A winding control system and method for processing of degradable protective clothing fabric

Abstract

This invention relates to the field of automated control technology for textile machinery, and more specifically, to a winding control system and method for processing biodegradable protective clothing fabrics. It addresses the problem that existing biodegradable protective clothing fabric processing technologies often rely on single temperature models or offline detection for state assessment, lacking real-time inversion capabilities for the multi-physics coupling effects of temperature, humidity, and stress, making it difficult to quantify micromechanical evolution and capture the spatial heterogeneity and abrupt change risks of local health. This invention constructs a mechanism-driven, spatiotemporal coupled, and dynamic early warning assessment system through a fabric degradation state calculation module. It introduces the Arrhenius wet stress coupling model, overcoming the limitations of a single temperature model, accurately inverting the true degradation rate and mapping the micromechanical state. It uniquely integrates the rate of change acceleration and the vulnerability coefficient of spatial inhomogeneity to quantify dynamic trends and capture heterogeneous risks, solving the problem of monitoring local weaknesses and accelerated abrupt changes.

Description

Technical Field

[0001] This invention relates to the field of automated control technology for textile machinery, and more specifically, to a winding control system and method for processing biodegradable protective clothing fabrics. Background Technology

[0002] With increasing global environmental awareness and growing pressure to dispose of medical waste, traditional petroleum-based protective clothing, which is difficult to degrade, is facing challenges. This has led to the development of PLA, PBAT, and other fabrics that can degrade into carbon dioxide and water. However, due to their high brittleness, low elongation at break, and sensitivity of modulus to temperature and humidity, these materials have an extremely narrow tension window during melt spinning, hydroentangling, and hot rolling processes. If the tension is too high, the fabric is prone to plastic deformation or breakage, thereby damaging the fiber structure and weakening the protective performance. If the tension is too low, it will cause loose rolls, collapsed edges, or star-shaped rolls, which will seriously affect subsequent cutting and sewing processes.

[0003] Reference patent application CN119703388A discloses a control system and method for laser processing of wrinkle-resistant denim fabric. The system includes: a pretreatment module for surface pretreatment of the target denim fabric to obtain a pretreated fabric and acquire a surface image of the pretreated fabric; an initial parameter determination module for determining the initial operating parameters of the laser equipment based on the surface image of the pretreated fabric; a first operating module for controlling the laser equipment to scan and heat the pretreated fabric according to the initial operating parameters to change the molecular structure of the pretreated fabric; a signal monitoring module for real-time monitoring of the temperature signal of the pretreated fabric; a second operating module for adjusting the real-time operating parameters of the laser equipment according to the temperature signal to obtain the processed fabric; and a post-processing module for cooling and shaping the processed fabric. This invention ensures the high efficiency of the laser processing process and reduces unnecessary downtime by real-time monitoring and dynamic adjustment of operating parameters. However, in existing biodegradable protective clothing fabric processing technologies, condition assessment methods often rely on a single temperature model or offline detection, lacking the ability to invert the multi-physics field coupling effect of temperature, humidity, and stress in real time. This makes it difficult to quantify micromechanical evolution and capture the spatial heterogeneity and abrupt risk of local health status, resulting in life predictions being mostly static estimates without dynamic early warning basis. At the same time, traditional winding control strategies use fixed roll diameter calculations, preset speed curves, and constant control parameters, without establishing a correlation mechanism with the real-time health status of the material. This makes it impossible to adaptively match the rapid time-varying degradation of the mechanical properties of biodegradable fabrics, which can easily lead to material breakage, wrinkles, and deformation due to acceleration / deceleration impacts or tension mismatch when material properties fluctuate, thus restricting the stability and consistency of finished products under high-speed processing.

[0004] To address the aforementioned problems, this invention proposes a winding control system and method for processing biodegradable protective clothing fabrics. Summary of the Invention

[0005] The objective of this invention is achieved through the following technical solution: A winding control system for processing biodegradable protective clothing fabrics, connected to a protective clothing fabric processing management platform, includes: The data acquisition and processing module is used to collect raw multimodal physical field response data in real time during the fabric processing operation, preprocess the collected raw multimodal physical field response data, and generate global degradation index, local health distribution and defect location information; The fabric degradation status calculation module is used to invert the degradation rate by coupling temperature, humidity and stress based on the global degradation index and local health distribution, using the Arrhenius humidity mechanism. It constructs the vulnerability coefficient based on the product logic of the index change rate and the health residual value, and outputs the health index, vulnerability coefficient and environmental acceleration factor. The velocity tension trajectory planning module is used to correct the roll diameter based on the health index, switch the velocity curve template according to the fragility coefficient and reconstruct the acceleration change rate limit parameter, and generate the reference tension feedforward value based on the power law relationship between the health index and the roll diameter, and output the linear velocity setting curve and the reference tension feedforward value. The adaptive tension fine control module is used to dynamically adjust the controller gain and observer parameters based on the health index, integrate the reference tension feedforward value and the local compensation amount to generate the tension set value, and output the motor torque and brake current.

