A system for automatically tracking the progress of textile processing based on plc

Abstract

The application relates to the technical field of automatic control of textile processing, in particular to a textile processing progress automatic tracking system based on a PLC, which comprises a critical threshold value determination module, which is used for determining a critical compaction tension threshold value according to a preset digital twin model; a risk index calculation module, which is used for calculating a prospective risk potential energy index according to a preset weight coefficient; a state evaluation module, which is used for calculating a safety margin index by combining the critical compaction tension threshold value determined by the critical threshold value determination module and the real-time acquired mechanical tension; and an adaptive control module, which is used for generating a temperature target value after compensation and adjustment based on the safety margin index calculated by the state evaluation module, the prospective risk potential energy index calculated by the risk index calculation module and a received plan speed adjustment instruction. The application overcomes the defect that traditional static process parameters cannot adapt to material state changes, and significantly improves the accuracy of a safety benchmark.

Description

Technical Field

[0001] This invention relates to the field of automated control technology for textile processing, specifically a PLC-based automatic tracking system for textile processing progress. Background Technology

[0002] As textile processing technology develops towards higher quality and higher efficiency, higher requirements are placed on the precise control of key process parameters during processing.

[0003] Currently, textile processing generally relies on static, experience-based process parameters. However, this static control method cannot dynamically adapt to real-time changes in the thermodynamic state of materials, nor can it anticipate potential risks from random disturbances such as speed fluctuations and thermal noise. It is prone to process defects such as wetting failure due to over-compaction, affecting the quality of the final product. Therefore, how to achieve dynamic and forward-looking control of the processing process and proactively avoid process risks has become an urgent technical problem to be solved in this field. Summary of the Invention

[0004] To solve the above-mentioned technical problems, the present invention provides a PLC-based automatic tracking system for textile processing progress. Specifically, the technical solution of the present invention includes:

[0005] The critical threshold determination module is used to determine the critical compaction tension threshold based on the real-time acquired fabric temperature and according to the preset digital twin model.

[0006] The risk index calculation module is used to calculate the forward-looking risk potential index based on real-time monitored speed fluctuations and thermal noise, and according to preset weighting coefficients.

[0007] The condition assessment module is used to combine the critical compaction tension threshold determined by the critical threshold determination module with the real-time mechanical tension to calculate the safety margin index.

[0008] The adaptive control module is used to generate a compensated and adjusted temperature target value based on the safety margin index calculated by the state assessment module, the forward-looking risk potential energy index calculated by the risk index calculation module, and the received planned speed adjustment command.

[0009] Preferably, the status assessment module is also used for:

[0010] The calculated safety margin index is compared with the preset safety threshold and warning threshold to output a stable, warning or critical state signal.

[0011] Preferably, the output logic for the state signal is as follows:

[0012] When the safety margin index is greater than the safety threshold, a steady-state signal is output;

[0013] When the safety margin index is less than or equal to the safety threshold and greater than the warning threshold, a warning status signal is output.

[0014] When the safety margin index is less than or equal to the warning threshold, a critical state signal is output.

[0015] Preferably, the adaptive control module performs the operation of generating the compensated and adjusted target temperature value when any of the following conditions are triggered:

[0016] The status signal output by the status assessment module is either a warning status or a critical status.

[0017] The forward-looking risk potential index calculated by the risk index calculation module exceeds the preset disturbance threshold;

[0018] Received the planned speed adjustment instruction.

[0019] Preferably, the adaptive control module generates a compensated and adjusted target temperature value, including:

[0020] Determine the feedforward temperature compensation amount;

[0021] Determine the feedback temperature compensation amount;

[0022] The current baseline process temperature setpoint, feedforward temperature compensation, and feedback temperature compensation are superimposed to generate the compensated and adjusted temperature target value.

[0023] Preferably, the determination of the feedforward temperature compensation amount includes:

[0024] The relative speed change rate is obtained by dividing the absolute value of the planned speed adjustment command by the current production process speed baseline setting.

[0025] The relative velocity change rate is multiplied by the preset feedforward compensation gain to obtain the feedforward temperature compensation amount.

[0026] Preferably, the determination of the feedback temperature compensation amount includes:

[0027] The forward-looking risk potential index calculated by the risk index calculation module is multiplied by the preset feedback compensation gain to obtain the feedback temperature compensation amount.

