Intelligent textile production line control method and system

Abstract

The invention discloses an intelligent textile production line control method and system, and relates to the technical field of industrial automation, and the method comprises the steps: collecting operation data in a textile production process in real time, and carrying out the data preprocessing; dynamically adjusting process parameters of the textile production line based on the preprocessed data; detecting the quality state in the textile production process in real time, and adjusting the operation of the production line based on the detection result; constructing a multi-feature fusion fault prediction model, and predicting the operation state of the production line equipment; potential faults are recognized in advance according to prediction results, and the operation mode of the production line is adjusted. The method combines multi-feature fusion analysis, dynamically optimizes the equipment operation state, identifies and adjusts potential faults in advance, improves the production quality and efficiency, reduces the maintenance cost and the shutdown risk, is significantly superior to a traditional method, and provides powerful technical support for intelligent upgrading of the textile industry.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial automation, and in particular to an intelligent textile production line control method and system. Background Art

[0002] With the rapid advancement of Industry 4.0, textile production lines are gradually developing towards intelligence and automation. Traditional textile production lines are mostly mechanized and rely on manual monitoring and experience to adjust production parameters. However, with the growing market demand for high-quality textiles, the limitations of traditional methods are gradually emerging. In recent years, the widespread application of Internet of Things (IoT) technology, artificial intelligence (AI) algorithms, and big data analysis technology has made it possible to upgrade textile production lines to intelligence. For example, real-time collection and analysis of operating data during the production process can optimize the production process; quality inspection technology based on machine vision significantly improves the efficiency of defect identification; combined with the prediction model of equipment operating status, it helps to reduce downtime caused by equipment failure. Despite this, the current textile production line control technology still has many shortcomings, making it difficult to achieve comprehensive optimization and intelligent control of the entire production process.

[0003] Although the existing textile production line control technology has made some progress in data collection, process adjustment, quality inspection, etc., it still has significant deficiencies and is difficult to meet the needs of efficient and intelligent production. First, in terms of data processing, most technologies only stay at the simple sensor data collection stage, and fail to achieve deep fusion and feature extraction of multi-source data, resulting in poor real-time and accuracy of production parameter adjustment. Secondly, in terms of dynamic adjustment of production line process parameters, the existing technology is often based on fixed rule control logic, lacks the ability to combine real-time data with intelligent adjustment, and is difficult to cope with complex production environment changes. Furthermore, in the field of quality inspection, most of the existing inspection systems are based on static rule settings and cannot efficiently identify dynamic quality problems of products, such as surface defects and color uniformity changes. In addition, the existing technology also has obvious deficiencies in equipment failure prediction, which is mainly reflected in: the monitoring means of equipment operation status is single, and potential multi-feature failures cannot be effectively predicted; there is a lack of a comprehensive health scoring mechanism, which makes it difficult to quantify equipment status and guide production line adjustments. Finally, in terms of adjustment of operation modes, the existing methods rely more on manual intervention, making it difficult to form real-time closed-loop optimization control. In summary, the existing technology cannot achieve comprehensive intelligent control of textile production lines. Summary of the invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problems solved by the present invention are: the existing textile production line control methods have insufficient data processing capabilities, lack of real-time dynamic process adjustment, limited equipment failure prediction capabilities, and how to achieve comprehensive intelligent control of the production line.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: In the first aspect, an embodiment of the present invention provides an intelligent textile production line control method, including real-time collection of operating data in the textile production process and data preprocessing; dynamically adjusting the process parameters of the textile production line based on the preprocessed data; real-time detection of the quality status in the textile production process, and adjusting the production line operation based on the detection results; constructing a multi-feature fusion fault prediction model to predict the operating status of production line equipment; identifying potential faults in advance according to the prediction results, and adjusting the operation mode of the production line.

[0007] As a preferred solution of the intelligent textile production line control method of the present invention, wherein: the real-time collection of operating data during the textile production process includes operating parameter data of production equipment, environmental parameter data and product quality data; The operating parameter data of the production equipment include motor speed, equipment load, bearing temperature and vibration signal; The environmental parameter data include temperature, humidity and air velocity in the production area; The product quality data includes the tension, weft yarn density and surface color uniformity of the textile.

[0008] As a preferred solution of the intelligent textile production line control method of the present invention, the data preprocessing includes noise filtering and time domain feature extraction of the operating parameter data of the production equipment, wherein the vibration signal is processed by wavelet denoising method, and the motor speed and equipment load data are smoothed and filtered to eliminate instantaneous fluctuations; Perform time series smoothing and multi-dimensional data fusion on environmental parameter data, process temperature and humidity data using the sliding average method, and perform trend analysis on air velocity to evaluate the stability of the production environment; Feature extraction and anomaly detection are performed on product quality data. The textile tension and weft density data are used to eliminate outliers through threshold judgment, and the surface color uniformity data is optimized using the histogram equalization method.

[0009] As a preferred solution of the intelligent textile production line control method of the present invention, the dynamically adjusting the process parameters of the textile production line includes calculating the real-time change trend of the motor speed by using a sliding average algorithm based on the operating parameter data of the production equipment, and dynamically adjusting the spinning speed and loom tension adjustment parameters in combination with the equipment load data; Based on environmental parameter data, the PID control method is used to adjust the operating status of the humidification device or air conditioning system, and the temperature and humidity of the production area are maintained within the target range by adjusting the output power in real time; Based on product quality data and the correlation between tension and weft yarn density, an optimization model is constructed to dynamically adjust the weft yarn density. Based on the color deviation analysis method, adjustment parameters of dye solution concentration and coating speed are generated to optimize the dyeing and finishing process.

