A method and system for rapid positioning of elastic yarns

Abstract

The application relates to the technical field of knitting machine yarn feeding control, and discloses a quick positioning method and system for elastic yarn, which obtains yarn deformation resistance capability information, and dynamically adjusts a yarn feeding target force according to the difference between the yarn deformation resistance capability information and target deformation resistance capability information, effectively solves the physical state drift problem caused by the influence of the viscoelasticity of yarn on the environment in the prior art, ensures the stability of the actual elongation of the yarn at a knitting point and the effective rigidity, and thus improves the stability of the loop density, gram weight and elastic recovery performance of cloth from the source.

Description

Technical Field

[0001] This invention relates to the field of yarn feeding control technology for knitting machines, and in particular to a rapid positioning method and system for elastic yarns. Background Technology

[0002] In modern textile manufacturing, knitting machines, especially circular knitting machines, play a central role in producing high-elasticity fabrics. These fabrics have extremely high requirements for loop density, weight, and stability of elastic recovery performance, all of which rely on precise tension control of each yarn fed into the knitting needles. Currently, each yarn path is equipped with an advanced active yarn feeding tension adjustment system. This system uses a high-precision tension sensor to monitor the instantaneous tension value of the yarn as it passes through, transmitting the data to the central control unit. The control unit compares the actual tension value with the preset process target tension value. If a deviation is found, it sends a command to the servo motor driving the yarn feeder to adjust the speed, thereby changing the yarn feeding amount and quickly pulling the yarn tension back within the preset process window, aiming to maintain stable yarn tension throughout the knitting process.

[0003] However, existing yarn tension control systems, while capable of monitoring and adjusting the instantaneous forces acting on the yarn in real time, often overlook the fact that elastic yarn, as a special material, is affected by subtle fluctuations in temperature and humidity in the production environment. This means that even if the system strives to maintain the tension at a set value, the yarn's elongation at the actual weaving point or its ability to resist deformation (effective stiffness) may have subtly changed. This invisible shift in physical state leads to imperceptible quality defects in the fabric, such as inconsistencies in loop formation, subtle differences in fabric feel, and uneven dye absorption.

[0004] Furthermore, even subtle changes in the viscoelastic state of yarn, though insignificant, can affect its long-term durability and resistance to mechanical stress. If a yarn is consistently subjected to slightly excessive effective elongation at its target tension, it may experience higher internal stress or accelerated fatigue at critical stress points, leading to gradual weakening over time and increased susceptibility to premature breakage. While rapid yarn breakage detection systems can accurately identify breakage locations and halt production, they cannot provide deeper reasons why breakage frequency increases or why yarns break at seemingly "normal" tension levels. This hinders effective preventative maintenance, making it difficult for technicians to identify and resolve potential systemic problems, resulting in an increase in "unexplained" or "random" breakages that disrupt production processes. Even highly efficient location systems cannot fundamentally solve the problem.

[0005] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0006] This invention provides a rapid positioning method and system for elastic yarns, aiming to solve the problems that existing knitting machine yarn feeding tension control systems cannot effectively cope with the physical state drift caused by the viscoelastic properties of elastic yarns affected by the environment, and that yarn breakage positioning systems cannot provide the underlying causes of breakage, thereby affecting product quality and production efficiency.

[0007] In a first aspect, this application discloses a rapid positioning method for elastic yarns, comprising: acquiring information on the yarn's ability to resist deformation in its current state; adjusting the target force for feeding the yarn based on the difference between the information on the ability to resist deformation and the preset target force for resisting deformation; adjusting the yarn feeding amount based on the adjusted target force to maintain the instantaneous force of the yarn consistent with the adjusted target force; when yarn breakage is detected, acquiring information on the yarn's physical state before and after the breakage, local environmental information, and yarn force fluctuation information; and analyzing the cause of the yarn breakage based on the yarn's physical state information, local environmental information, and yarn force fluctuation information.

[0008] This technical solution enables real-time sensing of the inherent viscoelastic changes in yarn and dynamic adjustment of the target force for yarn feeding. This effectively compensates for the drift in the physical state of the yarn caused by environmental changes, ensuring the stability of yarn elongation and effective stiffness during weaving and fundamentally improving fabric quality. Furthermore, through comprehensive analysis of multi-dimensional information before and after breakage, the underlying causes of yarn breakage can be deeply explored, providing data support for preventative maintenance and process optimization, and effectively reducing the frequency of "unexplained" breakage.

[0009] Furthermore, based on the above method, the target force for yarn feeding is adjusted according to the difference between the deformation resistance information and the preset target deformation resistance information, including: obtaining the reliability of the deformation resistance information; and adjusting the target force for yarn feeding according to the deformation resistance information and its corresponding reliability.

[0010] This technical solution introduces an assessment of the reliability of information regarding the yarn's resistance to deformation, making the adjustment of the target force more intelligent and robust. When the reliability of the information is low, the system can adopt a more cautious adjustment strategy, avoiding misoperations caused by inaccurate data, thereby improving the stability and reliability of the control system.

[0011] Furthermore, in some preferred embodiments, the reliability of obtaining deformation resistance information includes: the clarity of the yarn's response signal to disturbance; the dispersion of the deformation resistance information obtained from continuous measurements; the degree of matching between the current deformation resistance information's changing trend and the changing trend of preset environmental parameters; adjusting the weights of each indicator based on the clarity of the yarn's response signal to disturbance, the dispersion of the continuously measured deformation resistance information, the degree of matching between the current deformation resistance information's changing trend and the changing trend of preset environmental parameters, as well as yarn speed, yarn batch type, and historical production data; and, based on the adjusted weights, integrating the clarity of the yarn's response signal to disturbance, the dispersion of the continuously measured deformation resistance information, and the degree of matching between the current deformation resistance information's changing trend and the changing trend of preset environmental parameters to obtain the reliability of the deformation resistance information.

[0012] Through this technical solution, this application achieves a more comprehensive and accurate quantification of the reliability of deformation resistance information by comprehensively evaluating multi-dimensional indicators and dynamically adjusting weights. This refined evaluation mechanism enables the system to distinguish between real changes and measurement noise, thereby providing a more reliable basis for adjusting the yarn feeding target force in complex and ever-changing production environments, further improving control accuracy and stability.

[0013] Based on the above, this application further proposes adjusting the target force for yarn feeding according to the information on resistance to deformation and its corresponding reliability, including: when the reliability is continuously lower than a preset threshold, activating a risk assessment module to analyze the current yarn speed, yarn batch type, local environmental parameters, and historical production data; judging whether there is a potential risk of rapid deterioration of the actual physical state of the yarn based on the analysis results; and when there is a potential risk, adjusting the target force in a controlled stepwise manner and triggering an enhanced monitoring mode.

[0014] This technical solution enables the application to promptly activate a risk assessment mechanism when confidence levels are low, proactively identifying potential risks of yarn physical condition deterioration. This forward-looking risk management allows the system to take preventative measures before problems occur, adjusting target forces in a controlled, step-by-step manner and initiating enhanced monitoring. This effectively avoids yarn breakage and quality issues caused by yarn condition deterioration, significantly improving production stability and safety.

[0015] As a further improvement, when the credibility level is consistently lower than a preset threshold, a risk assessment module is activated, including: dynamically adjusting the assessment threshold of the credibility level of the resistance to deformation information based on yarn speed, yarn batch type, and local environmental parameters; when the credibility level is consistently lower than the dynamically adjusted assessment threshold, the risk assessment module is activated.

[0016] This technical solution introduces a mechanism for dynamically adjusting the assessment threshold, making the triggering conditions for risk assessment more flexible and adaptable. The system can intelligently adjust the sensitivity of risk identification based on the actual production environment and yarn characteristics, avoiding false alarms or missed alarms that might occur with fixed thresholds, thus making the activation of the risk assessment module more accurate and efficient.

[0017] Based on the above, this application further proposes that when the credibility level is continuously lower than the dynamically adjusted assessment threshold, after the risk assessment module is activated, the following steps are taken: after the risk assessment module is activated, an independent operation status monitoring unit is started. This monitoring unit continuously acquires the processor usage of the risk assessment module, the memory usage of the risk assessment module, the completion flags of the key internal data processing flow of the risk assessment module, and the communication link status between the risk assessment module and the external data source; when the processor usage continuously exceeds the preset range, the memory usage reaches the preset upper limit, the key internal data processing flow is not completed within a specified time, or the communication link is interrupted, an abnormal alarm is triggered; the operation status information of the risk assessment module is recorded; and the system switches to emergency control mode, in which the yarn feeding target force is adjusted using preset conservative parameters.

[0018] Through this technical solution, after the risk assessment module is activated, it is comprehensively monitored by an independent operation status monitoring unit to ensure the reliability and stability of the risk assessment process itself. Once an anomaly is detected, the system can promptly trigger an alarm and switch to emergency control mode, adjusting the yarn feeding target force using conservative parameters. This effectively prevents system loss of control due to malfunctions in the risk assessment module itself, further ensuring production safety.

[0019] Preferably, after the risk assessment module is started, an independent operation status monitoring unit is started, including: performing resource reservation operations through the operation status monitoring unit to pre-allocate processor time slices and memory space for itself; when the operation status monitoring unit executes a self-test program, it verifies the connectivity of core functions and communication links; it performs preliminary sampling of the processor usage and memory usage of the risk assessment module through the operation status monitoring unit; when the sampled data exceeds the preset instantaneous fluctuation range, it triggers an early anomaly warning and starts the full monitoring function.

[0020] This technical solution ensures the stability and reliability of the operational status monitoring unit by reserving resources and performing self-checks. Simultaneously, through preliminary sampling and early warning mechanisms, potential anomalies in the risk assessment module can be quickly detected, and comprehensive monitoring can be initiated before the problem escalates. This enables rapid response and effective management of the risk assessment module's operational status, further enhancing the overall robustness of the system.

