Method for adjusting speed of fluff machine based on intelligent sensor

Abstract

The invention relates to the technical field of equipment intelligent adjustment, and discloses a fluff machine speed adjustment method based on an intelligent sensor, which comprises the following steps: acquiring an image of target cloth, judging whether the tension of the cloth is stable, respectively executing tension adjustment or galling effect evaluation, and determining final adjustment data, according to the speed adjusting method of the fluff machine based on the intelligent sensor, production shutdown and quality problems caused by unstable tension are reduced, tension control and gear ratio adjustment are optimized, the quality stability of products is improved through accurate napping effect simulation, and the speed adjusting method of the fluff machine based on the intelligent sensor is suitable for popularization and application. The cloth tension and napping effect can be monitored in real time, problems can be found and adjusted in time, the defective rate is reduced, the device can adapt to different cloth materials, densities and thicknesses, the device is suitable for various production conditions through simulation and optimization, equipment abrasion caused by the tension problem is reduced through accurate tension control, and the maintenance cost is reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent adjustment of equipment, in particular to a speed adjustment method of a pile machine based on an intelligent sensor. BACKGROUND

[0002] With the improvement of people's living standards, the demand for pile products (such as clothing, automotive interiors, toys, etc.) is growing, and market competition is becoming increasingly fierce, and higher requirements are put forward for product quality and production efficiency. Consumers have higher and higher quality requirements for the appearance, feel, durability, etc. of pile products, which means that more accurate control is needed during production to ensure that the density, distribution, length, etc. of the pile meet the standards. Market competition and profit pressure prompt enterprises to seek ways to improve production efficiency and reduce production costs. Reducing downtime, reducing resource waste, and improving automation are key. More and more consumers want to customize pile products that suit their own style, which puts higher requirements on the flexibility and adaptability of the production process. It becomes important to quickly and accurately adjust production parameters to meet the needs of different products. Manufacturing is in the period of digital transformation, and intelligent and automated production is the trend. Pile machines, as a key piece of equipment, also need to be intelligently upgraded to adapt to industry trends. Manufacturing is increasingly focusing on environmental protection and hoping to reduce resource consumption and waste emissions. Pile machines have room for optimization in terms of fiber utilization and energy consumption during the production process. The pile machine speed adjustment method based on intelligent sensors is proposed to address the growing industry needs and make up for the shortcomings of traditional control methods. This method integrates advanced sensor technology, control engineering, motor control, data analysis, and other multidisciplinary cutting-edge technologies. It achieves optimal control of the pile machine production process through intelligent means, improving production efficiency and product quality. It is an important development direction in the field of industrial automation. The existing pile machine speed adjustment method based on intelligent sensors cannot determine whether the cloth tension is stable through the image of the cloth. When the tension is unstable, it cannot adjust the tension controller in a timely manner according to the needs. When the tension is stable, it cannot determine whether the desired pile effect can be achieved under the current speed and other parameters of the equipment. It cannot adjust to the most energy-efficient and time-saving gear ratio in a timely manner while meeting the desired effect, which can easily lead to an increase in product failure rate and increase production costs. Its practicality has certain limitations. SUMMARY

[0003] The present application provides a pile machine speed adjustment method based on an intelligent sensor to promote the solution to the problems in the background art.

[0004] The present application provides the following technical solution: a pile machine speed adjustment method based on an intelligent sensor, comprising: acquiring an image of target cloth; Analyze the target fabric to determine whether the fabric tension is stable; If the fabric tension is unstable, perform tension adjustment until the fabric tension is stable; If the fabric tension is stable, perform the napping effect evaluation; Determine the final adjustment data, and adjust the gear ratio of the napping roller and the tension controller according to the final adjustment data; The determination of whether the fabric tension is stable is specifically as follows: Extract preprocessed image ; Calculate the tension balance index; Calculate the balance evaluation of over-feed; Define a threshold adjustment judgment function and calculate the exponential threshold; Define a tension stability judgment function to determine whether the cloth tension is stable: ; like , then the fabric tension is determined to be stable; like , it is determined that the fabric tension is unstable.

[0005] As an optional solution of the method for adjusting the speed of a pile machine based on an intelligent sensor according to the present invention, the method includes: collecting an image of a target fabric, specifically: connecting a camera and performing initialization settings; using the camera to collect a real-time image of the fabric and storing it as an image file; preprocessing the collected image; calculating the gray-level co-occurrence matrix of the preprocessed image to extract texture features; extracting local texture features of the image; calculating the color moment of the image; calculating the color histogram of the image; combining the above texture features and color features to form a final feature vector .

[0006] As an optional solution of the method for adjusting the speed of a pile machine based on an intelligent sensor according to the present invention, wherein: performing tension adjustment until the tension of the fabric is stable, including determining tension adjustment data, specifically: obtaining the sensor type; installing and initializing the sensor; using the sensor to collect gear ratio related data; calculating and recording the gear ratio based on the collected data; obtaining the pre-processed image ;Call the tension simulation model and extract the preprocessed image and gear ratio corresponding to the target tension value, and the tension value corresponding to the tension adjustment data is defined as the target tension adjustment data, denoted as .

[0007] As an optional solution of the speed adjustment method of the pile machine based on the intelligent sensor, the tension adjustment is performed until the fabric tension is stable, including adjusting the tension controller according to the tension adjustment data, specifically: obtaining the target tension adjustment data ; adjusting the tension controller parameters; performing stability test; recording the adjustment results.