[0010] As a preferred solution of the intelligent textile production line control method of the present invention, wherein: the adjusting the operation of the production line includes acquiring a surface image of the textile by a machine vision system, analyzing the defect type and position by a convolutional neural network algorithm, and generating an adjustment signal to control the operation mode of the loom; The real-time tension data is obtained through the tension sensor, and the tension deviation value is calculated to generate a signal for adjusting the loom running speed and the weft yarn input speed; Real-time detection of weft density data, and optimization of the feeding speed and operation mode of the spinning equipment in combination with the dynamic adjustment model; Based on the surface color uniformity test results, the dynamic color difference analysis model is used to calculate the operating parameters of the dyeing and finishing equipment, and the dye concentration and coating speed are adjusted; Through the above detection and adjustment, a closed-loop feedback mechanism is formed to continuously update the equipment operating parameters to adapt to real-time changes in quality status.

[0011] As a preferred solution of the intelligent textile production line control method of the present invention, the multi-feature fusion fault prediction model is constructed, which includes: based on the vibration signal of the production line equipment, the collected vibration time series data is Perform fast Fourier transform and calculate the frequency domain energy eigenvalue. The calculation formula is: ; in, is the characteristic value of the vibration signal, and are the lower and upper limits of the frequency of the vibration signal, respectively. is the frequency domain eigenvalue of the vibration signal; is the attenuation factor of the high-frequency signal, which is used to reduce the impact of high-frequency noise on the results; is the frequency variable of the current integration; It is a small increment of frequency and is the basic unit of frequency domain integration, used to gradually accumulate signal characteristics within the frequency domain; Based on the bearing temperature data, the time series Perform second-order derivative calculation and integral trend analysis to extract temperature change characteristic values. The calculation formula is: ; in, is the temperature characteristic value, is the time series function of bearing temperature; The second derivative of temperature change is used to evaluate the acceleration trend of temperature; is the partial derivative; is the decay integral of the temperature trend, is the temperature fluctuation suppression factor; For time series The sinusoidal transformation reflects the periodic fluctuation characteristics of temperature changes; is time, which is the basic variable of time series data; Based on the motor current data, the collected current signal Perform time series analysis and extract the abnormal characteristics of the current signal through the autoregressive moving average model. The characteristic value calculation formula is: ; in, is the current characteristic value, For the The current sampling value, is the nonlinear amplification factor; Express The summation process of current sampling points; The prediction of the operating status of the production line equipment includes: synthesizing the characteristic values ​​to generate the equipment health score , and its calculation formula is: ; in, They are the normalized results of vibration, temperature and current characteristic values ​​respectively; is the total number of feature values, indicating the number of features considered in the calculation of the health score; For the A characteristic value represents the operating status of the device in a certain dimension; For the The reference value of the characteristic value is used to compare the deviation between the actual characteristic value and the target state; It is a characteristic difference inhibitor; The normalization formula is: ; in, is the normalized eigenvalue, is the original eigenvalue, is the maximum eigenvalue, is the minimum eigenvalue.

[0012] As a preferred solution of the intelligent textile production line control method of the present invention, the method of identifying potential faults in advance according to the prediction results includes: The potential failure risks are divided into multiple levels according to different value ranges, and corresponding production line adjustment strategies are adopted for different levels of risks; When health score In the high-risk range, When the load is too low, the equipment with the abnormality will be stopped and the standby equipment will be switched to ensure the continuity of the production line. The overall speed of the production line will be reduced and the spinning load will be redistributed to the low-load equipment. The adjusted load distribution formula is: ; in, For the The load distribution value of each device, is the total load, Weight the device health score; When health score In the medium risk range, When the equipment operating parameters are adjusted dynamically, including reducing the spinning speed, tension adjustment value and coating speed of the dyeing and finishing process, the dynamic adjustment formula is: ; in, are the adjusted operating parameters, is the current running parameter value, For reference health scores; monitor the equipment operating status in real time to detect whether there is a new downward trend in health scores; When health score In the low risk range, When the device is in a state of emergency, the current operation mode is maintained, and equipment health reports are generated regularly to assess possible long-term risks. The adjustment process is achieved through a closed-loop feedback mechanism, that is, after adjusting the operating mode, the equipment health score is recalculated and the operating status of the production line is continuously optimized until the health score of all equipment is higher than the safety threshold.

[0013] In a second aspect, an embodiment of the present invention provides an intelligent textile production line control system, including: Data acquisition module: real-time collection of operating data during the textile production process and data preprocessing; Parameter adjustment module: dynamically adjust the process parameters of the textile production line based on the pre-processed data; Operation adjustment module: Real-time detection of the quality status of the textile production process and adjustment of the production line operation based on the detection results; Fault prediction module: builds a multi-feature fusion fault prediction model to predict the operating status of production line equipment; Operation mode optimization module: Identify potential failures in advance based on prediction results and adjust the operation mode of the production line.

[0014] In a third aspect, an embodiment of the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0015] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps of the method described above when executed by a processor.

