logic, which is suitable for dealing with uncertainty and fuzziness problems. In the present invention, the fuzzy control algorithm dynamically adjusts the yarn tension and spinning speed according to the process adaptation index. Through fuzzy control, the system can automatically adjust the parameters in the production process according to the real-time changing process adaptation index to ensure that the yarn quality is optimized during the production process. Step S4: Textile equipment location information: refers to the specific location of the textile equipment at the production site. This information is usually provided by a positioning system (such as GPS, RFID or other sensors) to help monitor and manage the location changes of the equipment. A* algorithm: A* algorithm is a heuristic search algorithm commonly used for path planning, which can find the shortest path from the starting point to the target point in a known map. In the present invention, A* algorithm monitors the process adaptation index of the equipment in real time, dynamically plans the inspection trajectory, and optimizes the inspection path, so that the inspection personnel can check the equipment status and perform maintenance in the shortest time.The application of the A-star algorithm can improve inspection efficiency and ensure that equipment can be repaired in a timely manner when an abnormality occurs. Functions of the MES system Receive information: The MES system can receive real-time data from textile equipment, including process parameters, equipment status, image data, etc. This information provides data support for subsequent production process analysis and optimization. Display textile equipment location information and report processing results: The system will display the equipment location information, process parameter matching, and processing results (such as abnormal status alarms, etc.) to production managers in real time to facilitate decision-making and adjustments.

[0024] Specifically, the process parameters include: yarn tension, spinning speed, spinning temperature, and air flow; the yarn surface image data includes yarn thickness change rate, yarn color difference value, and yarn roughness.

[0025] In this embodiment, yarn tension: yarn tension refers to the tension or pressure on the yarn during the spinning process. This parameter has an important influence on the shape, strength and quality of the yarn. Too high or too low yarn tension will cause the yarn to break or deform, thereby affecting the quality of the final product. Spinning speed: spinning speed refers to the speed at which the spinning machine rotates or the textile equipment processes the yarn during the spinning process. The spinning speed directly affects the spinning quality and production efficiency of the yarn. Too high a spinning speed may cause uneven stretching or breakage of the yarn, while too low a speed may cause a decrease in production efficiency. Spinning temperature: spinning temperature refers to the temperature of the environment or the inside of the equipment during the spinning process. Spinning temperature plays an important role in the processing of fiber materials, and too high or too low a temperature may cause the quality of the yarn to decrease. Air flow: air flow refers to the air flow speed or air volume used to support or adjust the yarn during the spinning process. In textile equipment, air flow can be used for processes such as yarn transportation, shaping or cooling. Yarn thickness change rate: yarn thickness change rate refers to the degree of thickness difference between different parts of the yarn during the production process. It is usually measured by image data of the yarn surface. This parameter is used to evaluate the consistency of the yarn. Yarns with large variations in thickness usually result in unstable product quality. By monitoring this parameter in real time, process parameters can be adjusted in time to ensure the consistency of the yarn. Yarn color difference value: Yarn color difference value refers to the color difference between different parts of the yarn or between different batches. It calculates the degree of color difference in each area of ​​the yarn surface through image processing technology. Yarns with excessive color difference are usually considered to be of unqualified quality and may affect the appearance quality of the final fabric. Real-time monitoring of the color difference value of the yarn can ensure the uniform color of the product. Yarn roughness: Yarn roughness refers to the unevenness of the yarn surface, which is usually quantified by image acquisition and analysis of the yarn surface morphology. Higher roughness may cause the yarn to be rough and affect the feel and appearance of the fabric. By controlling the roughness, the quality of the yarn can be improved, making its surface smoother and more uniform.

[0026] Specifically, the specific process of constructing a textile production process monitoring model is as follows: data cleaning, denoising and standardization of process parameters and yarn surface image data; extracting surface quality features from yarn surface image data, combining process parameters, constructing a data set, training the data set through a neural network, establishing a textile production process monitoring model, and analyzing the relationship between process parameters and yarn quality.

[0027] In this implementation scheme, the collected process parameters and yarn surface image data are preprocessed, including: Data cleaning: removing missing or inconsistent data points to ensure data integrity. Denoising: eliminating noise in image data to ensure the clarity of surface images and improve the accuracy of subsequent analysis. Standardization: normalizing the data so that each feature value is within the same range and eliminating the impact of different scales.