[0008] As an optional solution of the speed adjustment method of the pile machine based on the intelligent sensor, the tension simulation model is specifically: collecting fabric feature data; for the fabric feature data, data cleaning is performed; for the fabric feature data after data cleaning, feature extraction is performed; for the fabric feature data after feature extraction, data standardization is performed; for the fabric feature data after data standardization, a selection and determination model is selected; the selected and determined model is trained; and the model after model training is verified. The fabric feature data after data standardization is input into the trained model; The tension value output by the model is calculated: ; According to the target tension value, the energy consumption value is calculated: ; According to the target tension value, the adjustment time is calculated: ; The fabric feature data, the trained model, the tension value, the energy consumption value and the time consumption are integrated to form a tension simulation model, denoted as .

[0009] As an optional solution of the speed adjustment method of the pile machine based on the intelligent sensor, the pulling effect evaluation is performed, specifically: obtaining the sensor type; installing and initializing the sensor; using the sensor to collect gear ratio related data; calculating the gear ratio according to the collected data; recording the gear ratio; and determining the gear ratio and fabric tension combination that meets the pulling effect.

[0010] As an optional solution of the smart sensor-based speed adjustment method of the napping machine, wherein: the gear ratio and the fabric tension combination satisfying the napping effect are determined, specifically: a combination simulation model is called to determine the pre-processed image and the gear ratio corresponding simulation effect; a target effect is obtained, denoted as ; it is judged whether the current gear ratio and the fabric tension satisfy the target effect; if , it is determined that the target effect is satisfied; then no parameter is adjusted, and the napping machine device operates at the current speed; if , it is determined that the target effect is not satisfied; then all possible combinations of the gear ratio and the fabric tension are extracted from the combination simulation model; the extracted combinations are recorded; each combination is evaluated to calculate the energy consumption value, the time consumption and the simulation effect of each combination; according to the evaluation result, the combination with the best comprehensive performance is selected as the optimal combination.

[0011] As an optional solution of the smart sensor-based speed adjustment method of the napping machine, wherein: the combination simulation model is specifically: different fabric characteristics are input; different production parameters are input; a virtual model of the fabric is generated according to the input fabric characteristics; a series of possible gear ratio and tension value combinations are generated by simulating different gear ratios and tension values; the production process of each combination is simulated to calculate the corresponding simulation effect; the evaluation standard of the simulation effect is defined; the simulation effect of each combination is scored according to the evaluation standard; all the fabric characteristics, production parameters, virtual model of the fabric, gear ratio and tension value combination, simulation effect and simulation effect score are integrated to generate the combination simulation model, denoted as .

[0012] The present application has the following advantages:

[0013] 1. The smart sensor-based speed adjustment method of the napping machine uses a camera to collect images of the fabric, judges whether the fabric tension is stable through image analysis, acquires the real-time state of the fabric through image collection, provides data support for subsequent tension judgment and simulation, monitors the fabric tension in real time, ensures the stability of the production process, and avoids quality problems caused by unstable tension.

[0014] 2、The smart sensor-based pile machine speed adjustment method, when the tension is unstable, calls the tension simulation model, determines the most energy-saving and time-saving tension adjustment data according to the current situation of the cloth and the current gear ratio of the raising roller, adjusts the parameters of the tension controller according to the tension adjustment data, ensures the stability of the cloth tension, and the tension simulation model generates a cloth by simulating different material, density, thickness and other data, simulates different gear ratios of the raising roller, simulates different data of the tension controller, determines the tension value of the cloth under different gear ratios and different data of the tension controller, and determines the energy consumption value and adjustment time length of the cloth from the initial tension value of the cloth to the tension stability in the process. Through the tension simulation model, the tension adjustment is optimized to ensure the stability of the cloth in the conveying process, the tension controller is accurately adjusted to ensure that the cloth tension reaches the optimal state, and the energy consumption and production downtime are reduced.

[0015] 3、The smart sensor-based pile machine speed adjustment method, when the tension is stable, collects the gear ratio of the raising roller, calls the combined simulation model, judges whether the current gear ratio and the cloth tension meet the current required raising effect, if yes, no adjustment is made, if not, extracts all combinations of the gear ratio and the cloth tension in the combined simulation model that can meet the current required raising effect, extracts the most energy-saving, time-saving and best combination as the final adjustment combination, adjusts the tension controller and the gear ratio of the raising roller according to the corresponding gear ratio and cloth tension of the final adjustment combination, the combined simulation model generates a cloth by simulating different material, density, thickness and other data, simulates different gear ratios of the raising roller, simulates different tension values, simulates different production time lengths, determines the corresponding raising effect of different cloths under different gear ratios of the raising roller, different tension values and different production time lengths, checks whether the current gear ratio and tension setting meet the raising effect, ensures the production efficiency and product quality, and finds the most energy-saving, time-saving and best gear ratio and tension setting through the combined simulation model to optimize the production process. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 The smart sensor-based pile machine speed adjustment method flowchart. DETAILED DESCRIPTION