[0016] The beneficial effects of the present invention are as follows: by collecting the operation data in the textile production process in real time and performing multi-source fusion and deep feature extraction, efficient use and standardization of data are achieved; by dynamically adjusting the process parameters of the textile production line, the real-time and accuracy of the production process are improved; by real-time quality detection and dynamically adjusting the production line operation based on the detection results, the product qualification rate and the stability of the production line are significantly improved; by constructing a multi-feature fusion fault prediction model, the equipment operation status is predicted, potential faults are identified in advance, and equipment downtime and maintenance costs are reduced; by optimizing and adjusting the production line operation mode based on the prediction results, a real-time closed-loop intelligent control system is formed, which realizes the comprehensive intelligent, automated and efficient operation of the textile production line. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work, among which: Figure 1 This is an overall flow chart of an intelligent textile production line control method provided for the first embodiment of the present invention. DETAILED DESCRIPTION

[0018] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0019] Example 1, reference Figure 1 , is an embodiment of the present invention, and provides an intelligent textile production line control method, comprising: S1: Real-time collection of operating data during the textile production process and data preprocessing.

[0020] Real-time collection of operating data during the textile production process includes operating parameter data of production equipment, environmental parameter data and product quality data.

[0021] The operating parameter data of production equipment include motor speed, equipment load, bearing temperature and vibration signal. Motor speed: used to monitor the operating speed of textile production line equipment, reflects the dynamic performance of production line equipment, and is an important input variable for dynamic adjustment of process parameters. Equipment load: indicates the current load status of textile production line equipment. By monitoring load changes, the stability and efficiency of equipment operation can be evaluated. Bearing temperature: Bearing temperature is an important indicator parameter of the healthy operation status of equipment. Abnormal temperature usually indicates the potential risk of equipment failure. Vibration signal: The vibration signal can characterize the dynamic changes in the operation of mechanical equipment and is one of the core parameters for detecting equipment failure.

[0022] Environmental parameter data include temperature, humidity and air velocity in the production area. These data are used to evaluate the stability of the textile production environment, especially in the production of high-quality textiles, where small changes in the environment may directly affect the physical properties of the textiles (such as weft density and tension).

[0023] Product quality data include textile tension, weft yarn density and surface color uniformity. Textile tension: It is the core indicator to measure the stress state of yarn during the textile process. Changes in tension will directly affect the weft yarn density and the uniformity of textiles. Weft yarn density: It characterizes the number of weft yarns per unit area of ​​textiles and is an important indicator for evaluating textile quality and production process parameters. Surface color uniformity: It is the core parameter to quantify the appearance quality of textiles through optical characteristics, and is usually optimized and evaluated using color histogram analysis methods.

[0024] Data preprocessing includes noise filtering and time domain feature extraction of the operating parameter data of the production equipment. The vibration signal is processed by wavelet denoising method, and the motor speed and equipment load data are smoothed to eliminate instantaneous fluctuations. Perform time series smoothing and multi-dimensional data fusion on environmental parameter data, process temperature and humidity data using the sliding average method, and perform trend analysis on air velocity to evaluate the stability of the production environment; Feature extraction and anomaly detection are performed on product quality data. The textile tension and weft density data are used to eliminate outliers through threshold judgment, and the surface color uniformity data is optimized using the histogram equalization method.

[0025] It should be noted that, in view of the non-stationary characteristics of vibration signals, the wavelet denoising method can effectively extract key frequency domain features, filter out high-frequency noise, and enhance signal quality. Smoothing filtering is used to eliminate instantaneous fluctuations in motor speed and equipment load to ensure that the data is more stable and reliable. The sliding average method processes temperature and humidity data to eliminate short-term fluctuation trends, retain long-term change trends, and improve the timeliness and accuracy of environmental data. Histogram equalization is used to optimize the distribution of color uniformity data and more accurately reflect the surface quality of textiles by enhancing contrast.

[0026] S2: Dynamically adjust the process parameters of the textile production line based on the preprocessed data.

[0027] Based on the operating parameter data of the production equipment, the sliding average algorithm is used to calculate the real-time change trend of the motor speed, and the spinning speed and loom tension adjustment parameters are dynamically adjusted in combination with the equipment load data.

[0028] It should be noted that in textile production lines, the collection of motor speed data is a continuous process. However, real-time motor speed data is often affected by instantaneous fluctuations, such as mechanical vibration, electrical noise, or short-term interference in the production process. Although these fluctuations will not significantly change the long-term operating state of the motor, if these raw data are used directly, it will lead to instability in the production control process, especially when adjusting the spinning speed and loom tension, which may introduce unnecessary fluctuations.

[0029] The sliding average algorithm is a commonly used method for smoothing data. It calculates the average value of a recent set of sampled data, removes short-term fluctuations, and retains the long-term trend of data. In the production line, the sliding average method can average these speed values ​​according to the set time window, such as the most recent 5 to 10 sampling points, to provide a more stable speed trend. This smooth speed change curve provides a stable input for subsequent dynamic adjustments.

[0030] In textile production, the spinning speed needs to be coordinated with the motor speed, and must also adapt to the real-time changes in the equipment load. The equipment load represents the working load state of the equipment. Too high a load may lead to increased equipment loss, while too low a load will affect production efficiency. The goal of dynamically adjusting the spinning speed is to ensure that the equipment operates efficiently within a safe range.

[0031] Combined with the steady speed trend calculated by the sliding average method, the adjustment of the spinning speed follows the following logic: when the steady trend of the motor speed shows that the speed is relatively high, the spinning speed can be appropriately increased to fully utilize the operating capacity of the equipment and improve production efficiency; if the equipment load shows that the working pressure is relatively high, the spinning speed needs to be appropriately reduced to avoid equipment overload and operating failures; when the motor speed is stable but the load is low, the utilization rate of the equipment can be improved by increasing the spinning speed, while preventing spinning quality problems caused by too low a load.

