A yarn tension intelligent adjusting method and system in a circular weaving machine

Abstract

The present application relates to the field of industrial regulation technology, in particular to a yarn tension intelligent adjustment method and system in large circular weaving, comprising: obtaining monitoring data of each dimension; obtaining a set of rapid rates according to the distribution of the change rate of the monitoring data between adjacent time points of each dimension; obtaining a dynamic mismatch degree according to the time distribution of the matching result of the rapid change rate of the yarn tension data and the motor speed data, combining the shock period of the yarn tension data and the number distribution of the set of rapid rates; obtaining a mismatch sensitivity degree of the current weaving process according to the numerical difference distribution and the time difference distribution of the corresponding change rate in the set of rapid rates of the yarn feeding speed data and the knitting amount data, combining the dynamic mismatch degree; obtaining an adjustment coefficient according to the coupling relationship between the mismatch sensitivity degree and the historical adjustment operation data, and adjusting the yarn tension. The present application fully considers the response delay, so that the regulation result is more accurate.

Description

Technical Field

[0001] This invention relates to the field of industrial adjustment technology, specifically to a method and system for intelligent yarn tension adjustment in circular knitting. Background Technology

[0002] Circular knitting machines, as one of the most widely used weaving equipment in the knitting industry, are mainly used for the production of knitted fabrics from various yarns such as cotton, polyester, spandex, and blends. Their weaving process is characterized by high speed, continuous operation, and multiple parallel needle paths. The quality of the fabric largely depends on the tension stability of the yarn during feeding and loop formation. Real-time monitoring and intelligent adjustment of yarn tension during circular knitting is a crucial technological direction for ensuring fabric quality, improving weaving efficiency, and reducing manual intervention.

[0003] Existing tension detection methods mostly rely on strain gauge sensors, whose sampling frequency and signal response time often cannot keep up with the rate of change of high-frequency tension fluctuations. This leads to delays that prevent the control system from responding to rapid tension fluctuations in a timely manner, resulting in lag in the adjustment results and inaccurate data adjustment results. Summary of the Invention

[0004] To address the technical problem of inaccurate data adjustment results due to the lag in existing adjustment methods, the present invention aims to provide an intelligent yarn tension adjustment method and system for circular knitting, the specific technical solution of which is as follows: Firstly, a method for intelligent yarn tension adjustment in circular knitting includes: Acquire monitoring data for various dimensions during the circular knitting process within the monitoring time window, including yarn tension data, motor speed data, yarn feeding speed data, and knitting amount data; Based on the distribution of the rate of change of the monitoring data between adjacent time points in each dimension, the rate of change of all monitoring data is filtered to obtain the set of drastic rates corresponding to the monitoring data of each dimension. Based on the time distribution of the matching results of the rate of change between the drastic rate sets of yarn tension data and motor speed data, combined with the oscillation period distribution of yarn tension data and the quantity distribution in the corresponding drastic rate sets, the dynamic mismatch degree of the current weaving process is obtained. Based on the numerical and temporal differences in the rate of change corresponding to the drastic rate sets of yarn feed speed data and knitting amount data, and combined with the dynamic mismatch degree, the mismatch sensitivity of the current weaving process is obtained. Based on the degree of mismatch sensitivity and the coupling relationship between yarn tension data and motor speed data in historical adjustment operations, an adjustment coefficient is obtained to adjust the yarn tension.

[0005] Preferably, the step of filtering the change rates of all monitoring data based on the distribution of change rates of monitoring data between adjacent time points in each dimension to obtain a set of dramatic rates corresponding to the monitoring data in each dimension specifically includes: For any dimension of monitoring data, the ratio of the difference between monitoring data at adjacent time points to the time interval is taken as the rate of change at the adjacent time points. All change rates are arranged in a preset order to obtain a change rate sequence. The maximum value of the first difference of the change rate sequence is used as the data boundary point. The subsequence containing the maximum value of the change rate constitutes the set of drastic rates corresponding to the monitoring data of any dimension.

[0006] Preferably, the step of obtaining the dynamic mismatch degree of the current weaving process based on the time distribution of the matching results of the rate of change between the sets of drastic rate changes in yarn tension data and motor speed data, combined with the oscillation period distribution of the yarn tension data and the quantity distribution in the corresponding sets of drastic rate changes, specifically includes: The first characteristic coefficient is obtained by matching the time interval corresponding to the minimum difference between each rate of change in the set of drastic rate changes in yarn tension data and motor speed data. The second characteristic coefficient is obtained based on the time distribution corresponding to the extreme points of the yarn tension data and the quantity distribution of the yarn tension data corresponding to the set of severe rates. The product of the first characteristic coefficient and the second characteristic coefficient is used as the dynamic mismatch degree of the current weaving process.

[0007] Preferably, the step of obtaining the first characteristic coefficient based on the time interval corresponding to the minimum difference between each rate of change in the set of drastic rate changes in yarn tension data and motor speed data specifically includes: When the minimum difference between the selected rate of change and each second rate of change is reached, a matching pair of the selected rate of change and the corresponding second rate of change is obtained; the average of the time intervals between the two rates of change in the matching pairs corresponding to all first rates of change is taken as the first feature coefficient. The selected rate of change refers to any first rate of change, which is each rate of change in the set of drastic rates of yarn tension data, and the second rate of change refers to each rate of change in the set of drastic rates of motor speed data.

[0008] Preferably, obtaining the second characteristic coefficient based on the time distribution corresponding to the extreme points of the yarn tension data and the quantity distribution of the yarn tension data corresponding to the set of severe rates specifically includes: The first coefficient is the reciprocal of the mean of the time intervals between adjacent extreme points of the yarn tension data; the second coefficient is the proportion of yarn tension data in the set of severe rates of yarn tension data; and the product of the first coefficient and the second coefficient is the second characteristic coefficient.