[0017] With reference to the drawings of the embodiments of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments of the present application, all the other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0018] Embodiment one, a speed adjustment method of a pile machine based on an intelligent sensor, referring to Figure 1 , comprising: collecting an image of target cloth; analyzing the target cloth to determine whether the cloth tension is stable; if the cloth tension is not stable, performing tension adjustment until the cloth tension is stable; if the cloth tension is stable, performing a pile effect evaluation; determining final adjustment data, and adjusting the pile roller gear ratio and the tension controller according to the final adjustment data; The determination of whether the cloth tension is stable is specifically: extracting the preprocessed image ; calculating a tension balance index: ; wherein, is a normalized value of the variance or standard deviation of all Euclidean distances, is the maximum value of all Euclidean distances, is the minimum value of all Euclidean distances, is a hyperparameter to avoid a zero denominator, is the tension balance index, and the specific process of calculating the tension balance index is: selecting a gray-scale image of the cloth area between the tension rollers; selecting two points symmetrical about a set axis on the gray-scale image as a pair of monitoring points; calculating the Euclidean distance between each pair of monitoring points; normalizing the variance or standard deviation of all Euclidean distances to obtain a normalized value; calculating the difference between the maximum value and the minimum value of all Euclidean distances, and adding a hyperparameter (to avoid a zero denominator) to obtain a sum value; taking the ratio of the normalized value to the sum value as the tension balance index; calculating a material balance evaluation, which evaluates the tension uniformity of the cloth passing through the tension rollers through the mechanical parameters of the tension rollers or the image features of the cloth area: ; wherein, is the deviation between the current torque of the tension roller and the historical average torque, is the historical average torque of the tension roller, is the shortest distance from the target pixel to the center axis of the target roller, is the total number of target pixels, is the balanced evaluation value of the material; Define a threshold adjustment judgment function to calculate the exponential threshold: ; in, is the initial threshold, are the preset adjustment parameters. To adjust the judgment function based on the preset threshold value, the initial threshold value is corrected to obtain the final exponential threshold value; Define a tension stability judgment function to determine whether the cloth tension is stable: ; like , then the fabric tension is determined to be stable; like , it is determined that the fabric tension is unstable.

[0019] This embodiment also provides the step of collecting an image of the target fabric, specifically: Connect the camera and perform initial settings to ensure it works properly: ; in, Indicates the state of the camera after initialization. If successful, it outputs True, otherwise it outputs False. Indicates the camera device ID. Indicates resolution, Indicates the frame rate, To initialize the camera's operating functions, including setting camera parameters and loading hardware modules, the camera must be initialized before use to ensure normal operation of the camera; Use the camera to capture real-time images of the cloth and save them as image files: ; in, Represents the collected cloth image, The image capture function is usually completed by a camera to collect image data for subsequent processing; Perform preprocessing on the collected images, such as grayscale conversion and filtering, to improve image quality: ; in, represents a pre-processed image, represents edge detection on the image of the cloth, represents contrast enhancement on the image to improve the clarity of the image and make the details in the image more obvious, represents filtering processing on the gray-scale image to remove noise and smooth the image, represents the filter kernel, which can be a Gaussian kernel, a mean kernel, etc., represents converting the color image to a gray-scale image to reduce the data volume while retaining the main features of the image; ; wherein the specific features include mean (Mean), standard deviation (Std), contrast (Contrast), dissimilarity (Dissimilarity), homogeneity (Homogeneity), angular second moment (ASM), energy (Energy), and entropy (Entropy); extracting local texture features of the image, with characteristics such as multi-resolution, gray-scale scale invariance, and rotation invariance: ; calculating color moments of the image, including first moments (mean), second moments (standard deviation), and third moments (skewness): ; wherein, is a color moment, a method for describing the color features of an image, including statistical quantities such as mean, standard deviation, and skewness, used for image analysis, especially in color feature extraction, which can help describe the color distribution of the image, is a color moment calculation function, used in image processing to calculate color moments for subsequent analysis; the function wherein the specific calculation formula of the first moment (mean) is: ; the function wherein the specific calculation formula of the second moment (standard deviation) is: ; the function wherein the specific calculation formula of the third moment (skewness) is: ; wherein, represents a color channel (such as R, G, B), represents the​ The first pixel value of the channel, N represents the total number of pixels in the image; Calculate the color histogram of the image, which describes the distribution of different colors in the image: ; Where, is the color histogram, which is a method of statistical distribution of colors in an image, used to describe the frequency of different colors in an image, used in image analysis, especially in color feature extraction, which can help identify and distinguish different color regions, is the calculation function of the color histogram, used in image processing to generate a color histogram for subsequent analysis; Where, the specific formula of the function is: ; Where, represents the frequency of color values appearing in the image; Combine the above texture features and color features to form the final feature vector : ; The specific steps of preprocessing the collected image are as follows: Convert the collected color image of the fabric into a grayscale image: ; Where, represents the grayscale image, is the function of the operation of converting the color image into a grayscale image, in image processing, grayscale can reduce the amount of data while retaining the main features of the image, facilitating subsequent processing; Filter the grayscale image: ; Where, represents the filter kernel, which can be a Gaussian kernel, mean kernel, etc., is the image after filtering, used for subsequent image analysis and processing, is the function of filtering the image to remove noise or smooth the image, in image preprocessing, filtering can improve image quality and enhance image usability; Contrast enhancement is performed on the filtered image to improve image clarity and make details more obvious: ; Where, ​The image after contrast enhancement processing is used to enhance the contrast of the image, so that the details in the image are more obvious, The function of the contrast enhancement operation is used to improve the definition of the image. In image preprocessing, contrast enhancement can make the details in the image more obvious, which is convenient for subsequent analysis. The image after contrast enhancement is subjected to edge detection. Common edge detection algorithms include Sobel, Canny, etc.: ; Among them, The image after edge detection processing is used to extract edge features in the image, which is convenient for subsequent image analysis, The edge detection operation function is used to extract edge features in the image. In image processing, edge detection can help identify the contours and structures in the image. The image after edge detection is taken as the final preprocessing result : .

