A speed adjusting method of a pile machine based on intelligent sensors

Abstract

The application relates to the technical field of intelligent adjustment of equipment, and discloses a pile machine speed adjustment method based on an intelligent sensor, which comprises the following steps: collecting the image of target cloth, judging whether the cloth tension is stable, and respectively executing tension adjustment or executing pile effect evaluation, determining final adjustment data, and adjusting the data of the pile roller gear ratio and the tension controller. The pile machine speed adjustment method based on the intelligent sensor reduces production downtime and quality problems caused by unstable tension, optimizes tension control and gear ratio adjustment, improves the quality stability of products through accurate pile effect simulation, supports real-time monitoring of cloth tension and pile effect, can discover and adjust problems in time, reduces the rate of defective products, can adapt to different cloth materials, densities and thicknesses, is suitable for various production conditions through simulation and optimization, reduces equipment wear caused by tension problems through accurate tension control, and reduces maintenance costs.

Description

Technical Field

[0001] This invention relates to the field of intelligent equipment adjustment technology, specifically a speed adjustment method for a plush machine based on intelligent sensors. Background Technology

[0002] As people's living standards improve, the demand for plush products (such as clothing, car interiors, and toys) is constantly growing, and market competition is becoming increasingly fierce. This places higher demands on product quality and production efficiency. Consumers have increasingly higher requirements for the appearance, feel, and durability of plush products, which means that more precise control is needed in the production process to ensure that the density, distribution, and length of the plush meet standards. Market competition and profit pressure are prompting companies to seek ways to improve production efficiency and reduce production costs. Reducing downtime, minimizing resource waste, and increasing automation are key. More and more consumers want to customize plush products to their own style, which places higher demands on the flexibility and adaptability of the production process. The ability to quickly and accurately adjust production parameters to meet the needs of different products is becoming important. The manufacturing industry is undergoing a digital transformation, and intelligent and automated production is the development trend. As a key piece of equipment, the pile machine also needs to be transformed into an intelligent system to adapt to the industry's development trend. The manufacturing industry is paying more and more attention to environmental protection and hopes to reduce resource consumption and waste emissions. In the production process of the pile machine, there is room for optimization in terms of fiber utilization and energy consumption. The speed adjustment method of the pile machine based on intelligent sensors is proposed to meet the above-mentioned growing industry demands and make up for the shortcomings of traditional control methods. This method integrates advanced sensor technology, control engineering, motor control, data analysis and other cutting-edge technologies from multiple disciplines. Through intelligent means, it achieves optimized control of the pile machine production process, improves production efficiency and product quality, and is an important development direction in the field of industrial automation.

[0003] Existing speed adjustment methods for pile machines based on smart sensors cannot determine whether the fabric tension is stable through images of the fabric. When the tension is unstable, the tension controller cannot be adjusted in time according to the needs. When the tension is stable, it cannot determine whether the required 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 time while meeting the required effect, which can easily lead to an increased product failure rate and increased production costs. Its practicality has certain limitations. Summary of the Invention

[0004] This invention provides a method for adjusting the speed of a plush machine based on intelligent sensors, which helps to solve the problems mentioned in the background art.

[0005] This invention provides the following technical solution: a method for adjusting the speed of a plush machine based on intelligent sensors, comprising:

[0006] Acquire images of the target fabric;

[0007] Analyze the target fabric to determine if the fabric tension is stable;

[0008] If the fabric tension is unstable, tension adjustment will be performed until the fabric tension is stable;

[0009] If the fabric tension is stable, perform a napping effect assessment;

[0010] Determine the final adjustment data, and adjust the gear ratio of the raising roller and the tension controller accordingly.

[0011] The determination of whether the fabric tension is stable specifically involves:

[0012] Extract the preprocessed image ;

[0013] Calculate the tension balance index;

[0014] Calculate the material flow balance evaluation;

[0015] Define a threshold adjustment judgment function and calculate the exponential threshold;

[0016] Define a tension stability judgment function to determine whether the fabric tension is stable:

[0017] ;

[0018] like If the fabric tension is stable, then it is determined that the fabric tension is stable.

[0019] like If the fabric tension is unstable, it is determined that the fabric tension is unstable.

[0020] As an optional solution to the speed adjustment method for a pile machine based on intelligent sensors described in this invention, the following steps are included: acquiring images of the target fabric, specifically: connecting a camera and performing initialization settings; acquiring real-time images of the fabric using the camera and storing them as image files; preprocessing the acquired images; calculating the gray-level co-occurrence matrix and extracting texture features from the preprocessed images; extracting local texture features from the images; calculating the color moments of the images; calculating the color histogram of the images; and combining the above texture features and color features to form the final feature vector. .

[0021] As an optional solution to the speed adjustment method for a pile machine based on intelligent sensors described in this invention, the following steps are included: performing tension adjustment until the fabric tension stabilizes, specifically determining tension adjustment data: acquiring 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; and acquiring a pre-processed image. Call the tension simulation model to extract the preprocessed image. and gear ratio All corresponding tension adjustment data are collected. For each tension adjustment data set, the tension value is simulated and applied to the target fabric to determine if the fabric tension is stable. If the fabric tension is stable, the tension adjustment data set corresponding to that tension value is designated as a stable data set. If the fabric tension is unstable, the tension adjustment data set corresponding to that tension value is designated as an unstable data set. All stable data sets are integrated to form a stable adjustment dataset. The stable data set with the lowest energy consumption and time energy consumption values ​​is extracted and designated as the adjustment data set. The tension adjustment data corresponding to the adjustment data set is designated as the target tension adjustment data, denoted as... .

[0022] As an optional solution to the speed adjustment method for a pile machine based on intelligent sensors described in this invention, the following step involves: performing tension adjustment until the fabric tension stabilizes, including adjusting the tension controller based on tension adjustment data, specifically: acquiring target tension adjustment data. Adjust the tension controller parameters; conduct a stability test; record the adjustment results.

[0023] As an optional solution to the speed adjustment method of the pile machine based on intelligent sensors described in this invention, the tension simulation model specifically includes: collecting fabric feature data; cleaning the fabric feature data; extracting features from the cleaned fabric feature data; standardizing the extracted fabric feature data; selecting and determining a model from the standardized fabric feature data; training the selected model; and validating the trained model.

[0024] The standardized fabric feature data is input into the trained model;

[0025] The tension value output by the calculation model: ;

[0026] Calculate the energy consumption value based on the target tension value: ;

[0027] Calculate the adjustment time based on the target tension value: ;

[0028] By integrating fabric feature data, training models, tension values, energy consumption values, and time consumption, a tension simulation model is formed, denoted as... .

[0029] As an optional solution for the speed adjustment method of the pile machine based on intelligent sensors described in this invention, the following steps are taken to perform a pile-raising effect evaluation: obtaining the sensor type; installing and initializing the sensor; using the sensor to collect gear ratio related data; calculating the gear ratio based on the collected data; recording the gear ratio; and determining the gear ratio and fabric tension combination that satisfies the pile-raising effect.

