An intelligent cutting control method for a yarn winding apparatus

Abstract

The application discloses an intelligent cutting control method for a spinning winding device, and relates to the technical field of intelligent control. The method comprises the following steps: the spinning winding device receives automatically issued spinning parameters through an embedded Internet of Things transmission component; the spinning winding device performs spinning winding based on the spinning parameters, and synchronously collects winding state data in real time through a sensor network; the collected winding state data is fed back to a cutting decision node in real time, winding state data strings are generated for winding measurement, cutting decisions are made based on winding measurement results, and cutting time, cutting speed and cutting tension are output as a cutting control parameter set; and the cutting control parameter set is transmitted to the spinning winding device to perform cutting control. The technical problem of insufficient cutting control precision of the spinning winding device in the prior art and the inability to adjust in real time according to actual conditions is solved, and the technical effect of improving cutting control precision is achieved through real-time data collection and feedback of the Internet of Things and the sensor network.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent control, and particularly relates to an intelligent cutting control method for a spinning winding device. BACKGROUND

[0002] The spinning winding process usually involves winding textile raw materials (such as yarns, fibers) into a roll shape through a winding machine, and the cutting operation is an important step that cannot be ignored. When performing cutting, the traditional spinning winding device often relies on preset programs and fixed parameters for operation, and cannot flexibly adjust the timing, speed and tension of cutting according to the real-time winding state. In particular, during the production process, the quality of the yarn, the tension change, the winding speed and other factors may change at any time, which makes it difficult for the traditional winding device to cope with complex production environments, resulting in insufficient cutting control precision, and even affecting the consistency and quality of the product. SUMMARY

[0003] The present application provides an intelligent cutting control method for a spinning winding device, which solves the technical problems of insufficient cutting control precision of the spinning winding device in the prior art and the inability to adjust in real time according to the actual situation.

[0004] In view of the above problems, the present application provides an intelligent cutting control method for a spinning winding device.

[0005] The present application provides an intelligent cutting control method for a spinning winding device, the method comprising:

[0006] The spinning winding device receives automatically issued spinning parameters through an embedded Internet of Things transmission component, wherein the spinning parameters include intrinsic spinning parameters and spinning winding parameters; the spinning winding device performs spinning winding based on the spinning parameters, and synchronously collects real-time winding state data through a sensor network; the collected winding state data is transmitted back to a cutting decision node in real time through the Internet of Things transmission component to generate a winding state data string for winding measurement, and cutting decisions are made based on the winding measurement results, and cutting timing, cutting speed and cutting tension are output as a cutting control parameter set; and the cutting control parameter set is transmitted to the spinning winding device to perform cutting control.

[0007] One or more technical solutions provided in the present application have at least the following technical effects or advantages:

[0008] Firstly, the spinning winding device receives the automatically issued spinning parameters through the embedded Internet of Things transmission component, wherein the spinning parameters include intrinsic spinning parameters and spinning winding parameters. Then, the spinning winding device performs spinning winding based on the spinning parameters and synchronously collects winding state data in real time through a sensor network. Then, the collected winding state data is fed back to the cutting decision node in real time through the Internet of Things transmission component, a winding state data string is generated for winding measurement, cutting decision is made based on the winding measurement result, and the cutting time, cutting speed and cutting tension are output as a cutting control parameter set. Finally, the cutting control parameter set is transmitted to the spinning winding device to perform cutting control. The technical problem of insufficient cutting control precision of the spinning winding device in the prior art and the inability to adjust in real time according to the actual situation is solved, and the technical effect of improving the cutting control precision is achieved through real-time data collection and feedback of the Internet of Things and the sensor network. BRIEF DESCRIPTION OF DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0010] Figure 1 A flowchart of an intelligent cutting control method for a spinning winding device provided by the embodiment of the present application is shown.

[0011] Figure 2 A flowchart of generating a winding state data string for winding measurement in an intelligent cutting control method for a spinning winding device provided by the embodiment of the present application is shown. DETAILED DESCRIPTION

[0012] The present application provides an intelligent cutting control method for a spinning winding device, which solves the technical problem of insufficient cutting control precision of the spinning winding device in the prior art and the inability to adjust in real time according to the actual situation.