[0021] In one implementation, a risk assessment module analyzes current yarn speed, yarn batch type, local environmental parameters, and historical production data, including: acquiring historical production data; performing interval boundary verification on the values ​​in the historical production data to identify outliers exceeding physically reasonable ranges, and smoothing the time series data in the historical production data to reduce the impact of instantaneous noise; filling in missing values ​​in the historical production data based on the trend of adjacent data or the average value of similar yarn batches to obtain processed historical production data; adjusting the weight of historical production data in risk assessment based on the processed historical production data and the degree of cleaning and filling of the historical production data, and combining it with current yarn speed, yarn batch type, and local environmental parameters to analyze the potential risks of the actual physical state of the yarn.

[0022] This technical solution significantly improves the accuracy and reliability of risk assessment by rigorously cleaning, smoothing, and filling historical production data, and dynamically adjusting its weight in risk assessment based on data quality. This refined data processing and weight adjustment mechanism enables the system to more comprehensively and objectively assess the potential risks of yarn physical conditions, providing a more solid data foundation for decision-making.

[0023] As an optional solution, when the credibility level remains below a preset threshold, the risk assessment module is activated, and the following steps are also taken: After the risk assessment module is activated, a readiness query signal is sent to the risk assessment module through the main control system; after receiving the readiness query signal, the risk assessment module checks whether the key initialization tasks within the risk assessment module have been completed. The key initialization tasks include self-testing of the core processing unit, connectivity verification of the data interface, and loading of initial parameters; based on the check results, the risk assessment module returns a readiness status signal to the main control system, indicating whether the risk assessment module is ready to receive tasks or provide output; after receiving the readiness status signal returned by the risk assessment module, and the readiness status signal indicating that the risk assessment module is ready, the main control system sends data to the risk assessment module or relies on the output of the risk assessment module to adjust the subsequent yarn feeding target force.

[0024] This technical solution introduces a readiness confirmation mechanism for the risk assessment module, ensuring that the main control system only interacts with and collaborates with the module after it is fully ready. This handshake protocol effectively avoids errors or data inconsistencies caused by incomplete module initialization, thereby improving the stability and reliability of system collaboration.

[0025] Secondly, this application also discloses a rapid positioning system for elastic yarns, the system comprising: The detection end is used to acquire information about the yarn's ability to resist deformation in its current state; based on the difference between the information about the ability to resist deformation and the preset target information about the ability to resist deformation, the target force for feeding the yarn is adjusted. The adjustment end is used to adjust the yarn feed rate according to the adjusted target force to maintain the instantaneous force of the yarn consistent with the adjusted target force; when yarn breakage is detected, it acquires information on the physical state of the yarn before and after the breakage, local environmental information, and yarn force fluctuation information. The processing unit is used to analyze the causes of yarn breakage based on information about the yarn's physical state, local environment, and yarn force fluctuations.

[0026] This application provides a rapid positioning system for elastic yarns. Through modular design, it achieves real-time sensing of yarn viscoelasticity changes, intelligent adjustment of target forces, and in-depth analysis of yarn breakage causes. The system can work collaboratively to comprehensively improve the quality control and fault diagnosis capabilities of knitting production from two dimensions: physical state compensation and fault cause analysis. Beneficial effects

[0027] This application provides a rapid positioning method and system for elastic yarns. By acquiring information on the yarn's resistance to deformation and dynamically adjusting the target force for yarn feeding based on the difference between the yarn's resistance to deformation and the target resistance information, it effectively solves the problem of physical state drift caused by environmental influences on the yarn's viscoelastic properties in existing technologies. This ensures the stability of the actual elongation and effective stiffness of the yarn at the knitting point, thereby improving the stability of the fabric's loop density, weight, and elastic recovery performance from the source. Furthermore, when yarn breakage is detected, this application can acquire information on the yarn's physical state before and after the breakage, local environmental information, and yarn force fluctuation information, and analyze the underlying causes of the yarn breakage in depth, overcoming the limitation of existing yarn breakage detection systems that can only locate the breakage but not provide the cause. This method not only enables adaptive compensation of yarn feeding tension, significantly reducing quality defects caused by yarn physical state drift, but also provides key data support for preventative maintenance and process optimization, effectively reducing the frequency of "unexplained" breakages, thereby comprehensively improving the efficiency and product quality of knitting production. Attached Figure Description

[0028] Figure 1 This is a flowchart illustrating a rapid positioning method for elastic yarn provided in an embodiment of the present invention; Figure 2 This is a flowchart of a method for adjusting the target force of yarn feeding according to an embodiment of the present invention; Figure 3 This is a flowchart of a method for obtaining the reliability of information on resistance to deformation provided by an embodiment of the present invention; Figure 4 This is a structural schematic diagram of a rapid positioning system for elastic yarn provided in an embodiment of the present invention. Detailed Implementation

[0029] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0030] Reference Figure 1 , Figure 1 This is a flowchart illustrating a rapid positioning method for elastic yarn provided in an embodiment of the present invention, including the following steps: S11, Obtain information on the yarn's ability to resist deformation in its current state; S12, adjust the target force for yarn feeding based on the difference between the deformation resistance information and the preset target deformation resistance information; S13, Adjust the yarn feed rate according to the adjusted target force to maintain the instantaneous force of the yarn consistent with the adjusted target force; S14, when the yarn breakage is detected, acquire the yarn physical state information, local environment information and yarn force fluctuation information before and after the breakage; S15, based on the yarn physical state information, the local environment information, and the yarn force fluctuation information, analyze the cause of the yarn breakage.

[0031] This application aims to fundamentally solve the blind spots in elastic yarn tension control and the inadequacies in yarn breakage cause analysis in existing technologies by introducing a real-time sensing and adaptive adjustment mechanism for information on the yarn's ability to resist deformation, as well as a comprehensive analysis of multi-dimensional information when yarn breakage occurs.

[0032] To better understand the technical solution proposed in this application, some key terms involved will be explained first.

[0033] "Information on the ability of yarn to resist deformation under current conditions" refers to the degree to which the internal structure of a yarn resists external tensile or compressive forces under specific temperature, humidity, and tension conditions. This can generally be understood as the yarn's effective stiffness or modulus of elasticity. This information reflects the physical response characteristics of the yarn during the actual weaving process and is a key indicator for assessing yarn health and predicting its behavior.

[0034] "Preset target resistance to deformation information" refers to the ideal yarn resistance to deformation information preset according to the specific fabric's process requirements and yarn type. It serves as the benchmark for the system's adaptive adjustments.

[0035] "Target force for yarn feeding" refers to the force that the yarn feeder needs to apply to the yarn to ensure that the yarn enters the weaving area with the desired tension. This force is dynamically adjusted to compensate for changes in the yarn's own physical state.

[0036] "Instantaneous force of yarn" refers to the tension that the yarn actually bears at a certain moment, which is obtained in real time by a high-precision tension sensor.

[0037] "Yarn physical state information" includes yarn diameter, linear density, moisture content, surface friction coefficient, etc., which directly affect the mechanical properties of the yarn.

[0038] "Local environmental information" refers to environmental parameters such as temperature, humidity, and airflow speed in the working area of ​​the knitting machine. These parameters have a significant impact on the viscoelastic behavior of elastic yarns.

[0039] "Yarn force fluctuation information" refers to data such as the trend, amplitude, and frequency of changes in the instantaneous force of the yarn within a period of time before and after breakage. These fluctuations may indicate abnormal conditions of the yarn or the direct cause of breakage.

[0040] This application provides a rapid positioning method for elastic yarns, the core of which lies in the deep perception and intelligent response of the yarn's physical state.

[0041] There are several ways to obtain information about a yarn's ability to resist deformation in its current state. For example, a non-contact laser diameter gauge and micro-tension sensor can be installed along the yarn path to measure the instantaneous diameter and tension response under minute deformations in real time. Combined with a pre-defined yarn material parameter model, its resistance to deformation can be calculated. Another approach is to use acoustic or vibration sensors to indirectly infer its resistance to deformation by analyzing the yarn's vibration characteristics or acoustic response at specific frequencies. Furthermore, more accurate information about its resistance to deformation can be obtained by directly measuring the internal strain of the yarn by coating or embedding micro-sensors on its surface.

[0042] To adjust the target force for yarn feeding based on the difference between the yarn's resistance to deformation information and the preset target resistance to deformation information, the following methods can be used. For example, the system can set a fixed proportional gain controller. When the yarn's resistance to deformation information deviates from the target value, the system directly calculates the target force to be adjusted by multiplying the deviation by the gain. Alternatively, a lookup table method can be used. A mapping table between the deviation of the resistance to deformation information and the target force adjustment amount can be pre-established, and the system can directly look up the corresponding adjustment amount based on the current deviation value.

[0043] To adjust the yarn feed rate based on the adjusted target force to maintain consistency between the instantaneous yarn force and the adjusted target force, the following methods can be used. For example, the system can employ a traditional PID controller, using the adjusted target force as the setpoint and the real-time measured instantaneous yarn force as the feedback value. The PID algorithm calculates the speed adjustment of the yarn feeding servo motor, thereby precisely controlling the yarn feed rate. Another method is to use a fuzzy logic controller. Based on the error between the target force and the instantaneous force and its rate of change, fuzzy inference rules are used to output control commands for the servo motor, thus achieving feed rate adjustment.

[0044] When yarn breakage is detected, the following methods can be used to acquire information on the yarn's physical state, local environment, and yarn force fluctuations before and after the breakage. For example, when the yarn breakage sensor is triggered, the system can immediately extract all relevant data from the connected database from the last few seconds before the breakage to the last few seconds after the breakage. Yarn physical state information can include data such as yarn diameter and moisture content recorded by online sensors. Local environment information can be acquired by temperature and humidity sensors installed near the knitting machine. Yarn force fluctuation information can be extracted from the data stream of a high-frequency tension sensor, including abnormal increases or decreases in tension before breakage and the waveform of a sudden drop in tension at the moment of breakage.

[0045] To analyze the causes of yarn breakage based on yarn physical state information, local environmental information, and yarn force fluctuation information, the following methods can be used. For example, the system can pre-set a series of rule-based expert systems, using the acquired multi-dimensional information as input. For instance, if the yarn diameter continuously decreases before breakage, and the local humidity is abnormally high, while the tension fluctuates drastically, the system may infer that the breakage is caused by the yarn becoming thinner due to moisture, leading to a decrease in strength. Alternatively, the system can use a machine learning model, trained on a large amount of historical yarn breakage data, to learn the complex relationship between different combinations of information and breakage causes, thereby automatically identifying and outputting the most likely breakage cause when a new yarn breakage event occurs.