[0032] This dynamic adjustment strategy ensures the stability of equipment operation and optimizes the spinning speed to match the real-time equipment status.

[0033] Loom tension is an important parameter that affects the quality of textiles and is closely related to spinning speed, motor speed and equipment load. During the dynamic adjustment process, it is necessary to adjust the tension in a timely manner according to the real-time equipment operating status, especially the load and speed changes, to ensure the consistency of spinning quality and weft density.

[0034] The adjustment logic of loom tension includes the following points: when the equipment load increases, the loom tension should be appropriately increased to ensure the stability of the yarn under high-load operation and prevent the yarn from relaxing and causing uneven weft density or yarn breakage; when the equipment load decreases, the loom tension should be appropriately reduced to avoid excessive tensile force on the yarn, causing yarn deformation or breakage; if the motor speed changes smoothly, the tension is adjusted according to the load change first; if the speed fluctuates greatly, the speed and load data are combined for dynamic optimization.

[0035] Through this dynamic adjustment, the loom tension is always adapted to the current equipment operating status, ensuring the quality and consistency of the textile products.

[0036] Furthermore, although the sliding average algorithm is an efficient and simple method, in certain scenarios, the following alternatives can be considered: Exponentially Weighted Moving Average (EWMA): EWMA gives higher weight to the latest data points and gradually reduces the weight of historical data. It can respond more quickly to the changing trend of motor speed and is suitable for production scenarios that require rapid adjustments.

[0037] Median filtering: If there are significant outliers (abnormal data) in the motor speed data, median filtering can effectively eliminate the interference of these anomalies on the adjustment process and provide more reliable trend judgment.

[0038] Kalman filter: In complex dynamic systems, Kalman filter provides optimized speed data through prediction and update cycles, which can not only smooth the data but also predict trend changes in advance.

[0039] Furthermore, the application of the sliding average algorithm significantly reduces the interference of short-term fluctuations in motor speed on the spinning speed and loom tension adjustment, providing more reliable input for subsequent dynamic adjustments. Dynamic adjustment of spinning speed can avoid equipment overload while maintaining high efficiency and improve the overall stability of the production line. Dynamic adjustment of loom tension ensures uniformity of yarn tension, reduces the probability of uneven weft density and yarn breakage, and greatly improves the quality of finished textile products.

[0040] Based on environmental parameter data, the PID control method is used to adjust the operating status of the humidification device or air-conditioning system, and the temperature and humidity in the production area are maintained within the target range by adjusting the output power in real time. The proportional integral differential (PID) control method calculates the error in real time and dynamically adjusts the output power to ensure that the temperature and humidity are maintained within the target range. It is a classic method in industrial control.

[0041] It should be noted that PID control is an automated regulation method widely used in industrial control systems. Through the coordinated work of three parts: proportional (P), integral (I) and differential (D), the output power of the humidification device or air-conditioning system is adjusted in real time: Proportional control: directly adjust the output power according to the size of the error. The larger the error, the larger the adjustment range, which is used to quickly respond to changes in temperature and humidity; Integral control: consider the accumulation of errors, gradually correct environmental deviations, and prevent the system from deviating from the target value for a long time; Differential control: make adjustments in advance according to the trend of error changes to avoid over-adjustment or under-adjustment.

[0042] The core of PID control is the role of the three parts of dynamic balance, which achieves precise regulation by coordinating the contributions of proportion, integration and differentiation.

[0043] According to the calculation results of PID control, the system adjusts the operating status of the humidification device or air conditioning system in real time: Humidification device: When the humidity is lower than the target value, the humidification device increases steam output and gradually increases the air humidity; when the humidity is higher than the target value, the steam output is reduced or suspended to avoid excessive humidity affecting the spinning quality; Air conditioning system: If the temperature is higher than the target value, the air conditioning system increases the cooling power to reduce the temperature in the production area; if the temperature is lower than the target value, the air conditioning system switches to heating mode to increase the temperature.

[0044] The adjustment process is always based on real-time data, and the adjustment actions are continuously updated as environmental conditions change, ensuring that the temperature and humidity of the production environment always remain within the target range.

[0045] After each output power adjustment, the system will continue to collect the latest temperature and humidity data and calculate the updated errors. These errors will be input into the PID control system again to correct the adjustment action. This closed-loop feedback mechanism can ensure that: the system can respond quickly to new environmental changes; avoid over-adjustment or under-adjustment; and maintain the stability of temperature and humidity for a long time.

[0046] Furthermore, PID control can adjust the output power in real time, and can quickly correct temperature and humidity deviations even when environmental parameters change rapidly, preventing environmental fluctuations from affecting the quality of textiles. Through the PID control method, the system can adjust the operating status of the humidifier and air conditioner in real time according to slight changes in the environment, significantly improving the control accuracy compared to traditional timing control or manual adjustment methods. A stable temperature and humidity environment helps to improve the consistency of yarn tension, reduce uneven weft density, and avoid the negative effects of excessive dryness or moisture on textile performance.

[0047] Based on product quality data and the correlation between tension and weft yarn density, an optimization model is constructed to dynamically adjust the weft yarn density. Based on the color deviation analysis method, adjustment parameters of dye solution concentration and coating speed are generated to optimize the dyeing and finishing process.