[0009] Preferably, the step of obtaining the mismatch sensitivity of the current weaving process based on the numerical and temporal difference distributions of the corresponding change rates in the drastic rate sets of yarn feed speed data and knitting amount data, combined with the dynamic mismatch degree, specifically includes: The third and fourth rates of change corresponding to the time sequence are obtained in chronological order to form a data group. The numerical difference between the third and fourth rates of change in each data group, as well as the time difference between the third and fourth rates of change, are obtained to obtain the feature difference value of each data group. Based on the mean of the characteristic differences and the degree of dynamic mismatch of all data sets, the mismatch sensitivity of the current weaving process is determined; where the third rate of change refers to each rate of change in the set of drastic rates of the yarn feed speed data, and the fourth rate of change refers to each rate of change in the set of drastic rates of the knitting quantity data.

[0010] Preferably, the step of obtaining an adjustment coefficient based on the mismatch sensitivity and the coupling relationship between yarn tension data and motor speed data in historical adjustment operations, and adjusting the yarn tension, specifically includes: Acquire the changes in motor speed and yarn tension during historical adjustment operations; The adjustment coefficient is obtained based on the degree of mismatch sensitivity, the change in motor speed, and the change in yarn tension; The proportional gain coefficient of the PID controller is adjusted using the adjustment coefficient.

[0011] Preferably, the step of obtaining the adjustment coefficient based on the degree of mismatch sensitivity, the change in motor speed, and the change in yarn tension specifically includes: The ratio between the change in yarn tension and the change in motor speed is used as the control effectiveness factor; the product of the negative correlation coefficient of the control effectiveness factor and the degree of mismatch sensitivity is normalized to obtain the adjustment coefficient.

[0012] Preferably, adjusting the proportional gain coefficient of the PID controller using an adjustment coefficient specifically includes: Obtain the control range of the proportional gain of the PID control, take the product of the adjustment coefficient and the control range as the adjustment degree, and obtain the adjusted proportional gain coefficient by summing the initial proportional gain coefficient and the adjustment degree.

[0013] Secondly, a yarn tension intelligent adjustment system for circular knitting includes a memory, a processor, and a computer program stored in the memory and running on the processor. When the computer program is executed by the processor, it implements the steps of an intelligent yarn tension adjustment method for circular knitting.

[0014] The embodiments of the present invention have at least the following beneficial effects: This invention first collects monitoring data from multiple dimensions and analyzes the drastic changes in the monitoring data for each dimension, filtering out sets of drastic rate variations and extracting data samples with relatively drastic amplitude changes to provide data basis for subsequent analysis of high-frequency disturbances and dynamic mismatch processes. Then, based on the drastic change characteristics of yarn tension data, it analyzes high-frequency disturbance phenomena in the weaving process and, combined with the time distribution of the matching results of the drastic change rates between speed and tension changes, obtains the mismatch situation of the weaving system. Furthermore, based on the response relationship between changes in knitting amount and yarn feeding speed, it quantifies the supply-consumption pattern in the current weaving process and assesses the dynamic mismatch sensitivity of the current weaving process. Finally, based on the coupling relationship between speed and tension changes corresponding to control operations in historical weaving processes, and combined with the dynamic mismatch sensitivity index, it obtains the adjustment coefficient in the current intelligent control system. Through the adjustment coefficient, intelligent adjustment and dynamic compensation of yarn tension are achieved, thereby maintaining stable yarn tension. By fully considering response delay, the control results are more accurate, improving fabric quality and equipment safety. Attached Figure Description

[0015] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a flowchart illustrating the steps of an intelligent yarn tension adjustment method in circular knitting provided by the present invention. Figure 2 This is a schematic diagram of the yarn tension data provided by the present invention; Figure 3 This is a schematic diagram of the motor speed data provided by the present invention; Figure 4 This is a flowchart of the steps of the method for obtaining the dynamic mismatch degree of the current weaving process provided by the present invention. Detailed Implementation

[0017] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a method and system for intelligent yarn tension adjustment in circular knitting according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0019] The following description, in conjunction with the accompanying drawings, details the specific scheme of the intelligent yarn tension adjustment method and system provided by this invention in circular knitting.

[0020] The specific implementation scenario targeted by this invention is as follows: The main process flow of circular knitting can be divided into: yarn release, yarn guiding and pretension control, yarn feeding and loop formation, and tensioning and fabric winding. Among them, the yarn feeding and loop formation stage is the stage with the most drastic changes in yarn tension and is also the most critical stage. In this stage, the yarn enters the loop formation area from the yarn storage device through the yarn guide nozzle. Affected by the combined effects of the speed of the yarn feeding wheel, the speed of the needle cylinder, the yarn guiding angle, and the friction of the knitting needles, the loop tension will experience periodic fluctuations of "stretching-relaxing-re-stretching" with each rotation. The embodiments of this invention mainly analyze the tension adjustment process of the yarn feeding and loop formation stage in the weaving process.

[0021] Please see Figure 1 The diagram illustrates a flowchart of a method for intelligent yarn tension adjustment in circular knitting according to an embodiment of the present invention. The method includes the following steps: Step S100: Obtain monitoring data for each dimension of the circular knitting machine weaving process within the monitoring time window, including yarn tension data, motor speed data, yarn feeding speed data, and knitting amount data.

[0022] As a concrete example, a tension detection component is installed along the path from the yarn feeder through the yarn guide into the knitting needle area to obtain yarn tension data. This data is used to acquire the dynamic stress signal of the yarn in real time. The distribution diagram of the yarn tension data is shown below. Figure 2 As shown. The tension detection component can be a tension sensor.

[0023] As a concrete example, a speed detection device, such as a Hall encoder, is installed at the servo motor control terminal of the yarn feeding mechanism. The Hall encoder collects the motor speed signal in real time to obtain motor speed data, which is used to subsequently determine whether the yarn feeding and take-up rates are matched. The distribution diagram of the motor speed data is shown below. Figure 3 As shown.