[0028] Extract features related to surface quality from yarn surface image data. Common features include: Thickness change rate: unevenness of yarn thickness. Color difference: the degree of change in yarn color. Roughness: the smoothness or roughness of the yarn surface. Construct a data set. Combine the extracted features with the corresponding process parameters (such as tension, speed, temperature, etc.) to construct a complete data set as the input data of the neural network. Each row of data includes the surface features of the yarn and the corresponding process parameter values. Use the constructed data set to train the neural network. The neural network forms a model by learning the complex relationship between process parameters and yarn surface quality. The network adjusts parameters according to the training data and gradually optimizes to accurately predict the impact of process parameters on yarn quality. Based on the trained neural network, a textile production process monitoring model is established. The model can input new process parameters and yarn image data in real time, and output the evaluation results of yarn quality, which helps monitor the stability and quality of textile production.

[0029] Specifically, the specific process of analyzing the matching degree between the yarn surface state and the process parameters is as follows: the yarn surface image data and the process parameters are input into the established textile production process monitoring model; the correlation between the characteristics of the yarn surface image data and the process parameter characteristics is extracted, and the various features in the process parameters and the surface image data are compared. Based on the correlation function that has been trained in the textile production process monitoring model, the matching degree between the process parameters and the yarn surface quality is calculated, and by analyzing the matching index, it is identified whether the process parameters match the yarn surface state.

[0030] In this embodiment, the yarn surface image data and process parameters collected in real time are input into the established textile production process monitoring model. The extracted features are compared based on the association function that has been trained in the textile production process monitoring model. Association function: A mathematical function obtained through model training, which is used to describe the relationship between process parameters and yarn surface quality characteristics. The degree of match between process parameters and yarn surface quality is calculated, and this value is usually expressed as a matching index. Matching analysis: By analyzing the matching index, it is determined whether the current process parameters are adapted to the surface state of the yarn. If the matching index is within a preset reasonable range, it means that the process parameters match the yarn surface state; otherwise, it means that the process parameters may need to be adjusted.

[0031] Specifically, the specific process of obtaining the process adaptation index is as follows: according to the trained correlation function in the textile production process monitoring model, the relationship between the yarn surface state characteristics and the process parameters is analyzed, the matching degree between the process parameters and the yarn surface quality is identified, and the process adaptation index is obtained.

[0032] In this embodiment, multi-level nonlinear correlation functions are used to extract the complex nonlinear relationship between process parameters and yarn surface state. The output of each layer is based on the weighted and activated function processing results of the previous layer. Specifically, the model can be expressed as: in: It is a vector of process parameters, including yarn tension, spinning speed, spinning temperature, and air flow. It is a vector of yarn surface state characteristics, including yarn thickness change rate, yarn color difference value, and yarn roughness. and are the first layer weights, corresponding to process parameters and surface state characteristics respectively. is the bias term of the first layer. is an activation function, used to introduce nonlinearity. This formula represents the weighted sum and activation processing of process parameters and yarn surface state characteristics in the first layer. In order to introduce more complex nonlinear relationships, we can use a multi-level structure in the neural network. Each layer takes the output of the previous layer as input to form a deep feature learning: ;in: It is the output of the second layer, which means the output of the first layer is processed by the activation function. and is the weight of the third layer. is the activation function of the third layer. This process can be extended to more layers to form a deeper network architecture. The weights and biases of each layer are trained through the back propagation algorithm, and the optimized parameters can eventually capture the relationship between complex input features. Based on the trained deep neural network, the process adaptation index (WED) is calculated by the network output layer: ;in: is the matching degree of the neural network output. is the weight coefficient, controlling the contribution of each item to the final matching index, i represents each element in the process parameters, j represents each element in the yarn surface state characteristics, n is the number of process parameters, and m is the number of yarn surface state characteristics. and The mean of process parameters and yarn surface state characteristics ensures that the model can be normalized and reduce the influence range of different characteristics. Specifically, according to the process adaptation index, the specific process of dynamically adjusting the yarn tension and spinning speed through the fuzzy control algorithm is as follows: input the process adaptation index into the fuzzy control system; perform fuzzy reasoning according to the preset fuzzy rule library to analyze the relationship between the process adaptation index and the yarn tension and spinning speed; perform defuzzification to obtain the adjustment value of the yarn tension and spinning speed; adjust the yarn tension and spinning speed according to the defuzzification results, and monitor the adjusted yarn tension and spinning speed in real time.