[0048] It should be noted that fluctuations in spinning tension usually result in changes in weft density. For example, too much tension may result in too high a weft density, while too little tension may result in uneven weft density.

[0049] By analyzing historical data, a correlation model between tension and weft density is constructed. This model directly correlates tension fluctuations with weft density changes and is used to predict the real-time change trend of weft density.

[0050] Based on the correlation model between tension and weft density, the optimal adjustment parameters of weft density are calculated in real time. For example, when excessive tension is detected, the system automatically reduces the speed of the weft density adjustment device to keep the density within the target range.

[0051] The goal of adjusting the weft density is to ensure its uniformity while meeting the physical performance requirements of the fabric. Traditional weft density adjustment methods usually rely on manual experience and are difficult to quickly respond to dynamic changes in tension during the production process. The present invention realizes automatic dynamic adjustment of weft density through an association relationship model, significantly improving the accuracy of adjustment.

[0052] Color deviation analysis: By comparing the actual color with the target color, a color deviation value is generated to guide the adjustment of dye concentration and coating speed.

[0053] When the deviation is large, increase the dye concentration to improve color saturation.

[0054] When the deviation is small, the concentration should be appropriately reduced to avoid over-dyeing.

[0055] Coating speed optimization: Color deviation is also used to adjust the coating speed: When the color deviation is large, slow down the coating speed and increase the penetration time of the dye.

[0056] When the color deviation is small, speed up the coating speed and improve production efficiency.

[0057] Furthermore, based on the color deviation analysis results, the system automatically generates adjustment parameters for dye concentration and coating speed, and transmits them to the dyeing and finishing equipment for execution in real time.

[0058] This process forms a closed-loop feedback mechanism, which continuously optimizes the operation status of the dyeing and finishing process by continuously monitoring color deviation. Traditional dyeing and finishing process control mostly uses fixed parameters and cannot respond to the dynamic changes of color deviation in real time. The present invention realizes intelligent optimization of the dyeing and finishing process by dynamically adjusting the dye concentration and coating speed through color deviation analysis.

[0059] S3: Conduct real-time detection of the quality status of the textile production process and adjust the production line operation based on the detection results.

[0060] The machine vision system acquires the textile surface image, and uses the convolutional neural network algorithm to analyze the defect type and location, and generates an adjustment signal to control the loom operation mode. The adjustment signal generated according to the defect type and location is as follows: If the warp yarn is identified as broken, the loom is controlled to stop running and a maintenance signal is activated. If uneven weft yarn distribution is detected, the weft yarn input speed is adjusted to improve uniformity.

[0061] The tension sensor obtains real-time tension data, calculates the tension deviation value, and generates a signal for adjusting the loom running speed and the weft yarn input speed. For example: collect spinning tension data, compare it with the target tension, and calculate the tension deviation value. When the tension is too high, reduce the loom running speed to reduce the pulling force. When the tension is too low, increase the weft yarn input speed to maintain yarn stability.

[0062] The weft yarn density data is detected in real time, and the feeding speed and operation mode of the spinning equipment are optimized in combination with the dynamic adjustment model. For example, when the weft yarn density is too high, the system slows down the feeding speed to avoid yarn accumulation; when the weft yarn density is too low, the system speeds up the feeding speed to increase the density.

[0063] Based on the surface color uniformity test results, the dynamic color difference analysis model is used to calculate the operating parameters of the dyeing and finishing equipment, and adjust the dye concentration and coating speed. For example, when the color difference is too large, the system increases the dye concentration or slows down the coating speed to increase the dyeing saturation; when the color difference is too small, the system reduces the dye concentration or speeds up the coating speed to improve production efficiency.

[0064] Through the above detection and adjustment, a closed-loop feedback mechanism is formed to continuously update the equipment operating parameters to adapt to real-time changes in quality status.

[0065] It should be noted that during the production process, the machine vision system detected spot defects on the surface of the textile, and the system immediately reduced the weft yarn input speed and adjusted the operating mode to prevent the defect from expanding. The tension sensor detected that the tension was too high, and the system reduced the loom speed and appropriately reduced the yarn tension, and finally returned to normal. In the dyeing and finishing process, the machine vision detected an increase in color deviation, and the system automatically increased the dye concentration and slowed down the coating speed to ensure uniform dyeing.

[0066] S4: Build a multi-feature fusion fault prediction model to predict the operating status of production line equipment.

[0067] Based on the vibration signals of the production line equipment, the collected vibration time series data Perform fast Fourier transform and calculate the frequency domain energy eigenvalue. The calculation formula is: ; in, is the characteristic value of the vibration signal, and are the lower and upper limits of the frequency of the vibration signal, respectively. is the frequency domain eigenvalue of the vibration signal; is the attenuation factor of the high-frequency signal, which is used to reduce the impact of high-frequency noise on the results; is the frequency variable of the current integration; It is a small increment of frequency and is the basic unit of frequency domain integration, used to gradually accumulate signal characteristics within the frequency domain.

[0068] The vibration signal reflects the dynamic state of the equipment during operation, and its frequency domain characteristic value It is calculated by Fast Fourier Transform (FFT) and represents the energy distribution of the signal within a specific frequency range. and upper limit Used to define the frequency range of interest to avoid the influence of interfering frequency bands. High frequency attenuation factor The introduction of suppresses the interference of high-frequency noise on the eigenvalue calculation, ensuring that the eigenvalue is more reliable.