[0024] As a concrete example, a speed sensor or angle encoder is installed on the cylinder drive spindle to collect the real-time cylinder speed signal to obtain yarn feed speed data. Simultaneously, the weaving control system acquires the number of knits participating in loop formation at each moment, and uses the recorded number of knits at each moment as the knit quantity data.

[0025] It should be noted that the actual process of circular knitting involves a control system and a cloud database, which can directly read the various parameter data of the current weaving process. That is, the monitoring data for each dimension is transmitted via a data bus, which is a well-known technology in the field and will not be elaborated upon further. It should also be noted that the data acquisition methods for each dimension are also well-known technologies. Implementers can choose appropriate methods for data acquisition based on the specific implementation scenario, and no restrictions are imposed here.

[0026] Furthermore, this embodiment sets a fixed monitoring time window to monitor and adjust the tension distribution in the current weaving process in real time. The time interval of the monitoring time window can be 5 minutes, which can be set by the implementer according to the specific implementation scenario.

[0027] Step S200: Based on the distribution of the rate of change of the monitoring data between adjacent time points in each dimension, the rate of change of all monitoring data is filtered to obtain the set of drastic rates corresponding to the monitoring data in each dimension.

[0028] Firstly, monitoring data from different dimensions needs to be quantified using a unified rate of change index to objectively distinguish between gentle and severe fluctuations. During circular knitting, the units and numerical ranges of data from different dimensions vary significantly. Directly comparing the differences in the original data cannot accurately determine the intensity of fluctuations. For example, a tension difference of 2N may correspond to severe fluctuations, while a speed difference of 2RPM may only represent a gentle change. Therefore, it is necessary to combine the differences in data from adjacent time points with the time interval to calculate the rate of change, that is, to analyze the amount of data change per unit time, eliminating the influence of time intervals and data units, and providing a unified standard for subsequent screening of fluctuation intensity across or within the same dimension.

[0029] Secondly, the rate of change in monitored data within the same dimension tends to exhibit a distribution pattern of mostly gradual changes and a few dramatic ones. For example, during normal weaving, the tension rate is mostly concentrated between 0.1-0.5 N / s, while during sudden disturbances, it can reach high values ​​of 2-3 N / s. If a fixed threshold is used, misjudgments may occur due to changes in weaving conditions (such as yarn material switching or pattern adjustments). Therefore, it is necessary to dynamically locate the dividing point based on the distribution of the rate of change within the current monitoring time window, so as to adaptively capture the natural segmentation of gradual and dramatic segments in the rate sequence, ensuring that the selected set of dramatic rates closely matches the actual fluctuation characteristics of the current working conditions, and avoiding the limitations of a fixed threshold.

[0030] Thirdly, selecting the set of drastic rate fluctuations is a core prerequisite for subsequent analysis of high-frequency disturbances and dynamic mismatches. Key issues in circular knitting, such as tension fluctuations and lag in speed adjustment, are often hidden at moments of drastic change. For example, a sudden increase in the rate of tension fluctuation may correspond to needle jamming, while a lag in the rate of speed fluctuation may correspond to a mismatch between yarn supply and consumption. Therefore, selecting the set of drastic rate fluctuations essentially involves extracting data samples with relatively drastic changes from massive amounts of monitoring data, providing a focused object for accurately analyzing the system's dynamic characteristics and identifying adjustment lag issues.

[0031] To address this, the method for obtaining the set of dramatic rates corresponding to the monitoring data of each dimension can be implemented through the following steps.

[0032] The first step is to take the ratio of the difference between monitoring data at adjacent time points to the time interval as the rate of change at adjacent time points for any dimension of monitoring data.

[0033] As a concrete example, by taking the difference between monitoring data points corresponding to each two adjacent time points and the time interval, a rate of change corresponding to each two adjacent time points can be calculated, reflecting the speed of change of monitoring data per unit time. More specifically, it can be the calculation of the difference between the monitoring data of each time point and the adjacent previous time point.

[0034] In other embodiments, curve fitting can be performed on all monitoring data to obtain the slope of the data point corresponding to each time point on the fitted curve, which serves as the rate of change, reflecting the speed of change for each data point. The curve fitting method is a well-known technique, such as the least squares method, and will not be elaborated upon here.

[0035] The second step is to arrange all the change rates in a preset order to obtain a change rate sequence. The maximum value of the first difference of the change rate sequence is used as the data boundary point, and the subsequence containing the maximum value of the change rate is obtained to form the set of drastic rates corresponding to the monitoring data of any one dimension.

[0036] It should be understood that a subsequence refers to dividing a rate of change sequence into two subsequences based on a data boundary point.

[0037] As a concrete example, the preset order can be either from smallest to largest rate of change or from largest to smallest rate of change. This embodiment uses the example of from smallest to largest rate of change. For monitoring data of any dimension, all rates of change are arranged in ascending order to obtain a rate of change sequence. The first-order difference value of the rate of change sequence is calculated. One first-order difference value corresponds to two rates of change. The smaller of the two rates of change corresponding to the maximum value of the first-order difference value is used as the data boundary point. This data boundary point can then be used to divide the rate of change sequence into two subsequences. The set of drastic rates is the subsequence containing the maximum value of the rate of change among the two subsequences. The set of drastic rates represents the data set with more drastic changes in magnitude.

[0038] Step S300: Based on the time distribution of the matching results of the rate of change between the drastic rate sets of yarn tension data and motor speed data, combined with the oscillation period distribution of yarn tension data and the quantity distribution in the corresponding drastic rate sets, the dynamic mismatch degree of the current weaving process is obtained.

[0039] The core of dynamic mismatch is to quantify the synergistic deviation between high-frequency disturbances of yarn tension and motor speed regulation response, focusing on measuring response delay, characterizing the intensity of the disturbance itself, and integrating the two types of features to reflect the overall mismatch.