[0046] The method proposed in this application effectively compensates for the viscoelastic drift of elastic yarns caused by environmental changes by real-time sensing and adaptive adjustment of the yarn's resistance to deformation. This maintains the stability of the yarn's actual physical state at the weaving point, avoiding the limitations of traditional tension control systems that only focus on instantaneous forces while ignoring changes in the yarn's intrinsic physical state. Specifically, when the yarn's resistance to deformation changes, the system dynamically adjusts the target force for feeding the yarn based on the difference between the change and a preset target value. For example, if the yarn becomes softer due to increased ambient humidity (reduced resistance to deformation), the system will correspondingly reduce the target force for feeding the yarn to ensure consistent elongation at the actual weaving point, thereby avoiding potential damage and fabric quality problems caused by excessive stretching.

[0047] Furthermore, when yarn breakage is detected, this application goes beyond simply locating the break; it delves deeper to analyze the cause. By acquiring information on the yarn's physical state before and after breakage, local environmental information, and yarn force fluctuations, the system can comprehensively determine the underlying cause of the breakage. For example, if the analysis shows that the yarn diameter was abnormally thinner, the local temperature was excessively high, and the tension fluctuated drastically before breakage, the system can accurately diagnose that the breakage was caused by the combined effects of yarn thermal aging and mechanical fatigue. This multi-dimensional, in-depth analytical capability enables production managers to address problems at their root, such as adjusting production environment parameters, replacing batches of yarn, or optimizing equipment maintenance strategies, thereby significantly reducing the frequency of yarn breakage and improving production efficiency and product quality.

[0048] Compared with existing technologies, the core innovation of this application lies in its deep perception and adaptive control of the intrinsic physical state of elastic yarn, as well as its intelligent analysis of the causes of yarn breakage. Existing technologies mainly rely on feedback control of the instantaneous tension of the yarn. When the viscoelastic properties of elastic yarn drift due to environmental influences, this method cannot fundamentally guarantee the stability of the actual physical state of the yarn at the weaving point, thus leading to fabric quality defects and "unexplained" yarn breakage.

[0049] This application overcomes the limitations of traditional tension control by introducing the step of "obtaining information on the yarn's ability to resist deformation in its current state." For example, in traditional systems, even if the yarn becomes stiffer due to environmental changes, the system still tries to maintain the tension at a preset value, which may result in insufficient actual elongation of the yarn at the knitting point, affecting loop formation. This application, however, can sense changes in the yarn's ability to resist deformation and adjust the "target force for feeding the yarn" accordingly, ensuring that the physical state of the yarn at the knitting point is always within the optimal range. This self-compensation mechanism significantly improves the stability of the knitting process and the consistency of fabric quality.

[0050] Furthermore, this application demonstrates significant advantages in handling yarn breakage. Traditional yarn breakage systems can only achieve rapid location and shutdown, but cannot provide the underlying causes of the breakage. This application, by acquiring and analyzing information on the physical state of the yarn before and after breakage, local environmental information, and yarn force fluctuations, can reveal the root cause behind the breakage. For example, if a traditional system frequently reports yarn breakage at a certain location, technicians may only be able to replace the yarn or inspect mechanical parts. However, this application can analyze whether the breakage is due to a sharp decline in strength of a specific batch of yarn under high temperature and humidity conditions, thereby guiding the production department to adjust environmental controls or change suppliers, solving the problem at its source. This preventative and fundamental problem-solving capability is not available in existing technologies, greatly improving the intelligence and efficiency of production management.

[0051] In some embodiments described above, the target force for yarn feeding is adjusted based on the difference between the yarn's current resistance to deformation and a preset target resistance to deformation. However, in practical applications, the acquired resistance to deformation information may be affected by various factors, such as sensor noise, environmental interference, or instantaneous fluctuations in the yarn's own characteristics, leading to uncertainty in its accuracy or reliability. If these uncertainties are not considered, and adjustments are made directly based on potentially inaccurate resistance information, it may result in unstable yarn feeding tension control and even affect the quality of the knitted fabric.

[0052] In response, this application further proposes an optimization scheme, referring to... Figure 2 , Figure 2 This is a flowchart of a method for adjusting the target force of yarn feeding according to an embodiment of the present invention. S12 includes: S121, Determine the reliability of the information regarding the ability to resist deformation; S122, adjust the target force for feeding yarn based on the information on resistance to deformation and its corresponding reliability.

[0053] Specifically, the reliability of the deformation resistance information refers to a quantitative assessment of the extent to which the currently acquired yarn deformation resistance information can be trusted or relied upon. Its purpose is to identify and quantify the potential uncertainties or errors in this information. For example, this reliability level can be a value between 0 and 1, where 1 indicates the information is completely reliable and 0 indicates the information is completely unreliable. Adjusting the target force of the yarn feeding based on the deformation resistance information and its corresponding reliability level can be understood as considering not only the difference between the deformation resistance information itself and the target information, but also its reliability. For example, when the reliability of the deformation resistance information is high, the target force can be adjusted more aggressively based on this information; while when the reliability is low, a more conservative or gradual adjustment strategy may be adopted, or even in extreme cases, the information may be temporarily ignored or a backup control strategy may be used to avoid misoperation due to unreliable information.

[0054] This application's solution effectively addresses the control instability issue caused by information uncertainty in the basic scheme by introducing an assessment of the reliability of information regarding the yarn's resistance to deformation. Specifically, after the system acquires information about the yarn's resistance to deformation, it no longer simply calculates the difference from the target value and makes adjustments. Instead, it first assesses the quality of this information to determine its reliability. This introduction of reliability makes the subsequent adjustment process of the target force more intelligent and robust. For example, a high reliability indicates that the acquired capability information is accurate and reliable, allowing the system to confidently make precise and rapid adjustments based on this information, quickly restoring the yarn tension to the target range. Conversely, a low reliability indicates that the information may be biased. In this case, a more cautious adjustment strategy is adopted, such as reducing the adjustment step size, extending the adjustment cycle, or combining other auxiliary information for comprehensive judgment, thereby avoiding over-adjustment or oscillation caused by erroneous information and ensuring the smoothness of the yarn feeding process.

[0055] Through the above technical solution, this application can significantly improve the robustness and accuracy of yarn feeding tension control in knitting machines. By evaluating the reliability of information on resistance to deformation, the system can dynamically adjust its dependence on this information and its response strategy, thereby effectively avoiding control deviations caused by factors such as sensor noise, environmental interference, or yarn characteristic fluctuations. This not only reduces the risk of yarn feeding tension fluctuations and the probability of yarn breakage, but also makes the yarn feeding process smoother, ultimately contributing to improved quality and production efficiency of knitted products. Compared to the basic solution, this application, by introducing the additional technical feature of reliability evaluation, makes the adjustment of the target force for yarn feeding more intelligent and adaptive, thus maintaining excellent control performance even in complex and changing production environments.

[0056] In some preferred embodiments, a specific example is illustrated below. Assume a knitting machine is running at high speed, requiring precise control of the feed tension of the elastic yarn. The system continuously acquires information about the yarn's resistance to deformation.

[0057] Specifically, when the system assesses that the reliability of the currently acquired information on resistance to deformation is high (e.g., above 0.8), it indicates that the information is accurate and reliable. At this point, if a significant difference is detected between the yarn's resistance to deformation information and the preset target value, the system will adjust the target force of the yarn feed directly based on this difference, with a fast response speed and a large adjustment range, thereby quickly pulling the yarn tension back to the target range and ensuring the continuity and stability of production.

[0058] Conversely, when the system assesses the reliability of the currently acquired information on resistance to deformation as low (e.g., below 0.3), it may indicate that the sensor readings have been affected by momentary interference or that the yarn condition has fluctuated abnormally. In this case, the system will not immediately make significant adjustments based on this low-reliability information, but will instead adopt a more conservative strategy. For example, the system may reduce the adjustment step size of the target force, extend the adjustment response time, or even trigger an internal warning to alert the operator or initiate further diagnostic procedures to verify the information's authenticity. In this way, even in situations of uncertainty, the system can avoid over-adjustment or control instability due to misjudgment, thereby ensuring the smooth operation of the yarn feeding process and product quality.

[0059] In some embodiments described above in this application, a degree of reliability for obtaining information on resistance to deformation was proposed. However, in practical applications, simply obtaining the degree of reliability may not fully reflect the complexity and dynamic changes of the actual state of the yarn, resulting in an inaccurate assessment of the resistance to deformation information, which in turn affects the adjustment effect of the yarn feeding target force. Therefore, reference is made to... Figure 3 , Figure 3 This is a flowchart of a method for obtaining the reliability of information on resistance to deformation provided by an embodiment of the present invention, which includes: S1211, to obtain the clarity of the yarn's response signal to disturbance; S1212, the degree of discreteness in obtaining information on the ability to resist deformation obtained from continuous measurements; S1213, Obtain the degree of matching between the current trend of deformation resistance information and the trend of preset environmental parameters; S1214, adjust the weight of each indicator based on the clarity of the yarn's response signal to disturbance, the dispersion of the deformation resistance information obtained by continuous measurement, the degree of matching between the current deformation resistance information and the preset environmental parameter change trend, as well as yarn speed, yarn batch type and historical production data. S1215, based on the adjusted weights, the clarity of the yarn's response signal to disturbance, the discreteness of the deformation resistance information obtained by continuous measurement, and the degree of matching between the current deformation resistance information and the preset environmental parameter change trend are integrated to obtain the reliability of the deformation resistance information.

[0060] Specifically, obtaining the clarity of the yarn's response signal to disturbances involves applying small, controllable disturbances to the yarn (e.g., through micro-amplitude vibrations or instantaneous tension changes) and monitoring the yarn's feedback signal to these disturbances. The clarity of the response signal can be quantified by the signal-to-noise ratio, waveform stability, or the prominence of characteristic peaks. The aim is to assess the sensitivity of the yarn to its current physical state and the accuracy of the measurement system.