[0033] In this embodiment, the process adaptation index is calculated by the previously established textile production process monitoring model to evaluate the matching degree between the current process parameters and the yarn quality. This index reflects the optimization space in the textile production process and helps the control system determine whether the process parameters need to be adjusted.

[0034] This index is passed into the fuzzy control system as an input variable to provide a basis for subsequent control decisions. Fuzzy reasoning is performed according to the preset fuzzy rule base: In a fuzzy control system, the fuzzy rule base contains a set of "if-then" type rules that define the relationship between the process adaptation index and the yarn tension and spinning speed. For example: If the process adaptation index is low, the yarn tension and spinning speed should be increased to improve the production effect. If the process adaptation index is high, the yarn tension and spinning speed should be reduced to avoid excessive tension causing yarn damage. The fuzzy reasoning process infers the input process adaptation index according to these rules to determine the adjustment direction and degree of yarn tension and spinning speed. Defuzzification is performed to obtain the adjustment values ​​of yarn tension and spinning speed: Defuzzification is a key step in fuzzy control systems. In fuzzy reasoning, the output variables (i.e., the adjustment values ​​of yarn tension and spinning speed) are usually fuzzy, and these values ​​are inferred based on fuzzy sets (such as "high", "medium", and "low"). The fuzzy output results are converted into precise adjustment values ​​through defuzzification technology. These adjustment values ​​will directly guide the textile equipment on how to adjust the yarn tension and spinning speed. Adjust the yarn tension and spinning speed according to the defuzzification results: According to the defuzzified adjustment values, the control system will issue adjustment instructions to change the settings of the textile equipment, thereby realizing dynamic adjustment of the yarn tension and spinning speed. This helps to optimize the production process in real time and ensure that the quality of the yarn always meets the standards. Real-time monitoring of adjusted yarn tension and spinning speed: After adjustment, the system will continue to monitor the yarn tension and spinning speed in real time and compare them with the target value. If the actual value deviates from the target value, the system will make dynamic adjustments again. This closed-loop control ensures that the quality of the yarn can always be maintained at an optimal level during the production process.

[0035] Specifically, the specific process of judging the abnormal working state of textile equipment based on the location information and process adaptation index of textile equipment is as follows: obtaining the location information and process adaptation index of the textile equipment; comparing the process adaptation index of the equipment with a preset threshold value based on the threshold range of the process adaptation index; when the process adaptation index exceeds the threshold range of the process adaptation index, it indicates that there is an abnormality in the textile equipment and triggers an alarm message and marks the abnormal position of the equipment.

[0036] In this embodiment, the location information of textile equipment: In the textile production process, the actual location of the equipment is very important, especially in large-scale production lines where multiple equipment is distributed in different locations. The location information helps the system accurately identify the location of the equipment for easy management and monitoring. Process adaptation index: As mentioned above, the process adaptation index is used to measure the degree of match between the current production process (such as tension, speed, etc.) and the yarn quality (such as surface smoothness, roughness, etc.). This index value can indicate whether the current production status of the textile equipment meets the expected process requirements. According to the threshold range of the process adaptation index, the process adaptation index of the equipment is compared with the preset threshold: The process adaptation index of each equipment has a normal range (i.e., the threshold range), which is usually preset based on historical data, process requirements, and production standards. Threshold comparison: According to the process adaptation index of the equipment, it is compared with the preset threshold range. For example: If the process adaptation index is higher than the threshold, it may indicate that the equipment has been over-adjusted or the production is abnormal (such as too high tension or speed), resulting in substandard product quality. If the process adaptation index is lower than the threshold, it may indicate that the equipment is not operating properly (such as equipment failure, resulting in substandard yarn quality). If the process adaptation index is within the normal range, the equipment status is normal; if it exceeds the threshold range, the system will consider that the equipment is abnormal. When the process adaptation index exceeds the threshold range of the process adaptation index, it means that the textile equipment is abnormal and triggers the alarm information and marks the abnormal position of the equipment: Abnormal state judgment: When the process adaptation index of the equipment exceeds the predetermined normal range, it means that the equipment may have a performance failure or improper process setting. For example, there may be excessive tension, excessive temperature or unreasonable speed, resulting in problems with the surface quality of the yarn. Triggering alarm information: When the system detects an abnormality, it will automatically trigger an alarm. This usually includes sound, light alarm, SMS notification or other alarm methods to notify the operator or maintenance personnel to deal with it immediately. Mark the abnormal position of the equipment: By combining with the location information of the equipment, the system can mark the location of the equipment where the abnormality occurs, helping staff to quickly locate the problem equipment for inspection or maintenance. This mark can also be displayed on the operation interface by the system or automatically recorded in the equipment log.