[0006] In a preferred embodiment of the present invention, the process of inverting the degradation rate in the fabric degradation state calculation module based on the global degradation index and local health distribution, and using the Arrhenius humidity mechanism to couple temperature, humidity, and stress, includes: Read the local health status and current global degradation index of a single grid point in the fabric geometry domain, calculate the difference between the initial and failure modulus and multiply it by the health status, add the failure modulus to obtain the current elastic modulus; multiply the global degradation index by the aging correction factor to obtain the benchmark correction amount, apply it to the current elastic modulus to establish the single-point mechanical state; Obtain absolute temperature, relative humidity and equivalent stress data at the same location, calculate the temperature index to obtain the basic reaction term, subtract the product of stress and reduction coefficient from the initial activation energy to obtain the corrected activation energy, calculate the product of humidity logarithm and entropy coefficient to obtain the entropy correction term, multiply the basic reaction term, the corrected activation energy index term and the entropy correction term by the global degradation index to obtain the site comprehensive reaction rate coefficient. The instantaneous degradation rate is calculated by multiplying the current elastic modulus of the point by the comprehensive reaction rate coefficient, and the instantaneous degradation rate field is integrated and output by traversing the global mesh.

[0007] In a preferred embodiment of the present invention, the process of constructing the fragility coefficient in the fabric degradation state calculation module based on the product logic of the exponential rate of change and the health residual value includes: Read continuous data points from the real-time global degradation index time series, perform a second differential operation on the time variable to calculate the instantaneous acceleration component, and extract the absolute value of the component as the benchmark value of the exponential rate of change at the current moment; Traverse the position coordinates of a single grid point within the fabric geometry domain, read the local health of the point and calculate the deviation from the current global average health. Square the deviation value as the point contribution. Perform spatial integration on the point contribution of all grid points over the total surface area of ​​the monitoring area. Multiply the integration result by the preset damage sensitivity coefficient to obtain the spatial non-uniformity penalty factor. The vulnerability coefficient at the current moment is calculated by multiplying the current exponential rate of change benchmark value with the spatial inhomogeneity penalty factor and outputting it directly.

[0008] In a preferred embodiment of the present invention, the process of correcting the roll diameter based on the health index, switching the velocity curve template according to the vulnerability coefficient, and reconstructing the acceleration change rate limit parameters in the velocity tension trajectory planning module includes: Read the original estimated volume size and real-time health index, calculate and subtract the health index to get the aging deviation rate, multiply the aging deviation rate by the preset maximum volume size correction coefficient to get the correction increment, and add the correction increment to the original estimated volume size to generate the corrected volume size. The system reads the baseline acceleration rate of change limit and the real-time vulnerability coefficient of the selected template. When the vulnerability coefficient is greater than or equal to the upper threshold, the baseline acceleration rate of change limit is multiplied by the vulnerability coefficient to obtain the reconstructed acceleration rate of change limit. When the vulnerability coefficient is between the lower and upper thresholds, the system calculates and subtracts the vulnerability coefficient to obtain the material vulnerability margin. The system then multiplies half of the material vulnerability margin and subtracts the product to obtain the attenuation factor. The system multiplies the baseline acceleration rate of change limit by the attenuation factor to obtain the reconstructed acceleration rate of change limit. The system then recalculates the transition time between the acceleration and deceleration phases and limits the acceleration rate of change using the reconstructed acceleration rate of change limit. Finally, the system calculates and outputs the linear velocity setting command sequence in conjunction with the corrected roll diameter.

[0009] In a preferred embodiment of the present invention, the process of generating a reference tension feedforward value based on the power law relationship between the health index and the roll diameter in the speed tension trajectory planning module, and outputting the linear velocity setting curve and the reference tension feedforward value, includes: Read the real-time health index, corrected roll diameter, and preset base constant, and multiply the base constant, health compensation term, and roll diameter scaling term to calculate the reference tension feedforward value for the current single moment; Based on the sampling frequency of the control cycle, each discrete time point within the planning time period is traversed. At each time point, the power law operation is repeatedly performed to obtain the single-point data of the reference tension feedforward value at the corresponding time. The single-point data generated at each time point are arranged point by point in chronological order to generate a reference tension feedforward value sequence covering the entire planning time period. Read the linear velocity setting curve sequence generated after reconstruction by the acceleration change rate limit parameter, strictly align and match the velocity command at each moment in the linear velocity setting curve sequence with the feedforward command at the corresponding moment in the reference tension feedforward value sequence on the time axis, and directly output the aligned linear velocity setting curve and the reference tension feedforward value sequence as two sets of time-synchronized trajectory commands.

[0010] In a preferred embodiment of the present invention, the process of dynamically adjusting the controller gain and observer parameters based on the health index in the adaptive tension fine control module includes: Read the real-time health index, calculate the device aging degree by subtracting the health index from the value, multiply it by the preset roll diameter correction coefficient to get the deviation ratio, add one to get the correction ratio, and multiply the original roll diameter by the correction ratio to generate the corrected roll diameter. Read the nominal observer bandwidth and multiply it by the health index to obtain the current observer bandwidth. Read the nominal gain matrix and multiply it by the health index to obtain the current gain matrix. Load the current observer bandwidth and the current gain matrix into the state observer model. The real-time vulnerability coefficient is read and compared with the preset lower and upper thresholds. When the vulnerability coefficient is less than the lower threshold, the proportional gain and integral gain are set to the minimum value, and the differential filter time constant is set to the maximum value. When the vulnerability coefficient is greater than or equal to the lower threshold and less than the upper threshold, the nominal proportional gain and nominal integral gain are multiplied by the attenuation factor, and the nominal differential filter time constant is divided by the attenuation factor. When the vulnerability coefficient is greater than or equal to the upper threshold, the proportional gain and integral gain are set to the maximum value, and the differential filter time constant is set to the minimum value. If the health index is lower than the preset health threshold, the differential gain is multiplied by the health index for fine-tuning.