[0028] Preferably, the critical threshold determination module determines the critical compaction tension threshold, including:

[0029] Call upon the reference critical tension, thermal softening coefficient, and reference temperature preset in the digital twin model;

[0030] The real-time fabric temperature is used as a real-time input and substituted into the digital twin model for calculation to output the critical compaction tension threshold.

[0031] Compared with the prior art, the present invention has the following beneficial effects:

[0032] 1. By introducing a digital twin model, this system can dynamically and accurately predict the critical compaction tension that the material can withstand based on the real-time fabric temperature, overcoming the shortcomings of traditional static process parameters that cannot adapt to changes in material state, and significantly improving the accuracy of safety benchmarks.

[0033] 2. This system constructs a forward-looking risk index by quantifying random disturbances such as speed fluctuations and thermal noise, and combines feedforward and feedback compensation control strategies to achieve early prediction and proactive avoidance of process risks, changing the previous lag control mode of passively responding to problems.

[0034] 3. This system transforms the continuously changing safety margin index into three discrete state signals: stable, early warning, and critical. This makes the assessment results of the system's safety status intuitive and clear, greatly simplifies the complexity of the control logic, and improves the reliability of the automated response.

[0035] 4. This system establishes a three-in-one intelligent control triggering mechanism that integrates response, prediction, and foresight. This ensures that compensation and adjustment only intervene when necessary, such as when the system's safety margin decreases, the risk of future disturbances increases, or before planned operations are executed. This achieves precise and efficient intervention, ensuring the stability of the processing process with minimal control costs. Attached Figure Description

[0036] The present invention will be further explained below with reference to the accompanying drawings and embodiments:

[0037] Figure 1 This is a structural diagram of the system of the present invention. Detailed Implementation

[0038] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments. Example 1

[0039] Please see Figure 1 A PLC-based automatic tracking system for textile processing progress includes:

[0040] The critical threshold determination module is used to determine the critical compaction tension threshold based on the real-time acquired fabric temperature and according to the preset digital twin model.

[0041] The risk index calculation module is used to calculate the forward-looking risk potential index based on real-time monitored speed fluctuations and thermal noise, and according to preset weighting coefficients.

[0042] The condition assessment module is used to combine the critical compaction tension threshold determined by the critical threshold determination module with the real-time mechanical tension to calculate the safety margin index.

[0043] The adaptive control module is used to generate a compensated and adjusted temperature target value based on the safety margin index calculated by the state assessment module, the forward-looking risk potential energy index calculated by the risk index calculation module, and the received planned speed adjustment command.

[0044] An automatic tracking system for textile processing progress based on PLC aims to achieve precise, forward-looking, and adaptive control of the wetting state during textile processing by constructing a prediction-evaluation-compensation closed-loop control system. In this embodiment, the system is deployed in a programmable logic controller (PLC) and forms a complete technical closed loop through interaction with field sensors and actuators. The interaction between the PLC and various external devices, including sensors, actuators, and the upper-level control system, is achieved through the PROFINET industrial Ethernet protocol. The data and output instructions required by each module are periodically exchanged in real time through the I / O data mapping area defined by this network. The system specifically includes a critical threshold determination module, a risk index calculation module, a state evaluation module, and an adaptive control module.

[0045] The critical threshold determination module aims to dynamically calculate the ultimate mechanical tension that the fabric can withstand to prevent irreversible compaction damage to the fiber bundles, based on the fabric's current thermodynamic state. The core of this module is a digital twin model deployed within a PLC's advanced function block; this model is based on real-time collected temperature parameters. It dynamically predicts and outputs the critical compaction tension threshold for the fiber fabric to experience wetting failure under the current working conditions. The critical compaction tension threshold The calculation is performed using the following formula:

[0046] ;

[0047] in: It is the predicted critical compaction tension threshold, in Pascals (Pa), and is the final output of this module, providing a key dynamic benchmark for the subsequent state assessment module.

[0048] The material at the reference temperature The reference critical tension, measured in Pascals (Pa), serves to provide a baseline anchor point for the model. It is derived from physical property constants precisely calibrated through laboratory tests such as offline dynamic thermomechanical analysis (DMA) of specific fiber-resin systems and is preset in the PLC's formulation data block.

[0049] It is the thermal softening coefficient of the fiber bundle matrix, with units of 1000 kJ / m². Its function is to quantify the sensitivity of material strength to temperature changes; its source is also offline experimental calibration, and it is preset in PLC;

[0050] This is the current fabric temperature, expressed in degrees Celsius (°C). Its function is to provide the model with real-time process condition input; it is obtained through real-time monitoring by a group of thermocouple sensors deployed in the processing area.