[0030] As an optional solution to the speed adjustment method of the pile machine based on intelligent sensors described in this invention, the determination of the gear ratio and fabric tension combination that satisfies the pile-raising effect specifically involves calling a combined simulation model to determine the pre-processed image. and gear ratio The corresponding simulation effect; obtain the target effect, denoted as ; Determine whether the current gear ratio and fabric tension meet the target effect; If If the target effect is met, then no parameters need to be adjusted, and the pile machine can continue operating at its current speed; if... If the target effect is not met, then all possible combinations of gear ratio and fabric tension are extracted from the combined simulation model; the extracted combinations are recorded; each combination is evaluated, and the energy consumption, time consumption, and simulation effect of each combination are calculated; based on the evaluation results, the combination with the best overall performance is selected as the optimal combination.

[0031] As an optional solution to the speed adjustment method for a pile machine based on intelligent sensors described in this invention, the combined simulation model specifically includes: inputting different fabric characteristics; inputting different production parameters; generating a virtual model of the fabric based on the input fabric characteristics; simulating different gear ratios and tension values ​​to generate a series of possible combinations of gear ratios and tension values; simulating the production process for each combination and calculating the corresponding simulation effect; defining an evaluation standard for the simulation effect; scoring the simulation effect of each combination according to the evaluation standard; and integrating all fabric characteristics, production parameters, the virtual model of the fabric, combinations of gear ratios and tension values, simulation effects, and scores of simulation effects to generate a combined simulation model, denoted as... .

[0032] The present invention has the following beneficial effects:

[0033] 1. This intelligent sensor-based speed adjustment method for a pile machine uses a camera to collect images of the fabric, analyzes the images to determine whether the fabric tension is stable, and obtains the real-time status of the fabric through image acquisition, providing data support for subsequent tension judgment and simulation. Real-time monitoring of fabric tension ensures the stability of the production process and avoids quality problems caused by unstable tension.

[0034] 2. This intelligent sensor-based speed adjustment method for a pile machine, when the tension is unstable, invokes a tension simulation model. Based on the current condition of the fabric and the gear ratio of the current pile roller, it determines the most energy-efficient and time-saving tension adjustment data. The parameters of the tension controller are adjusted according to this data to ensure stable fabric tension. The tension simulation model generates a fabric with different materials, densities, and thicknesses, simulates different gear ratios of the pile roller, and simulates different tension controller data. It determines the tension value of different fabrics under different gear ratios and tension controller data. Multiple experiments are conducted for each data point to determine all the tension controller data required for different fabrics to achieve tension stability during transport under different gear ratios. The model also determines the energy consumption and adjustment time required to adjust the fabric from its initial tension value to a stable tension. Through the tension simulation model, tension adjustment is optimized to ensure the fabric remains stable during transport. Precise adjustment of the tension controller ensures optimal fabric tension, reducing energy consumption and production downtime.

[0035] 3. This intelligent sensor-based speed adjustment method for a pile machine collects the gear ratio of the pile-raising roller when the tension is stable. It then calls a combined simulation model to determine if the current gear ratio and fabric tension meet the required pile-raising effect. If they do, no adjustment is made. If not, it extracts all combinations of gear ratios and fabric tensions from the combined simulation model that meet the required pile-raising effect. The most energy-efficient, time-saving, and effective combination is selected as the final adjustment combination. Based on the gear ratio and fabric tension corresponding to the final adjustment combination, the tension controller and the gear ratio of the pile-raising roller are adjusted. The combined simulation model simulates generating a fabric with different materials, densities, thicknesses, etc., simulating different gear ratios of the pile-raising roller, different tension values, and different production times. It determines the pile-raising effect corresponding to different fabrics under different gear ratios, tension values, and production times. It checks whether the current gear ratio and tension settings meet the pile-raising effect, ensuring production efficiency and product quality. Through the combined simulation model, it finds the most energy-efficient, time-saving, and effective gear ratio and tension settings, optimizing the production process. Attached Figure Description

[0036] Figure 1 This is a flowchart of the speed adjustment method for a plush machine based on intelligent sensors according to the present invention. Detailed Implementation

[0037] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0038] Example 1: A method for adjusting the speed of a plush machine based on intelligent sensors, see [link / reference]. Figure 1 ,include:

[0039] Acquire images of the target fabric;

[0040] Analyze the target fabric to determine if the fabric tension is stable;

[0041] If the fabric tension is unstable, tension adjustment will be performed until the fabric tension is stable;

[0042] If the fabric tension is stable, perform a napping effect assessment;

[0043] Determine the final adjustment data, and adjust the gear ratio of the raising roller and the tension controller accordingly.

[0044] The determination of whether the fabric tension is stable specifically involves:

[0045] Extract the preprocessed image ;

[0046] Calculate the tension balance index:

[0047] ;

[0048] in, This represents the normalized value of the variance or standard deviation of all Euclidean distances. This is the maximum value of all Euclidean distances. This is the minimum value of all Euclidean distances. To avoid the denominator being zero, this is a hyperparameter. The tension balance index is the specific process for calculating the tension balance index.

[0049] Select a grayscale image of the fabric area between the tension rollers;

[0050] Select two points symmetrical about a set axis on the grayscale image as a pair of monitoring points;

[0051] Calculate the Euclidean distance between each pair of monitoring points;

[0052] Normalize the variance or standard deviation of all Euclidean distances to obtain normalized values;

[0053] Calculate the difference between the maximum and minimum values ​​of all Euclidean distances, and add it to the hyperparameter (to avoid the denominator being zero) to obtain the sum;

[0054] The ratio of the normalized value to the summed value is used as the tension balance index;

[0055] The material flow balance evaluation is calculated, which assesses the uniformity of fabric tension as it passes through the tension roller using the mechanical parameters of the tension roller or image features of the fabric area.

[0056] ;

[0057] in, This represents the deviation between the current torque of the tension roll and its historical average torque. The historical average torque of the tension roller. The shortest distance from the target pixel to the centerline of the target roller. The total number of target pixels. This is the evaluation value for material flow balance;

[0058] Define a threshold adjustment judgment function to calculate the exponential threshold:

[0059] ;

[0060] in, As the initial threshold, These are the preset adjustment parameters. The final exponential threshold is obtained by adjusting the judgment function according to the preset threshold to correct the initial threshold.

[0061] Define a tension stability judgment function to determine whether the fabric tension is stable:

[0062] ;

[0063] like If the fabric tension is stable, then it is determined that the fabric tension is stable.

[0064] like If the fabric tension is unstable, it is determined that the fabric tension is unstable.