[0011] As a preferred embodiment of the present invention, a winding control method for processing biodegradable protective clothing fabric includes the following steps: Step 1: Collect raw multimodal physical field response data in real time during the fabric processing operation, preprocess the collected raw multimodal physical field response data, and generate global degradation index, local health distribution and defect location information; Step 2: Based on the global degradation index and local health distribution, the degradation rate is inverted by coupling temperature, humidity and stress using the Arrhenius humidity mechanism. The vulnerability coefficient is constructed according to the product logic of the index change rate and the health residual value, and the health index, vulnerability coefficient and environmental acceleration factor are output. Step 3: Adjust the roll diameter based on the health index, switch the speed curve template according to the fragility coefficient and reconstruct the acceleration change rate limit parameter, and generate the reference tension feedforward value based on the power law relationship between the health index and the roll diameter, and output the linear velocity setting curve and the reference tension feedforward value. Step 4: Based on the health index, dynamically adjust the controller gain and observer parameters, integrate the reference tension feedforward value and local compensation amount to generate the tension setpoint, and output the motor torque and brake current.

[0012] Compared with the prior art, the advantages of this invention are: (1) In this invention, a mechanism-driven, spatiotemporal coupling and dynamic early warning assessment system is constructed through the fabric degradation state calculation module. The Arrhenius wet stress coupling model is introduced to break through the single temperature limitation, accurately invert the real degradation rate and map the micro-mechanical state. The vulnerability coefficient of the rate of change acceleration and spatial non-uniformity is uniquely integrated to quantify the dynamic trend and capture the heterogeneity risk, solve the problem of monitoring local shortcomings and abrupt acceleration. Combined with real-time correction of environmental acceleration factors and rigorous verification of numerical algorithms, the physical interpretability of the indicators is ensured, and the fabric life prediction is upgraded from static estimation to dynamic full-domain high-precision real-time intelligent decision-making. (2) In this invention, a health-adaptive roll diameter correction and vulnerability-driven trajectory reconstruction mechanism is constructed through the speed tension trajectory planning module. The roll diameter is dynamically compensated and the speed template and reconstruction acceleration limit are intelligently switched according to the vulnerability. The real-time bearing capacity of the material is matched. Combined with the power law feedforward algorithm, the nonlinear influence of aging and geometric changes on tension is accurately quantified, and the speed tension command is synchronized in time and space. This mechanism data dual-drive strategy effectively avoids the risks of material breakage, wrinkles and deformation caused by fabric degradation, and significantly improves the stability, smoothness and consistency of high-speed processing. Attached Figure Description

[0013] Figure 1 This is a system block diagram of Embodiment 1 of the present invention; Figure 2 This is a system block diagram of Embodiment 2 of the present invention; Figure 3 This is a flowchart of the steps in the protective clothing fabric processing and winding control method of the present invention. Detailed Implementation

[0014] 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. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0015] Example 1: As Figure 1 As shown, the present invention proposes a winding control system for processing biodegradable protective clothing fabrics, which is connected to a protective clothing fabric processing management platform, and includes: The data acquisition and processing module is used to collect raw multimodal physical field response data in real time during the fabric processing operation, including multi-band near-infrared reflected light intensity, ultrasonic echo flight time, ultrasonic echo peak voltage and micro-area surface temperature. The module preprocesses the collected raw multimodal physical field response data. The preprocessing operations include spatiotemporal synchronization alignment of multi-source signals, adaptive noise filtering and baseline correction, characteristic calculation of spectral absorbance and ultrasonic attenuation coefficient, partial least squares multidimensional regression mapping and spatial statistical anomaly identification, generating global degradation index, local health distribution and defect location information. The implementation of the data acquisition and processing module first establishes a quantitative mapping model based on offline calibration. During the initialization phase, the system selects laboratory calibration samples covering different degradation states, simultaneously acquires their multimodal raw signals, and uses high-precision instruments to measure the true degree of hydrolysis and elastic modulus as the benchmark. Subsequently, feature extraction is performed on the raw signals to calculate the spectral absorbance and characteristic peak area reflecting the absorption characteristics of chemical bonds, and the ultrasonic flight time and echo attenuation coefficient reflecting the degree of internal compactness and microcracks. The partial least squares regression algorithm is used to establish the multivariate statistical mapping relationship between the above physical characteristics and the degree of material hydrolysis and elastic modulus. After cross-validation and optimization, the generated regression coefficient matrix and model parameters are preset as the benchmark for online inversion, thereby establishing the scientific conversion logic from sensor signals to the internal state of the material. During online operation, the system synchronously acquires raw response data such as multi-band near-infrared reflected light intensity, ultrasonic echo flight time, echo peak voltage, and micro-area surface temperature at a high-frequency sampling rate. First, it uses a high-precision time synchronization protocol and spatial interpolation compensation technology to eliminate clock deviations and installation position differences between sensors, achieving strict spatiotemporal alignment of multi-source signals. Then, it uses adaptive algorithms such as smoothing filtering and wavelet transform to remove high-frequency electronic noise and environmental mechanical vibration interference, and corrects baseline drift caused by light source fluctuations and coupling changes. Furthermore, based on the optical absorption principle and ultrasonic propagation theory, it converts the cleaned light intensity into absorbance and characteristic peak area to characterize the degree of chemical hydrolysis, and converts time and voltage into flight time and echo attenuation coefficient to characterize internal density changes and microcrack propagation, completing the essential leap from raw electrical signals to characteristic quantities with clear physicochemical significance. The system inputs the calculated real-time physical feature vector into a preset regression mapping model. Through weighted summation and linear transformation, it inverts the global degradation index, which represents the overall degree of hydrolysis, and the local health, calculated point by point along the width direction. The health of all points is arranged in space to form a local health distribution. Based on this, the system performs spatial statistical anomaly identification. Within a sliding local window, it calculates the statistical mean and standard deviation of the health. Points that are significantly lower than the statistical mean or whose differences from their neighborhood exceed a preset outlier threshold are identified as degradation hotspots and defects. The length and width coordinates of these points are accurately recorded. Finally, the system integrates and generates multi-dimensional degradation feature data containing the global degradation index, local health distribution, and defect location information. This data is then sent to downstream modules in real time to support subsequent state measurement and control decisions. By integrating near-infrared, ultrasonic, and temperature multimodal data through the data acquisition and processing module, the limitations of single detection are overcome, enabling real-time full-dimensional vision of the fabric inside and out. Relying on algorithms such as spatiotemporal synchronization, adaptive filtering, and partial least squares regression, noise is effectively reduced and physical characteristics are accurately calculated. Finally, the global degradation index and defect location are output, upgrading post-event sampling inspection to real-time quantitative monitoring during the process, significantly improving the accuracy of quality control and the level of intelligent production.