[0013] The technical solutions in the embodiments of the present application will be described clearly and completely with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0014] It is to be understood that the terms "including", "comprising", "having" and "encompassing" are open-ended, and do not exclude additional, unrecited elements or method steps. It is to be understood that such terms are merely used to describe or inform the disclosed embodiments; other embodiments can certainly include additional steps or elements.

[0015] As shown in the embodiments of the present application, a method for intelligent cutting control of a yarn winding device is provided, wherein the method comprises: Figure 1

[0016] The yarn winding device receives automatically issued yarn parameters through the embedded Internet of Things transmission component, wherein the yarn parameters include yarn intrinsic parameters and yarn winding parameters.

[0017] The yarn winding device can receive automatically issued yarn parameters in real time through the embedded Internet of Things transmission component; these yarn parameters are divided into two categories: one category is yarn intrinsic parameters, which include various data related to the physical properties of the yarn itself, such as yarn thickness, strength, elasticity, diameter, etc.; the other category is yarn winding parameters, which involve specific control requirements in the winding process, such as winding speed, tension, load of the winding drum, and material stretch, etc. The yarn winding device receives externally automatically issued yarn parameters through the embedded Internet of Things component; these parameters include the physical properties of the yarn and the specific operating parameters that need to be controlled in the winding process, and the device performs intelligent operation according to these parameters.

[0018] The yarn winding device performs yarn winding based on the yarn parameters and synchronously collects winding state data in real time through a sensor network.

[0019] After receiving the yarn parameters, the yarn winding device performs winding operation of the yarn according to these parameters; the device adjusts control variables such as speed and tension in the winding process according to the yarn intrinsic parameters and winding parameters to ensure the accuracy and stability of the winding process; at the same time, the device also synchronously collects winding state data in real time through the embedded sensor network during the winding process, which may include key indicators such as tension change, winding speed, and yarn deformation during the winding process. With the support of the sensor network, the device can continuously monitor and optimize the winding process in a dynamically changing production environment to ensure that the winding quality meets the predetermined requirements.

[0020] Further, the winding state data includes winding speed, winding tension, and winding material quality, the yarn winding device performs yarn winding based on the yarn parameters and synchronously collects winding state data in real time through a sensor network, including:

[0021] ​The sensor network includes a rotation sensor, a tension sensor, and a pressure sensor. The rotation sensor is used to monitor and record the rotation data of the winding shaft in real time, and the winding speed is calculated based on the rotation data. The tension sensor is used to monitor and record the tension during the winding process in real time, and the output is the winding tension. The pressure sensor is used to monitor and record the pressure data of the winding disc shaft in real time, and the winding material quality is calculated based on the mass of the winding disc and the pressure data. The winding speed, winding tension, and winding material quality are output as the winding state data.

[0022] The winding state data includes winding speed, winding tension, and winding material quality, which are monitored and recorded in real time by multiple sensors to ensure accurate control of the winding process. Specifically, the sensor network consists of a rotation sensor, a tension sensor, and a pressure sensor, which work together to obtain comprehensive winding state information. The rotation sensor is responsible for monitoring and recording the rotation data of the winding shaft (such as the rotation data of the shaft of the winding disc and the tensioning wheel). Through analysis of these rotation data, the system can accurately calculate the actual winding speed, ensuring that the winding process meets the predetermined operating requirements. The tension sensor is used to monitor the tension changes of the yarn during the winding process, and records and outputs the winding tension data in real time. The tension sensor includes a wheel-type tension sensor, a cantilever beam sensor, and a bearing sensor. The wheel-type tension sensor measures the tension directly on the yarn, suitable for dynamic real-time measurement. The cantilever beam sensor is installed on the guide rod or tension control device, and measures the tension through displacement or force changes. The bearing sensor is installed on the winding shaft, and measures the force on the bearing to indirectly obtain the tension. The pressure sensor monitors and records the pressure data of the winding disc shaft in real time, and calculates the winding material quality based on the mass of the winding disc and the pressure data. The winding material quality is an important standard for measuring the quality of the winding, and is directly related to the quality and production efficiency of the final product. Through the coordinated action of these sensors, the equipment can output three key winding state data: winding speed, winding tension, and winding material quality, providing accurate basis for subsequent cutting decisions.