[0061] The degree of dispersion in obtaining resistance to deformation information from continuous measurements can be understood as statistically analyzing the resistance to deformation information collected continuously over a period of time, calculating dispersion indices such as standard deviation, variance, or coefficient of variation. The smaller the dispersion, the more stable the measurement data and the higher its reliability. Its purpose is to reflect the inherent stability of yarn performance and the repeatability of the measurement process.

[0062] In practical applications, the degree of matching between the changing trend of the current resistance to deformation information and the changing trend of preset environmental parameters is obtained. Specifically, this involves monitoring the changing trend of the resistance to deformation information over time and comparing it with the preset or real-time changing trends of environmental parameters (such as temperature, humidity, and air pressure). For example, when the ambient temperature rises, the resistance to deformation of some elastic yarns may decrease. If the measured resistance to deformation information also shows a similar decreasing trend, it indicates a high degree of reliability. The purpose is to cross-validate the rationality of the yarn's condition changes using external environmental information.

[0063] Furthermore, based on the clarity of the yarn's response signal to disturbances, the dispersion of the deformation resistance information obtained from continuous measurements, the degree of matching between the current trend of deformation resistance information and the trend of preset environmental parameters, as well as yarn speed, yarn batch type, and historical production data, the weights of each indicator can be adjusted. For example, during high-speed production, the weight of yarn speed may be increased; for new batches of yarn, the weight of historical production data may be decreased. These weights can be dynamically adjusted based on machine learning models, expert experience, or preset rules. The aim is to flexibly allocate the importance of different evaluation indicators according to actual working conditions and yarn characteristics.

[0064] Therefore, based on the adjusted weights, and by integrating the clarity of the yarn's response signal to disturbances, the dispersion of the deformation resistance information obtained from continuous measurements, and the degree of matching between the current deformation resistance information's changing trend and the preset environmental parameter's changing trend, the reliability of the deformation resistance information can be obtained. The fusion method can employ various algorithms such as weighted averaging, fuzzy logic reasoning, or neural networks to synthesize the evaluation results from multiple dimensions into a unified reliability score.

[0065] This application's solution comprehensively and accurately assesses the reliability of deformation resistance information from multiple dimensions by considering the clarity of the yarn's response signal to disturbances, the dispersion of deformation resistance information obtained through continuous measurement, and the degree of matching between the current trend of deformation resistance information and the trend of preset environmental parameters. It also dynamically adjusts the weights of each indicator based on yarn speed, yarn batch type, and historical production data. This multi-indicator, dynamically weighted evaluation mechanism enables the system to more robustly determine the reliability of the acquired deformation resistance information when faced with yarn performance fluctuations, environmental changes, or measurement noise. It is precisely this precise assessment of reliability that makes subsequent decisions to adjust the yarn feeding target force based on this information more scientific and reasonable, avoiding misjudgments and control deviations caused by unreliable information.

[0066] The above technical solution overcomes the limitation of insufficient reliability in assessing the ability to resist deformation using a single indicator, significantly improving the accuracy and robustness of the reliability assessment. Specifically, by introducing multiple dimensions such as response signal clarity, data dispersion, and trend matching, and dynamically adjusting the weights based on production parameters, the system can more precisely capture subtle changes in yarn condition and the impact of external interference. This comprehensive assessment mechanism ensures that the information on the ability to resist deformation is highly reliable before adjusting the target force of the yarn feed, effectively avoiding the problem of unstable or inaccurate yarn feed tension control caused by information distortion, and further improving the accuracy and stability of the knitting machine's self-compensation for yarn feed tension.

[0067] In some embodiments described above, the target force for yarn feeding is adjusted based on information about the yarn's resistance to deformation in its current state and its corresponding reliability. However, in practical applications, when the reliability of this resistance information remains consistently low, adjusting the target force solely based on the reliability may not adequately address the potential risk of rapid deterioration in the yarn's actual physical state. In such cases, without deeper analysis and more careful control, instability in the yarn feeding process may occur, potentially leading to yarn breakage or a decline in product quality.

[0068] In response, this application further proposes an optimization scheme, wherein adjusting the target force for yarn feeding based on the deformation resistance information and its corresponding reliability includes: When the credibility level is consistently lower than a preset threshold, the risk assessment module is activated to analyze the current yarn speed, yarn batch type, local environmental parameters, and historical production data. Based on the analysis results, determine whether there is a potential risk of rapid deterioration of the actual physical state of the yarn; When the aforementioned potential risk exists, the target force is adjusted in a controlled, step-by-step manner, and an enhanced monitoring mode is triggered.

[0069] Specifically, "the reliability level is consistently below a preset threshold" means that within a certain period of time, such as N consecutive measurements or M seconds, the reliability level of the yarn's resistance to deformation information is consistently below a preset value. This preset threshold can be set based on yarn type, production environment, and historical experience, or it can be dynamically adjusted. When this condition is met, it indicates that the currently acquired information on the yarn's resistance to deformation may have significant uncertainty or bias, requiring a more in-depth risk assessment.

[0070] The "risk assessment module" is a standalone functional unit or integrated into the main control system, whose primary responsibility is to comprehensively analyze the potential risks of the current yarn feeding process. This module is configured to receive and process multi-source data, including current yarn speed, yarn batch type, local environmental parameters (e.g., temperature, humidity), and historical production data. The "historical production data" may include performance data of the same or similar yarn batches, yarn breakage records, quality anomaly records, etc., to provide more comprehensive background information.

[0071] In practical applications, the risk assessment module performs comprehensive analysis of the aforementioned multi-source data, such as using machine learning algorithms, statistical models, or expert systems, to "determine whether there is a potential risk of rapid deterioration of the actual physical state of the yarn." This "rapid deterioration of the actual physical state" can manifest as a sharp decrease in yarn strength, abnormal changes in elastic modulus, and an increase in the surface friction coefficient. These changes may indicate impending breakage or quality problems.

[0072] In a preferred implementation, when the risk assessment module determines that a potential risk exists, the system will no longer simply adjust the target force according to the degree of confidence, but will instead adjust the target force in a "controlled step-by-step manner." This "controlled step-by-step manner" means that the adjustment range of the target force is limited to a small range, and the adjustment speed is slow, to avoid exacerbating the yarn's instability due to excessive or sudden adjustments. Simultaneously, the system will "trigger an enhanced monitoring mode," which can increase the sensor sampling frequency, monitor more dimensions of yarn parameters (such as vibration and surface defects), and initiate additional diagnostic procedures to more precisely track changes in the yarn's state, providing more accurate data support for subsequent decision-making.

[0073] This application's solution effectively addresses the potential blindness and lag issues of traditional adjustment methods when the reliability of information regarding resistance to deformation remains consistently low, by introducing a risk assessment module and a tiered response mechanism. Specifically, when the reliability level consistently falls below a preset threshold, the system no longer relies solely on a single reliability indicator but activates the risk assessment module to comprehensively analyze current yarn speed, yarn batch type, local environmental parameters, and historical production data. This multi-dimensional data fusion analysis enables the system to more comprehensively and accurately identify potential risks of rapid deterioration in the actual physical state of the yarn, thereby avoiding misjudgments or insufficient responses due to information uncertainty. Once a potential risk is identified, the system adopts a "controlled step-by-step adjustment of the target force," avoiding the negative impacts of potentially aggressive adjustments and ensuring the smoothness of the yarn feeding process. Simultaneously, the "trigger-enhanced monitoring mode" ensures the acquisition of more detailed and real-time yarn status data during risk periods, providing a solid data foundation for subsequent precise control and intervention.

[0074] Through the above technical solution, this application can significantly improve the robustness and predictability of yarn feeding tension control in knitting machines. In complex operating conditions where the reliability of yarn condition information is low, the system no longer passively waits for problems to occur, but can proactively identify and assess potential risks of yarn physical condition deterioration. This proactive risk management mechanism, combined with controlled target force adjustment and enhanced monitoring, effectively reduces the risk of yarn breakage caused by sudden changes in yarn condition, reduces downtime, and improves production efficiency and product quality. Furthermore, through comprehensive analysis of multi-source data, this solution can more accurately understand yarn behavior, providing strong support for achieving smarter and more reliable knitting production.

[0075] In some preferred embodiments, a specific example is given below. Suppose that during the knitting machine production process, the system continuously monitors and detects that the reliability of information regarding the resistance to deformation of a certain elastic yarn is below a preset threshold of 0.6 for 10 consecutive seconds. At this time, the main control system immediately activates the risk assessment module. This risk assessment module collects the current yarn feed speed (e.g., 500 m / min), yarn batch type (e.g., polyester core-spun yarn, batch number XYZ123), local environmental parameters (e.g., temperature 28°C, humidity 65%), and retrieves historical production data of this batch of yarn or similar yarns from the database, including past yarn breakage frequency, tension fluctuation range, and performance under similar environmental conditions. The risk assessment module performs a comprehensive analysis of this data. For example, it may find that this batch of yarn has a high breakage tendency in historical data, especially under the current speed and humidity conditions. Based on this analysis, the risk assessment module determines that there is a potential risk of rapid deterioration of the actual physical state of the yarn. Once a potential risk is identified, the system will no longer drastically adjust the target force for yarn feeding according to the conventional algorithm. Instead, it will fine-tune the target force in a controlled, step-by-step manner, for example, by 0.5 cN every 5 seconds, limiting the single adjustment to no more than 2 cN. Simultaneously, the system will immediately trigger an enhanced monitoring mode, increasing the sampling frequency of the yarn tension sensor from the conventional 100Hz to 500Hz and activating an additional optical sensor to scan the yarn surface in real time to detect early defects such as fuzzing and abrasion. In this way, the system can adopt a more cautious and precise control strategy before potential risks escalate into actual problems, effectively preventing yarn breakage and ensuring production continuity and product quality.

[0076] In some embodiments of this application, a risk assessment module is activated when the reliability of the deformation resistance information remains below a preset threshold. However, in actual production environments, factors such as yarn speed, yarn batch type, and local environmental parameters change dynamically. If a fixed preset threshold is used to determine the reliability, it may not accurately reflect the true risk level under the current operating conditions, resulting in inaccurate activation timing of the risk assessment module and affecting the system's response efficiency and stability. For example, during high-speed production or when handling specific vulnerable yarn batches, a higher tolerance for reliability may be required, and a fixed threshold may lead to false alarms; conversely, during low-speed or stable production, a fixed threshold may be too lenient, failing to detect potential risks in a timely manner.