[0020] The embodiment also provides that the final adjustment data is determined, and data adjustment is performed on the napping roller gear ratio and the tension controller according to the final adjustment data. Specifically, An optimal combination is obtained ; According to the tension value in the optimal combination, the parameters of the tension controller are adjusted: ; Among them, The target tension value extracted from the optimal combination represents the target tension that the cloth needs to maintain during the conveying process in order to achieve the best napping effect, P is a proportional coefficient, I is an integral time, and D is a differential time, The function of adjusting the parameters of the proportional-integral-differential (PID) controller, the proportional-integral-differential (PID) controller includes a proportional coefficient P, an integral time I, and a differential time D. These parameters determine the response mode of the PID controller to the error, thereby affecting the stability and performance of the system. According to the target tension value extracted from the optimal combination , the parameters of the PID controller are adjusted to achieve the most energy-saving and time-saving tension control. Generally, the best PID parameters need to be determined through experiments or simulations. The specific steps of adjusting the parameters of the tension controller are as follows: Adjust the proportional coefficient P to make the controller respond to the error more quickly. Generally, start from a small value and gradually increase until the system is stable. Adjust the integral time I to reduce the steady-state error. The integral time should start from a large value and gradually decrease until the system no longer oscillates. Adjust the differential time D to reduce the overshoot, the differential time should start from a small value, gradually increase, until the system response is stable; Perform stability test: ; Where, The result of the stability test is a Boolean value, output as True or False, indicating whether the system is stable, in the control system, stability refers to the system's ability to return to equilibrium after being disturbed, used to determine whether the tension control system is stable after adjusting the PID parameters, if is True, it means the system is stable, if is False, it means the system is not stable, further adjustment of parameters is needed until the system is stable, is a test function, used to test the stability of the adjusted PID parameters, to ensure the system can run stably, the test method includes manual adjustment, automatic adjustment and tension calibration, to ensure that the adjusted PID parameters can make the system run stably, avoid oscillation or instability, the test result is used to judge whether the system is stable, the specific steps of stability test are: First, manually run the system to check if the tension control system is working properly, confirm that the tension display and actuator are normal; Switch the control mode to automatic mode, the controller will automatically adjust the working state of the actuator according to the difference between the set value and the sensor feedback value, to ensure that the system can stably output the required electrical signal; In the tension monitoring mode, perform automatic span adjustment, use standard weights for calibration to ensure that the tension value output by the controller is consistent with the actual value; In the tension monitoring mode, perform automatic span adjustment, use standard weights for calibration to ensure that the tension value output by the controller is consistent with the actual value; According to the gear ratio in the optimal combination, adjust the gear ratio of the raising roller; According to the motor encoder resolution and the number of pulses required for one rotation of the motor, calculate the electronic gear ratio: ; Where, Electronic gear ratio, Electronic gear ratio is a proportional relationship set in the motor controller, used to proportionally transform the rotation angle or speed of the motor shaft and the rotation angle or speed of the load. It is usually represented as a ratio of two integers P / Q, where P is the unit of movement on the load side and Q is the unit of movement on the motor side. It is used for speed matching (by adjusting the electronic gear ratio, the motor speed can be matched with the required speed of the load), position control (the electronic gear ratio can accurately convert the rotation angle of the motor to the displacement of the load, achieving high-precision position control), and simplifying mechanical structure (by electronic gear ratio, mechanical gear transmission can be reduced or eliminated, simplifying mechanical structure, reducing cost and maintenance difficulty), Motor encoder resolution, Motor encoder resolution refers to the number of pulses generated by the encoder per revolution. Common encoder resolutions include 2000 lines, 2500 lines, etc. After 4 times frequency processing, the motor can generate 8000 or 10000 pulses per revolution, which is used to calculate the precise position and speed of the motor, ensuring the accuracy and consistency of the motor movement, Pulses required for one revolution of the motor, Pulses required for one revolution of the motor refers to the number of pulses received by the motor from the upper controller, which is used to drive the motor to complete one revolution, and is used to calculate the electronic gear ratio, ensuring the accuracy of speed and position control between the motor and the load; Set the calculated electronic gear ratio to the control system of the raising roller; Test the transmission efficiency of the adjusted gear ratio to ensure that it can operate efficiently and stably: ; Where, Transmission efficiency test result, Transmission efficiency test result is a Boolean value indicating whether the system is running efficiently. After adjusting the gear ratio, the efficiency test is used to determine whether the system achieves the expected efficiency, which is used to determine whether the adjusted gear ratio can operate efficiently, ensuring the best balance between energy saving and performance, Transmission efficiency test function, Transmission efficiency test function is used to test whether the adjusted gear ratio can operate efficiently. It usually evaluates the energy consumption, response time and stability of the system, and through the test of the adjusted gear ratio, it ensures the best balance between energy saving and performance.