[0023] Through the Internet of Things transmission component, the collected winding state data is transmitted back to the cutting decision node in real time, and a winding state data string is generated for winding measurement. Based on the winding measurement results, cutting decisions are made, and the cutting timing, cutting speed, and cutting tension are output as the cutting control parameter set.

[0024] In the data collection phase, the yarn winding equipment transmits the real-time collected winding state data back to the cutting decision node through the Internet of Things transmission component. The cutting decision node is responsible for analyzing and processing these winding state data, generating a winding state data string for winding measurement. The purpose of winding measurement is to monitor the progress, quality and other key parameters of winding according to real-time data, to ensure that the winding process is performed according to the predetermined standard. The winding state data string includes winding speed, winding tension and roll quality data. Through these data, the cutting decision node can comprehensively evaluate the state of the winding process.

[0025] Based on the winding measurement results, the cutting decision node makes cutting decisions, mainly determining appropriate cutting timing, cutting speed and cutting tension control parameters. These decisions ensure the synchronization of cutting operations and winding processes, avoiding unstable product quality or equipment damage due to inappropriate cutting timing or incorrect cutting parameters. Finally, the system will output a set of cutting control parameters, including cutting timing, cutting speed and cutting tension, and transmit them back to the yarn winding equipment. The yarn winding equipment performs cutting operations according to the transmitted cutting control parameter set, thereby achieving automatic and precise control of the entire winding and cutting process.

[0026] Further, as shown in Figure 2 The collected winding state data is transmitted back to the cutting decision node in real time through the Internet of Things transmission component to generate a winding state data string for winding measurement, including:

[0027] The received real-time winding speed, winding tension and roll quality are stored in the corresponding winding speed data string, winding tension data string and roll quality data string according to the time stamp. The winding speed data string is smoothed, and the smoothed winding speed data string is accumulated based on the integral method to obtain the cumulative winding length. Based on the roll quality data string, fitting analysis is performed, and the intuitive winding length is calculated according to the fitting analysis result. The difference length between the cumulative winding length and the intuitive winding length is compared. If the difference length is less than or equal to the preset difference control limit, the average of the cumulative winding length and the intuitive winding length is output as the winding measurement result.

[0028] The real-time returned winding state data includes winding speed, winding tension and roll mass, each group of data is stored according to its corresponding time stamp, and is respectively saved in a winding speed data string, a winding tension data string and a roll mass data string, so that each item of data can be accurately corresponded to its collection time, and a complete information basis is provided for subsequent analysis. Next, the winding speed data string is smoothed to reduce mutations caused by equipment or external interference, so that the data is more stable. Then, based on the integral method, the accumulated winding length is obtained by cumulatively calculating the smoothed winding speed data string. At the same time, based on the roll mass data string, the system performs fitting analysis, calculates and obtains the intuitive winding length by analyzing the trend of historical data and the change of current data. Specifically, historical roll mass data is collected, and the trend of the historical data is analyzed to identify the relationship between roll mass and winding length. By mathematical modeling or statistical analysis of historical data, the system extracts the correlation pattern between roll mass change and winding length. Combined with the real-time collected current roll mass data, the current roll mass data is compared and fused with the historical trend to accurately reflect the quality change under the current production condition. By fitting analysis of the current data, the system generates a mathematical model suitable for the existing production environment, and calculates the intuitive winding length according to the mathematical model. The intuitive winding length reflects the actual cumulative winding length under a specific winding quality. Finally, by comparing the difference between the cumulative winding length and the intuitive winding length, and calculating the difference length, if the difference length is less than or equal to the preset difference control limit, the average of the cumulative winding length and the intuitive winding length is output as the final winding measurement result. This measurement result is used to judge whether the current winding process meets the expected requirements, and to ensure the accuracy of winding before cutting operation.