[0077] In response, this application further proposes an optimization scheme, wherein when the credibility level remains below a preset threshold, a risk assessment module is activated, including: The evaluation threshold for the reliability of the deformation resistance information is dynamically adjusted based on the yarn speed, the yarn batch type, and the local environmental parameters. When the credibility level remains below the dynamically adjusted assessment threshold, the risk assessment module is activated.

[0078] Specifically, the assessment threshold for dynamically adjusting the reliability of the deformation resistance information refers to the system's real-time monitoring and acquisition of the current yarn speed, yarn batch type, and local environmental parameters. Yarn speed can be understood as the speed at which the yarn moves during feeding, affecting the dynamic stress on the yarn. Yarn batch type refers to yarns of different materials, structures, or production batches, which may have different physical properties and sensitivities to deformation resistance. Local environmental parameters include, but are not limited to, external conditions such as temperature and humidity that may affect yarn performance. Based on these dynamically changing parameters, the system calculates an assessment threshold that matches the current operating conditions using a preset algorithm model or lookup table. For example, when the yarn speed is high or the yarn batch is of a vulnerable type, the assessment threshold may be appropriately increased to allow for a wider range of reliability fluctuations and avoid oversensitivity; conversely, when the yarn speed is low or the yarn batch is stable, the assessment threshold may be decreased to increase sensitivity to potential risks.

[0079] This application's solution addresses the problem of poor applicability of fixed thresholds under varying operating conditions by introducing a mechanism for dynamically adjusting the evaluation threshold. Specifically, when yarn speed, yarn batch type, and local environmental parameters change, the system can intelligently adjust the evaluation threshold for the reliability of deformation resistance information based on this real-time information. For example, in high-speed production mode, the yarn may experience more instantaneous disturbances, leading to increased instantaneous fluctuations in reliability. If a fixed threshold from the low-speed mode is still used, the risk assessment module may be frequently triggered, resulting in wasted resources and unnecessary intervention. By dynamically increasing the evaluation threshold, the system can tolerate normal fluctuations within a certain range, avoiding false alarms. Conversely, when dealing with known vulnerable yarn batches, even a small decrease in reliability may indicate a greater risk. In this case, by dynamically lowering the evaluation threshold, the system can detect potential anomalies earlier and more sensitively, thereby promptly initiating the risk assessment module for in-depth analysis. This dynamic adjustment mechanism makes the activation conditions of the risk assessment module more closely aligned with actual operating conditions, improving the system's decision-making accuracy and response efficiency.

[0080] Through the above technical solution, this application can adaptively adjust the triggering conditions of the risk assessment module according to the actual production environment and yarn characteristics. This significantly improves the system's perception accuracy and robustness in detecting changes in yarn state, effectively avoiding false alarms or missed alarms caused by fixed thresholds. Therefore, the risk assessment module is activated more promptly and accurately, enabling the system to intervene before potential risks become actual problems, thereby improving the stability and safety of the knitting machine's yarn feeding process, reducing the incidence of yarn breakage accidents, and optimizing production efficiency.

[0081] In some preferred embodiments, a specific example is given below. Suppose that a knitting machine needs to process two different types of elastic yarns: one is standard polyester yarn (batch A), and the other is a high-elastic spandex blended yarn (batch B), and the machine may operate at two speeds: low speed (500 rpm) and high speed (1000 rpm).

[0082] In traditional approaches, a fixed confidence level assessment threshold might be set, such as 0.8.

[0083] However, in the scheme of this application, the evaluation threshold is dynamically adjusted.

[0084] Specifically: When processing standard polyester yarn (batch A) and running at low speed (500 rpm), the system dynamically adjusts the evaluation threshold to 0.75 based on yarn speed, yarn batch type, and local environmental parameters. This means that the risk assessment module is only activated when the confidence level remains below 0.75.

[0085] When processing high-elastic spandex blended yarn (batch B) and running at high speed (1000 rpm), the system dynamically adjusts the assessment threshold to 0.85 because high-elastic yarn is more prone to instantaneous fluctuations at high speeds and has a higher risk of breakage. At this point, even if the confidence level decreases slightly, as long as it does not fall below 0.85, the system will not immediately activate the risk assessment module, thus avoiding false alarms caused by normal fluctuations.

[0086] Conversely, if processing high-elastic spandex blended yarn (batch B) but running at a low speed (500 rpm), the system may adjust the evaluation threshold to 0.80. This is slightly higher than the threshold when processing polyester yarn, reflecting the inherently higher risk of high-elastic yarn, which requires more careful monitoring even at low speeds.

[0087] Through this dynamic adjustment, the system can flexibly set the sensitivity of risk assessment according to specific operating conditions. For example, when processing high-elasticity yarn at high speeds, the system can tolerate greater fluctuations in reliability, avoiding frequent triggering of risk assessments due to normal production fluctuations; while when processing standard yarns or operating at low speeds, the system can maintain appropriate sensitivity to ensure timely detection of potential problems. This adaptive threshold adjustment mechanism significantly improves the accuracy and efficiency of risk assessment, reduces unnecessary interventions, and enhances the overall stability of production.

[0088] In some embodiments of this application, when the reliability of the yarn's resistance to deformation information is consistently lower than the dynamically adjusted evaluation threshold, a risk assessment module is activated to analyze potential risks. However, after the risk assessment module is activated, its operational status and reliability are not adequately monitored and guaranteed. If the risk assessment module malfunctions during operation, such as processor overload, memory overflow, or data processing interruption, the risk assessment results may be inaccurate or delayed, thereby affecting the correct adjustment of the yarn feeding target force and even causing production accidents such as yarn breakage. Therefore, this application further proposes a scheme to continuously monitor the operational status of the risk assessment module after its activation and to take corresponding emergency measures.

[0089] In this regard, this application further proposes the following steps after the risk assessment module is activated: After the risk assessment module is started, an independent operation status monitoring unit is started. The monitoring unit continuously acquires the processor usage of the risk assessment module, the memory usage of the risk assessment module, the completion status of the key internal data processing flow of the risk assessment module, and the communication link status between the risk assessment module and the external data source. An abnormal alarm is triggered when the processor usage continues to exceed a preset range, the memory usage reaches a preset limit, the critical internal data processing flow is not completed within a specified time, or the communication link is interrupted. Record the operational status information of the risk assessment module; Switch to emergency control mode, in which the target force for yarn feeding is adjusted using preset conservative parameters.

[0090] Specifically, the independent operational status monitoring unit is designed as a lightweight, high-priority software or hardware module. Its main responsibility is to monitor the internal health and external interaction status of the risk assessment module in real time and continuously. Processor usage refers to the utilization rate of the central processing unit resources by the risk assessment module within a specific time period. A sustained exceedance of this threshold may indicate that the module is stuck in an infinite loop or is overloaded. Memory usage refers to the memory space occupied by the risk assessment module during operation; reaching a preset upper limit may indicate memory leaks or resource exhaustion. The completion marker of the key internal data processing flow refers to the phased or final completion status of the core algorithm or data analysis task within the risk assessment module. Failure to complete within a specified time indicates potential processing stagnation or inefficiency. The communication link status refers to the connectivity and stability of the data transmission channel between the risk assessment module and sensors, databases, or other control systems; an interruption signifies obstruction of data input or output.

[0091] When any of the above-mentioned abnormal conditions are detected by the monitoring unit and persist, the system will immediately trigger an alarm, such as through audible and visual signals, interface prompts, or remote notifications, to alert the operator. Simultaneously, the operational status information of the risk assessment module, including the time, type, duration of the abnormality, and relevant system parameters, will be recorded in detail for subsequent fault diagnosis and system optimization. Furthermore, to prevent potential risks from escalating, the system will automatically switch to emergency control mode. In this mode, the adjustment of the yarn feeding target force will no longer rely entirely on the output of the risk assessment module, but will be adjusted using preset conservative parameters, such as setting the yarn feeding tension within a relatively stable and safe range to minimize yarn breakage or overstretching, ensuring production continuity and yarn quality.

[0092] This application's solution addresses the issue of insufficient reliability in the risk assessment module by introducing an independent operational status monitoring unit. Once the risk assessment module is activated, the monitoring unit continuously tracks its key operational indicators in real time, including processor usage, memory consumption, completion markers of critical data processing flows, and communication link status. This continuous monitoring mechanism ensures that the risk assessment module's healthy operation remains within a controllable range. If any indicator consistently exceeds a preset safety threshold, it indicates a potential functional abnormality or performance degradation in the risk assessment module. In this case, the system can quickly trigger an alarm and immediately record detailed operational status information, providing a basis for subsequent troubleshooting. More importantly, by promptly switching to emergency control mode and adjusting the yarn feeding target force using preset conservative parameters, this application's solution can quickly take preventative measures when the risk assessment module itself malfunctions, avoiding errors in the yarn feeding control strategy due to the module's failure, thereby effectively preventing serious production accidents such as yarn breakage. It is precisely because of this proactive management and emergency response mechanism for the reliability of the risk assessment module itself that the entire yarn feeding tension self-compensation system can maintain higher robustness and safety when facing complex and ever-changing production environments and potential internal system failures.

[0093] Through the above technical solution, this application significantly improves the overall reliability and safety of the knitting machine yarn feeding tension self-compensation system. When the risk assessment module is activated to address potential yarn risks, this solution effectively compensates for the lack of effective monitoring of the risk assessment module's own operating status in existing technologies through an independent operating status monitoring unit. This enables the system to promptly detect and respond to potential internal faults or performance bottlenecks in the risk assessment module, avoiding misjudgments or control failures caused by the module's malfunction. Specifically, when abnormal conditions such as processor overload, memory overflow, critical process stagnation, or communication interruption are detected, the system can quickly trigger an alarm and switch to emergency control mode, adopting conservative yarn feeding parameters. This ensures that even when the risk assessment module itself malfunctions, production stability and yarn quality are maintained, minimizing production risks caused by internal system faults. This self-protection and emergency handling capability of the core decision-making module greatly enhances the fault tolerance and continuous operational stability of the entire yarn feeding control system under complex operating conditions.