[0051] This is a reference temperature, measured in degrees Celsius (°C), used to correlate with the real-time temperature. A comparison is made to determine the deviation of the current state from the calibration state; the source of this deviation is the reference temperature used during material calibration, which is stored as a preset physical property constant.

[0052] The risk index calculation module aims to integrate multiple production disturbances during the processing to construct a comprehensive risk index capable of proactively predicting the possibility of future tension shocks or process window contraction. In this embodiment, it calculates a forward-looking risk potential index through real-time monitoring of speed fluctuations and thermal noise. The index is calculated using the following formula:

[0053] ;

[0054] in: It is a forward-looking risk potential energy index, a dimensionless value, and is the output of this module; its function is to quantify the overall random disturbance intensity of the current system and provide feedback adjustment basis for the adaptive control module.

[0055] and It is a dimensionless weighting coefficient for the effects of speed fluctuations and thermal noise. Its function is to weight the impact of different disturbance sources on product quality according to their relative importance. Its source is determined through sensitivity analysis experiments and preset in the PLC.

[0056] This is the speed fluctuation, measured in meters per second (m / s), which characterizes the stability of the transmission system. It is derived from the real-time motor speed calculated by monitoring the servo motor encoder through high-frequency sampling, comparing it to the set speed. Deviation;

[0057] Thermal noise, measured in degrees Celsius (°C), is precisely defined in this embodiment as the standard deviation of all temperature readings collected by the thermocouple array inside the curing oven at a given instant. Its function is to quantify the non-uniformity of the temperature field or the intensity of turbulence, which is an innovative quantitative expression of the physical concept of thermal noise.

[0058] and These are the speed and temperature reference settings, which are the current production process settings and serve as the reference for calculating relative disturbances.

[0059] To ensure robustness of the calculation, this module performs a check on the denominator term before performing the calculation. and Conduct an inspection. When or When the value is close to or equal to zero, such as during the device startup or shutdown phase, the risk index... The calculation will be paused or set to a safe initial value, such as 0, to avoid calculation anomalies caused by division by zero. The effective calculation of the risk index will only be started after the system enters a stable operating condition.

[0060] The state assessment module aims to transform abstract physical predictions into standardized, easily judged relative risk indicators, thereby assessing the safety level of the fabric's microscopic wetting state online. In this embodiment, this module incorporates the output of the critical threshold determination module. With real-time collected mechanical tension The safety margin index is calculated. The index is calculated using the following formula:

[0061] ;

[0062] in: It is the safety margin index, a dimensionless value, and is the output of this module; its function is to intuitively reflect the distance between the current operating point and the failure boundary.

[0063] It is the critical compaction tension threshold, measured in Pascals (Pa), and is determined by the critical threshold determination module based on real-time temperature. The result is obtained through dynamic calculation;

[0064] It is the mechanical tension of the fabric, measured in Pascals (Pa), and is obtained in real time by a tension sensor installed on the processing path.

[0065] The adaptive control module aims to generate a compensatory temperature regulation command based on the system's current safety status, future disturbance risks, and planned operating instructions, in order to proactively and intelligently maintain or broaden the safe process window. In this embodiment, this module receives the output from the status evaluation module. The corresponding status signal and risk index calculation module output And the planned speed adjustment instructions issued by the higher-level system As input, the final compensated and adjusted temperature target value is generated. The target value is calculated using a hybrid feedforward-feedback control law:

[0066] ;

[0067] in: It is the temperature target value after compensation and adjustment, in degrees Celsius (°C), which is sent as the final instruction to temperature control actuators such as heaters;

[0068] This is the current baseline process temperature setting, in degrees Celsius (°C).

[0069] and These are feedforward compensation gain and feedback compensation gain, both in degrees Celsius (°C). Their function is to convert the standardized relative velocity change rate and risk index into specific temperature compensation values. They are derived from the optimal control parameters obtained through experiments or simulation calibration and are preset in the PLC.

[0070] This is the planned speed adjustment command, measured in meters per second (m / s). It originates from the production command received by the PLC from the host control system. Specifically, this command is written to a designated data block (DB) in the PLC via a non-periodic communication method through the PROFINET network. This data block contains a predefined structure whose members include at least one 32-bit floating-point number representing the speed adjustment amount. A 16-bit unsigned integer is used as the instruction sequence number. The adaptive control module reads and executes the new speed adjustment amount only when it detects a change in the instruction sequence number within a scan cycle. After execution, the status flag is reset or updated to ensure the uniqueness and reliability of the instruction.