[0029] Further, through the Internet of Things transmission component, the collected winding state data is transmitted to the cutting decision node in real time to generate a winding state data string for winding measurement, which further includes:

[0030] If the difference length is greater than the preset difference control limit, the relative elongation coefficient string is calculated and obtained according to the winding tension data string and the intrinsic parameters of the spinning line. Based on the integral method, the deformation compensation coefficient is obtained by cumulatively calculating the relative elongation coefficient string, and the first modified winding length is obtained by deforming the cumulative winding length according to the deformation compensation coefficient. Combined with the winding tension data string and the roll mass data string, the common mutation point is extracted to determine the slip node, and the second modified winding length is obtained by slip correction according to the mutation amplitude and mutation duration of the slip node. The cumulative winding length is updated based on the second modified winding length.

[0031] The system compares the accumulated winding length and the intuitive winding length, and if the difference between them is greater than the preset difference control limit, the system will continue to make correction calculation; according to the winding tension data string collected during the winding process and the intrinsic parameters of the yarn, the relative elongation coefficient string is calculated, which reflects the relative elongation of the yarn under different tension conditions and provides a basis for subsequent deformation correction. The specific calculation formula is: wherein, is the relative elongation coefficient at the i th time point, is the tension value in the winding tension data string, and E is the elastic modulus of the yarn, is the original length of the yarn. Then, based on the integral method, the relative elongation coefficient string is accumulated to obtain the deformation compensation coefficient, which reflects the cumulative deformation degree of the yarn during the winding process and considers the influence of tension change, yarn elongation and other factors on the winding length; according to this deformation compensation coefficient, the system makes deformation correction on the accumulated winding length to obtain the first corrected winding length, which is more in line with the actual situation. Specifically, the first corrected winding length = accumulated winding length × (1 + deformation compensation coefficient). In addition, the system also analyzes the winding tension data string and the winding material quality data string to find the mutation points, which are points where the data changes sharply at a certain time, usually representing the occurrence of some abnormal situation or key event; by comparing the two data strings, the system identifies the time points that have occurred simultaneously, i.e. the common mutation points, which are the slip nodes, i.e. the yarn slips due to uneven tension or other factors during the winding process, which will cause fluctuations in the winding quality of the yarn; by analyzing the mutation amplitude and duration of the slip nodes, i.e. the size and duration of the mutation, the severity of the slip is evaluated. Slip nodes with larger mutation amplitude and longer duration usually indicate more serious slip phenomenon, which may cause larger quality fluctuations; according to the analysis results of these slip nodes, the system makes slip correction by adjusting the winding tension, speed and other control parameters, so that the system can effectively reduce or eliminate the slip phenomenon and ensure the stability and consistency of the winding process. Finally, the second corrected winding length is obtained, which further improves the accuracy of the winding length. Based on the second corrected winding length, the system updates the accumulated winding length, so as to realize more accurate control of the winding process and ensure the consistency between the winding data and the actual winding length, thereby improving the accuracy and stability of the cutting control.

[0032] Further, based on the winding measurement results, the cutting decision is made, and the cutting time, cutting speed and cutting tension are output as the cutting control parameter set, including:

[0033] The winding measurement result is compared with the rated yarn length, and a decision trigger is made in combination with a preset cutting window length; if the difference between the winding measurement result and the rated yarn length is less than or equal to the cutting window length, the standard cutting tension and the standard cutting speed are determined based on the intrinsic parameters of the yarn; the time stamp corresponding to the winding measurement result is obtained, and the cutting timing is determined in combination with the preset cutting window length; the standard cutting tension and the standard cutting speed are output as the cutting tension and the cutting speed, and the cutting control parameter set is generated in combination with the cutting timing.