[0016] The fabric degradation status calculation module is used to invert the degradation rate by coupling temperature, humidity and stress based on the global degradation index and local health distribution, using the Arrhenius humidity mechanism. It constructs the vulnerability coefficient based on the product logic of the index change rate and the health residual value, and outputs the health index, vulnerability coefficient and environmental acceleration factor. The fabric degradation state calculation module, based on the global degradation index and local health distribution, utilizes the Arrhenius humidity mechanism to couple temperature, humidity, and stress to invert the degradation rate. The process includes: Read the local health status and current global degradation index of a single grid point in the fabric geometry domain, calculate the difference between the initial and failure modulus and multiply it by the health status, add the failure modulus to obtain the current elastic modulus; multiply the global degradation index by the aging correction factor to obtain the benchmark correction amount, apply it to the current elastic modulus to establish the single-point mechanical state; Obtain absolute temperature, relative humidity and equivalent stress data at the same location, calculate the temperature index to obtain the basic reaction term, subtract the product of stress and reduction coefficient from the initial activation energy to obtain the corrected activation energy, calculate the product of humidity logarithm and entropy coefficient to obtain the entropy correction term, multiply the basic reaction term, the corrected activation energy index term and the entropy correction term by the global degradation index to obtain the site comprehensive reaction rate coefficient. The instantaneous degradation rate is calculated by multiplying the current elastic modulus of the point with the comprehensive reaction rate coefficient. The instantaneous degradation rate field is integrated and output by traversing the global grid. The damage accumulation increment is obtained by integrating the rate field over time within the period. The increment is added to the global degradation index of the previous period to generate the latest global degradation index. The process of constructing the vulnerability coefficient in the fabric degradation state calculation module based on the product logic of the exponential change rate and the health residual value includes: Read continuous data points from the real-time global degradation index time series, perform a second differential operation on the time variable to calculate the instantaneous acceleration component, and extract the absolute value of the component as the benchmark value of the exponential rate of change at the current moment; Traverse the position coordinates of a single grid point within the fabric geometry domain, read the local health of the point and calculate the deviation from the current global average health. Square the deviation value as the point contribution. Perform spatial integration on the point contribution of all grid points over the total surface area of ​​the monitoring area. Multiply the integration result by the preset damage sensitivity coefficient to obtain the spatial non-uniformity penalty factor. The vulnerability coefficient at the current moment is calculated by multiplying the current exponential rate of change benchmark value with the spatial inhomogeneity penalty factor and outputting it directly. The vulnerability coefficient is calculated using the following formula: ,in To represent the vulnerability coefficient at time t, the result is a dimensionless scalar. To represent the global degradation exponent, which is a function of time t, To represent the total surface area of ​​the fabric monitoring area, To represent the geometric domain of the fabric, that is, the spatial range of the integration operation, The damage sensitivity coefficient is used to adjust the model's response sensitivity to changes in local health. To represent the local health of the position coordinates (x, y) at time t, the value ranges from 0 to 1; The process of outputting the health index, vulnerability coefficient, and environmental acceleration factor in the fabric degradation status calculation module includes: Read the local health status and stress gradient data of each grid point in the entire domain, assign weights to each point according to the magnitude of the stress gradient and perform a weighted average calculation to obtain the basic health value. At the same time, read the current global degradation index and calculate the reciprocal of the square root as the correction coefficient. Multiply the basic health value and the correction coefficient to generate the health index. The actual cumulative damage is obtained by reading the instantaneous reaction rate time series under the actual time-varying environment and performing time integration. Simultaneously, the baseline reaction rate time series under the standard reference environment is read and the same time length integration is performed to obtain the baseline cumulative damage. The environmental acceleration factor is calculated by dividing the actual cumulative damage by the baseline cumulative damage. Read the generated vulnerability coefficient, integrate the health index, environmental acceleration factor and vulnerability coefficient into the final output vector, perform Crank-Nicholson format calculation and verify the uniqueness of the parameters through the full rank condition of the Jacobian matrix, compare the vulnerability coefficient with the preset upper limit threshold and compare the health index with the preset lower limit threshold. If the vulnerability coefficient exceeds the upper limit or the health index is lower than the lower limit, an alarm signal is triggered immediately. By constructing a precise assessment system driven by mechanism, coupled with spatiotemporal factors and dynamic early warning through a fabric degradation state measurement module, an Arrhenius humidity mechanism and stress coupling model are introduced to overcome the limitations of single temperature. The system utilizes modified activation energy and entropy correction terms to accurately invert the actual degradation rate under the synergy of temperature, humidity and stress, achieving a deep mapping from the macroscopic environment to the microscopic mechanical state. By adopting the vulnerability coefficient logic that integrates the acceleration of the rate of change of the exponential growth rate and the spatial inhomogeneity of the health residual value, the dynamic trend of degradation is quantified and the risk of local heterogeneity is captured, solving the problem that traditional indicators cannot reflect local shortcomings and abrupt acceleration. Combined with real-time correction of the environmental acceleration factor and the Crank-Nicholson scheme solution and Jacobi matrix verification, the mathematical rigor and physical interpretability of the indicators are ensured, upgrading fabric life prediction from static empirical estimation to dynamic, full-domain, high-precision, real-time intelligent decision-making.