[0065] This embodiment also provides the method of acquiring images of the target fabric, specifically:

[0066] Connect the camera and perform initial setup to ensure it is working properly:

[0067] ;

[0068] in, This indicates the state of the camera after initialization; output True if successful, and False if unsuccessful. Indicates the camera device identifier. Indicates resolution, Indicates frame rate. The camera initialization functions include setting camera parameters and loading hardware modules. The camera must be initialized before use to ensure that it works properly.

[0069] Use a camera to capture real-time images of the fabric and save them as image files:

[0070] ;

[0071] in, This represents the captured image of the fabric. The image capture operation function is usually implemented by the camera to acquire image data for subsequent processing;

[0072] Preprocessing of the acquired images, such as grayscale conversion and filtering, is performed to improve image quality.

[0073] ;

[0074] in, This represents the preprocessed image. This indicates that edge detection is being performed on an image of the fabric. This indicates that the image contrast has been enhanced to improve image sharpness and make details in the image more apparent. This indicates that a grayscale image is filtered to remove noise and smooth the image. This represents the filter kernel, which can be a Gaussian kernel, a mean kernel, etc. This means converting a color image to a grayscale image to reduce the amount of data while preserving the main features of the image;

[0075] For the preprocessed image, calculate the gray-level co-occurrence matrix and extract texture features such as mean, standard deviation, contrast, dissimilarity, homogeneity, second moment of angle, energy, and entropy.

[0076] ;

[0077] Specific features include mean, standard deviation, contrast, dissimilarity, homogeneity, second moment of angle (ASM), energy, and entropy.

[0078] It extracts local texture features from images, exhibiting characteristics such as multi-resolution, grayscale invariance, and rotation invariance.

[0079] ;

[0080] Calculate the color moments of an image, including the first moment (mean), second moment (standard deviation), and third moment (skewness):

[0081] ;

[0082] in, Color moments are a method for describing the color features of an image, including statistical measures such as mean, standard deviation, and skewness. They are used in image analysis, especially in color feature extraction, and can help describe the color distribution of an image. This is the function for calculating color moments. In image processing, it is used to calculate color moments for subsequent analysis.

[0083] function In this context, the specific formula for calculating the first moment (mean) is:

[0084] ;

[0085] function In this context, the specific formula for calculating the second moment (standard deviation) is:

[0086] ;

[0087] function In this context, the specific formula for calculating the third moment (skewness) is:

[0088] ;

[0089] in, Indicates color channels (such as R, G, B). Indicates the first The first channel N represents the total number of pixels in the image;

[0090] Calculate the color histogram of an image to describe the distribution of different colors within the image:

[0091] ;

[0092] in, A color histogram is a method for statistically analyzing the distribution of colors in an image. It describes the frequency of different colors in an image and is used in image analysis, particularly in color feature extraction, where it helps to identify and distinguish regions of different colors. This is a function for calculating color histograms, used in image processing to generate color histograms for subsequent analysis.

[0093] Among them, the function The specific formula is:

[0094] ;

[0095] in, Indicates color value Frequency of occurrence in the image;

[0096] The texture and color features described above are combined to form the final feature vector. :

[0097] ;

[0098] The specific steps for preprocessing the acquired images are as follows:

[0099] Convert the captured color image of the fabric to a grayscale image:

[0100] ;

[0101] in, This represents the image after grayscale conversion. This is a function that converts a color image to a grayscale image. In image processing, grayscale conversion can reduce the amount of data while preserving the main features of the image, making it easier for subsequent processing.

[0102] Filtering the grayscale image:

[0103] ;

[0104] in, This represents the filter kernel, which can be a Gaussian kernel, a mean kernel, etc. The filtered image is used for subsequent image analysis and processing. This is a function that performs filtering operations on an image to remove noise or smooth the image. In image preprocessing, filtering can improve image quality and enhance the usability of the image.

[0105] Contrast enhancement is applied to the filtered image to improve image sharpness and make details in the image more apparent.

[0106] ;

[0107] in, This is an image that has undergone contrast enhancement processing, used to increase the contrast of the image and make the details in the image more apparent. This is a function for contrast enhancement operations, used to improve the sharpness of an image. In image preprocessing, contrast enhancement can make the details in the image more obvious, which is convenient for subsequent analysis.

[0108] Edge detection is then performed on the contrast-enhanced image. Common edge detection algorithms include Sobel and Canny.

[0109] ;

[0110] in, This is an image that has undergone edge detection processing, used to extract edge features from the image to facilitate subsequent image analysis. This is an edge detection operation function used to extract edge features in an image. In image processing, edge detection can help identify contours and structures in an image.

[0111] The image after edge detection is used as the final preprocessing result. :

[0112] .

[0113] This embodiment also provides determining the final adjustment data and adjusting the gear ratio of the raising roller and the tension controller based on the final adjustment data, specifically as follows:

[0114] Get the optimal combination ;

[0115] Adjust the parameters of the tension controller based on the tension values ​​in the optimal combination:

[0116] ;

[0117] in, To obtain the optimal combination The target tension value extracted represents the target tension that the fabric needs to maintain during the conveying process to achieve the best napping effect. P is the proportionality coefficient, I is the integral time, and D is the derivative time. A proportional-integral-derivative (PID) controller is a function that adjusts the parameters of its components. A PID controller includes a proportional gain P, integral time I, and derivative time D. These parameters determine how the PID controller responds to errors, thus affecting the system's stability and performance. The optimal combination of parameters is used to determine the optimal response. The target tension value is extracted, and the parameters of the PID controller are adjusted to achieve the most energy-efficient and time-saving tension control. This usually requires determining the optimal PID parameters through experiments or simulations. The specific steps for adjusting the tension controller parameters are as follows:

[0118] Adjust the proportional coefficient P to make the controller respond more quickly to errors. Generally, start with a small value and gradually increase it until the system is stable.

[0119] To reduce steady-state error, the integral time I should be adjusted. The integral time should start from a large value and gradually decrease until the system stops oscillating.

[0120] To reduce overshoot, adjust the derivative time D. The derivative time should start from a small value and gradually increase until the system response is stable.

[0121] Conduct stability testing:

[0122] ;

[0123] in, The result of the stability test is a Boolean value, outputting either True or False, indicating whether the system is stable. In control systems, stability refers to whether the system can recover to an equilibrium state after being disturbed. It is used to determine whether the tension control system is stable after adjusting the PID parameters. If True, it indicates that the system is stable; if If the value is False, it indicates that the system is unstable and further parameter adjustments are needed until the system is stable. This is a test function used to perform stability tests on the adjusted PID parameters to ensure stable system operation. Test methods include manual adjustment, automatic adjustment, and tension calibration to ensure the adjusted PID parameters enable stable system operation and avoid oscillations or instability. The test results are used to determine system stability. The specific steps of the stability test are as follows:

[0124] First, manually run the system to check if the tension control system is working properly, and confirm that the tension display and actuator are both normal.