[0069] Based on the bearing temperature data, the time series Perform second-order derivative calculation and integral trend analysis to extract temperature change characteristic values. The calculation formula is: ; in, is the temperature characteristic value, is the time series function of bearing temperature; The second derivative of temperature change is used to evaluate the acceleration trend of temperature; is the partial derivative; is the decay integral of the temperature trend, is the temperature fluctuation suppression factor; For time series The sinusoidal transformation reflects the periodic fluctuation characteristics of temperature changes; is time, which is the basic variable of time series data.

[0070] Bearing temperature reflects the thermal state of the equipment, and its changing trend is an important basis for judging equipment abnormalities. Perform second-order derivative and integral analysis to extract acceleration trend and long-term fluctuation trend characteristics. Used to evaluate the severity of temperature changes, integral trend Used to quantify the periodic fluctuation characteristics of temperature changes. Temperature fluctuation suppression factor The influence of violent fluctuations on eigenvalues ​​is suppressed, ensuring the stability of feature extraction.

[0071] Based on the motor current data, the collected current signal Perform time series analysis and extract the abnormal characteristics of the current signal through the autoregressive moving average model. The characteristic value calculation formula is: ; in, is the current characteristic value, For the The current sampling value, is the nonlinear amplification factor; Express The summation of the current sampling points is performed.

[0072] The current signal is an important indicator of the electrical status of the equipment. Abnormal features are extracted through time series analysis and autoregressive moving average model (ARMA). is the value of the collected current signal, and the summation operation reflects the overall trend of the signal. Nonlinear amplification factor Used to amplify the impact of key feature points and enhance the model's sensitivity to abnormal features.

[0073] Predict the operating status of production line equipment, including comprehensive feature values ​​and generating equipment health scores , and its calculation formula is: ; in, They are the normalized results of vibration, temperature and current characteristic values ​​respectively; is the total number of feature values, indicating the number of features considered in the calculation of the health score; For the A characteristic value represents the operating status of the device in a certain dimension; For the The reference value of the characteristic value is used to compare the deviation between the actual characteristic value and the target state; It is a characteristic difference inhibitor.

[0074] The normalization formula is: ; in, is the normalized eigenvalue, is the original eigenvalue, is the maximum eigenvalue, is the minimum eigenvalue.

[0075] Health Score It is generated by the normalization of vibration, temperature and current eigenvalues ​​and is used to quantify the operating status of the equipment. The normalization formula ensures that the eigenvalues ​​have the same dimension, and the lowest eigenvalue dominates the calculation of the score result (using the suppression factor Dynamically adjust weights to avoid high eigenvalues ​​masking the impact of key low eigenvalues ​​and improve the accuracy of prediction results.

[0076] S5: Identify potential failures in advance based on the prediction results and adjust the operation mode of the production line.

[0077] Based on device health score The potential failure risks are divided into multiple levels based on the different value ranges of , and corresponding production line adjustment strategies are adopted for different levels of risks.

[0078] When health score In the high-risk range, When the load is too low, the equipment with the abnormality will be stopped and the standby equipment will be switched to ensure the continuity of the production line. The overall speed of the production line will be reduced and the spinning load will be redistributed to the low-load equipment. The adjusted load distribution formula is: ; in, For the The load distribution value of each device, is the total load, Assign a weight to the device health score.

[0079] When health score In the medium risk range, When the equipment operating parameters are adjusted dynamically, including reducing the spinning speed, tension adjustment value and coating speed of the dyeing and finishing process, the dynamic adjustment formula is: ; in, are the adjusted operating parameters, is the current running parameter value, For reference health score; monitor the equipment operation status in real time to detect whether there is a new downward trend in health score.

[0080] When health score In the low risk range, When the device is in a critical condition, the current operating mode is maintained, equipment health reports are generated regularly, and possible long-term risks are evaluated.

[0081] The adjustment process is achieved through a closed-loop feedback mechanism, that is, after adjusting the operating mode, the equipment health score is recalculated and the operating status of the production line is continuously optimized until the health score of all equipment is higher than the safety threshold.

[0082] It should be noted that dividing the health score into multiple risk levels (high, medium, and low) can quickly identify the different risk levels of equipment status, avoid the shortcomings of a unified strategy, and improve the targeted nature of adjustments. Traditional fault management methods are often unable to distinguish risk levels, resulting in overly conservative or aggressive adjustment strategies. The present invention achieves risk classification and differentiated adjustments by finely dividing the health score value range. Equipment in the high-risk range stops running and switches to backup equipment, effectively preventing the chain reaction of serious faults on the production line; in the medium-risk range, operating parameters are dynamically adjusted to delay equipment degradation and significantly improve equipment utilization.

[0083] Embodiment 2 is the second embodiment of the present invention, which is different from the previous embodiment in that: If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention can be essentially or partly embodied in the form of a software product that contributes to the prior art or partly in the current technical solution. The current computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0084] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0085] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0086] Embodiment 3 is an embodiment of the present invention, which provides an intelligent textile production line control system, including a data acquisition module, a parameter adjustment module, an operation adjustment module, a fault prediction module and an operation mode optimization module.

[0087] Data acquisition module: collects operation data in the textile production process in real time and performs data preprocessing.

[0088] Parameter adjustment module: Dynamically adjust the process parameters of the textile production line based on preprocessed data.