[0040] Firstly, the time lag in motor speed regulation after a severe tension disturbance is the core manifestation of dynamic mismatch, requiring the extraction of the degree of delay in the regulation response through time distribution characteristics. In circular knitting, when yarn tension fluctuates drastically (e.g., a sudden increase in tension rate due to abrupt needle friction), ideally, the motor should adjust its speed synchronously to compensate for the load change. In this case, the time interval between the matching rate of tension and speed fluctuations approaches zero. However, if there is a lag in motor regulation (e.g., signal transmission delay, slow actuator response), the time interval between the two will increase, and the longer the interval, the more severe the regulation lag and the higher the risk of dynamic mismatch. Therefore, by using the time interval between tension and speed fluctuations under matching conditions, the delay characteristics of the regulation response can be reflected, providing data on the timeliness of the response for subsequent quantification of mismatch.

[0041] Secondly, the oscillation period distribution and the distribution of the number of violent rates in yarn tension data jointly determine the intensity of the disturbance. It is necessary to extract the high-frequency disturbance characteristics of the yarn tension data through their coupling relationship. The oscillation period distribution is reflected by calculating the average time interval of the data, and the frequency characteristics of the disturbance are reflected by calculating the proportion of violent rates. Furthermore, coupling these two factors reflects the synergistic characteristics of frequency and relative frequency, providing data basis for quantifying the severity of the disturbance.

[0042] Finally, based on the combined results of the two feature analyses, the mismatch in the current weaving process is assessed.

[0043] In this regard, such as Figure 4 As shown, the method for obtaining the dynamic mismatch degree of the current weaving process can be implemented by steps S301 to S303.

[0044] Step S301: Based on the time interval corresponding to the minimum difference between each rate of change in the set of drastic rate changes in yarn tension data and motor speed data, the first characteristic coefficient is obtained.

[0045] As a concrete example, each rate of change in the set of drastic rates of yarn tension data is denoted as the first rate of change, and each rate of change in the set of drastic rates of motor speed data is denoted as the second rate of change. Then, we take any one of the first rates of change as an example for explanation, that is, we denot any one of the first rates of change as the selected rate of change.

[0046] Specifically, when the minimum difference between the selected rate of change and each second rate of change is reached, a matching pair of the selected rate of change and the corresponding second rate of change is obtained; the average of the time intervals corresponding to the two rates of change in all matching pairs corresponding to the first rates of change is used as the first feature coefficient. It should be understood that the selected rate of change refers to any one first rate of change, and the first rate of change refers to each rate of change in the set of abrupt rate changes in the yarn tension data; the second rate of change refers to each rate of change in the set of abrupt rate changes in the motor speed data.

[0047] More specifically, firstly, the difference between the selected rate of change and each second rate of change is calculated. The second rate of change corresponding to the minimum difference among all differences is then matched with the selected rate of change. This process represents filtering out the objects from the drastic change characteristics of the motor speed data that have the smallest difference from the drastic change characteristics of the current yarn tension data. The matched pairs of selected rates of change indicate that the difference between the change trends of the yarn tension data and the motor speed data is small and relatively similar.

[0048] Then, each first rate of change corresponds to a matching pair. The time interval between the two time points corresponding to the two rates of change in each matching pair is obtained. The arithmetic mean of the time intervals of the matching pairs of all first rates of change can be used to obtain the first characteristic coefficient.

[0049] It should be noted that this embodiment obtains the longest time interval corresponding to the matching pair. More specifically, one rate of change corresponds to two time points, and thus the longest time interval between the two time points corresponding to the two rates of change in the matching pair can be obtained.

[0050] The first characteristic coefficient is the average time delay between the severe disturbance of yarn tension and the response of motor speed regulation, which is the core characterization of the timeliness of the control end's response to high-frequency tension disturbances.

[0051] The time interval corresponding to a matching pair represents the time difference between the moment when tension fluctuates drastically and the moment when the motor makes a targeted speed adjustment. The average of the time intervals corresponding to the matching pairs for the first rate of change reflects the average response lag time of the motor to drastic tension disturbances during the current weaving process. A larger value indicates a slower response from the motor to drastic tension fluctuations, meaning poorer timing coordination between the control unit and tension changes; a smaller value indicates that the motor can adjust to tension disturbances almost synchronously, with better timeliness and lower risk of delay in the control link.

[0052] Step S302: Based on the time distribution corresponding to the extreme points of the yarn tension data and the quantity distribution of the yarn tension data corresponding to the set of severe rates, the second characteristic coefficient is obtained.

[0053] Specifically, the reciprocal of the mean of the time intervals between adjacent extreme points of yarn tension data is used as the first coefficient; the proportion of yarn tension data corresponding to the set of severe rates of yarn tension data is used as the second coefficient, and the product of the first coefficient and the second coefficient is the second characteristic coefficient.

[0054] Among these methods, the extreme points of yarn tension data can be statistically analyzed based on the distribution of all yarn tension data. It should be understood that the method for obtaining the extreme points of the data is a well-known technique and will not be described in detail here. Implementers can choose an appropriate method for data processing according to the specific implementation scenario.

[0055] As a concrete example, the method for obtaining the second characteristic coefficient can be expressed by the formula: in, Represents the second characteristic coefficient. This indicates the number of yarn tension data points contained in the set of drastic rate data. This indicates the number of all yarn tension data. This represents the time interval between the i-th extreme point and the (i+1)-th extreme point of the yarn tension data. This indicates the number of extreme points in the yarn tension data.

[0056] The second coefficient represents the proportion of the number of drastic rate sets in the yarn tension data, reflecting the frequency of disturbances. The higher the proportion, the more times the tension changes drastically per unit time, and the more frequent the instantaneous instability events of tension during the weaving process.