[0021] Example two, this example is an improvement based on example one. The speed adjustment method of the smart sensor-based raising machine, executes tension adjustment until the fabric tension is stable, including determining tension adjustment data, specifically: Obtain sensor type, including encoder and hall effect sensor: ; Where, Sensor type, represents an encoder, HES represents a Hall Effect sensor; Install and initialize the sensor, install the sensor near the raising roller, ensure the distance between the sensor and the gear teeth is within the optimal sensing range, for Hall Effect sensors, the sensing distance should typically be less than 1mm, for encoders, ensure the encoder is connected to the motor shaft so that it rotates with the motor, initialize the sensor, set its parameters such as sampling rate, output format, etc., for Hall Effect sensors, calibration is required to eliminate offset and amplitude mismatch: ; where, is the data of the sensor after initialization, is the sensor initialization function, used to initialize the sensor and set its parameters, making it ready to collect data, typically includes setting the sensor's sampling rate, output format, connection to the device, etc., before collecting data, the sensor must be initialized to ensure it works correctly, the initialization process can set the sensor's basic parameters such as sampling rate, resolution, etc.; Collect gear ratio related data using the sensor, for Hall Effect sensors, record the pulse signal of the magnetic field change, for encoders, record the number of output pulses: ; where, data is the data collected by the sensor, such as the speed and position data of the gear, for Hall Effect sensors, the data collected is the pulse signal of the magnetic field change, for encoders, the data collected is the number of output pulses, is the data collection function, i.e. the process of collecting data from the sensor, depending on the sensor type and settings, the collected data can be real-time or periodic, the collected data can be used for analysis, monitoring or control, in industrial applications, the collected data is usually used for subsequent processing and analysis; According to the collected data, calculate the gear ratio: ; ; where, represents the gear ratio calculation formula used when the sensor type is an encoder, is the number of pulses collected by the sensor per revolution of the encoder, is the number of revolutions collected by the sensor per revolution of the motor, represents the gear ratio calculation formula used when the sensor type is a Hall Effect sensor, is the number of gear teeth collected by the sensor, is the number of pulses detected by the sensor; Record the gear ratio for subsequent tension adjustment and production process control: ; wherein, is the recorded gear ratio and its related data, is the recording function for recording the gear ratio, i.e. storing the calculated gear ratio for subsequent use, in mechanical systems, recording the gear ratio helps to monitor and adjust the running state of the equipment; Obtain the pre-processed image ; Call the tension simulation model to extract the pre-processed image and the gear ratio All corresponding tension adjustment data: ; ; wherein, is the tension simulation model for extracting the corresponding tension adjustment data according to the input image and gear ratio, which can help optimize the tension control of the equipment, improve production efficiency and energy utilization, is the tension adjustment data, including tension value, energy consumption value and time consumption, is the target tension value, is the energy consumption required to reach the target tension, is the time required to reach the target tension; For the tension value in each set of tension adjustment data, respectively simulate the application to the target cloth, judge whether the cloth tension is stable; If the cloth tension is stable, the set of tension adjustment data corresponding to the tension value is defined as the stable data set; If the cloth tension is not stable, the set of tension adjustment data corresponding to the tension value is defined as the unstable data set; Integrate all stable data sets to form a stable adjustment data set; Extract the stable data set in the stable adjustment data set, whose energy consumption value and time energy consumption value are the smallest, and define it as the adjustment data set; The tension adjustment data corresponding to the adjustment data set is defined as the target tension adjustment data, denoted as .

[0022] The embodiment also provides that the tension adjustment is performed until the cloth tension is stable, including adjusting the tension controller according to the tension adjustment data, specifically: Obtain the target tension adjustment data ; Adjust the tension controller parameters: ; Among them, P is the proportional coefficient, I is the integral time, and D is the differential time. It is a function to adjust the parameters of the proportional-integral-derivative (PID) controller. The proportional-integral-derivative (PID) controller includes the proportional coefficient P, the integral time I and the differential time D. These parameters determine the response of the PID controller to the error, thus affecting the stability and performance of the system. The data is adjusted according to the target tension. , adjust the parameters of the PID controller to achieve the most energy-saving and time-saving tension control. Usually, it is necessary to determine the optimal PID parameters through experiments or simulations. The specific steps for adjusting the tension controller parameters are: Adjust the proportional coefficient P to make the controller respond more quickly to the error. Generally, start with a smaller value and gradually increase it until the system is stable. Adjust the integral time I to reduce the steady-state error. The integral time should start from a larger value and gradually decrease until the system no longer oscillates. Adjust the differential time D to reduce overshoot. The differential time should start from a smaller value and gradually increase until the system responds smoothly. Conduct stability testing: ; in, The result of the stability test is a Boolean value. The output is True or False, indicating whether the system is stable. In the control system, stability refers to whether the system can return to a balanced state after being disturbed. It is used to determine whether the tension control system is stable after adjusting the PID parameters. If If True, the system is stable. If it is False, it means the system is unstable and further parameter adjustment is required until the system is stable. This is a test function used to perform stability testing on the adjusted PID parameters to ensure that the system can run stably. The test methods include manual adjustment, automatic adjustment, and tension calibration to ensure that the adjusted PID parameters can make the system run stably and avoid oscillation or instability. The test results are used to determine whether the system is stable. The specific steps of the stability test are as follows: First, manually run the system to check whether the tension control system is working properly and confirm that the tension display and actuator are normal; Switch the control mode to automatic mode. The controller will automatically adjust the working state of the actuator according to the difference between the set value and the sensor feedback value to ensure that the system can stably output the required electrical signal. In tension monitoring mode, automatic span adjustment is performed and calibration is performed using standard weights to ensure that the tension value output by the controller is consistent with the actual value; The adjustment result is recorded, and the adjusted parameters and test results are recorded for subsequent reference and use: ; wherein, is the recorded adjustment result, i.e. the adjusted parameters and test results, is the adjustment result recording function, used to record the adjusted PID parameters and test results for subsequent reference and analysis, save the adjusted parameters and stability test results for subsequent system maintenance and optimization, and provide historical data for analyzing the long-term performance and stability of the system.