[0034] Specifically, the winding measurement result is compared with the rated yarn length, and it is determined whether to trigger the cutting operation in combination with the preset cutting window length; the cutting window length defines an allowable error range, and if the difference between the winding measurement result and the rated yarn length is less than or equal to the cutting window length, it indicates that the winding has approached the target yarn length, meeting the requirements of the cutting timing. Then, if the difference meets the condition, the standard cutting tension and the standard cutting speed are calculated according to the intrinsic parameters of the yarn (such as the elastic modulus and strength of the yarn), which are dynamically adjusted according to the physical properties of the yarn and the winding state, aiming to ensure the optimal tension and speed during the cutting operation, thereby reducing the error and instability in the cutting process. Then, the system obtains the time stamp corresponding to the winding measurement result, and calculates the ideal cutting timing in combination with the preset cutting window length; the determination of the cutting timing not only considers whether the winding length meets the requirements, but also predicts the best time for cutting according to the real-time data of the winding, to avoid cutting too early or too late, so as to achieve the best winding effect and cutting efficiency. Finally, the system generates a complete cutting control parameter set according to the above parameters, which includes the standard cutting tension, cutting speed and cutting timing, and is used to guide the execution of the cutting operation, ensuring accurate control throughout the process.

[0035] Further, the method further comprises:

[0036] The historical cutting data of the yarn winding device is obtained; the central value analysis is performed according to the historical cutting data, to obtain the typical cutting tension and the typical cutting speed of the yarn winding device; the historical time stamp corresponding to the typical cutting tension is determined, and extension analysis is performed in combination with the standard cutting tension, to obtain the predicted cutting tension; the historical time stamp corresponding to the typical cutting speed is determined, and extension analysis is performed in combination with the standard cutting speed, to obtain the predicted cutting speed; the cutting control parameter set is constructed based on the predicted cutting tension and the predicted cutting speed.

[0037] Preferably, historical cutting data of the yarn winding equipment is acquired, including key information such as tension and speed recorded in past cutting operations. Next, ensemble analysis is performed on this historical cutting data. By analyzing the distribution and trends of the historical data, the typical cutting tension and typical cutting speed of the equipment are determined. These typical parameters reflect the standard range of cutting operations under normal operating conditions, providing important reference for subsequent cutting decisions. Then, the historical timestamps corresponding to the typical cutting tensions are determined, i.e., the time points when these typical cutting tension values ​​occurred. Through these timestamps, the system can understand the optimal or most common cutting tensions that the equipment has achieved under different winding states. Based on the standard cutting tension value in the current operation, the typical cutting tensions in the historical data are extended and analyzed. By mathematically modeling or algorithmically analyzing the relationship between historical cutting tensions and the current standard cutting tension, the system can predict the cutting tension that may occur under the current winding state, i.e., predict the cutting tension. Similarly, the historical timestamps corresponding to the typical cutting speeds are determined, and extended analysis is performed in conjunction with the real-time standard cutting speed to obtain the predicted cutting speed, ensuring that the cutting speed matches the historical performance of the equipment. Finally, based on the predicted cutting tension and predicted cutting speed, a complete set of cutting control parameters is constructed, including the predicted tension, speed and other control elements.

[0038] The cutting control parameter set is transmitted to the yarn winding equipment to perform cutting control.

[0039] The calculated set of cutting control parameters is transmitted in real time to the yarn winding equipment via an IoT transmission component. This set of parameters includes the latest winding data and cutting requirements. Upon receiving the control command, the equipment executes the cutting operation based on these parameters. By transmitting the parameter set, the system ensures that the equipment can perform cutting tasks according to the preset cutting timing, speed, and tension, thereby improving the accuracy and consistency of the cutting operation. Through real-time feedback and parameter transmission, the system achieves dynamic control of the yarn winding process, enabling the equipment to adjust according to real-time conditions during winding, ensuring that each cut is completed under optimal conditions, improving production efficiency and guaranteeing product quality.

[0040] Furthermore, the methods also include:

[0041] During cutting control, cutting execution data is collected synchronously, including tool status data and motor status data. Tool anomaly detection is performed based on the tool status data and a preset tool status transition matrix, and motor anomaly detection is performed based on the preset motor status control limits. If the detection results show an anomaly, an anomaly alarm message is generated for an anomaly response.