[0125] When the control mode is switched 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, ensuring that the system can stably output the required electrical signal;

[0126] In tension monitoring mode, automatic span adjustment is performed, and standard weights are used for calibration to ensure that the tension value output by the controller is consistent with the actual value.

[0127] In tension monitoring mode, automatic span adjustment is performed, and standard weights are used for calibration to ensure that the tension value output by the controller is consistent with the actual value.

[0128] Adjust the gear ratio of the raising roller according to the gear ratio in the optimal combination;

[0129] Calculate the electronic gear ratio based on the motor encoder resolution and the number of pulses required for one revolution of the motor:

[0130] ;

[0131] in, The electronic gear ratio is a proportional relationship set in the motor controller to proportionally convert the rotation angle or speed of the motor shaft to the rotation angle or speed of the load. It is usually expressed as a ratio of two integers, P / Q, where P is the unit of displacement on the load side and Q is the unit of displacement 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 into the displacement of the load, thereby achieving high-precision position control), and simplifying the mechanical structure (through the electronic gear ratio, mechanical gear transmission can be reduced or eliminated, thereby simplifying the mechanical structure, reducing costs and maintenance difficulty). The encoder resolution refers to the number of pulses generated per revolution of the encoder. Common encoder resolutions include 2000 lines and 2500 lines. After a 4x frequency multiplication, the encoder can generate 8000 or 10000 pulses per revolution, used to calculate the precise position and speed of the motor, ensuring the accuracy and consistency of the motor's movement. The number of pulses required for the motor to rotate one revolution refers to the number of pulses received by the motor from the upper-level controller. This number is used to drive the motor to complete one revolution and to calculate the electronic gear ratio, ensuring the speed and position control accuracy between the motor and the load.

[0132] The calculated electronic gear ratio is set into the control system of the napping roller;

[0133] The adjusted gear ratio was tested for transmission efficiency to ensure efficient and stable operation.

[0134] ;

[0135] in, The result of the transmission efficiency test is a Boolean value indicating whether the system is operating efficiently. After adjusting the gear ratio, the efficiency test is used to determine whether the system has achieved the expected efficiency. This is used to determine whether the adjusted gear ratio can operate efficiently, ensuring that the system achieves the optimal balance between energy saving and performance. This is a transmission efficiency test function used to test whether an adjusted gear ratio can operate efficiently. It typically evaluates the system's energy consumption, response time, and stability, ensuring that the system achieves an optimal balance between energy saving and performance by testing the adjusted gear ratio.

[0136] Example 2 is an improvement on Example 1. This method for adjusting the speed of a pile machine based on intelligent sensors performs tension adjustment until the fabric tension stabilizes, including determining the tension adjustment data, specifically:

[0137] Obtain the sensor type, including encoders and Hall effect sensors:

[0138] ;

[0139] in, For the type of sensor, HES stands for Hall effect sensor;

[0140] Install and initialize the sensor. Mount the sensor near the raising roller, ensuring 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 1 mm. For encoders, ensure the encoder is connected to the motor shaft and rotates with the motor. Initialize the sensor by setting its parameters, such as sampling rate and output format. For Hall effect sensors, calibration is required to eliminate offset and amplitude mismatch.

[0141] ;

[0142] in, The sensor data after sensor initialization. The sensor initialization function is used to initialize the sensor, set its parameters, and prepare it for data acquisition. This typically includes setting the sensor's sampling rate, output format, and connection to the device. The sensor must be initialized before data acquisition to ensure it works correctly. The initialization process can set the sensor's basic parameters, such as sampling rate and resolution.

[0143] Sensors are used to collect data related to the gear ratio. For Hall effect sensors, the pulse signal of the magnetic field change is recorded; for encoders, the number of output pulses is recorded.

[0144] ;

[0145] Here, 'data' represents the data collected by the sensor, such as the rotational speed and position data of a gear. For a Hall effect sensor, it collects the pulse signal of the magnetic field change; for an encoder, it collects the number of output pulses. This is a data acquisition function, which is the process of collecting data from sensors. 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.

[0146] Based on the collected data, calculate the gear ratio:

[0147] ;

[0148] ;

[0149] in, This indicates the formula for calculating the gear ratio when the sensor type is an encoder. The number of pulses per revolution of the encoder, collected by the sensor. This refers to the number of revolutions per revolution of the motor, collected by the sensor. This indicates the gear ratio calculation formula used when the sensor type is a Hall effect sensor. The number of gear teeth collected by the sensor. The number of pulses detected by the sensor;

[0150] Record the gear ratio for subsequent tension adjustment and production process control:

[0151] ;

[0152] in, For the recorded gear ratio and related data, This is a recording function used to record the gear ratio, that is, to store the calculated gear ratio for later use. In mechanical systems, recording the gear ratio helps to monitor and adjust the operating status of the equipment.

[0153] Obtain the preprocessed image ;

[0154] Call the tension simulation model to extract the preprocessed image. and gear ratio All corresponding tension adjustment data:

[0155] ;

[0156] ;

[0157] in, This is a tension simulation model used to extract corresponding tension adjustment data based on the input image and gear ratio. This model can help optimize equipment tension control, improve production efficiency and energy utilization. For tension adjustment data, including tension value, energy consumption value, and time consumption, For the target tension value, The energy required to achieve the target tension The time required to achieve the target tension;

[0158] For each set of tension adjustment data, the tension value is simulated and applied to the target fabric to determine whether the fabric tension is stable.

[0159] If the fabric tension is stable, then the set of tension adjustment data corresponding to that tension value is defined as the stable data set;

[0160] If the fabric tension is unstable, then the set of tension adjustment data corresponding to that tension value is defined as the unstable data set;

[0161] Integrate all stable data sets to form a stable, regulated dataset;

[0162] Extract the stable data set from the stable adjustment dataset where both energy consumption and time energy consumption are the smallest, and designate it as the adjustment data set;

[0163] The tension adjustment data corresponding to the adjustment data group is defined as the target tension adjustment data, denoted as . .

[0164] This embodiment also provides performing tension adjustment until the fabric tension stabilizes, including adjusting the tension controller based on tension adjustment data, specifically:

[0165] Acquire target tension adjustment data ;

[0166] Adjust the tension controller parameters:

[0167] ;

[0168] Where P is the proportionality coefficient, I is the integral time, and D is the derivative time. A proportional-integral-derivative (PID) controller is a function that adjusts the parameters of its components. A PID controller includes a proportional gain P, an integral time I, and a derivative time D. These parameters determine how the PID controller responds to errors, thus affecting the system's stability and performance. The control is adjusted based on the target tension data. To achieve the most energy-efficient and time-saving tension control, the parameters of the PID controller need to be adjusted. This usually requires determining the optimal PID parameters through experiments or simulations. The specific steps for adjusting the tension controller parameters are as follows:

[0169] Adjust the proportional coefficient P to make the controller respond more quickly to errors. Generally, start with a small value and gradually increase it until the system is stable.