[0057] The average time interval between extreme points of yarn tension data, and the average period of anti-tension oscillation. The first coefficient represents the tension oscillation frequency; the shorter the average time interval, the larger the value of the second coefficient, indicating that the tension oscillation period is shorter, the tension fluctuation frequency is higher, and the oscillation is more intense.

[0058] The product of the first and second coefficients constitutes the second characteristic coefficient, which couples the disturbance frequency with the oscillation frequency to fully characterize the intensity of the tension disturbance. The larger the value of the second characteristic coefficient, the more frequent and rapid the tension fluctuations, indicating a high-frequency, high-intensity disturbance state, which poses a greater threat to weaving quality (such as yarn breakage and uneven loop formation).

[0059] It should be noted that when At that time, it can be Set the value to a minimum, for example, set the value to a minimum. This indicates that there is no strong disturbance.

[0060] Step S303: The product of the first characteristic coefficient and the second characteristic coefficient is used as the dynamic mismatch degree of the current weaving process.

[0061] Dynamic mismatch is a collaborative mismatch index between the intensity of high-frequency tension disturbance and the lag in motor adjustment response. It characterizes the dynamic coordination deviation between yarn tension changes and motor speed adjustments, and is the core basis for judging whether there is tension runaway caused by adjustment lag in the weaving system.

[0062] If only the first characteristic coefficient (response delay) is used for evaluation, the difference between weak disturbances with long delays and strong disturbances with long delays will be ignored. Long delays under strong disturbances will lead to rapid accumulation of tension deviations, with risks far exceeding those under weak disturbances. If only the second characteristic coefficient (disturbance intensity) is used for evaluation, the difference between strong disturbances with short delays and strong disturbances with long delays will be missed. Short delays can offset the impact of disturbances through rapid adjustment, while long delays will amplify the harm of disturbances. Therefore, the second characteristic coefficient (disturbance intensity) needs to be multiplied by the first characteristic coefficient (response delay) so that the final dynamic mismatch index can reflect both the severity of the disturbance itself and the lag in the adjustment response, fully characterizing the mismatch state where tension disturbances cannot be suppressed in time.

[0063] The higher the value of the dynamic mismatch degree in the current weaving process, the more severe the mismatch state of the current system with strong disturbance and slow response. At this time, the tension fluctuates frequently and rapidly, and the motor adjustment cannot keep up. This can easily lead to the yarn tension continuously deviating from the process standard, causing fabric defects (such as missed needles, sparse or dense patterns) or even equipment failure (such as yarn entanglement).

[0064] The smaller the value of the dynamic mismatch degree in the current weaving process, the more it indicates that the system is in a coordinated state of weak disturbance, fast response, or strong disturbance but timely response. At this time, the tension fluctuation is either mild or the motor can adjust in time to offset the disturbance, the overall tension remains stable, and the reliability of the weaving process is high.

[0065] Step S400: Based on the numerical and temporal difference distributions of the corresponding change rates in the drastic rate sets of yarn feeding speed data and knitting amount data, and combined with the dynamic mismatch degree, the mismatch sensitivity of the current weaving process is obtained.

[0066] The core of mismatch sensitivity is to quantify the superposition effect of yarn feeding-knitting load coordination and existing dynamic mismatch in the system, to judge the vulnerability of the weaving system to disturbances, and to focus on analyzing the basic coordination deviation of yarn feeding and load, as well as the correlation between basic deviation and existing dynamic mismatch.

[0067] Firstly, the numerical matching degree and temporal synchronization of yarn feeding speed and knitting quantity jointly determine the basic coordination deviation between the two, which needs to be quantified through the characteristic difference values ​​of the data set. In circular knitting, a sudden change in the number of knits (such as an increase in the number of working needles due to pattern switching) means that the demand for yarn consumption increases instantaneously. At this time, the yarn feeding speed needs to increase synchronously to meet the consumption. Ideally, the numerical values ​​of the two drastic rates should be similar (supply matching consumption) and the timing should be synchronized (supply adjustment without delay). Therefore, it is necessary to first analyze the characteristic deviation between yarn feeding speed and knitting quantity through the characteristic performance of two dimensions.

[0068] Secondly, the basic coordination deviation needs to be combined with the existing dynamic mismatch to fully reflect the system's sensitivity to disturbances. Simply using the mean of characteristic difference values ​​(reflecting the level of basic coordination deviation) cannot determine the actual impact of the deviation on the system: if the system already has a low degree of dynamic mismatch (e.g., small tension disturbances, timely motor response), even if the basic coordination deviation is slightly large, the system can compensate through adjustment, resulting in low sensitivity; however, if the system already has a high degree of dynamic mismatch (e.g., strong tension disturbances, lag in motor response), the addition of the basic coordination deviation will lead to amplification of supply-consumption imbalance and tension regulation lag, making the system highly susceptible to chain fluctuations triggered by minor disturbances, resulting in high sensitivity.

[0069] To address this, the current method for obtaining the mismatch sensitivity of the weaving process can be achieved through the following steps. As a specific example, in this embodiment, each rate of change in the set of drastic rates of the yarn feed speed data is designated as the third rate of change, and each rate of change in the set of drastic rates of the knitting amount data is designated as the fourth rate of change.

[0070] The first step is to obtain the third and fourth rates of change corresponding to the time sequence to form a data set.

[0071] Specifically, all third rates of change are arranged in chronological order, as are all fourth rates of change. Each rate of change corresponds to a time sequence number based on the arrangement order. Third and fourth rates of change with the same time sequence number are grouped together as a data set. Under normal response conditions, the numerical and temporal differences between the third and fourth rates of change within the same data set should be small.

[0072] The second step is to obtain the numerical difference between the third and fourth change rates in each data group, as well as the time difference between the third and fourth change rates, to obtain the characteristic difference value of each data group; based on the mean of the characteristic difference values ​​of all data groups and the degree of dynamic mismatch, the mismatch sensitivity of the current weaving process is determined.