[0089] Operation adjustment module: Real-time detection of the quality status of the textile production process and adjustment of the production line operation based on the detection results.

[0090] Fault prediction module: Build a multi-feature fusion fault prediction model to predict the operating status of production line equipment.

[0091] Operation mode optimization module: Identify potential failures in advance based on prediction results and adjust the operation mode of the production line.

[0092] Example 4 is an embodiment of the present invention, which provides an intelligent textile production line control method. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation / comparative experiments.

[0093] The purpose of this experiment is to verify the effectiveness of the textile production line control method, including real-time data collection and preprocessing, dynamic adjustment of process parameters, real-time quality status detection, application of multi-feature fusion fault prediction model, and adjustment of production line operation mode according to the prediction results. The test environment is a production workshop of a textile factory, and the experimental objects are 6 main production equipment (equipment A to equipment F).

[0094] Test preparation: Data acquisition devices are installed on the production line to record the equipment's operating data in real time, including tension, weft yarn density, equipment health score, and operating speed.

[0095] Equipped with intelligent control system to realize dynamic adjustment of production line process parameters and real-time quality status detection functions.

[0096] A multi-feature fusion fault prediction model is introduced in the test scenario to analyze the equipment operation status and generate a health score.

[0097] There are three preset health score intervals: high risk (score <80); Medium risk (80≤score≤90); Low risk (score > 90).

[0098] Implementation process: Real-time data acquisition and preprocessing: The equipment’s tension, weft yarn density and running speed data are recorded every 5 minutes, and the data is preprocessed through noise filtering and sliding average algorithm to eliminate the interference of sudden fluctuations on the analysis.

[0099] Dynamic adjustment of process parameters: Analysis of the operating data of equipment A and equipment B found that the weft yarn density deviated from the target value (25 yarns / cm); the process parameters were optimized in real time by dynamically adjusting the weft yarn input speed and loom tension; The higher operating speed of equipment C increased the tension fluctuation, and the production process was stabilized by reducing the operating speed.

[0100] Real-time quality detection: The surface defects of textiles in device D were detected by machine vision system, and the uneven distribution of weft yarns was found; Adjust the tension value based on the detection results and reallocate the spinning task to other equipment.

[0101] Failure prediction and health score updates: Use multi-feature fusion model to comprehensively analyze tension, weft density and equipment running speed to generate health score; The health scores of devices D and F are lower than the medium risk range, triggering the adjustment strategy.

[0102] Adjust the operating mode: Stop the operation of equipment D, switch to the standby equipment, and reduce the overall speed of the production line to relieve the load; For equipment F, dynamically adjust process parameters, reduce operating speed and redistribute load.

[0103] For specific experimental data, please refer to Table 1.

[0104] Table 1 Experimental data reference table

[0105] Before the adjustment, the weft yarn density of device A and device B was low, and after the adjustment it returned to the target value of 25 yarns / cm.

[0106] After the equipment D was adjusted, the tension dropped from 10.9N to 10.2N, and the weft yarn density increased from 24.8 to 25.2, indicating that the adjustment significantly improved the production quality.

[0107] Equipment D and equipment F had lower scores of 78 and 80 respectively, both triggering adjustment strategies. After stopping equipment D, the load distribution of the production line was optimized, and the health scores of the remaining equipment did not drop significantly.

[0108] The data shows that through real-time detection and dynamic adjustment, the equipment operating parameters quickly returned to the target range, significantly reducing quality fluctuations. The multi-feature fusion model successfully identified potential faults, optimized the operating mode in advance, and avoided the impact of equipment damage on the production line. The health scores were all higher than 80 after adjustment, verifying the effectiveness of the closed-loop feedback mechanism and ensuring the continuity and stability of production.

[0109] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. An intelligent textile production line control method, characterized in that: include: Collect operation data in the textile production process in real time and perform data preprocessing; Dynamically adjust the process parameters of the textile production line based on preprocessed data; Real-time detection of the quality status of textile production and adjustment of production line operation based on the detection results; Construct a multi-feature fusion fault prediction model to predict the operating status of production line equipment; Identify potential failures in advance based on prediction results and adjust the operation mode of the production line; The data preprocessing includes noise filtering and time domain feature extraction of the operating parameter data of the production equipment, wherein the vibration signal is processed by wavelet denoising method, and the motor speed and equipment load data are smoothed to eliminate instantaneous fluctuations; Perform time series smoothing and multi-dimensional data fusion on environmental parameter data, process temperature and humidity data using the sliding average method, and perform trend analysis on air velocity to evaluate the stability of the production environment; Feature extraction and anomaly detection are performed on product quality data. The textile tension and weft density data are judged by threshold to eliminate outliers, and the surface color uniformity data is optimized by histogram equalization method. The dynamic adjustment of the process parameters of the textile production line includes calculating the real-time change trend of the motor speed using a sliding average algorithm based on the operating parameter data of the production equipment, and dynamically adjusting the spinning speed and loom tension adjustment parameters in combination with the equipment load data; Based on environmental parameter data, the PID control method is used to adjust the operating status of the humidification device or air conditioning system, and the temperature and humidity of the production area are maintained within the target range by adjusting the output power in real time; Based on product quality data and the correlation between tension and weft yarn density, an optimization model is constructed to dynamically adjust the weft yarn density. Based on the color deviation analysis method, adjustment parameters of dye solution concentration and coating speed are generated to optimize the dyeing and finishing process.