[0023] The embodiment also provides a tension simulation model, specifically: Collecting fabric feature data, including fabric material, density, thickness, and other feature data, gear ratio data of the raising roller, and preprocessed image data: ; wherein, is the fabric feature data, is the fabric material, such as cotton, hemp, silk, etc., is the fabric density, in grams per cubic centimeter, is the fabric thickness, in millimeters, is the gear ratio of the raising roller, is the preprocessed image data of the fabric; For fabric feature data, data cleaning is performed to handle missing values, outliers, duplicate records, and noise data: ; wherein, is the cleaned fabric feature data, with missing values, outliers, and noise removed, is the data cleaning function, used to handle problems such as missing, abnormal, and noise in the data; For the data-cleaning fabric feature data, feature extraction is performed to extract features such as texture features, color features, etc. from the preprocessed image: ; wherein, is the feature extracted from the data-cleaning fabric feature data, such as texture features, color features, etc., is the feature extraction function, used to extract useful information from the image; For the feature-extracted fabric feature data, data standardization is performed to standardize the feature data, ensuring that features of different dimensions and orders of magnitude are treated fairly in the model: ; wherein, standardize the fabric feature data, making different dimensional features comparable, is a standardization function for adjusting the dimension of the data; For the standardized fabric feature data, select a determination model, select a suitable machine learning model such as support vector machine (SVM), neural network, linear regression, etc.: ; where model is the selected machine learning model such as support vector machine (SVM), neural network, linear regression, etc. is a model selection function for selecting a suitable model; For the selected determination model, model training is performed, historical data is used to train the model, and model parameters are adjusted to optimize performance: ; where, is the trained model with prediction ability, is a model training function for training the model; For the model trained model, model validation is performed, and the performance of the model is evaluated through cross-validation to ensure the generalization ability of the model: ; where, is the model validation result for evaluating the performance of the model, is a model validation function for evaluating the generalization ability of the model; The data standardized fabric feature data is input into the trained model; Calculate the tension value output by the model: ; where, is the target tension value, unit: Newton; According to the target tension value, calculate the energy consumption value: ; where, is the energy consumption value, unit: Joule, is an energy consumption calculation function for calculating the energy consumption required to reach the target tension; According to the target tension value, calculate the adjustment time: ; where, is the time consumption, unit: second, is a time calculation function for calculating the time required to reach the target tension; Integrate the fabric feature data, the trained model, the tension value, the energy consumption value and the time consumption to form a tension simulation model, denoted as .

[0024] In this embodiment, the pile effect evaluation is performed, specifically: Get the sensor type, including encoder and hall effect sensor: ; Wherein, is the type of sensor, represents the encoder, and HES represents the hall effect sensor; Install and initialize the sensor, install the sensor near the raising roller, ensure that the distance between the sensor and the gear teeth is within the optimal sensing range, wherein for the hall effect sensor, the sensing distance should be less than 1mm, for the encoder, ensure that the encoder is connected with the motor shaft, so that the encoder rotates with the motor, initialize the sensor, set its parameters such as sampling rate, output format, etc., for the hall effect sensor, calibration is needed to eliminate offset and amplitude mismatch: ; Wherein, is the data of the sensor after initialization, is the sensor initialization function, which is used to initialize the sensor and set its parameters, so that it is ready to collect data, usually including setting the sampling rate, output format, connection with the device, etc. of the sensor, before collecting data, the sensor must be initialized to ensure its correct work, the initialization process can set the basic parameters of the sensor such as sampling rate, resolution, etc. Collect gear ratio related data using the sensor, for the hall effect sensor, record the pulse signal of the magnetic field change, for the encoder, record the output pulse number: ; Wherein, data is the data collected by the sensor, such as the speed and position data of the gear, for the hall effect sensor, the pulse signal of the magnetic field change is collected, for the encoder, the output pulse number is collected, is the data collection function, that is, the process of collecting data from the sensor, according to the type and setting of the sensor, the collected data can be real-time or periodic, the collected data can be used for analysis, monitoring or control, in industrial applications, the collected data is usually used for subsequent processing and analysis; According to the collected data, calculate the gear ratio: ; ; wherein, is a gear ratio calculation formula used when the sensor type is an encoder, is the number of pulses collected by the sensor per revolution of the encoder, is the number of revolutions collected by the sensor per revolution of the motor, is a gear ratio calculation formula used when the sensor type is a Hall effect sensor, is the number of teeth of the gear collected by the sensor, is the number of pulses detected by the sensor; record the gear ratio for subsequent tension adjustment and production process control: ; wherein, is the recorded gear ratio and its related data, is a recording function for recording the gear ratio, i.e. storing the calculated gear ratio for subsequent use, in a mechanical system, recording the gear ratio helps to monitor and adjust the running state of the equipment; determine the gear ratio and fabric tension combination that meets the pulling effect.

[0025] The embodiment also provides for determining the gear ratio and fabric tension combination that meets the pulling effect, specifically: call the combination simulation model to determine the pre-processed image and the gear ratio corresponding simulation effect, the simulation effect is the pulling effect: ; wherein, is the pre-processed image and the gear ratio corresponding simulation effect in the combination simulation model, is the combination simulation model; obtain the target effect, denoted as ; determine whether the current gear ratio and fabric tension meet the target effect: ; if , it is determined that the target effect is met; then do not adjust any parameters, and the pile machine equipment can run at the current speed; if , it is determined that the target effect is not met; then extract all possible combinations of gear ratio and fabric tension from the combination simulation model: ; wherein, is a list containing all possible combinations of gear ratio and fabric tension that meet the target effect under the fabric image, is a function for generating a list of combinations from the combination simulation model, which extracts all possible combinations of gear ratio and fabric tension and generates a list containing multiple combinations for subsequent evaluation and selection of the optimal combination, providing a comprehensive list of combinations to evaluate which combinations can meet the desired napping effect, records the extracted combinations for subsequent selection, evaluates each combination to calculate the energy consumption, time consumption, and simulation effect of each combination, ; where, is the result obtained after evaluating each combination, typically including energy consumption, time consumption, and napping effect, and is used to store the evaluation results of each combination for subsequent selection of the optimal combination, is a function for evaluating each extracted combination, which considers energy consumption, time, and effect to generate an evaluation result for each combination, evaluates the performance of each combination, and provides a basis for selecting the optimal combination, selects the combination with the best overall performance as the optimal combination, i.e., prioritizes the combination with the lowest energy consumption, prioritizes the combination with the shortest time consumption if the energy consumption is the same, and prioritizes the combination with the best effect if the energy consumption and time are the same: ; where, is the best combination selected from all evaluated combinations, which is the combination with the lowest energy consumption, shortest time, and best effect, and is used to adjust the gear ratio of the tension controller and the napping roller to achieve the best production effect, is a function for selecting the optimal combination, which selects the combination with the best overall performance based on the evaluation results to determine the final adjustment scheme and achieve the most energy-efficient, time-saving, and effective production process.