[0073] As a concrete example, the method for obtaining the mismatch sensitivity of the current weaving process can be expressed by the formula: in, This indicates the degree of mismatch sensitivity in the current weaving process. Indicates the degree of dynamic mismatch. This represents the mean of the characteristic differences across all data groups. Indicates the number of data sets. This represents the feature difference value of the x-th data group. This represents the third rate of change in the x-th data set. This represents the fourth rate of change in the x-th data set. This represents the earliest time point corresponding to the third rate of change in the x-th data set. This represents the earliest time point corresponding to the fourth rate of change in the x-th data set.

[0074] This represents the numerical difference between the third and fourth rates of change in the x-th data set. The smaller the value, the more similar the numerical changes, and thus the stronger the regularity between supply and consumption. This represents the time difference between the third and fourth rates of change in the x-th data set. The smaller the value, the stronger the coupling between the yarn feed rate change event and the knitting quantity change event.

[0075] The mean of the characteristic difference value reflects the average level of the supply-consumption coordination deviation. The smaller the value, the higher the amplitude matching degree between the yarn feeding speed and the number of knits, the stronger the temporal synchronization, and the stable law formed by supply and consumption (such as when the number of knits increases sharply due to pattern switching, the yarn feeding speed can quickly and accurately match the consumption demand). The yarn tension is not easy to fluctuate due to oversupply (yarn accumulation and loosening) or undersupply (yarn stretching too tight), and the basic stability of the weaving process is good.

[0076] The larger the mean value of the characteristic difference, the greater the coordination deviation between the yarn feeding speed and the number of knits, the disordered supply and consumption pattern, which can easily cause high-frequency tension fluctuations and create hidden dangers for subsequent dynamic mismatch.

[0077] The current mismatch sensitivity of the weaving process is a quantitative indicator of the system's vulnerability to disturbances after the superposition of supply-consumption coordination deviation and existing dynamic mismatch. It reflects the sensitivity level of the weaving system to the amplified risk of dynamic mismatch caused by supply-consumption imbalance and is a key basis for judging whether the system needs to strengthen adjustment.

[0078] The product of the mean of characteristic differences and the degree of dynamic mismatch is essentially the superposition of supply-consumption coordination deviation and existing dynamic mismatch. The more disordered the supply-consumption, the more it will amplify the mismatch between tension disturbance and motor regulation, making the system's response to small disturbances (such as changes in needle friction and yarn material fluctuations) more severe.

[0079] A higher mismatch sensitivity value indicates a highly sensitive system, posing a high risk to weaving quality and equipment safety, requiring immediate and enhanced parameter adjustments. Conversely, a lower mismatch sensitivity value indicates a less sensitive system, with higher tolerance to disturbances and stronger stability in the weaving process.

[0080] Step S500: Based on the degree of mismatch sensitivity and the coupling relationship between yarn tension data and motor speed data in historical adjustment operations, an adjustment coefficient is obtained, and the yarn tension is adjusted.

[0081] The main purpose of this step is to establish a correlation between the current sensitive state of the system and historical adjustment experience, generate a tension adjustment strategy that is adapted to the current operating conditions, and achieve the goal of optimizing adjustment parameters based on dynamic mismatch risk and historical adjustment experience.

[0082] Firstly, the coupling relationship between changes in motor speed and yarn tension during historical adjustment operations forms the basis for adjustment, requiring the extraction of reusable empirical parameters. In circular knitting, there is a clear causal feedback relationship between motor speed and yarn tension: increasing speed leads to faster yarn traction, usually accompanied by increased tension (if yarn feeding is not increased synchronously); decreasing speed may lead to decreased tension (if there is an oversupply of yarn). The strength of this relationship varies across different historical adjustment cases, essentially a reflection of operating conditions such as yarn material, weaving density, and equipment status. Therefore, it is necessary to extract sample pairs of changes in motor speed and yarn tension from historical data, quantify the coupling strength under different operating conditions, and provide an empirical benchmark for subsequent adjustment coefficients.

[0083] Secondly, the degree of mismatch sensitivity determines the strength and aggressiveness of the adjustment, which needs to be dynamically adapted to the current risk state of the system by combining it with historical coupling relationships. High mismatch sensitivity means the system is highly vulnerable to disturbances, requiring more precise and rapid adjustments (e.g., increasing the proportional gain to speed up the response) to prevent small deviations from accumulating into severe mismatches. Low mismatch sensitivity indicates good system stability, allowing for smoother and more conservative adjustments (e.g., reducing the proportional gain to avoid overshoot), preventing over-adjustment from triggering new fluctuations. Calculating the adjustment coefficient solely based on historical coupling relationships ignores the current system's risk level. Therefore, mismatch sensitivity must be used as a weighting factor, combined with historical speed-tension coupling strength, to obtain the adjustment coefficient, ensuring that the adjustment strategy matches the current system's risk state.

[0084] The specific method for adjusting yarn tension can be achieved through the following steps.

[0085] The first step is to obtain the changes in motor speed and yarn tension during historical adjustment operations.

[0086] In the historical database of the circular knitting machine process, the data change corresponding to each adjustment operation of the control system is obtained. Specifically, at the initial moment of each system control command, after a time window, the absolute value of the difference between the motor speed data and the initial moment is recorded as the motor speed change corresponding to one historical adjustment operation. The time window length can be set to 5 minutes, and the implementer can set it according to the specific implementation scenario; there is no limitation here.

[0087] At the initial moment of each system control command, the ratio of the average of all peak points in the yarn tension data after a time window to the average of all peak points in the yarn tension data before the initial moment is recorded as the yarn tension change, reflecting the increase in tension peak value before and after the control operation. It should be understood that the method for obtaining peak points is a well-known technique and will not be discussed further here.