[0071] It is a forward-looking risk potential index, dimensionless, and its source is the calculation result of the risk index calculation module;

[0072] Through the collaborative work of the above four modules, this invention constructs a complete closed loop from dynamic prediction of physical state to quantification of multi-source disturbance risks, to real-time safety margin assessment, and finally to feedforward-feedback hybrid adaptive compensation control. It completely changes the traditional textile processing mode that relies on static process parameters and ex-post adjustments. This system can predict the degradation of material properties caused by temperature changes in real time, quantify the potential risks brought by current random disturbances, and proactively adjust process parameters in a forward-looking manner to avoid risks. Thus, while ensuring production efficiency, it greatly improves the final quality of the product and the stability of the processing process, and effectively prevents serious process defects such as impregnation failure caused by over-compaction.

[0073] Example 2:

[0074] The status assessment module is also used for:

[0075] The calculated safety margin index is compared with the preset safety threshold and warning threshold to output a stable, warning or critical state signal;

[0076] The output logic for the status signal is as follows:

[0077] When the safety margin index is greater than the safety threshold, a steady-state signal is output;

[0078] When the safety margin index is less than or equal to the safety threshold and greater than the warning threshold, a warning status signal is output.

[0079] When the safety margin index is less than or equal to the warning threshold, a critical state signal is output.

[0080] Based on the system described in Example 1, this example specifies its functions to make the output of the state assessment module more instructive and operable; the state assessment module not only calculates the safety margin index. Furthermore, it compares the result with a preset threshold to output a discretized state signal, providing a clear basis for subsequent control decisions;

[0081] This module has two preset key thresholds: a security threshold. and warning threshold These two thresholds define the boundaries of the three state intervals: safety, warning, and critical. Their values ​​are based on historical production data statistical analysis, product quality acceptance standards, and the experience of process experts.

[0082] The output logic of the state signal is strictly defined as follows:

[0083] When the calculated safety margin index Greater than the safety threshold At that time, the module outputs a stable state signal;

[0084] When the safety margin index Less than or equal to the safety threshold And greater than the warning threshold When this happens, the module outputs a warning status signal;

[0085] When the safety margin index Less than or equal to the warning threshold At that time, the module outputs a critical state signal;

[0086] By introducing a hierarchical state assessment logic, a continuously changing safety margin index that requires professional knowledge to interpret is transformed into three state signals—stable, warning, and critical—that any operator or control system can instantly understand. This design greatly reduces the complexity of the control logic, enabling subsequent adaptive control modules to trigger corresponding control strategies based on clear and explicit signals. This not only improves the system's automation level and response reliability but also lays the foundation for realizing more refined hierarchical risk response strategies.

[0087] Example 3:

[0088] The adaptive control module generates the compensated target temperature value when any of the following conditions are triggered:

[0089] The status signal output by the status assessment module is either a warning status or a critical status.

[0090] The forward-looking risk potential index calculated by the risk index calculation module exceeds the preset disturbance threshold;

[0091] Received the planned speed adjustment instruction.

[0092] Based on the system described in Example 1, this example provides a clear and multi-dimensional definition of the activation timing of the adaptive control module. The module does not continuously perform compensation and adjustment, but only performs the operation of generating the temperature target value after compensation and adjustment when specific triggering conditions are met, thereby achieving efficient, accurate and necessary intervention.

[0093] The adaptive control module is designed to be activated based on one of the following three conditions:

[0094] The status signal output by the status assessment module is a warning status or a critical status; this is a reactive triggering mechanism that immediately initiates compensation when the system's safety margin has dropped to a level that requires attention or intervention.

[0095] The forward-looking risk potential index calculated by the risk index calculation module The disturbance exceeds a preset threshold; this threshold is set to identify statistically significant process fluctuations, and its value can be taken from historical normal production conditions. The 95th percentile of the value; this is a forward-looking triggering mechanism that predicts future risks based on the current intensity of the disturbance, even with the current safety margin. It's acceptable, but if the system exhibits high instability, we should intervene early to prevent problems before they occur.

[0096] Received planned speed adjustment instruction This is a predictive feedforward triggering mechanism that proactively compensates for and widens the process window before the system is about to execute an operation known to cause tension shock, such as acceleration.