[0170] To reduce steady-state error, the integral time I should be adjusted. The integral time should start from a large value and gradually decrease until the system stops oscillating.

[0171] To reduce overshoot, adjust the derivative time D. The derivative time should start from a small value and gradually increase until the system response is stable.

[0172] Conduct stability testing:

[0173] ;

[0174] in, The result of the stability test is a Boolean value, outputting either True or False, indicating whether the system is stable. In control systems, stability refers to whether the system can recover to an equilibrium state after being disturbed. It is used to determine whether the tension control system is stable after adjusting the PID parameters. If True, it indicates that the system is stable; if If the value is False, it indicates that the system is unstable and further parameter adjustments are needed until the system is stable. This is a test function used to perform stability tests on the adjusted PID parameters to ensure stable system operation. Test methods include manual adjustment, automatic adjustment, and tension calibration to ensure the adjusted PID parameters enable stable system operation and avoid oscillations or instability. The test results are used to determine system stability. The specific steps of the stability test are as follows:

[0175] First, manually run the system to check if the tension control system is working properly, and confirm that the tension display and actuator are both normal.

[0176] When the control mode is switched 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, ensuring that the system can stably output the required electrical signal;

[0177] In tension monitoring mode, automatic span adjustment is performed, and standard weights are used for calibration to ensure that the tension value output by the controller is consistent with the actual value.

[0178] Record the adjustment results, including the adjusted parameters and test results, for future reference and use:

[0179] ;

[0180] in, The recorded adjustment results, namely the adjusted parameters and test results, The result recording function is used to record the adjusted PID parameters and test results for subsequent reference and analysis. It saves the adjusted parameters and stability test results to facilitate subsequent system maintenance and optimization, and provides historical data for analyzing the long-term performance and stability of the system.

[0181] This embodiment also provides a tension simulation model, specifically:

[0182] Collect fabric feature data, including fabric material, density, thickness, and other feature data, napping roller gear ratio data, and preprocessed image data:

[0183] ;

[0184] in, For fabric feature data, The material of the fabric, such as cotton, linen, silk, etc. The density of the fabric is expressed in grams per cubic centimeter. The thickness of the fabric is expressed in millimeters. The gear ratio of the napping roller. Image data of the preprocessed fabric;

[0185] For fabric feature data, data cleaning is performed to handle missing values, outliers, duplicate records, and noisy data:

[0186] ;

[0187] in, For the fabric feature data after cleaning, missing values, outliers, and noise were removed. These are data cleaning functions used to handle problems in the data, such as missing data, anomalies, and noise.

[0188] For the cleaned fabric feature data, feature extraction is performed to extract features from the preprocessed image, such as texture features and color features:

[0189] ;

[0190] in, Features extracted from cleaned fabric feature data, such as texture and color features. This is a feature extraction function used to extract useful information from an image;

[0191] For the fabric feature data after feature extraction, data standardization is performed to ensure that features of different dimensions and magnitudes are treated fairly in the model:

[0192] ;

[0193] in, To standardize fabric feature data and make features of different dimensions comparable, This is a standardization function used to adjust the dimensions of data;

[0194] For the standardized fabric feature data, select a suitable model, such as Support Vector Machine (SVM), neural network, linear regression, etc.

[0195] ;

[0196] Here, `model` represents the selected machine learning model, such as Support Vector Machine (SVM), Neural Network, Linear Regression, etc. A model selection function is used to select a suitable model.

[0197] For the selected model, train the model using historical data and adjust the model parameters to optimize performance:

[0198] ;

[0199] in, The trained model has predictive capabilities. This is the model training function, used to train the model;

[0200] After training, the model is validated by cross-validation to assess its performance and ensure its generalization ability.

[0201] ;

[0202] in, The model validation results are used to evaluate the model's performance. This is the model validation function, used to evaluate the model's generalization ability;

[0203] The standardized fabric feature data is input into the trained model;

[0204] The tension value output by the calculation model:

[0205] ;

[0206] in, The target tension value is expressed in Newtons.

[0207] Calculate the energy consumption value based on the target tension value:

[0208] ;

[0209] in, This is the energy consumption value, measured in joules. This is an energy consumption calculation function used to calculate the energy consumption required to achieve the target tension;

[0210] Calculate the adjustment time based on the target tension value:

[0211] ;

[0212] in, The time elapsed is measured in seconds. This is a time calculation function used to calculate the time required to reach the target tension;

[0213] By integrating fabric feature data, training models, tension values, energy consumption values, and time consumption, a tension simulation model is formed, denoted as... .

[0214] Example 3 is an improvement on Example 2. In this example, the effect of the napping is evaluated, specifically as follows:

[0215] Obtain the sensor type, including encoders and Hall effect sensors:

[0216] ;

[0217] in, For the type of sensor, HES stands for Hall effect sensor;

[0218] Install and initialize the sensor. Mount the sensor near the raising roller, ensuring 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 1 mm. For encoders, ensure the encoder is connected to the motor shaft and rotates with the motor. Initialize the sensor by setting its parameters, such as sampling rate and output format. For Hall effect sensors, calibration is required to eliminate offset and amplitude mismatch.

[0219] ;

[0220] in, The sensor data after sensor initialization. The sensor initialization function is used to initialize the sensor, set its parameters, and prepare it for data acquisition. This typically includes setting the sensor's sampling rate, output format, and connection to the device. The sensor must be initialized before data acquisition to ensure it works correctly. The initialization process can set the sensor's basic parameters, such as sampling rate and resolution.

[0221] Sensors are used to collect data related to the gear ratio. For Hall effect sensors, the pulse signal of the magnetic field change is recorded; for encoders, the number of output pulses is recorded.

[0222] ;

[0223] Here, 'data' represents the data collected by the sensor, such as the rotational speed and position data of a gear. For a Hall effect sensor, it collects the pulse signal of the magnetic field change; for an encoder, it collects the number of output pulses. This is a data acquisition function, which is the process of collecting data from sensors. 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.

[0224] Based on the collected data, calculate the gear ratio:

[0225] ;

[0226] ;

[0227] in, This indicates the formula for calculating the gear ratio when the sensor type is an encoder. The number of pulses per revolution of the encoder, collected by the sensor. This refers to the number of revolutions per revolution of the motor, collected by the sensor. This indicates the gear ratio calculation formula used when the sensor type is a Hall effect sensor. The number of gear teeth collected by the sensor. The number of pulses detected by the sensor;

[0228] Record the gear ratio for subsequent tension adjustment and production process control:

[0229] ;

[0230] in, For the recorded gear ratio and related data, This is a recording function used to record the gear ratio, that is, to store the calculated gear ratio for later use. In mechanical systems, recording the gear ratio helps to monitor and adjust the operating status of the equipment.

[0231] Determine the optimal gear ratio and fabric tension combination to achieve the desired napping effect.