[0017] The velocity tension trajectory planning module is used to correct the roll diameter based on the health index, switch the velocity curve template according to the fragility coefficient and reconstruct the acceleration change rate limit parameter, and generate the reference tension feedforward value based on the power law relationship between the health index and the roll diameter, and output the linear velocity setting curve and the reference tension feedforward value. The process in the velocity tension trajectory planning module that corrects the roll diameter based on the health index, switches the velocity curve template according to the vulnerability coefficient, and reconstructs the acceleration change rate limit parameters includes: Read the original estimated volume size and real-time health index, calculate and subtract the health index to get the aging deviation rate, multiply the aging deviation rate by the preset maximum volume size correction coefficient to get the correction increment, and add the correction increment to the original estimated volume size to generate the corrected volume size. Read the real-time vulnerability coefficient and compare it with the preset lower and upper thresholds. If it is less than the lower threshold, call the standard polynomial velocity curve template. If it is greater than or equal to the lower threshold and less than the upper threshold, call the modified trapezoidal velocity curve template with a sinusoidal transition segment. If it is greater than or equal to the upper threshold, call the seven-segment smooth S-shaped velocity curve template. Read the reference acceleration rate of change limit and real-time vulnerability coefficient of the selected template. When the vulnerability coefficient is greater than or equal to the upper threshold, multiply the reference acceleration rate of change limit by the vulnerability coefficient to obtain the reconstructed acceleration rate of change limit. When the vulnerability coefficient is between the lower threshold and the upper threshold, calculate one and subtract the vulnerability coefficient to obtain the material vulnerability margin. Multiply one-half by the material vulnerability margin and subtract one from the product to obtain the attenuation factor. Multiply the reference acceleration rate of change limit by the attenuation factor to obtain the reconstructed acceleration rate of change limit. Use the reconstructed acceleration rate of change limit to recalculate the transition time between the acceleration and deceleration segments and limit the acceleration rate of change. Combine the corrected roll diameter to calculate the linear velocity setting command sequence and output it. The process of generating a baseline tension feedforward value based on the power law relationship between the health index and the roll diameter in the velocity tension trajectory planning module, and outputting the linear velocity setting curve and the baseline tension feedforward value includes: Read the real-time health index, corrected roll diameter, and preset base constant; call the preset health decay index and roll diameter power index; calculate the negative health decay exponent of the health index to obtain the health compensation term; calculate the roll diameter power of the corrected roll diameter to obtain the roll diameter scaling term; multiply the base constant, health compensation term, and roll diameter scaling term to calculate the reference tension feedforward value for the current single moment. Based on the sampling frequency of the control cycle, each discrete time point within the planning time period is traversed. At each time point, the power law operation is repeatedly performed to obtain the single-point data of the reference tension feedforward value at the corresponding time. The single-point data generated at each time point are arranged point by point in chronological order to generate a reference tension feedforward value sequence covering the entire planning time period. Read the linear velocity setting curve sequence generated after reconstruction by the acceleration change rate limit parameter, strictly align and match the velocity command at each moment in the linear velocity setting curve sequence with the feedforward command at the corresponding moment in the reference tension feedforward value sequence on the time axis, and directly output the aligned linear velocity setting curve and the reference tension feedforward value sequence as two sets of time-synchronized trajectory commands. By constructing a health-adaptive roll diameter correction and vulnerability-driven trajectory reconstruction mechanism through a speed-tension trajectory planning module, this approach breaks through the limitations of traditional fixed parameters. It dynamically compensates for roll diameter using aging deviation rate and intelligently switches speed curve templates and reconstructs acceleration limits based on vulnerability coefficient to match the real-time load-bearing capacity of the material. Combined with a power-law feedforward algorithm for health and roll diameter, it accurately quantifies the nonlinear effects of aging and geometric changes on tension, achieving spatiotemporal synchronization of speed and tension commands. This mechanism- and data-driven strategy effectively avoids the risks of material breakage, wrinkles, and deformation caused by fabric degradation, significantly improving high-speed processing stability, trajectory smoothness, and finished product consistency.