[0097] This embodiment establishes a highly intelligent control triggering system that integrates reaction, prediction, and anticipation. It overcomes the limitations of traditional control systems that can only passively respond to errors. By integrating comprehensive judgments of the current state, future risks, and established plans, this system ensures that every intervention of compensatory control is timely: it can react quickly when danger occurs, suppress danger in advance when it is brewing, and proactively avoid danger before executing planned operations, thereby maximizing the protection of process stability with minimal control costs.

[0098] Example 4:

[0099] The adaptive control module generates the compensated and adjusted target temperature value, including:

[0100] Determine the feedforward temperature compensation amount;

[0101] Determine the feedback temperature compensation amount;

[0102] The current baseline process temperature setpoint, feedforward temperature compensation, and feedback temperature compensation are superimposed to generate the compensated and adjusted temperature target value.

[0103] The determination of the feedforward temperature compensation amount includes:

[0104] The relative speed change rate is obtained by dividing the absolute value of the planned speed adjustment command by the current production process speed baseline setting.

[0105] The relative velocity change rate is multiplied by the preset feedforward compensation gain to obtain the feedforward temperature compensation amount;

[0106] The determination of the feedback temperature compensation amount includes:

[0107] The forward-looking risk potential index calculated by the risk index calculation module is multiplied by the preset feedback compensation gain to obtain the feedback temperature compensation amount.

[0108] This embodiment details how the adaptive control module generates the compensated and adjusted temperature target value. The specific algorithm structure and calculation process; its core technology lies in decoupling the control quantities that respond to disturbances of different natures, calculating them separately, and then superimposing them to achieve more precise control and generate... The overall framework is as follows:

[0109] ;

[0110] This formula indicates that the final temperature target value is the current baseline process temperature setpoint. Based on this, two independent compensation quantities are superimposed: feedforward temperature compensation quantity. and feedback temperature compensation amount ;

[0111] Feedforward temperature compensation The determination method is as follows:

[0112] The planned speed adjustment command received by the PLC The absolute value divided by the current production process speed reference setting. To obtain a dimensionless rate of change of relative velocity. The relative velocity change rate is compared with the preset feedforward compensation gain. Multiply to obtain the feedforward temperature compensation amount. The calculation formula is as follows:

[0113] ;

[0114] The purpose of this compensation is to proactively avoid tension shocks caused by planned speed changes;

[0115] Feedback temperature compensation amount The determination method is as follows:

[0116] The forward-looking risk potential index will be calculated in real time by the risk index calculation module. With the preset feedback compensation gain Multiply by the product to directly obtain the feedback temperature compensation amount. The calculation formula is as follows:

[0117] ;

[0118] The purpose of this compensation is to suppress the impact of random disturbances such as velocity fluctuations and thermal turbulence on system stability in real time.

[0119] By decoupling the total temperature compensation into two independent channels, feedforward and feedback, this invention achieves a synergistic effect; based on known future instructions, it broadens the safety process window in advance by raising the temperature before the actual impact occurs, demonstrating predictive control; the feedback compensation, as described in claim 7, is based on the real-time system comprehensive risk index. The system continuously and finely adjusts the temperature to suppress random disturbances, demonstrating adaptive control. The linear superposition of these two compensation mechanisms enables the system to cope with large adjustments in the plan and to precisely smooth out random fluctuations during operation. This hybrid control mode, which combines strategic preset and tactical fine-tuning, has a stability and robustness far exceeding that of a single feedforward or feedback control system.

[0120] Example 5:

[0121] The critical threshold determination module determines the critical compaction tension threshold, including:

[0122] Call upon the reference critical tension, thermal softening coefficient, and reference temperature preset in the digital twin model;

[0123] The real-time fabric temperature is used as a real-time input and substituted into the digital twin model for calculation to output the critical compaction tension threshold.

[0124] Based on the system described in Example 1, this example determines the critical compaction tension threshold using the critical threshold determination module. The specific implementation details have been supplemented to ensure the full disclosure and reproducibility of the plan;

[0125] Calling preset parameters determined during the calibration phase: Before performing calculations, the module calls three core physical property constants preset in the digital twin model from the PLC's non-volatile storage area, such as the recipe data block.

[0126] Reference critical tension Thermal softening coefficient and reference temperature These parameters were precisely calibrated through rigorous offline laboratory testing of the fiber-resin system used, providing a solid foundation for the physical realism of the model.

[0127] The fabric temperature is obtained in real time through a thermocouple sensor array. As the sole real-time variable input, it is substituted into the mathematical expression of the digital twin model. Perform calculations in it;

[0128] After the calculation is complete, the module will display the results. The critical compaction tension threshold is output as a condition assessment module for use in the next stage.