[0037] Specifically, the specific process of dynamically planning the inspection trajectory through the A-star algorithm and optimizing the inspection path is as follows: determine the textile equipment that needs to be inspected based on the alarm information, and obtain the process adaptation index of the textile equipment; build a map model of the location of the textile equipment and define the connection relationship between the equipment; according to the process adaptation index of the equipment, use the A-star algorithm to analyze the process adaptation index and path cost of the equipment, calculate the shortest path from the current equipment to the target equipment, and generate the inspection trajectory; monitor the process adaptation index in real time and dynamically adjust the inspection path.

[0038] In this embodiment, the textile equipment that needs to be inspected is determined according to the alarm information, and the process adaptation index of the textile equipment is obtained: the system identifies the equipment that needs to be inspected through the alarm information received by the system (for example, equipment abnormality, process adaptation index exceeds the threshold, etc.). The process adaptation index associated with each device is obtained, which represents the matching degree between the current operating state of the equipment and the optimal process parameters, and is used to determine whether the equipment needs to be inspected or maintained. A map model of the location of the textile equipment is constructed, and the connection relationship between the equipment is defined: according to the physical location of the equipment, a map model of the location of the equipment is constructed. This model converts the equipment location into a network graph, in which the equipment nodes represent different textile equipment, and the connection relationship between the equipment represents the path between them. The connection relationship between the equipment can be a directly connected path (such as physical distance, connection of the production line, etc.) or connected through a preset path. According to the process adaptation index of the equipment, the process adaptation index and path cost of the equipment are analyzed by the A-star algorithm: the A-star algorithm is a heuristic search algorithm for finding the shortest path in a graph. In this step, the A-star algorithm not only considers the physical distance between the equipment (path cost), but also combines the process adaptation index of each equipment as an additional cost. Path cost: including the distance between devices, the working status of the devices (such as process adaptation index), etc. The higher the process adaptation index, the closer the status of the device is to the ideal process, and the lower the path cost may be; conversely, devices with low process adaptation index may require longer inspection time or adjustment, so the path cost may be higher. Calculate the shortest path from the current device to the target device and generate the inspection trajectory: Use the A-star algorithm to calculate the shortest path from the current inspection device to the target device. This path not only considers the distance, but also adjusts the weight according to the process adaptation index of each device to ensure that the inspector inspects the equipment along the optimal path. Generate inspection trajectory: Based on the calculated shortest path, the system plans the route that the inspector should follow to ensure the optimal use of resources and time. Real-time monitoring of the process adaptation index and dynamic adjustment of the inspection path: As the inspection progresses, the system continuously monitors the process adaptation index of the equipment to ensure real-time acquisition of equipment status updates. If the process adaptation index of a certain device changes during the inspection, the system will automatically adjust the inspection path to ensure that the actual status of the device matches the planned inspection path, thereby achieving dynamic optimization.

[0039] In summary, this application has at least the following effects: The textile production process optimization method based on intelligent terminals can obtain process parameters and yarn surface state in real time, automatically adjust yarn tension and spinning speed, optimize production process, reduce manual intervention, and improve the automation and efficiency of the production process. By accurately analyzing the matching degree between process parameters and yarn surface state, the best matching between production process and yarn quality can be ensured, thereby improving the stability and quality of products and reducing the generation of defective products. Based on dynamic monitoring of process adaptation index, combined with A star algorithm to optimize inspection path, equipment abnormality can be found in time and alarm can be triggered, ensuring that equipment problems can be found and repaired as soon as possible, thereby reducing the risk of failure rate and production interruption. The application of A star algorithm in inspection path planning enables inspectors to efficiently complete equipment inspection tasks. By combining the process adaptation index of equipment, optimizing inspection sequence and path, ensuring the reasonable allocation of inspection resources, and improving inspection efficiency. Through the combination of fuzzy control algorithm and real-time monitoring system, this application can intelligently adjust production parameters and optimize equipment management, reduce human intervention, and improve the flexibility and adaptability of production process.

[0040] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0041] The present invention is described with reference to flowcharts and / or block diagrams of systems, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0042] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1A function specified in one or more boxes.