[0088] Thus, each historical adjustment operation corresponds to a change in motor speed and a change in yarn tension. In this embodiment, subsequent adjustment analysis is performed based on the average of the data changes corresponding to a preset number of historical adjustment operations prior to the current weaving process. In other embodiments, the implementer may also perform subsequent adjustment analysis based on the data changes corresponding to the most recent historical adjustment operation prior to the current weaving process. For ease of description, the following descriptions will use the changes in motor speed and yarn tension.

[0089] The second step is to obtain the adjustment coefficient based on the degree of mismatch sensitivity, the change in motor speed, and the change in yarn tension.

[0090] Specifically, the ratio between the change in yarn tension and the change in motor speed is used as the control effectiveness factor; the product between the negative correlation coefficient of the control effectiveness factor and the degree of mismatch sensitivity is normalized to obtain the adjustment coefficient.

[0091] As a concrete example, the method for obtaining the adjustment coefficient can be expressed by the formula: in, This represents the adjustment coefficient. This indicates the degree of mismatch sensitivity in the current weaving process. Indicates the control effectiveness factor. express This function is used to normalize the product. There are no restrictions here; implementers can choose the appropriate normalization method based on the specific implementation scenario. It is a very small positive number, and in this embodiment, it is taken as 0.01 to avoid the case where the denominator is 0.

[0092] Control effectiveness factor The control efficiency factor characterizes the tension improvement effect achievable by a unit change in motor speed during historical regulation operations. A small control efficiency factor indicates low historical regulation efficiency, and the negative correlation coefficient of the control efficiency factor needs to be used to address this. Increase the demand for adjustment to ensure that current adjustments can cover risks; a large control effectiveness factor indicates high historical adjustment efficiency, and the negative correlation coefficient of the control effectiveness factor... This will reduce the need for adjustment and avoid over-adjustment.

[0093] Mismatch sensitivity It reflects the comprehensive risks of the current supply-consumption imbalance and tension-regulation mismatch in the system. The larger the value of , the more vulnerable the system is to disturbances. For example, if there is a severe imbalance between yarn feeding and knitting load, and the motor responds slowly to tension disturbances, a stronger adjustment force is required to suppress tension fluctuations. The adjustment coefficient establishes a positive correlation between higher risk and lower efficiency, and stronger adjustment requirements. Furthermore, the value is constrained to 0-1 to ensure that the adjustment force is controllable.

[0094] The third step is to adjust the proportional gain coefficient of the PID controller using the adjustment coefficient.

[0095] As a concrete example, the adjustment process can be expressed by the formula: in, Adjusted proportional gain value, This represents the system's preset initial proportional gain. This represents the adjustment coefficient. This indicates the maximum allowable proportional gain value of the system.

[0096] In a PID controller, the proportional gain directly determines the ratio between the current deviation and the adjustment amount. The larger the proportional gain Kp, the more drastic the adjustment action for the same tension deviation. When the system experiences high-frequency disturbances (such as a sudden change in the amount of knitting due to pattern switching), a larger adjustment coefficient pushes Kp closer to its maximum value, accelerating the adjustment response to suppress fluctuations. When the system is stable (such as uniform weaving with gentle yarn tension), a smaller adjustment coefficient keeps Kp at a low level, avoiding overshoot and disruption of stability. Ultimately, this achieves the process goal of rapidly suppressing fluctuations and gently maintaining stability, ensuring the quality of fabric loop formation (such as avoiding sparse or dense loops and missed stitches) and equipment safety (such as preventing yarn tangling and motor overload).

[0097] The adjusted proportional gain value is input into the PID controller, and combined with fixed integral and derivative parameters, feedback adjustment is performed on the current tension error to generate a new control output signal for adjusting the motor drive parameters. The system monitors the weaving process in real time to form a continuous adaptive closed-loop intelligent adjustment. If abnormal tension fluctuations or sudden changes in motor current are detected, the gain can be automatically reduced and the adjustment coefficient can be decreased to ensure safety. The mapping between the adjustment coefficient and the proportional gain ensures that the control strength adapts to changes in the system state. During high-frequency disturbances or pattern switching, the gain will not be too large to avoid tension overshoot. When the system is stable, the gain can be amplified to improve the response speed and further effectively improve fabric quality and equipment safety.

[0098] In summary, this embodiment of the invention first sets a fixed monitoring time window. Within this window, it analyzes high-frequency disturbances in the weaving process based on the instantaneous changes in yarn tension data. Then, based on the difference between speed changes and tension changes, it obtains the matching mismatch degree of the weaving system. Based on the response relationship between motor load changes (such as changes in knitting quantity) and yarn feeding speed, it quantifies the supply-consumption pattern in the current weaving process. Finally, it combines the feature analysis results to assess the dynamic mismatch sensitivity of the current weaving process. Furthermore, it obtains a control efficiency factor based on the coupling relationship between speed changes and tension changes corresponding to control operations in historical weaving processes. This factor, combined with the dynamic mismatch sensitivity index, yields the adjustment coefficient in the current intelligent adjustment system. The adjustment coefficient enables intelligent adjustment and dynamic compensation of yarn tension, thereby maintaining stable yarn tension and improving fabric quality and equipment safety.

[0099] This invention also provides an intelligent yarn tension adjustment system for circular knitting, comprising a memory, a processor, and a computer program stored in the memory and running on the processor. When executed by the processor, the computer program implements the steps of an intelligent yarn tension adjustment method for circular knitting. Since an embodiment of an intelligent yarn tension adjustment method for circular knitting has already been described in detail, it will not be elaborated further here.