[0026] The embodiment also provides a combination simulation model, specifically: inputs different fabric characteristics, including material, density, thickness, and other data: ; where, is the characteristic data of the fabric, including material, density, thickness, and other data, which is used to generate a fabric model to simulate the napping effect of different fabrics under different production conditions, is the material of the fabric, such as cotton, hemp, silk, etc., which affects the physical properties of the fabric, such as strength, elasticity, etc., density of the fabric, in grams per cubic centimeter, used to affect the weight and strength of the fabric, thickness of the fabric, in millimeters, used to affect the flexibility and durability of the fabric; input different production parameters, including the gear ratio of the raising roller, tension value, production duration, etc.: ; where, production parameters, including the gear ratio of the raising roller, tension value, production duration, etc., used to simulate different production conditions and evaluate the raising effect under different parameters, gear ratio of the raising roller, affecting the conveying speed and tension of the fabric, used to adjust the conveying speed and tension of the fabric to achieve the best raising effect, tension value of the fabric, in Newton, used to control the tension of the fabric during conveying, ensuring uniform raising effect, production duration, in seconds, used to affect the raising effect of the fabric, too long or too short production time may result in poor effect; generate a virtual model of the fabric according to the input fabric characteristics: ; where, virtual model generated according to fabric characteristics, used to simulate the raising effect of different fabrics under different production conditions, function for generating fabric model, used to generate a virtual model of the fabric according to the input fabric characteristics; simulate different gear ratios and tension values, generate a series of possible gear ratio and tension value combinations: ; ; where, a series of possible gear ratios, used to simulate different production conditions and evaluate the raising effect under different gear ratios, function for generating a series of possible gear ratios, used to provide multiple gear ratio options for simulation and evaluation, a series of possible tension values, used to simulate different production conditions and evaluate the raising effect under different tension values, function for generating a series of possible tension values, used to provide multiple tension value options for simulation and evaluation; simulate the production process for each combination and calculate the corresponding simulation effect: ; where, the result of simulating the production process, including the evaluation of the raising effect, used to select the optimal production parameter combination, It is a function for simulating the production process, which is used to simulate the production process and evaluate the napping effect according to the input cloth model and production parameters; Define the evaluation criteria for simulation effects based on actual needs: ; in, The standards for evaluating the napping effect, such as texture, density, thickness, etc., are used to evaluate the napping effect of each combination and select the optimal combination. The texture of the fabric affects the appearance and feel of the napped effect and is used as one of the criteria for evaluating the napped effect. The density of the fabric, expressed in grams per cubic centimeter, affects the weight and strength of the fabric. The thickness of the fabric, in millimeters, affects the flexibility and durability of the fabric. The simulation effect of each combination is scored according to the evaluation criteria: ; in, The evaluation score of each combination is used to select the optimal production parameter combination. is a function for evaluating the hair pulling effect, used to score the hair pulling effect of each combination according to the evaluation criteria; Integrate all fabric features, production parameters, fabric virtual model, gear ratio and tension value combination, simulation effect and simulation effect score to generate a combined simulation model, denoted as .

[0027] In this embodiment, precise tension control and gear ratio adjustment are used to ensure that the fabric remains stable during the production process, reducing production downtime and quality problems caused by unstable tension. The algorithm optimizes tension control and gear ratio adjustment to ensure that energy consumption is minimized while meeting production requirements. Through precise simulation of the napping effect, the final effect of the fabric is ensured to meet production requirements, thereby improving product quality stability. The algorithm supports real-time monitoring of fabric tension and napping effect, and can promptly detect and adjust problems to reduce the defective rate. The algorithm can adapt to different fabric materials, densities and thicknesses, and through simulation and optimization, it is applicable to a variety of production conditions. Through precise tension control, equipment wear caused by tension problems is reduced, thereby reducing maintenance costs.

Claims

1. A method for adjusting the speed of a velvet machine based on an intelligent sensor, characterized in that: include: Collect images of target fabrics; Analyze the target fabric to determine whether the fabric tension is stable; If the fabric tension is unstable, perform tension adjustment until the fabric tension is stable; If the fabric tension is stable, perform the napping effect evaluation; Determine the final adjustment data, and adjust the gear ratio of the napping roller and the tension controller according to the final adjustment data; The determination of whether the fabric tension is stable is specifically as follows: Extract preprocessed image ; Calculate the tension balance index: ； Calculate the balance evaluation of the material: ； Define a threshold adjustment judgment function to calculate the exponential threshold: ； Define a tension stability judgment function to determine whether the cloth tension is stable: ； like , then the fabric tension is determined to be stable; like , it is determined that the fabric tension is unstable.