[0018] Example 2: The technical solution of this embodiment of the invention differs from that of Example 1 in that: like Figure 2As shown, the adaptive tension fine control module is used to dynamically adjust the controller gain and observer parameters based on the health index, integrate the reference tension feedforward value and the local compensation amount to generate the tension set value, and output the motor torque and brake current. The process of dynamically adjusting the controller gain and observer parameters based on the health index in the adaptive tension fine control module includes: Read the real-time health index, calculate the device aging degree by subtracting the health index from the value, multiply it by the preset roll diameter correction coefficient to get the deviation ratio, add one to get the correction ratio, and multiply the original roll diameter by the correction ratio to generate the corrected roll diameter. Read the nominal observer bandwidth and multiply it by the health index to obtain the current observer bandwidth. Read the nominal gain matrix and multiply it by the health index to obtain the current gain matrix. Load the current observer bandwidth and the current gain matrix into the state observer model. The real-time vulnerability coefficient is read and compared with the preset lower and upper thresholds. When the vulnerability coefficient is less than the lower threshold, the proportional gain and integral gain are set to the minimum value, and the differential filter time constant is set to the maximum value. When the vulnerability coefficient is greater than or equal to the lower threshold and less than the upper threshold, the vulnerability coefficient is subtracted to obtain the material vulnerability margin. Then, the product of the material vulnerability margin and half of the vulnerability coefficient is subtracted to obtain the attenuation factor. The nominal proportional gain and nominal integral gain are multiplied by the attenuation factor, and the nominal differential filter time constant is divided by the attenuation factor. When the vulnerability coefficient is greater than or equal to the upper threshold, the proportional gain and integral gain are set to the maximum value, and the differential filter time constant is set to the minimum value. If the health index is lower than the preset health threshold, the differential gain is multiplied by the health index for fine-tuning. The final determined proportional gain, integral gain, differential gain, and filter parameters are loaded into the closed-loop control algorithm. The process of generating the tension setpoint and outputting the motor torque and brake current in the adaptive tension fine control module by integrating the reference tension feedforward value and the local compensation amount includes: Read the preset system tension stiffness coefficient, roll diameter influence index and health influence index, and simultaneously read the real-time corrected roll diameter and health index. Calculate the roll diameter influence index power of the corrected roll diameter to obtain the roll diameter power term, and calculate the negative health influence index power of the health index to obtain the health compensation term. Multiply the system tension stiffness coefficient, roll diameter power term and health compensation term to calculate the reference tension feedforward value. The tension deviation is obtained by reading the actual tension fed back by the tension sensor and the process target tension and calculating the difference. The proportional gain, integral gain and derivative gain after dynamic adjustment are read. The local compensation amount is obtained by performing proportional-integral-derivative operation on the tension deviation. The reference tension feedforward value and the local compensation amount are algebraically added to generate the total tension set value. Read the real-time linear velocity setting value and the corrected roll diameter, multiply the linear velocity setting value by two and divide it by the corrected roll diameter to obtain the spindle angular velocity setting value, and send it to the spindle servo driver after discretization to calculate the motor torque command. Read the total tension setting value and convert it into the target current according to the preset tension current mapping relationship. After power conversion, output it to the brake coil to generate the brake current command. An adaptive tension fine control module is constructed to establish a health-adaptive gain scheduling and vulnerability-driven parameter reconstruction mechanism. Based on the real-time health status, the observer bandwidth and gain matrix are dynamically matched, and the PID parameters and filter constants are intelligently adjusted based on the vulnerability coefficient. This enhances damping and fracture prevention when the material is vulnerable and improves response and wave suppression when it is strong and tough. Combined with a feedforward algorithm that integrates roll diameter power law and health compensation, the nonlinear interference caused by geometric changes and material degradation is accurately offset. This achieves a deep integration of feedforward prediction and feedback correction, effectively solving the problem of tension instability caused by time-varying fabric performance, and significantly improving the accuracy of torque and current output, dynamic response capability, and finished product tension uniformity.

[0019] Example 3: The technical solution of this embodiment of the invention differs from that of Example 1 and Example 2 in that: like Figure 3 As shown, a winding control method for processing biodegradable protective clothing fabric includes the following steps: Step 1: Collect raw multimodal physical field response data in real time during the fabric processing operation, preprocess the collected raw multimodal physical field response data, and generate global degradation index, local health distribution and defect location information; Step 2: Based on the global degradation index and local health distribution, the degradation rate is inverted by coupling temperature, humidity and stress using the Arrhenius humidity mechanism. The vulnerability coefficient is constructed according to the product logic of the index change rate and the health residual value, and the health index, vulnerability coefficient and environmental acceleration factor are output. Step 3: Adjust the roll diameter based on the health index, switch the speed curve template according to the fragility coefficient and reconstruct the acceleration change rate limit parameter, and generate the reference tension feedforward value based on the power law relationship between the health index and the roll diameter, and output the linear velocity setting curve and the reference tension feedforward value. Step 4: Based on the health index, dynamically adjust the controller gain and observer parameters, integrate the reference tension feedforward value and local compensation amount to generate the tension setpoint, and output the motor torque and brake current; This winding control method for processing biodegradable protective clothing fabrics constructs a closed-loop system encompassing perception, evaluation, planning, and control. It integrates multimodal data to quantify the degradation status in real time, utilizes the Arrhenius wet stress coupling mechanism to invert the rate, and constructs a vulnerability coefficient to capture heterogeneity risks. Based on this, it dynamically corrects the roll diameter, intelligently switches the speed curve, and reconstructs the acceleration limit. Combined with power-law feedforward and adaptive gain scheduling, it effectively solves the problems of tension instability and material breakage caused by the time-varying performance of biodegradable fabrics. This significantly improves the dynamic response of processing, trajectory smoothness, and finished product consistency, achieving a leap from passive adaptation to active predictive control.