[0129] This implementation method clarifies that the model parameters are derived from offline precise calibration and the model's computational mechanism is based on real-time temperature calculation, ensuring the scientific validity and feasibility of the critical threshold determination module. Compared with existing technologies that use fixed and conservative safety thresholds, this solution achieves dynamic and accurate prediction of critical tension through a digital twin model based on real physical principles and calibrated with experimental data. This method not only greatly improves the accuracy of the safety threshold, avoiding production efficiency losses due to overly conservative thresholds or product quality risks due to overly aggressive thresholds, but also features a simple calculation method that fully meets the millisecond-level real-time computation requirements of PLCs, thus ensuring high precision while possessing extremely high industrial practical value.

[0130] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A PLC-based automatic tracking system for textile processing progress, characterized in that, include: The critical threshold determination module is used to determine the critical compaction tension threshold based on the real-time acquired fabric temperature and according to the preset digital twin model. The risk index calculation module is used to calculate the forward-looking risk potential index based on real-time monitored speed fluctuations and thermal noise, and according to preset weighting coefficients. The condition assessment module combines the critical compaction tension threshold determined by the critical threshold determination module with the real-time acquired mechanical tension to calculate the safety margin index. : ， in, It is a safety margin index. It is the critical compaction tension threshold. It is the mechanical tension of the fabric; The adaptive control module is used to generate a compensated and adjusted temperature target value based on the safety margin index calculated by the state assessment module, the forward-looking risk potential energy index calculated by the risk index calculation module, and the received planned speed adjustment command. : ， in, This is the target temperature value after compensation and adjustment. This is the current baseline process temperature setpoint. and These are the feedforward compensation gain and the feedback compensation gain, respectively. It is the amount of planned speed adjustment instructions. It is a forward-looking risk potential index; The adaptive control module generates a compensated and adjusted target temperature value, including: Determine the feedforward temperature compensation amount; Determine the feedback temperature compensation amount; The current baseline process temperature setpoint, feedforward temperature compensation, and feedback temperature compensation are superimposed to generate the compensated and adjusted temperature target value. The determination of the feedforward temperature compensation amount includes: The relative speed change rate is obtained by dividing the absolute value of the planned speed adjustment command by the current production process speed baseline setting. The relative velocity change rate is multiplied by the preset feedforward compensation gain to obtain the feedforward temperature compensation amount; The determination of the feedback temperature compensation amount includes: The forward-looking risk potential index calculated by the risk index calculation module Multiply by the preset feedback compensation gain to obtain the feedback temperature compensation amount; ， in, It is a forward-looking risk potential index. and It is a dimensionless weighting coefficient for the effects of velocity fluctuations and thermal noise. It is the velocity fluctuation. It is a specific thermal noise that characterizes the standard deviation of all temperature readings collected by the thermocouple array inside the curing oven at a given instant. and These are the speed and temperature reference settings, respectively. The critical threshold determination module determines the critical compaction tension threshold, including: Call upon the reference critical tension, thermal softening coefficient, and reference temperature preset in the digital twin model; The real-time fabric temperature is used as a real-time input and substituted into the digital twin model for calculation to output the critical compaction tension threshold. : ， in, It is the predicted critical compaction tension threshold. The material at the reference temperature The reference critical tension is specified below. It is the thermal softening coefficient of the fiber bundle matrix. This is the current fabric temperature. This is a reference temperature.

2. The automatic tracking system for textile processing progress based on PLC according to claim 1, characterized in that, The status assessment module is also used for: The calculated safety margin index is compared with the preset safety threshold and warning threshold to output a stable, warning or critical state signal.

3. The automatic tracking system for textile processing progress based on PLC according to claim 2, characterized in that, The output logic for the state signal is as follows: When the safety margin index is greater than the safety threshold, a steady-state signal is output; When the safety margin index is less than or equal to the safety threshold and greater than the warning threshold, a warning status signal is output. When the safety margin index is less than or equal to the warning threshold, a critical state signal is output.

4. The automatic tracking system for textile processing progress based on PLC according to claim 1, characterized in that, The adaptive control module performs the operation of generating a compensated and adjusted target temperature value when any of the following conditions are triggered: The status signal output by the status assessment module is either a warning status or a critical status. The forward-looking risk potential index calculated by the risk index calculation module exceeds the preset disturbance threshold; Received the planned speed adjustment instruction.

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