2. The method for adjusting the speed of a velveting machine based on an intelligent sensor according to claim 1, characterized in that: Collect images of the target fabric, specifically: Connect the camera and perform initial settings: ； Use the camera to capture real-time images of the cloth and save them as image files: ； Preprocess the collected images: ； For the preprocessed image, calculate the gray level co-occurrence matrix and extract texture features: ； Extract local texture features of an image: ； Compute the color moments of an image: ； Compute the color histogram of an image: ； Combine the above texture features and color features to form the final feature vector : 。 3. The method for adjusting the speed of a velveting machine based on an intelligent sensor according to claim 1, characterized in that: Perform tension adjustment until the fabric tension is stable, including determining the tension adjustment data, specifically: Get the sensor type: ； Install and initialize the sensor: ； Use sensors to collect gear ratio related data: ； According to the collected data, calculate the gear ratio: ； ； Record the gear ratio: ； Get the preprocessed image ; Call the tension simulation model and extract the preprocessed image and gear ratio All corresponding tension adjustment data: ； ； For each set of tension adjustment data, the tension values ​​are simulated and applied to the target fabric to determine whether the fabric tension is stable. If the fabric tension is stable, a set of tension adjustment data corresponding to the tension value is defined as a stable data set; If the fabric tension is unstable, a group of tension adjustment data corresponding to the tension value is defined as an unstable data group; Integrate all stable data sets to form a stable adjustment data set; Extract the stable adjustment data set, and the stable data group with the smallest energy consumption value and time energy consumption value is defined as the adjustment data group; The tension adjustment data corresponding to the adjustment data group is defined as the target tension adjustment data, which is recorded as .

4. The method for adjusting the speed of a velveting machine based on an intelligent sensor according to claim 3, characterized in that: Perform tension adjustment until the fabric tension is stable, including adjusting the tension controller according to the tension adjustment data, specifically: Get target tension adjustment data ; Adjust the tension controller parameters: ； Conduct stability testing: ； Record adjustment results: 。 5. The method for adjusting the speed of a velveting machine based on an intelligent sensor according to claim 4, characterized in that: Tension simulation model, specifically: Collect fabric feature data: ； For fabric feature data, perform data cleaning: ； For the fabric feature data after data cleaning, feature extraction is performed: ； For the fabric feature data after feature extraction, data standardization is performed: ； For the cloth feature data after data standardization, select Determine Model: ； For the selected model, perform model training: ； After model training, perform model validation: ； Input the normalized fabric feature data into the trained model; Calculate the tension value output by the model: ； Calculate the energy consumption value according to the target tension value: ； According to the target tension value, calculate the adjustment time: ； Integrate fabric feature data, training model, tension value, energy consumption value and time consumption to form a tension simulation model, which is recorded as .

6. The method for adjusting the speed of a velveting machine based on an intelligent sensor according to claim 1, characterized in that: Perform roughening effect evaluation, specifically: Get the sensor type: ； Install and initialize the sensor: ； Use sensors to collect gear ratio related data: ； According to the collected data, calculate the gear ratio: ； ； Record the gear ratio: ； Determine the gear ratio and fabric tension combination that achieves the desired napping effect.

7. The method for adjusting the speed of a velveting machine based on an intelligent sensor according to claim 6, characterized in that: Determine the gear ratio and fabric tension combination that meets the napping effect, specifically: Call the combined simulation model to determine the preprocessed image and gear ratio The corresponding simulation effect: ； Get the target effect, recorded as ; Determine whether the current gear ratio and fabric tension meet the target effect: ； like , then it is determined that the target effect is met; Then no parameters need to be adjusted and the fluffing machine can run at the current speed; like , it is determined that the target effect is not met; Then all possible combinations of gear ratios and cloth tensions are extracted from the combined simulation model: ； Record the extracted combination; Evaluate each combination and calculate the energy consumption, time consumption and simulation effect of each combination: ； Based on the evaluation results, the combination with the best comprehensive performance is selected as the optimal combination: 。 8. The method for adjusting the speed of a velveting machine based on an intelligent sensor according to claim 7, characterized in that: Combined simulation model, specifically: Enter different cloth characteristics: ； Enter different production parameters: ； Generate a virtual model of the cloth based on the input cloth features: ； Simulate different gear ratios and tension values ​​to generate a range of possible gear ratio and tension value combinations: ； ； Simulate the production process for each combination and calculate the corresponding simulation effect: ； Define the evaluation criteria for simulation performance: ； The simulation effect of each combination is scored according to the evaluation criteria: ； Integrate all fabric features, production parameters, fabric virtual model, gear ratio and tension value combination, simulation effect and simulation effect score to generate a combined simulation model, denoted as .

9. The method for adjusting the speed of a velveting machine based on an intelligent sensor according to claim 1, characterized in that: Determine the final adjustment data, and adjust the gear ratio of the napping roller and the tension controller according to the final adjustment data, specifically: Get the best combination ; According to the tension value in the optimal combination, adjust the parameters of the tension controller: ； Conduct stability testing: ； In tension monitoring mode, automatic span adjustment is performed; Adjust the gear ratio of the napping roller according to the gear ratio in the optimal combination; Calculate the electronic gear ratio based on the motor encoder resolution and the number of pulses required for the motor to rotate one circle: ； The calculated electronic gear ratio is set to the control system of the nap roller; Perform transmission efficiency tests on the adjusted gear ratios to ensure they can operate efficiently and stably: 。

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