[0020] The above are merely preferred embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and its improved concept, should be covered within the scope of protection of the present invention.

Claims

1. A winding control system for processing biodegradable protective clothing fabrics, connected to a protective clothing fabric processing management platform, characterized in that, include: The data acquisition and processing module is used to collect raw multimodal physical field response data in real time during the fabric processing operation, preprocess the collected raw multimodal physical field response data, and generate global degradation index, local health distribution and defect location information; The fabric degradation status calculation module is used to invert the degradation rate by coupling temperature, humidity and stress based on the global degradation index and local health distribution, using the Arrhenius humidity mechanism. It constructs the vulnerability coefficient based on the product logic of the index change rate and the health residual value, and outputs the health index, vulnerability coefficient and environmental acceleration factor. The velocity tension trajectory planning module is used to correct the roll diameter based on the health index, switch the velocity curve template according to the fragility coefficient and reconstruct the acceleration change rate limit parameter, and generate the reference tension feedforward value based on the power law relationship between the health index and the roll diameter, and output the linear velocity setting curve and the reference tension feedforward value. The adaptive tension fine control module is used to dynamically adjust the controller gain and observer parameters based on the health index, integrate the reference tension feedforward value and the local compensation amount to generate the tension set value, and output the motor torque and brake current.

2. The winding control system for processing biodegradable protective clothing fabric according to claim 1, characterized in that, The process by which the fabric degradation state calculation module, based on the global degradation index and local health distribution, couples temperature, humidity, and stress using the Arrhenius humidity mechanism to invert the degradation rate includes: Read the local health status and current global degradation index of a single grid point in the fabric geometry domain, calculate the difference between the initial and failure modulus and multiply it by the health status, add the failure modulus to obtain the current elastic modulus; multiply the global degradation index by the aging correction factor to obtain the benchmark correction amount, apply it to the current elastic modulus to establish the single-point mechanical state; Obtain absolute temperature, relative humidity and equivalent stress data at the same location, calculate the temperature index to obtain the basic reaction term, subtract the product of stress and reduction coefficient from the initial activation energy to obtain the corrected activation energy, calculate the product of humidity logarithm and entropy coefficient to obtain the entropy correction term, multiply the basic reaction term, the corrected activation energy index term and the entropy correction term by the global degradation index to obtain the site comprehensive reaction rate coefficient. The instantaneous degradation rate is calculated by multiplying the current elastic modulus of the point by the comprehensive reaction rate coefficient, and the instantaneous degradation rate field is integrated and output by traversing the global mesh.

3. A winding control system for processing biodegradable protective clothing fabric according to claim 2, characterized in that, The process of constructing the vulnerability coefficient in the fabric degradation state calculation module based on the product logic of the exponential rate of change and the residual health value includes: Read continuous data points from the real-time global degradation index time series, perform a second differential operation on the time variable to calculate the instantaneous acceleration component, and extract the absolute value of the component as the benchmark value of the exponential rate of change at the current moment; Traverse the position coordinates of a single grid point within the fabric geometry domain, read the local health of the point and calculate the deviation from the current global average health. Square the deviation value as the point contribution. Perform spatial integration on the point contribution of all grid points over the total surface area of ​​the monitoring area. Multiply the integration result by the preset damage sensitivity coefficient to obtain the spatial non-uniformity penalty factor. The vulnerability coefficient at the current moment is calculated by multiplying the current exponential rate of change benchmark value with the spatial inhomogeneity penalty factor and outputting it directly.

4. A winding control system for processing biodegradable protective clothing fabric according to claim 3, characterized in that, The process of outputting the health index, vulnerability coefficient, and environmental acceleration factor in the fabric degradation state calculation module includes: Read the local health status and stress gradient data of each grid point in the entire domain, assign weights to each point according to the magnitude of the stress gradient and perform a weighted average calculation to obtain the basic health value. At the same time, read the current global degradation index and calculate the reciprocal of the square root as the correction coefficient. Multiply the basic health value and the correction coefficient to generate the health index. The actual cumulative damage is obtained by reading the instantaneous reaction rate time series under the actual time-varying environment and performing time integration. Simultaneously, the baseline reaction rate time series under the standard reference environment is read and the same time length integration is performed to obtain the baseline cumulative damage. The environmental acceleration factor is calculated by dividing the actual cumulative damage by the baseline cumulative damage. Read the generated vulnerability coefficients and integrate the health index, environmental acceleration factor, and vulnerability coefficients into the final output vector.

5. A winding control system for processing biodegradable protective clothing fabric according to claim 1, characterized in that, The process in the velocity tension trajectory planning module that corrects the roll diameter based on the health index, switches the velocity curve template according to the vulnerability coefficient, and reconstructs the acceleration change rate limit parameters includes: Read the original estimated volume size and real-time health index, calculate and subtract the health index to get the aging deviation rate, multiply the aging deviation rate by the preset maximum volume size correction coefficient to get the correction increment, and add the correction increment to the original estimated volume size to generate the corrected volume size. The system reads the baseline acceleration rate of change limit and the real-time vulnerability coefficient of the selected template. When the vulnerability coefficient is greater than or equal to the upper threshold, the baseline acceleration rate of change limit is multiplied by the vulnerability coefficient to obtain the reconstructed acceleration rate of change limit. When the vulnerability coefficient is between the lower and upper thresholds, the system calculates and subtracts the vulnerability coefficient to obtain the material vulnerability margin. The system then multiplies half of the material vulnerability margin and subtracts the product to obtain the attenuation factor. The system multiplies the baseline acceleration rate of change limit by the attenuation factor to obtain the reconstructed acceleration rate of change limit. The system then recalculates the transition time between the acceleration and deceleration phases and limits the acceleration rate of change using the reconstructed acceleration rate of change limit. Finally, the system calculates and outputs the linear velocity setting command sequence in conjunction with the corrected roll diameter.