[0232] This embodiment also provides a method for determining the gear ratio and fabric tension combination that satisfies the napping effect, specifically:

[0233] Call the combined simulation model to determine the preprocessed image. and gear ratio The corresponding simulation effect is a textured finish:

[0234] ;

[0235] in, For the preprocessed image and gear ratio The corresponding simulation effect in the combined simulation model For combined simulation models;

[0236] To achieve the desired effect, let's call it... ;

[0237] Determine if the current gear ratio and fabric tension meet the target effect:

[0238] ;

[0239] like If so, the target effect is determined to be satisfied;

[0240] Then, without adjusting any parameters, the plush machine can operate at its current speed.

[0241] like If so, it is determined that the target effect is not met;

[0242] Then, extract all possible combinations of gear ratios and fabric tension from the combined simulation model:

[0243] ;

[0244] in, It is a list containing all possible combinations of gear ratios and fabric tension for that fabric image that achieve the desired effect. The list generation function is used to extract all possible combinations of gear ratios and fabric tension from the combined simulation model and generate a list containing multiple combinations for subsequent evaluation and selection of the optimal combination. It provides a comprehensive list of combinations to evaluate which combinations can meet the required napping effect.

[0245] Record the extracted combinations for later selection;

[0246] For each combination, evaluate the energy consumption, time consumption, and simulation results:

[0247] ;

[0248] in, The results obtained after evaluating each combination typically include indicators such as energy consumption, time consumption, and lint removal effect. These evaluation results are stored for subsequent selection of the optimal combination. This is a function that evaluates each extracted combination. It considers factors such as energy consumption, time, and effect, generates an evaluation result for each combination, assesses the performance of each combination, and provides a basis for selecting the optimal combination.

[0249] Based on the evaluation results, the combination with the best overall performance is selected as the optimal combination. This means that, while meeting the performance requirements, the combination with the lowest energy consumption is prioritized; if energy consumption is the same, the combination with the shortest time consumption is prioritized; and if energy consumption and time are the same, the combination with the best performance is prioritized.

[0250] ;

[0251] in, This is the optimal combination selected from all evaluated options; it features the lowest energy consumption, shortest time, and best results. It's used to adjust the gear ratio of the tension controller and the napping roller to achieve the best production performance. To select the optimal combination of functions, it selects the combination with the best overall performance based on the evaluation results, and determines the final adjustment scheme to achieve the most energy-efficient, time-saving, and effective production process.

[0252] This embodiment also provides a combined simulation model, specifically:

[0253] Input different fabric characteristics, including data such as material, density, and thickness:

[0254] ;

[0255] in, This includes fabric feature data such as material, density, and thickness, used to generate fabric models that simulate the napping effect of different fabrics under various production conditions. The material of the fabric, such as cotton, linen, or silk, is used to influence its physical properties, such as strength and elasticity. Density, measured in grams per cubic centimeter, is used to determine the weight and strength of the fabric. The thickness of the fabric, in millimeters, affects its flexibility and durability.

[0256] Input different production parameters, including the gear ratio of the napping roller, tension value, production time, etc.:

[0257] ;

[0258] in, These are production parameters, including the gear ratio of the napping roller, tension value, and production time, used to simulate different production conditions and evaluate the napping effect under different parameters. The gear ratio of the napping roller affects the fabric's conveying speed and tension, and is used to adjust the fabric's conveying speed and tension to achieve the best napping effect. This is the fabric tension value, measured in Newtons, used to control the fabric tension during transport and ensure a uniform napping effect. Production time, measured in seconds, affects the napping effect of the fabric. Too long or too short a production time may result in poor results.

[0259] Based on the input fabric features, generate a virtual model of the fabric:

[0260] ;

[0261] in, This is a virtual model generated based on fabric characteristics, used to simulate the napping effect of different fabrics under different production conditions. This is a function for generating a cloth model, used to generate a virtual model of the cloth based on the input cloth features;

[0262] Simulate different gear ratios and tension values ​​to generate a series of possible combinations of gear ratios and tension values:

[0263] ;

[0264] ;

[0265] in, A series of possible gear ratios were used to simulate different production conditions and evaluate the scratching effect under different gear ratios. A function to generate a range of possible gear ratios, providing multiple gear ratio options for simulation and evaluation. A series of possible tension values ​​are used to simulate different production conditions and evaluate the napping effect under different tension values. A function to generate a range of possible tension values, providing multiple tension value options for simulation and evaluation;

[0266] For each combination of production process simulations, calculate the corresponding simulation effect:

[0267] ;

[0268] in, The results of simulating the production process, including the evaluation of the roughening effect, are used to select the optimal combination of production parameters. This is a function for simulating the production process, used to simulate the production process and evaluate the napping effect based on the input fabric model and production parameters;

[0269] Based on actual needs, define the evaluation criteria for simulation effects:

[0270] ;

[0271] in, Standards for evaluating the napping effect, such as texture, density, and thickness, 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 finish, and is used as one of the standards for evaluating the napped effect. Density, measured in grams per cubic centimeter, is used to determine the weight and strength of the fabric. The thickness of the fabric, in millimeters, affects its flexibility and durability.

[0272] The simulation effect of each combination is scored according to the evaluation criteria:

[0273] ;

[0274] in, An evaluation score is assigned to each combination, which is used to select the optimal combination of production parameters. A function for evaluating the napping effect, used to score the napping effect of each combination according to evaluation criteria;

[0275] By integrating all fabric characteristics, production parameters, virtual fabric models, gear ratios and tension values, simulation results, and simulation result scores, a combined simulation model is generated, denoted as... .

[0276] In this embodiment, precise tension control and gear ratio adjustment ensure the fabric remains stable during production, reducing production downtime and quality issues caused by unstable tension. The algorithm optimizes tension control and gear ratio adjustment to ensure minimal energy consumption while meeting production requirements. Through precise simulation of the napping effect, it ensures the final fabric finish meets production requirements, improving product quality stability. The algorithm supports real-time monitoring of fabric tension and napping effect, enabling timely detection and adjustment of problems, reducing defect rates. The algorithm can adapt to different fabric materials, densities, and thicknesses, and through simulation and optimization, it is applicable to various production conditions. Precise tension control reduces equipment wear caused by tension issues, lowering maintenance costs.