[0094] In some preferred embodiments, assuming the knitting machine is running at high speed, the reliability of the yarn's resistance to deformation information continuously falls below the dynamically adjusted assessment threshold due to environmental disturbances, thus activating the risk assessment module. At this time, an independent operational status monitoring unit is activated and begins continuously monitoring the operational status of the risk assessment module. Specifically, the monitoring unit samples the processor usage and memory usage of the risk assessment module every 50 milliseconds. At a certain moment, the monitoring unit detects that the processor usage of the risk assessment module exceeds the preset upper limit of 95% for one second consecutively, and the completion flag of its critical internal data processing flow fails to update within a specified time (e.g., 200 milliseconds). The monitoring unit immediately determines that the risk assessment module is malfunctioning and triggers an audible and visual alarm. Simultaneously, the system automatically records detailed information about this abnormal event, including the time of occurrence, the type of abnormality (processor overload and processing flow stagnation), and the system parameters at the time. To prevent yarn feeding tension control errors due to risk assessment module failure, the system quickly switches to emergency control mode. In this mode, the target force for yarn feeding is adjusted to a preset conservative value. For example, the yarn feeding tension is set to the average tension value corresponding to the yarn batch type, allowing for a small fluctuation range. This ensures that the yarn continues to be fed under a relatively safe tension until operator intervention or the risk assessment module returns to normal. In this way, even if the risk assessment module itself malfunctions, it can effectively avoid serious impacts on production.

[0095] In some of the embodiments described above in this application, an independent operational status monitoring unit is activated after the risk assessment module is started. However, simply activating this monitoring unit may not be sufficient to ensure its own real-time operational stability and effective monitoring capability of the risk assessment module. In the initial stage of the monitoring unit's activation, there may be issues such as resource allocation delays, internal function initialization failures, or unstable communication links with the monitored module. These potential initial defects may prevent the monitoring unit from obtaining the operational status of the risk assessment module in a timely and accurate manner, thereby affecting the risk warning and control effectiveness of the entire system.

[0096] In this regard, this application further proposes the following steps for activating an independent operational status monitoring unit after the risk assessment module is started: The operation status monitoring unit performs a resource reservation operation to pre-allocate processor time slices and memory space for itself. When the operation status monitoring unit executes the self-test program, it verifies the connectivity of the core functions and communication links; The operation status monitoring unit performs preliminary sampling of the processor usage and memory usage of the risk assessment module; When the sampled data exceeds the preset instantaneous fluctuation range, an early anomaly warning is triggered and the full monitoring function is activated.

[0097] Specifically, upon startup, the operational status monitoring unit proactively performs resource reservation operations. This means that the monitoring unit requests and pre-allocates necessary processor time slices and memory space from the system to ensure it has stable computing resources when performing monitoring tasks, avoiding performance degradation or response delays caused by system resource contention. For example, a higher running priority can be assigned to the monitoring unit, and a certain amount of dedicated memory area can be reserved to ensure that its critical monitoring tasks can be completed in a timely and efficient manner.

[0098] When the operational status monitoring unit executes its self-test procedure, its purpose is to verify its core functions and the connectivity of its communication links with external systems. This includes checking whether its internal data acquisition module, data processing module, and data interfaces with the risk assessment module or other main control systems are functioning correctly. For example, the monitoring unit can send internal test signals and verify reception, or check the status registers of key hardware components to ensure that it is in a healthy and usable state.

[0099] In practical applications, the runtime monitoring unit performs initial sampling of the processor usage and memory usage of the risk assessment module. This initial sampling is a fast, high-frequency monitoring method designed to capture any sudden anomalies in resource consumption that may occur during the initial startup of the risk assessment module. For example, the monitoring unit can continuously acquire its CPU utilization and memory allocation at millisecond-level frequencies for a short period after the risk assessment module starts up.

[0100] The preset instantaneous fluctuation range refers to the short-term, non-continuous range of changes in processor usage and memory consumption allowed during the normal startup and initialization of the risk assessment module. When the initial sampled data exceeds this preset instantaneous fluctuation range, it indicates that the risk assessment module may have an anomaly in the initial startup phase, such as program crashes, resource leaks, or infinite loops. At this time, the monitoring unit will immediately trigger an early anomaly warning, a rapid-response alarm mechanism designed to alert system administrators or the main control system to potential problems. Simultaneously, the monitoring unit will activate full monitoring functions, switching from the initial sampling mode to a more comprehensive and in-depth continuous monitoring mode to obtain more detailed operational status data, providing a basis for subsequent fault diagnosis and handling.

[0101] The proposed solution ensures the stability and reliability of the independent operational status monitoring unit by enabling it to proactively perform resource reservation operations and self-check procedures upon startup. This avoids monitoring failures caused by unit malfunctions or insufficient resources. Simultaneously, by performing preliminary sampling and setting instantaneous fluctuation ranges for the risk assessment module, the monitoring unit can detect potential instantaneous anomalies at the initial startup stage of the risk assessment module, triggering early warnings before problems escalate into serious malfunctions. This mechanism effectively compensates for the potential lag in routine monitoring only after the risk assessment module starts, enhancing the system's ability to detect anomalies in the risk assessment module's operational status early.

[0102] Through the above technical solution, the independent operation status monitoring unit can proactively ensure the integrity of its own operating resources and functions upon startup, avoiding monitoring blind spots caused by problems with the monitoring unit itself. More importantly, by introducing preliminary sampling and early anomaly warning mechanisms, the system can quickly respond to and identify instantaneous anomalies in the risk assessment module at the initial startup stage, thereby significantly improving the system's early warning capability and response speed for potential risks. This helps to discover and resolve potential software or hardware problems before the risk assessment module has fully entered a stable operating state, ensuring the accuracy and reliability of subsequent risk assessments, and further enhancing the overall robustness of the knitting machine yarn feeding tension self-compensation and yarn breakage rapid positioning method.

[0103] In some preferred embodiments, it is assumed that the main control system of the knitting machine activates the risk assessment module when it detects that the reliability of the yarn's resistance to deformation information is consistently below a preset threshold. At this time, an independent operation status monitoring unit also activates. This monitoring unit first requests and successfully reserves 50MB of memory space and a high-priority CPU time slice from the operating system to ensure stable operation of its monitoring task. Subsequently, the monitoring unit executes a self-test program to verify that its internal data acquisition sensors and communication interface with the risk assessment module are in normal working order. Next, the monitoring unit begins preliminary sampling of the processor utilization and memory usage of the risk assessment module 10 times per second. Three seconds after startup, the monitoring unit detects that the processor utilization of the risk assessment module momentarily spikes to 95% and lasts for 0.5 seconds, far exceeding the preset instantaneous fluctuation range (e.g., the instantaneous peak should not exceed 70% during normal startup). The monitoring unit immediately triggers an early anomaly warning and displays "Risk assessment module startup anomaly: CPU instantaneous overload" on the main control system interface. Simultaneously, the monitoring unit automatically activated its full monitoring functions to track the various operational indicators of the risk assessment module in greater detail and prepare to switch to emergency control mode if necessary. This early warning mechanism allows operators to intervene promptly, check the risk assessment module's startup logs, identify and resolve potential configuration errors or resource conflicts that could cause momentary overload, thereby preventing more serious failures that might occur in the risk assessment module during subsequent operation.

[0104] In some embodiments of this application described above, when the reliability of the information regarding resistance to deformation consistently falls below a preset threshold, a risk assessment module is activated. This module analyzes the current yarn speed, yarn batch type, local environmental parameters, and historical production data to determine if there is a potential risk of rapid deterioration in the actual physical state of the yarn. However, if the historical production data contains noise, outliers, or missing values, and these are not effectively processed, the accuracy and reliability of the risk assessment may be reduced, thereby affecting the correct assessment of potential yarn risks and the effectiveness of subsequent adjustments to the target force of yarn feeding.

[0105] In this regard, this application further proposes that the steps of analyzing the current yarn speed, yarn batch type, local environmental parameters, and historical production data through the risk assessment module include: Obtain the historical production data; The values ​​in the historical production data are checked for interval boundaries to identify outliers that exceed the physical reasonable range, and the time series data in the historical production data are smoothed to reduce the impact of instantaneous noise. Based on the trend of adjacent data or the average value of similar yarn batches, the missing values ​​in the historical production data are filled in to obtain the processed historical production data. Based on the processed historical production data and the degree to which the historical production data has been cleaned and filled, the weight of the historical production data in the risk assessment is adjusted, and the potential risks of the actual physical state of the yarn are analyzed in combination with the current yarn speed, the yarn batch type, and the local environmental parameters.

[0106] Specifically, acquiring historical production data refers to retrieving historical information related to the yarn production process from system databases, sensor logs, or other data storage media. This includes past yarn feeding tension, yarn breakage frequency, ambient temperature and humidity, yarn batch characteristics, and equipment operating status. This historical data provides important reference and background information for assessing the potential risks of the current yarn condition.

[0107] Among these measures, range boundary verification is performed on historical production data to ensure the physical reasonableness of the data. For example, yarn tension values ​​should not be negative, and ambient temperature values ​​should be within a preset reasonable environmental range. Values ​​exceeding these preset physical boundaries will be identified as outliers, which may be caused by sensor malfunctions, data acquisition errors, or extreme operating conditions. Simultaneously, time-series data in historical production data undergoes smoothing processing, such as using algorithms like moving averages, exponential smoothing, or Kalman filtering. This effectively filters out instantaneous noise and random fluctuations in the data, making the data trends clearer and more stable, and avoiding misjudgments caused by short-term, non-essential fluctuations.

[0108] In practical applications, filling in missing values ​​in historical production data based on trends in adjacent data or average values ​​of similar yarn batches is essential to ensure data integrity and continuity. For example, if tension data for a certain point in time is missing, it can be supplemented by linear interpolation or polynomial fitting based on tension data from points before and after it, or by filling in the missing data based on the historical average tension of the same batch of yarn under similar production conditions. This method yields cleaned, smoothed, and filled historical production data, providing a high-quality and complete data foundation for subsequent risk assessments.