[0043] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0044] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0045] 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. A textile production process optimization method based on intelligent terminals, characterized in that: The following steps are involved: S1. By connecting the sensor integrated in the smart glasses with the interface module of the textile equipment, the process parameters are obtained in real time, and the image of the yarn surface state is collected to obtain the yarn surface image data; S2. Input the process parameters and yarn surface image data into the MES system, build a textile production process monitoring model, analyze the matching degree between the yarn surface state and the process parameters, and obtain the process adaptation index; S3. dynamically adjust the yarn tension and spinning speed through fuzzy control algorithm according to the process adaptation index; S4. According to the location information of textile equipment and the process adaptation index, the abnormal working state of textile equipment is judged, and the inspection trajectory is dynamically planned through the A-star algorithm to optimize the inspection path; The MES system has the functions of receiving information, displaying textile equipment location information and reporting processing results; The process adaptation index is used to evaluate the matching degree between the yarn quality and the current production process parameters.

2. The textile production process optimization method based on intelligent terminal according to claim 1 is characterized in that: The process parameters include yarn tension, spinning speed, spinning temperature, air flow, and twist; The yarn surface image data includes yarn thickness change rate, yarn color difference value, and yarn roughness.

3. The textile production process optimization method based on intelligent terminal according to claim 2 is characterized in that: The specific process of building a textile production process monitoring model is as follows: Perform data cleaning, denoising and standardization on process parameters and yarn surface image data; Surface quality features are extracted from yarn surface image data, combined with process parameters, a data set is constructed, the data set is trained through a neural network, a textile production process monitoring model is established, and the relationship between process parameters and yarn quality is analyzed.

4. The textile production process optimization method based on intelligent terminal according to claim 3 is characterized in that: The specific process of analyzing the matching degree between yarn surface state and process parameters is as follows: Input yarn surface image data and process parameters into the established textile production process monitoring model; The correlation between the features of the yarn surface image data and the process parameter features is extracted, and the features in the process parameters and surface image data are compared. Based on the trained correlation function in the textile production process monitoring model, the matching degree between the process parameters and the yarn surface quality is calculated. By analyzing the matching index, it is identified whether the process parameters match the yarn surface state.

5. The textile production process optimization method based on intelligent terminal according to claim 4 is characterized in that: The specific process of obtaining the process adaptation index is as follows: According to the trained correlation function in the textile production process monitoring model, the relationship between the yarn surface state characteristics and the process parameters is analyzed, the matching degree between the process parameters and the yarn surface quality is identified, and the process adaptation index is obtained.

6. The textile production process optimization method based on intelligent terminal according to claim 5 is characterized in that: According to the process adaptation index, the specific process of dynamically adjusting the yarn tension and spinning speed through the fuzzy control algorithm is as follows: Inputting the process adaptation index into the fuzzy control system; Fuzzy reasoning is performed based on the preset fuzzy rule base to analyze the relationship between the process adaptation index and the yarn tension and spinning speed; Perform defuzzification to obtain adjustment values ​​of yarn tension and spinning speed; The yarn tension and spinning speed are adjusted according to the defuzzification results, and the adjusted yarn tension and spinning speed are monitored in real time.

7. The textile production process optimization method based on intelligent terminal according to claim 6 is characterized in that: According to the location information of textile equipment and the process adaptation index, the specific process of judging the abnormal working state of textile equipment is as follows: Obtain location information and process adaptation index of textile equipment; According to the threshold range of the process fitness index, the process fitness index of the equipment is compared with a preset threshold value; When the process adaptation index exceeds the threshold range of the process adaptation index, it indicates that there is an abnormality in the textile equipment and triggers an alarm message and marks the abnormal location of the equipment.

8. The method for optimizing textile production process based on intelligent terminal according to claim 7 is characterized in that: The specific process of optimizing the inspection path by dynamically planning the inspection trajectory through the A-star algorithm is as follows: Determine the textile equipment that needs to be inspected based on the alarm information, and obtain the process adaptation index of the textile equipment; Build a map model of the location of textile equipment and define the connection relationship between equipment; According to the process adaptation index of the equipment, the A-star algorithm is used to analyze the process adaptation index and path cost of the equipment, calculate the shortest path from the current equipment to the target equipment, and generate the inspection trajectory; Monitor the process adaptation index in real time and dynamically adjust the inspection path.

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