Claims

1. A method for speed adjustment of a plush machine based on intelligent sensors, characterized in that: include: Acquire images of the target fabric; Analyze the target fabric to determine if the fabric tension is stable; If the fabric tension is unstable, tension adjustment will be performed until the fabric tension is stable; If the fabric tension is stable, perform a napping effect assessment; Determine the final adjustment data, and adjust the gear ratio of the raising roller and the tension controller accordingly. The determination of whether the fabric tension is stable specifically involves: Extract the preprocessed image ; Calculate the tension balance index: ； in, This represents the normalized value of the variance or standard deviation of all Euclidean distances. This is the maximum value of all Euclidean distances. This is the minimum value of all Euclidean distances. To avoid the denominator being zero, this is a hyperparameter. The tension balance index; Calculate the material balance evaluation: ； in, This represents the deviation between the current torque of the tension roll and its historical average torque. The historical average torque of the tension roller. The shortest distance from the target pixel to the centerline of the target roller. The total number of target pixels. This is the evaluation value for material flow balance; Define a threshold adjustment judgment function to calculate the exponential threshold: ； in, As the initial threshold, These are the preset adjustment parameters. The final exponential threshold is obtained by adjusting the judgment function according to the preset threshold to correct the initial threshold. Define a tension stability judgment function to determine whether the fabric tension is stable: ； like If the fabric tension is stable, then it is determined that the fabric tension is stable. like If so, the fabric tension is determined to be unstable; Perform tension adjustment until the fabric tension stabilizes, including determining the tension adjustment data, specifically: Get sensor type: ； in, For the type of sensor, Indicates encoder, Indicates a Hall effect sensor; Install and initialize the sensor: ； in, The sensor data after sensor initialization. A sensor initialization function is provided to initialize the sensor. Use sensors to collect gear ratio related data: ； in, The data collected by the sensor varies. For a Hall effect sensor, it collects the pulse signal of the magnetic field change; for an encoder, it collects the number of output pulses. This is the data acquisition function, which is the process of acquiring data from the sensor; Based on the collected data, calculate the gear ratio: ； ； in, This indicates the formula for calculating the gear ratio when the sensor type is an encoder. The number of pulses per revolution of the encoder, collected by the sensor. This refers to the number of revolutions per revolution of the motor, collected by the sensor. This indicates the gear ratio calculation formula used when the sensor type is a Hall effect sensor. The number of gear teeth collected by the sensor. The number of pulses detected by the sensor; Record the gear ratio: ； in, For the recorded gear ratio and related data, This is a recording function used to record the gear ratio; Obtain the preprocessed image ; Call the tension simulation model to extract the preprocessed image. and gear ratio All corresponding tension adjustment data: ； ； in, This is a tension simulation model used to extract corresponding tension adjustment data based on the input image and gear ratio. For tension adjustment data, For the target tension value, The energy required to achieve the target tension The time required to achieve the target tension; For each set of tension adjustment data, the tension value is simulated and applied to the target fabric to determine whether the fabric tension is stable. If the fabric tension is stable, then the set of tension adjustment data corresponding to that tension value is defined as the stable data set; If the fabric tension is unstable, then the set of tension adjustment data corresponding to that tension value is defined as the unstable data set; Integrate all stable data sets to form a stable, regulated dataset; Extract the stable data set from the stable adjustment dataset where both energy consumption and time energy consumption are the smallest, and designate it as the adjustment data set; The tension adjustment data corresponding to the adjustment data group is defined as the target tension adjustment data, denoted as . ; The evaluation of the texturing effect is as follows: Get sensor type: ； in, For the type of sensor, Indicates encoder, Indicates a Hall effect sensor; Install and initialize the sensor: ； in, The sensor data after sensor initialization. A sensor initialization function is provided to initialize the sensor. Use sensors to collect gear ratio related data: ； in, The data collected by the sensor varies. For a Hall effect sensor, it collects the pulse signal of the magnetic field change; for an encoder, it collects the number of output pulses. This is the data acquisition function, which is the process of acquiring data from the sensor; Based on the collected data, calculate the gear ratio: ； ； in, This indicates the formula for calculating the gear ratio when the sensor type is an encoder. The number of pulses per revolution of the encoder, collected by the sensor. This refers to the number of revolutions per revolution of the motor, collected by the sensor. This indicates the gear ratio calculation formula used when the sensor type is a Hall effect sensor. The number of gear teeth collected by the sensor. The number of pulses detected by the sensor; Record the gear ratio: ； in, For the recorded gear ratio and related data, This is a recording function used to record the gear ratio; The optimal gear ratio and fabric tension combination for achieving the desired napping effect is determined as follows: Call the combined simulation model to determine the preprocessed image. and gear ratio Corresponding simulation effect: ； in, For the preprocessed image and gear ratio The corresponding simulation effect in the combined simulation model For combined simulation models; To achieve the desired effect, let's call it... ; Determine if the current gear ratio and fabric tension meet the target effect: ； like If so, the target effect is determined to be satisfied; Then, without adjusting any parameters, the plush machine can operate at its current speed. like If so, it is determined that the target effect is not met; Then, extract all possible combinations of gear ratios and fabric tension from the combined simulation model: ； in, It is a list containing all possible combinations of gear ratios and fabric tension that would satisfy the target effect for this fabric image. The list generation function is used to extract all possible combinations of gear ratios and fabric tension from the combined simulation model and generate a list containing multiple combinations for subsequent evaluation and selection of the optimal combination. It provides a comprehensive list of combinations to evaluate which combinations can meet the required napping effect. Record the extracted combinations; For each combination, evaluate the energy consumption, time consumption, and simulation results: ； in, The results obtained after evaluating each combination are stored for subsequent selection of the optimal combination. This function evaluates each extracted combination, generates an evaluation result for each combination, assesses the performance of each combination, and provides a basis for selecting the optimal combination. Based on the evaluation results, the combination with the best overall performance is selected as the optimal combination: ； in, This is the optimal combination selected from all evaluated options; it features the lowest energy consumption, shortest time, and best results. It's used to adjust the gear ratio of the tension controller and the napping roller to achieve the best production performance. To select the optimal combination of functions, it selects the combination with the best overall performance based on the evaluation results, and determines the final adjustment scheme to achieve the most energy-efficient, time-saving and effective production process. After determining the final adjustment data, adjust the gear ratio of the raising roller and the tension controller accordingly, specifically as follows: Get the optimal combination ; Adjust the parameters of the tension controller based on the tension values ​​in the optimal combination: ； in, To obtain the optimal combination The target tension value extracted represents the target tension that the fabric needs to maintain during the conveying process to achieve the best napping effect. This is the proportionality coefficient. For integration time, For differential time, A function for adjusting the parameters of a proportional-integral-derivative (PID) controller, which includes a proportional coefficient. Integral Time and differential time ; Conduct stability testing: ； in, The result of the stability test is a boolean value, outputting either True or False, indicating whether the system is stable. This is a test function used to perform stability tests on the adjusted PID parameters to ensure that the system can operate stably. Automatic span adjustment is performed in tension monitoring mode; Adjust the gear ratio of the raising 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 one revolution of the motor: ； in, For electronic gear ratio, For motor encoder resolution, The number of pulses required for the motor to rotate one revolution; The calculated electronic gear ratio is set into the control system of the napping roller; The adjusted gear ratio was tested for transmission efficiency to ensure efficient and stable operation. ； in, The result of the transmission efficiency test indicates whether the system is operating efficiently. This is a transmission efficiency test function used to test whether the adjusted gear ratio can operate efficiently.