[0109] Furthermore, based on the processed historical production data and the degree to which it has been cleaned and filled, the weight of historical production data in risk assessment is adjusted. This means that the higher the data quality (i.e., the better its completeness, accuracy, and reliability after cleaning and filling), the greater its reference value in risk assessment, and its weight should be increased accordingly. Conversely, if the data quality is low, such as with too many missing values ​​or outliers that are difficult to completely correct, its weight should be reduced to avoid negatively impacting the risk assessment results. Finally, by combining the current yarn speed, yarn batch type, and local environmental parameters, a comprehensive analysis of the potential risks in the actual physical state of the yarn is conducted to arrive at a more accurate and reliable risk judgment.

[0110] This application's solution addresses the potential noise, outliers, and missing values ​​in raw historical production data through systematic preprocessing, significantly improving data quality and the accuracy of risk assessment. Specifically, interval boundary verification and outlier identification effectively eliminate data that does not conform to physical laws or is caused by errors, preventing interference with risk assessment. Smoothing of time-series data filters out instantaneous noise, making data trends more realistic and reliable, and helping to identify long-term trends rather than short-term fluctuations. Missing value imputation ensures the integrity of the data chain, enabling the risk assessment model to analyze based on more comprehensive and uninterrupted information. Furthermore, the weight of historical production data in risk assessment is dynamically adjusted according to the degree of data cleaning and imputation, allowing high-quality data to play a greater role while appropriately mitigating the impact of low-quality data, thus ensuring the accuracy and robustness of risk assessment. Therefore, the risk assessment module can conduct a more precise analysis of the potential risks of the actual physical state of the yarn based on more reliable and comprehensive historical data combined with current real-time parameters.

[0111] Through the above technical solutions, this application can significantly improve the accuracy and reliability of risk assessment. By systematically cleaning, smoothing, and filling historical production data, interference factors in the data are effectively eliminated, and data gaps are compensated for, so that risk assessment is no longer limited by the quality defects of the original data. Furthermore, by dynamically adjusting the weights according to the degree of data processing, the risk assessment model can more intelligently utilize historical information and avoid misjudgments caused by low-quality data. This enables the system to identify potential risks of rapid deterioration of the actual physical state of the yarn earlier and more accurately, thereby enabling timely and controlled adjustments to the target force and triggering of enhanced monitoring modes, effectively preventing yarn breakage or quality problems, and ensuring the stability and efficiency of knitting production.

[0112] In some preferred embodiments, a specific example is given below. Suppose that during a certain production cycle, the knitting machine system needs to perform a risk assessment on elastic yarn. First, the system retrieves historical production data from a historical database, including yarn tension, breakage frequency, ambient temperature, and humidity for the past month. Among the retrieved data, several days of tension data are found to be negative, which clearly exceeds the physically reasonable range. The system identifies these negative values ​​as outliers through interval boundary checks and marks or removes them. Simultaneously, the ambient temperature data at certain points in time exhibits drastic and short-lived fluctuations, which are considered transient noise. The system uses a 5-point moving average filter to smooth the time series data of ambient temperature to eliminate the influence of these transient noises, making the temperature trend more stable. Furthermore, due to sensor malfunction, several hours of humidity data are completely missing. The system fills in the missing data using linear interpolation based on the humidity data adjacent to the missing time points, or, if historical data from other production lines for this batch of yarn is available, uses the average humidity of similar yarn batches.

[0113] After the above processing, the system obtains "processed historical production data" that has been cleaned, smoothed, and filled. At this point, the system evaluates the degree of cleaning and filling of this data. For example, if data for a certain period is severely missing and difficult to fill, or if there are many outliers, the weight of historical data for that period in the risk assessment will be appropriately reduced. Conversely, if the data quality is high and the cleaning and filling are well done, its weight will be increased accordingly. Finally, the risk assessment module combines this weighted historical production data with currently acquired real-time yarn speed, yarn batch type, and local environmental parameters (such as current temperature and humidity) for comprehensive analysis. For example, if historical data shows that the breakage risk of a certain batch of yarn increases significantly under specific temperature and humidity conditions, and the current environmental parameters are similar to historical high-risk conditions, even if the current yarn tension fluctuation is not large, the system will determine that there is a potential risk of rapid deterioration of the actual physical state of the yarn, and accordingly adjust the target force for yarn feeding and trigger an enhanced monitoring mode.

[0114] In some embodiments of this application, when the reliability of the yarn's resistance to deformation information remains below a preset threshold, a risk assessment module is activated to analyze potential risks. However, in practical applications, simply activating the risk assessment module does not guarantee that it is immediately fully ready. There may be delays in module initialization, incomplete self-checks of internal components, or incomplete establishment of data interfaces. If these issues are not addressed, the main control system may send data or rely on its output before the risk assessment module is ready, leading to data processing errors, inaccurate assessment results, or system response delays, affecting the timeliness and accuracy of adjusting the yarn feeding target force.

[0115] In response, this application further proposes that after the risk assessment module is started, a readiness query signal is sent to the risk assessment module through the main control system; after receiving the readiness query signal, the risk assessment module checks whether the key initialization tasks inside the risk assessment module have been completed. The key initialization tasks include self-testing of the core processing unit, connectivity verification of the data interface, and loading of initial parameters; based on the check results, the risk assessment module returns a readiness status signal to the main control system, indicating whether the risk assessment module is ready to receive tasks or provide output; after receiving the readiness status signal returned by the risk assessment module, and the readiness status signal indicating that the risk assessment module is ready, the main control system sends data to the risk assessment module or relies on the output of the risk assessment module to perform subsequent adjustment of the yarn feeding target force.

[0116] Specifically, after activating the risk assessment module, the main control system proactively sends a readiness query signal to detect the current operating status of the risk assessment module. Upon receiving this readiness query signal, the risk assessment module immediately performs a series of internal checks to ensure that its core functions are fully activated and ready. These key initialization tasks include: self-testing of the core processing unit to verify whether hardware resources such as the processor and memory are functioning correctly; connectivity verification of the data interface to ensure unobstructed data transmission channels between the module and external data sources (such as sensors and historical databases) and the main control system; and loading of initial parameters to ensure that the configuration information such as the algorithm model and thresholds required for risk assessment are correctly loaded. After completing all checks, the risk assessment module generates a readiness status signal based on the check results and returns it to the main control system. This readiness status signal explicitly indicates whether the risk assessment module is ready to receive further task instructions or provide risk assessment results. Only when the main control system receives the readiness status signal indicating that the risk assessment module is ready will it begin sending specific yarn physical state information, local environmental parameters, and other data to the module, or adjust the subsequent yarn feeding target force based on the output of the risk assessment module.

[0117] This application's solution effectively addresses potential readiness issues after the risk assessment module starts by introducing a "handshake" mechanism between the main control system and the risk assessment module. Specifically, the sending of a readiness query signal and the return of a readiness status signal establish a clear communication protocol, ensuring that the main control system can accurately determine the operational status of the risk assessment module before data interaction. Key initialization tasks such as the core processing unit's self-test, data interface connectivity verification, and initial parameter loading comprehensively guarantee the reliability and data processing capabilities of the risk assessment module from both internal functionality and external connectivity perspectives. It is precisely this two-way confirmation mechanism that allows the main control system to avoid sending data to an unready module, thereby preventing potential errors or system instability caused by inconsistent module states.

[0118] Through the above technical solution, this application ensures that the risk assessment module is always in optimal working condition when invoked, significantly improving the accuracy and timeliness of risk assessment. This solution effectively avoids assessment errors or system failures caused by incomplete module initialization, thereby enhancing the robustness and reliability of the entire yarn feeding tension self-compensation and yarn breakage rapid positioning method. Furthermore, through explicit readiness status feedback, the main control system can more intelligently schedule tasks and optimize resource utilization, further improving the stability and efficiency of the knitting machine production process.

[0119] In some preferred embodiments, when the confidence level remains below a preset threshold, the main control system sends a specific readiness query message to the risk assessment module, such as a data packet containing the "CMD_READY_QUERY" instruction. Upon receiving this message, the risk assessment module immediately initiates its internal initialization sequence. For example, it first performs self-tests on core processing units such as CPU register checks and memory integrity tests; then, it attempts to establish communication with connected sensor interfaces and historical database interfaces to verify the connectivity of the data link; finally, it loads the preset risk assessment algorithm model and relevant threshold parameters. Once all these tasks are successfully completed, the risk assessment module returns a readiness status message to the main control system, such as a response containing "STATUS_READY_OK". Only after the main control system receives this "STATUS_READY_OK" response will it begin sending data such as the current yarn speed, yarn batch type, local environmental parameters, and historical production data to the risk assessment module for analysis, and wait for its output risk assessment results to guide subsequent adjustments to the yarn feeding target force. If the risk assessment module returns "STATUS_READY_FAIL" or other error codes, the main control system will trigger an exception handling process, such as retrying the query, logging errors, or switching to standby mode, to ensure the stable operation of the system.

[0120] refer to Figure 4 , Figure 4 This is a schematic diagram of a rapid positioning system for elastic yarn provided in an embodiment of the present invention. The system includes: The detection end is used to acquire information about the yarn's ability to resist deformation in its current state; and to adjust the target force for feeding the yarn based on the difference between the information about the ability to resist deformation and the preset target information about the ability to resist deformation. The adjustment end is used to adjust the yarn feeding amount according to the adjusted target force to maintain the instantaneous force of the yarn consistent with the adjusted target force; when the yarn breakage is detected, the yarn physical state information, local environmental information and yarn force fluctuation information before and after the breakage are acquired. The processing unit is used to analyze the cause of yarn breakage based on the yarn physical state information, the local environment information, and the yarn force fluctuation information.

[0121] The system proposed in this application integrates deep sensing of yarn physical state, adaptive adjustment of yarn feeding tension, and intelligent analysis of yarn breakage causes into different functional modules, forming a collaborative whole. The detection end is responsible for acquiring information on the yarn's resistance to deformation in real time and making preliminary mechanical parameter adjustments; the adjustment end precisely controls the yarn feeding amount according to the adjusted target force and comprehensively collects relevant data when yarn breakage occurs; the processing end performs in-depth analysis of this data to reveal the root cause of yarn breakage. This modular design enables the system to effectively compensate for the viscoelastic drift of elastic yarn caused by environmental changes, fundamentally solving the blind spots in elastic yarn tension control and the inadequacy of yarn breakage cause analysis in existing technologies, thereby significantly improving the stability of knitting production and product quality.