6. A winding control system for processing biodegradable protective clothing fabric according to claim 5, characterized in that, The process of generating a reference tension feedforward value based on the power law relationship between the health index and the roll diameter in the velocity tension trajectory planning module, and outputting the linear velocity setting curve and the reference tension feedforward value, includes: Read the real-time health index, corrected roll diameter, and preset base constant, and multiply the base constant, health compensation term, and roll diameter scaling term to calculate the reference tension feedforward value for the current single moment; Based on the sampling frequency of the control cycle, each discrete time point within the planning time period is traversed. At each time point, the power law operation is repeatedly performed to obtain the single-point data of the reference tension feedforward value at the corresponding time. The single-point data generated at each time point are arranged point by point in chronological order to generate a reference tension feedforward value sequence covering the entire planning time period. Read the linear velocity setting curve sequence generated after reconstruction by the acceleration change rate limit parameter, strictly align and match the velocity command at each moment in the linear velocity setting curve sequence with the feedforward command at the corresponding moment in the reference tension feedforward value sequence on the time axis, and directly output the aligned linear velocity setting curve and the reference tension feedforward value sequence as two sets of time-synchronized trajectory commands.

7. A winding control system for processing biodegradable protective clothing fabric according to claim 1, characterized in that, The process of dynamically adjusting the controller gain and observer parameters based on the health index in the adaptive tension fine control module includes: Read the real-time health index, calculate the device aging degree by subtracting the health index from the value, multiply it by the preset roll diameter correction coefficient to get the deviation ratio, add one to get the correction ratio, and multiply the original roll diameter by the correction ratio to generate the corrected roll diameter. Read the nominal observer bandwidth and multiply it by the health index to obtain the current observer bandwidth. Read the nominal gain matrix and multiply it by the health index to obtain the current gain matrix. Load the current observer bandwidth and the current gain matrix into the state observer model. The real-time vulnerability coefficient is read and compared with the preset lower and upper thresholds. When the vulnerability coefficient is less than the lower threshold, the proportional gain and integral gain are set to the minimum value, and the differential filter time constant is set to the maximum value. When the vulnerability coefficient is greater than or equal to the lower threshold and less than the upper threshold, the nominal proportional gain and nominal integral gain are multiplied by the attenuation factor, and the nominal differential filter time constant is divided by the attenuation factor. When the vulnerability coefficient is greater than or equal to the upper threshold, the proportional gain and integral gain are set to the maximum value, and the differential filter time constant is set to the minimum value. If the health index is lower than the preset health threshold, the differential gain is multiplied by the health index for fine-tuning.

8. A winding control system for processing biodegradable protective clothing fabric according to claim 7, characterized in that, The process by which the adaptive tension fine control module integrates the reference tension feedforward value and the local compensation amount to generate the tension setpoint, and outputs the motor torque and brake current includes: Read the preset system tension stiffness coefficient, roll diameter influence index and health influence index, and simultaneously read the real-time correction roll diameter and health index. Multiply the system tension stiffness coefficient, roll diameter power term and health compensation term to calculate the reference tension feedforward value. The tension deviation is obtained by reading the actual tension fed back by the tension sensor and the process target tension and calculating the difference. The proportional gain, integral gain and derivative gain after dynamic adjustment are read. The local compensation amount is obtained by performing proportional-integral-derivative operation on the tension deviation. The reference tension feedforward value and the local compensation amount are algebraically added to generate the total tension set value. The system reads the real-time linear velocity setting value and the corrected roll diameter, multiplies the linear velocity setting value by two and divides it by the corrected roll diameter to obtain the spindle angular velocity setting value. After discretization, the value is sent to the spindle servo driver to calculate the motor torque command. The system reads the total tension setting value and converts it into the target current according to the preset tension current mapping relationship. After power conversion, the value is output to the brake coil to generate the brake current command.

9. A winding control method for processing biodegradable protective clothing fabrics, applied to a winding control system for processing biodegradable protective clothing fabrics as described in any one of claims 1-8, characterized in that, Includes the following steps: Step 1: Collect raw multimodal physical field response data in real time during the fabric processing operation, preprocess the collected raw multimodal physical field response data, and generate global degradation index, local health distribution and defect location information; Step 2: Based on the global degradation index and local health distribution, the degradation rate is inverted by coupling temperature, humidity and stress using the Arrhenius humidity mechanism. The vulnerability coefficient is constructed according to the product logic of the index change rate and the health residual value, and the health index, vulnerability coefficient and environmental acceleration factor are output. Step 3: Adjust the roll diameter based on the health index, switch the speed curve template according to the fragility coefficient and reconstruct the acceleration change rate limit parameter, and generate the reference tension feedforward value based on the power law relationship between the health index and the roll diameter, and output the linear velocity setting curve and the reference tension feedforward value. Step 4: Based on the health index, dynamically adjust the controller gain and observer parameters, integrate the reference tension feedforward value and local compensation amount to generate the tension setpoint, and output the motor torque and brake current.

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