[0100] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for intelligent adjustment of yarn tension in circular knitting, characterized in that, The method includes the following steps: Acquire monitoring data for various dimensions during the circular knitting process within the monitoring time window, including yarn tension data, motor speed data, yarn feeding speed data, and knitting amount data; Based on the distribution of the rate of change of the monitoring data between adjacent time points in each dimension, the rate of change of all monitoring data is filtered to obtain the set of drastic rates corresponding to the monitoring data of each dimension. Based on the time distribution of the matching results of the rate of change between the drastic rate sets of yarn tension data and motor speed data, combined with the oscillation period distribution of yarn tension data and the quantity distribution in the corresponding drastic rate sets, the dynamic mismatch degree of the current weaving process is obtained. Based on the numerical and temporal differences in the rate of change corresponding to the drastic rate sets of yarn feed speed data and knitting amount data, and combined with the dynamic mismatch degree, the mismatch sensitivity of the current weaving process is obtained. Based on the degree of mismatch sensitivity and the coupling relationship between yarn tension data and motor speed data in historical adjustment operations, an adjustment coefficient is obtained to adjust the yarn tension.

2. The intelligent yarn tension adjustment method in circular knitting according to claim 1, characterized in that, The process involves filtering the change rates of all monitoring data based on the distribution of change rates between adjacent time points in each dimension, resulting in a set of dramatic rates corresponding to the monitoring data in each dimension. Specifically, this includes: For any dimension of monitoring data, the ratio of the difference between monitoring data at adjacent time points to the time interval is taken as the rate of change for the adjacent time points. All change rates are arranged in a preset order to obtain a change rate sequence. The maximum value of the first difference of the change rate sequence is used as the data boundary point. The subsequence containing the maximum value of the change rate constitutes the set of drastic rates corresponding to the monitoring data of any dimension.

3. The intelligent yarn tension adjustment method in circular knitting according to claim 1, characterized in that, The dynamic mismatch degree of the current weaving process is obtained by combining the time distribution of the matching results of the rate of change between the sets of drastic rate changes in yarn tension data and motor speed data, along with the oscillation period distribution of the yarn tension data and the quantity distribution in the corresponding sets of drastic rate changes. Specifically, this includes: The first characteristic coefficient is obtained by matching the time interval corresponding to the minimum difference between each rate of change in the set of drastic rate changes in yarn tension data and motor speed data. The second characteristic coefficient is obtained based on the time distribution corresponding to the extreme points of the yarn tension data and the quantity distribution of the yarn tension data corresponding to the set of severe rates. The product of the first characteristic coefficient and the second characteristic coefficient is used as the dynamic mismatch degree of the current weaving process.

4. The intelligent yarn tension adjustment method in circular knitting according to claim 3, characterized in that, The first characteristic coefficient is obtained by matching the time interval corresponding to the minimum difference between each rate of change in the set of drastic rate changes in yarn tension data and motor speed data, specifically including: When the minimum difference between the selected rate of change and each second rate of change is reached, a matching pair of the selected rate of change and the corresponding second rate of change is obtained; the average of the time intervals between the two rates of change in the matching pairs corresponding to all first rates of change is taken as the first feature coefficient. The selected rate of change refers to any first rate of change, which is each rate of change in the set of drastic rates of yarn tension data, and the second rate of change refers to each rate of change in the set of drastic rates of motor speed data.

5. The intelligent yarn tension adjustment method in circular knitting according to claim 3, characterized in that, The second characteristic coefficient is obtained based on the time distribution corresponding to the extreme points of the yarn tension data and the quantity distribution of the yarn tension data corresponding to the set of severe rates, specifically including: The first coefficient is the reciprocal of the mean of the time intervals between adjacent extreme points of the yarn tension data; the second coefficient is the proportion of yarn tension data in the set of severe rates of yarn tension data; and the product of the first coefficient and the second coefficient is the second characteristic coefficient.

6. The intelligent yarn tension adjustment method in circular knitting according to claim 1, characterized in that, The method of determining the mismatch sensitivity of the current weaving process by combining the numerical and temporal differences in the rate of change corresponding to the drastic rate sets of yarn feed speed data and knitting amount data with the dynamic mismatch degree specifically includes: The third and fourth rates of change corresponding to the time sequence are obtained in chronological order to form a data group. The numerical difference between the third and fourth rates of change in each data group, as well as the time difference between the third and fourth rates of change, are obtained to obtain the feature difference value of each data group. Based on the mean of the characteristic differences and the degree of dynamic mismatch of all data sets, the mismatch sensitivity of the current weaving process is determined; where the third rate of change refers to each rate of change in the set of drastic rates of the yarn feed speed data, and the fourth rate of change refers to each rate of change in the set of drastic rates of the knitting quantity data.

7. The intelligent yarn tension adjustment method in circular knitting according to claim 1, characterized in that, The step of obtaining an adjustment coefficient based on the mismatch sensitivity and the coupling relationship between yarn tension data and motor speed data in historical adjustment operations, and adjusting the yarn tension specifically includes: Acquire the changes in motor speed and yarn tension during historical adjustment operations; The adjustment coefficient is obtained based on the degree of mismatch sensitivity, the change in motor speed, and the change in yarn tension; The proportional gain coefficient of the PID controller is adjusted using the adjustment coefficient.

8. The intelligent yarn tension adjustment method in circular knitting according to claim 7, characterized in that, The adjustment coefficient is obtained based on the degree of mismatch sensitivity, the change in motor speed, and the change in yarn tension, specifically including: The ratio between the change in yarn tension and the change in motor speed is used as the control effectiveness factor; the product of the negative correlation coefficient of the control effectiveness factor and the degree of mismatch sensitivity is normalized to obtain the adjustment coefficient.

9. The intelligent yarn tension adjustment method in circular knitting according to claim 7, characterized in that, The adjustment of the proportional gain coefficient of the PID controller using the adjustment coefficient specifically includes: Obtain the control range of the proportional gain of the PID control, take the product of the adjustment coefficient and the control range as the adjustment degree, and obtain the adjusted proportional gain coefficient by summing the initial proportional gain coefficient and the adjustment degree.

10. A yarn tension intelligent adjustment system for circular knitting, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the computer program is executed by the processor, it implements the steps of the intelligent yarn tension adjustment method in circular knitting as described in any one of claims 1-9.

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