[0122] In some embodiments of this application, the various functional modules of the above system can be specifically implemented.

[0123] Specifically, the detection end can be configured as an intelligent module integrating multiple sensors. For example, it can include a laser diameter gauge for real-time measurement of yarn diameter and a micro-tension sensor for sensing minute tension changes. Alternatively, the detection end can be a distributed sensor network, where multiple independent sensor units, such as acoustic or vibration sensors, aggregate data through a communication interface to a local processing unit responsible for initial data integration and preprocessing. The aforementioned detection end is used to acquire information about the yarn's resistance to deformation in its current state and adjust the target force for yarn feeding based on the difference between this resistance information and a preset target resistance information. For details on acquiring the yarn's resistance to deformation information and adjusting the target force for yarn feeding, please refer to the relevant descriptions in the foregoing embodiments of this application; further details will not be repeated here.

[0124] Furthermore, the adjustment end can be a control module integrating a servo motor controller and a data acquisition unit. For example, the adjustment end can receive adjustment commands from the detection end and drive the servo motor of the yarn feeder to precisely adjust the yarn feeding amount. Simultaneously, the adjustment end can also be connected to a yarn breakage sensor, an environmental sensor, and other physical state sensors to quickly acquire yarn physical state information, local environmental information, and yarn force fluctuation information before and after the breakage when yarn breakage is detected. As a preferred embodiment, the adjustment end can be a controller with edge computing capabilities, capable of performing partial data preprocessing and real-time control response locally. The aforementioned adjustment end is used to adjust the yarn feeding amount according to the adjusted target force to maintain the instantaneous force of the yarn consistent with the adjusted target force; and to acquire yarn physical state information, local environmental information, and yarn force fluctuation information before and after the breakage when yarn breakage is detected. For specific methods of adjusting the yarn feeding amount and acquiring information before and after breakage, please refer to the relevant descriptions in the foregoing embodiments of this application, which will not be repeated here.

[0125] Furthermore, the processing end can be an independent computing unit, such as an industrial computer or embedded processor, running specialized analysis software and algorithms. This processing end can receive yarn physical state information, local environmental information, and yarn force fluctuation information from the adjustment end or other data sources, and perform deep analysis using a preset rule base or a trained machine learning model. Alternatively, the processing end can be a service module on a cloud server, receiving data and providing analysis results via a network. The aforementioned processing end is used to analyze the causes of yarn breakage based on the yarn physical state information, the local environmental information, and the yarn force fluctuation information. Specific methods for analyzing the causes of yarn breakage can be referred to the relevant descriptions in the foregoing embodiments of this application, and will not be repeated here.

[0126] Compared with existing technologies, the core innovation of the rapid positioning system for elastic yarn proposed in this application lies in its ability to achieve deep perception and adaptive control of the inherent physical state of elastic yarn through structured functional modules, as well as intelligent analysis of the causes of yarn breakage. Existing technologies mainly rely on feedback control of the instantaneous tension of the yarn. This method cannot fundamentally guarantee the stability of the actual physical state of the yarn at the weaving point when the viscoelastic properties of elastic yarn drift due to environmental influences, thus leading to fabric quality defects and "unexplained" yarn breakage.

[0127] This application's system acquires real-time information on the yarn's resistance to deformation through a detection end, overcoming the limitations of traditional tension control. For example, in traditional systems, even if the yarn becomes stiffer due to environmental changes, the system still tries to maintain the tension at a preset value, which may result in insufficient actual elongation of the yarn at the knitting point, affecting loop formation. However, the detection end of this application can sense changes in the yarn's resistance to deformation and guide the adjustment end to adjust the target force of the yarn feed accordingly, ensuring that the physical state of the yarn at the knitting point is always within the optimal range. This self-compensation mechanism significantly improves the stability of the knitting process and the consistency of fabric quality.

[0128] Furthermore, the system of this application also demonstrates significant advantages in handling yarn breakage. Traditional yarn breakage systems can only achieve rapid location and shutdown, but cannot provide the underlying cause of the breakage. The adjustment end of this application comprehensively acquires multi-dimensional information when a yarn breakage occurs, and the processing end performs in-depth analysis, revealing the root cause behind the breakage. For example, if a traditional system frequently reports yarn breakage at a certain location, technicians may only be able to replace the yarn or inspect mechanical parts. However, the system of this application may analyze that the yarn breakage is due to a sharp decline in strength of a specific batch of yarn under high temperature and high humidity conditions, thereby guiding the production department to adjust environmental controls or change suppliers, solving the problem at its source. This preventative and fundamental problem-solving capability is not available in existing technologies, greatly improving the intelligence and efficiency of production management.

[0129] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A rapid positioning method for elastic yarns, characterized in that, include: Obtain information about the yarn's ability to resist deformation in its current state; Based on the difference between the deformation resistance capability information and the preset target deformation resistance capability information, the target force for yarn feeding is adjusted. Specifically, this includes: acquiring the clarity of the yarn's response signal to disturbance; acquiring the dispersion of continuously measured deformation resistance capability information; acquiring the degree of matching between the current deformation resistance capability information's changing trend and the preset environmental parameter changing trend; adjusting the weights of each indicator based on the clarity of the yarn's response signal to disturbance, the dispersion of continuously measured deformation resistance capability information, the degree of matching between the current deformation resistance capability information's changing trend and the preset environmental parameter changing trend, as well as yarn speed, yarn batch type, and historical production data; integrating the clarity of the yarn's response signal to disturbance, the dispersion of continuously measured deformation resistance capability information, and the degree of matching between the current deformation resistance capability information's changing trend and the preset environmental parameter changing trend based on the adjusted weights to obtain the reliability of the deformation resistance capability information; and adjusting the target force for yarn feeding based on the deformation resistance capability information and its corresponding reliability. Adjust the yarn feed rate according to the adjusted target force to maintain the instantaneous force of the yarn consistent with the adjusted target force; When the yarn breakage is detected, information on the yarn's physical state before and after the breakage, local environmental information, and yarn force fluctuation information are acquired. The cause of yarn breakage is analyzed based on the yarn physical state information, the local environment information, and the yarn force fluctuation information.

2. The rapid positioning method for elastic yarn according to claim 1, characterized in that, The step of adjusting the target force for yarn feeding based on the deformation resistance information and its corresponding reliability includes: When the credibility level is consistently lower than a preset threshold, the risk assessment module is activated to analyze the current yarn speed, yarn batch type, local environmental parameters, and historical production data. Based on the analysis results, determine whether there is a potential risk of rapid deterioration of the actual physical state of the yarn; When the aforementioned potential risk exists, the target force is adjusted in a controlled, step-by-step manner, and an enhanced monitoring mode is triggered.

3. The rapid positioning method for elastic yarn according to claim 2, characterized in that, When the credibility level remains below a preset threshold, the risk assessment module is activated, including: The evaluation threshold for the reliability of the deformation resistance information is dynamically adjusted based on the yarn speed, the yarn batch type, and the local environmental parameters. When the credibility level remains below the dynamically adjusted assessment threshold, the risk assessment module is activated.

4. The rapid positioning method for elastic yarn according to claim 3, characterized in that, When the credibility level remains below the dynamically adjusted assessment threshold, the risk assessment module is activated, including: After the risk assessment module is started, an independent operation status monitoring unit is started. The monitoring unit continuously acquires the processor usage of the risk assessment module, the memory usage of the risk assessment module, the completion status of the key internal data processing flow of the risk assessment module, and the communication link status between the risk assessment module and the external data source. An abnormal alarm is triggered when the processor usage continues to exceed a preset range, the memory usage reaches a preset limit, the critical internal data processing flow is not completed within a specified time, or the communication link is interrupted. Record the operational status information of the risk assessment module; Switch to emergency control mode, in which the target force for yarn feeding is adjusted using preset conservative parameters.

5. The rapid positioning method for elastic yarn according to claim 4, characterized in that, After the risk assessment module is started, an independent operation status monitoring unit is activated, including: The operation status monitoring unit performs a resource reservation operation to pre-allocate processor time slices and memory space for itself. When the operation status monitoring unit executes the self-test program, it verifies the connectivity of the core functions and communication links; The operation status monitoring unit performs preliminary sampling of the processor usage and memory usage of the risk assessment module; When the sampled data exceeds the preset instantaneous fluctuation range, an early anomaly warning is triggered and the full monitoring function is activated.

6. The rapid positioning method for elastic yarn according to claim 2, characterized in that, The risk assessment module analyzes current yarn speed, yarn batch type, local environmental parameters, and historical production data, including: Obtain the historical production data; The values ​​in the historical production data are checked for interval boundaries to identify outliers that exceed the physical reasonable range, and the time series data in the historical production data are smoothed to reduce the impact of instantaneous noise. Based on the trend of adjacent data or the average value of similar yarn batches, the missing values ​​in the historical production data are filled in to obtain the processed historical production data. Based on the processed historical production data and the degree to which the historical production data has been cleaned and filled, the weight of the historical production data in the risk assessment is adjusted, and the potential risks of the actual physical state of the yarn are analyzed in combination with the current yarn speed, the yarn batch type, and the local environmental parameters.

7. The rapid positioning method for elastic yarn according to claim 2, characterized in that, After the step of activating the risk assessment module when the credibility level remains below a preset threshold is mentioned, the following further steps are included: After the risk assessment module is started, a readiness query signal is sent to the risk assessment module through the main control system; After receiving the readiness query signal through the risk assessment module, it checks whether the key initialization tasks inside the risk assessment module have been completed. The key initialization tasks include the self-test of the core processing unit, the connectivity verification of the data interface, and the loading of initial parameters. Based on the inspection results, the risk assessment module returns a ready status signal to the main control system. The ready status signal indicates whether the risk assessment module is ready to receive tasks or provide output. After receiving the ready status signal returned by the risk assessment module, and the ready status signal indicates that the risk assessment module is ready, the main control system sends data to the risk assessment module or relies on the output of the risk assessment module to adjust the subsequent yarn feeding target force.

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