2. The intelligent textile production line control method according to claim 1, characterized in that: The real-time collection of operating data during the textile production process includes operating parameter data of production equipment, environmental parameter data and product quality data; The operating parameter data of the production equipment include motor speed, equipment load, bearing temperature and vibration signal; The environmental parameter data include temperature, humidity and air velocity in the production area; The product quality data includes the tension, weft yarn density and surface color uniformity of the textile.

3. The intelligent textile production line control method according to claim 2, characterized in that: The adjusting the operation of the production line includes acquiring a surface image of the textile through a machine vision system, analyzing the type and location of defects using a convolutional neural network algorithm, and generating an adjustment signal to control the operation mode of the loom; The real-time tension data is obtained through the tension sensor, and the tension deviation value is calculated to generate a signal for adjusting the loom running speed and the weft yarn input speed; Real-time detection of weft density data, and optimization of the feeding speed and operation mode of the spinning equipment in combination with the dynamic adjustment model; Based on the surface color uniformity test results, the dynamic color difference analysis model is used to calculate the operating parameters of the dyeing and finishing equipment, and the dye concentration and coating speed are adjusted; Through the above detection and adjustment, a closed-loop feedback mechanism is formed to continuously update the equipment operating parameters to adapt to real-time changes in quality status.

4. The intelligent textile production line control method according to claim 3, characterized in that: The multi-feature fusion fault prediction model is constructed by: based on the vibration signal of the production line equipment, the collected vibration time series data is Perform fast Fourier transform and calculate the frequency domain energy eigenvalue. The calculation formula is: ； in, is the vibration signal characteristic value, and are the lower and upper limits of the frequency of the vibration signal, respectively. is the frequency domain eigenvalue of the vibration signal; is the attenuation factor of the high-frequency signal, which is used to reduce the impact of high-frequency noise on the results; is the frequency variable of the current integration; It is a small increment of frequency and is the basic unit of frequency domain integration, used to gradually accumulate signal characteristics within the frequency domain; Based on the bearing temperature data, the time series Perform second-order derivative calculation and integral trend analysis to extract temperature change characteristic values. The calculation formula is: ； in, is the temperature characteristic value, is the time series function of bearing temperature; The second derivative of temperature change is used to evaluate the acceleration trend of temperature; is the partial derivative; is the decay integral of the temperature trend, is the temperature fluctuation suppression factor; For time series The sinusoidal transformation reflects the periodic fluctuation characteristics of temperature changes; is time, which is the basic variable of time series data; Based on the motor current data, the collected current signal Perform time series analysis and extract the abnormal characteristics of the current signal through the autoregressive moving average model. The characteristic value calculation formula is: ； in, is the current characteristic value, For the The current sampling value, is the nonlinear amplification factor; Express The summation process of current sampling points; The prediction of the operating status of the production line equipment includes: synthesizing the characteristic values ​​to generate the equipment health score , and its calculation formula is: ； in, They are the normalized results of vibration, temperature and current characteristic values ​​respectively; is the total number of feature values, indicating the number of features considered in the calculation of the health score; For the A characteristic value represents the operating status of the device in a certain dimension; For the The reference value of the characteristic value is used to compare the deviation between the actual characteristic value and the target state; It is a characteristic difference inhibitor; The normalization formula is: ； in, is the normalized eigenvalue, is the original eigenvalue, is the maximum eigenvalue, is the minimum eigenvalue.

5. The intelligent textile production line control method according to claim 4, characterized in that: The identification of potential failures in advance based on the prediction results includes: The potential failure risks are divided into multiple levels according to different value ranges, and corresponding production line adjustment strategies are adopted for different levels of risks; When health score In the high-risk range, When the load is too low, the equipment with the abnormality will be stopped and the standby equipment will be switched to ensure the continuity of the production line. The overall speed of the production line will be reduced and the spinning load will be redistributed to the low-load equipment. The adjusted load distribution formula is: ； in, For the The load distribution value of each device, is the total load, Weight the device health score; When health score In the medium risk range, When the equipment operating parameters are adjusted dynamically, including reducing the spinning speed, tension adjustment value and coating speed of the dyeing and finishing process, the dynamic adjustment formula is: ； in, are the adjusted operating parameters, is the current running parameter value, For reference health scores; monitor the equipment operating status in real time to detect whether there is a new downward trend in health scores; When health score In the low risk range, When the device is in a state of emergency, the current operation mode is maintained, and equipment health reports are generated regularly to assess possible long-term risks. The adjustment process is achieved through a closed-loop feedback mechanism, that is, after adjusting the operating mode, the equipment health score is recalculated and the operating status of the production line is continuously optimized until the health score of all equipment is higher than the safety threshold.

6. An intelligent textile production line control system, used to implement the intelligent textile production line control method according to any one of claims 1 to 5, characterized in that: include: Data acquisition module: real-time collection of operating data during the textile production process and data preprocessing; Parameter adjustment module: dynamically adjust the process parameters of the textile production line based on the pre-processed data; Operation adjustment module: Real-time detection of the quality status of the textile production process and adjustment of the production line operation based on the detection results; Fault prediction module: builds a multi-feature fusion fault prediction model to predict the operating status of production line equipment; Operation mode optimization module: Identify potential failures in advance based on prediction results and adjust the operation mode of the production line.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the intelligent textile production line control method described in any one of claims 1 to 5 are implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the intelligent textile production line control method according to any one of claims 1 to 5 are implemented.

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