2. The speed adjustment method for a plush machine based on intelligent sensors according to claim 1, characterized in that: Acquire images of the target fabric, specifically: Connect the camera and perform initial setup: ； in, This indicates the state of the camera after initialization. Indicates the camera device identifier. Indicates resolution, Indicates frame rate, Functions for initializing the camera; Use a camera to capture real-time images of the fabric and save them as image files: ； in, This represents the captured image of the fabric. Functions for capturing images; Preprocess the acquired images: ； in, This represents the preprocessed image. This indicates that edge detection is being performed on an image of the fabric. This indicates that the image contrast has been enhanced to improve image sharpness and make details in the image more apparent. This indicates that a grayscale image is filtered to remove noise and smooth the image. Indicates the filter kernel. This indicates that a color image will be converted to a grayscale image. For the preprocessed image, calculate the gray-level co-occurrence matrix and extract texture features: ； Specific features include mean, standard deviation, contrast, dissimilarity, homogeneity, second moment (ASM), energy, and entropy. Extracting local texture features from an image: ； Calculate the color moments of the image: ； in, For color dimensions, This is the function for calculating color moments. In image processing, it is used to calculate color moments for subsequent analysis. Calculate the color histogram of the image: ； in, For color histograms, This is a function for calculating color histograms, used in image processing to generate color histograms for subsequent analysis. The texture and color features described above are combined to form the final feature vector. : 。 3. The method for adjusting the speed of a plush machine based on intelligent sensors according to claim 1, characterized in that: Perform tension adjustment until the fabric tension stabilizes, including adjusting the tension controller based on the tension adjustment data, specifically: Acquire target tension adjustment data ; Adjust the tension controller parameters: ； in, This is the proportionality coefficient. For integration time, For differential time, A function for adjusting the parameters of a proportional-integral-derivative (PID) controller, which includes a proportional coefficient. Integral Time and differential time ; Conduct stability testing: ； in, The result of the stability test is a boolean value, outputting either True or False, indicating whether the system is stable. This is a test function used to perform stability tests on the adjusted PID parameters to ensure that the system can operate stably. Record the adjustment results: ； in, The recorded adjustment results, namely the adjusted parameters and test results, This is an adjustment result recording function used to record the adjusted PID parameters and test results.

4. The speed adjustment method for a plush machine based on a smart sensor according to claim 3, characterized in that: The tension simulation model is as follows: Collect fabric feature data: ； in, For fabric feature data, The material of the fabric. The density of the fabric is expressed in grams per cubic centimeter. The thickness of the fabric is expressed in millimeters. The gear ratio of the napping roller. Image data of the preprocessed fabric; For fabric feature data, data cleaning is performed: ； in, For the fabric feature data after cleaning, missing values, outliers, and noise were removed. These are data cleaning functions used to handle problems in the data; For the cleaned fabric feature data, feature extraction is performed: ； in, Features extracted from the cleaned fabric feature data. This is a feature extraction function used to extract useful information from an image; For the fabric feature data after feature extraction, data standardization is performed: ； in, To standardize fabric feature data and make features of different dimensions comparable, This is a standardization function used to adjust the dimensions of data; For the standardized fabric feature data, select the appropriate model: ； in, For the selected machine learning model, A model selection function is used to select a suitable model. For the selected model, perform model training: ； in, The trained model has predictive capabilities. This is the model training function, used to train the model; After training, the model is validated. ； in, The model validation results are used to evaluate the model's performance. This is the model validation function, used to evaluate the model's generalization ability; The standardized fabric feature data is input into the trained model; The tension value output by the calculation model: ； in, The target tension value is expressed in Newtons. Calculate the energy consumption value based on the target tension value: ； in, This is the energy consumption value, measured in joules. This is an energy consumption calculation function used to calculate the energy consumption required to achieve the target tension; Calculate the adjustment time based on the target tension value: ； in, The time elapsed is measured in seconds. This is a time calculation function used to calculate the time required to reach the target tension; By integrating fabric feature data, training models, tension values, energy consumption values, and time consumption, a tension simulation model is formed, denoted as... .

5. The method for adjusting the speed of a plush machine based on a smart sensor according to claim 1, characterized in that: The combined simulation model is as follows: Input different fabric features: ； in, The feature data of the fabric is used to generate a fabric model to simulate the napping effect of different fabrics under different production conditions. The material of the fabric is used to influence its physical properties. Density, measured in grams per cubic centimeter, is used to determine the weight and strength of the fabric. The thickness of the fabric, in millimeters, affects its flexibility and durability. Input different production parameters: ； in, These are production parameters used to simulate different production conditions and evaluate the napping effect under different parameters. The gear ratio of the napping roller affects the fabric's conveying speed and tension, and is used to adjust the fabric's conveying speed and tension to achieve the best napping effect. This is the fabric tension value, measured in Newtons, used to control the fabric tension during transport and ensure a uniform napping effect. Production time, measured in seconds, affects the napping effect of the fabric. Too long or too short a production time may result in poor results. Based on the input fabric features, generate a virtual model of the fabric: ； in, This is a virtual model generated based on fabric characteristics, used to simulate the napping effect of different fabrics under different production conditions. This is a function for generating a cloth model, used to generate a virtual model of the cloth based on the input cloth features; Simulate different gear ratios and tension values ​​to generate a series of possible combinations of gear ratios and tension values: ； ； in, A series of possible gear ratios were used to simulate different production conditions and evaluate the scratching effect under different gear ratios. A function to generate a range of possible gear ratios, providing multiple gear ratio options for simulation and evaluation. A series of possible tension values ​​are used to simulate different production conditions and evaluate the napping effect under different tension values. A function to generate a range of possible tension values, providing multiple tension value options for simulation and evaluation; For each combination of production process simulations, calculate the corresponding simulation effect: ； in, The results of simulating the production process, including the evaluation of the roughening effect, are used to select the optimal combination of production parameters. This is a function for simulating the production process, used to simulate the production process and evaluate the napping effect based on the input fabric model and production parameters; Define the evaluation criteria for simulation results: ； in, To evaluate the napping effect, a standard is used to assess the napping effect of each combination and select the optimal combination. The texture of the fabric affects the appearance and feel of the napped finish, and is used as one of the standards for evaluating the napped effect. Density, measured in grams per cubic centimeter, is used to determine the weight and strength of the fabric. The thickness of the fabric, in millimeters, affects its flexibility and durability. The simulation effect of each combination is scored according to the evaluation criteria: ； in, An evaluation score is assigned to each combination, which is used to select the optimal combination of production parameters. A function for evaluating the napping effect, used to score the napping effect of each combination according to evaluation criteria; By integrating all fabric characteristics, production parameters, virtual fabric models, gear ratios and tension values, simulation results, and simulation result scores, a combined simulation model is generated, denoted as... .

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