[0042] In the execution of the cutting control, the system synchronously collects cutting execution data, including tool state data and motor state data. By monitoring the state of the tool and the motor in real time, the system can timely understand the running condition of the equipment in the cutting process. Specifically, the working state, wear condition, vibration and other indicators of the tool are collected, and abnormal detection is performed according to the preset tool state transition matrix. By comparing with historical data or preset rules, the system can identify whether the tool has abnormal conditions such as excessive wear or jamming. At the same time, the system also collects the speed, load, current and other parameters of the motor, and performs abnormal detection of the motor based on the preset motor state control limit. When the state data of the motor exceeds the preset range, the system determines that the motor is abnormal, such as overload or overheating. If the detection result shows that the tool or the motor is abnormal, the system will generate an abnormal alarm information, and trigger the corresponding abnormal response mechanism according to the alarm information, such as suspending the operation of the equipment, adjusting the working parameters or starting the maintenance process, to ensure the normal operation of the equipment and avoid greater failure. Through this real-time monitoring and abnormal response mechanism, the system can improve the reliability and production efficiency of the equipment, and ensure the smooth progress of the cutting operation.

[0043] Further, the winding state data string for winding measurement further includes:

[0044] Based on the acquired winding state data string, a winding state tensor is formed, and trend fitting and periodicity analysis are performed according to the winding state tensor. According to the analysis result, the forward-looking winding state data in the preset time window is predicted. The forward-looking winding state data and the winding state data string are combined for winding measurement, and the forward-looking winding measurement result is obtained. If the forward-looking winding measurement result meets the preset rated thread length, the cutting decision and cutting control are triggered.

[0045] Specifically, based on the acquired winding state data string, the system forms a winding state tensor, which contains multiple dimensions of winding state data at different time points, such as winding speed, tension and roll mass, etc. Then, the system performs trend fitting and periodicity analysis on the winding state tensor. Through these analyses, the system can identify long-term trends and periodic fluctuations in the winding process, providing a basis for subsequent prediction. Next, based on the results of trend fitting and periodicity analysis, the system predicts the forward-looking winding state data within a preset time window. These forward-looking data provide an expected performance of the winding state in the future, helping to make control decisions in advance. The forward-looking winding state data is combined with the existing winding state data string to perform winding measurement, thereby obtaining the forward-looking winding measurement result. Finally, the system determines whether the forward-looking winding measurement result meets the preset rated thread length. If the condition is met, the system triggers the cutting decision and cutting control. Through this method, the system can predict the winding progress based on forward-looking data before the winding process is completely finished, thereby starting the cutting operation in time to ensure high synchronization and precision of the winding and cutting processes, further improving production efficiency and product quality.

[0046] In summary, the embodiments of the present application have at least the following technical effects:

[0047] First, the thread winding device receives automatically issued thread parameters through the embedded Internet of Things transmission component, wherein the thread parameters include thread intrinsic parameters and thread winding parameters. Next, the thread winding device performs thread winding based on the thread parameters and synchronously collects winding state data in real time through the sensor network. Then, the collected winding state data is transmitted back to the cutting decision node in real time through the Internet of Things transmission component, a winding state data string is generated for winding measurement, and cutting decisions are made based on the winding measurement results, outputting the cutting timing, cutting speed and cutting tension as the cutting control parameter set. Finally, the cutting control parameter set is transmitted to the thread winding device to perform cutting control. The technical problem of insufficient cutting control precision of the thread winding device in the prior art, which cannot be adjusted in real time according to the actual situation, is solved. Through real-time data collection and feedback of the Internet of Things and the sensor network, the technical effect of improving the cutting control precision is achieved.

[0048] It should be noted that the above sequence of the embodiments of the present application is only for description, not representing the advantages and disadvantages of the embodiments. The above describes specific embodiments of the present application. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-task processing and parallel processing are possible or can be advantageous.

[0049] The above description is only the preferred embodiment of the present application, and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

[0050] The specification and drawings are only exemplary and illustrative of the present application and are considered to cover any and all modifications, variations, combinations or equivalents that are within the scope of the present application. Obviously, those skilled in the art can make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application belong to the scope of the present application and its equivalents, the present application is intended to include these modifications and variations.

Claims

1. An intelligent cutting control method for a yarn winding machine, characterized in that, The method includes: The spinning and winding equipment receives automatically transmitted spinning parameters through an embedded Internet of Things (IoT) transmission component. These spinning parameters include intrinsic spinning parameters and spinning and winding parameters. The spinning and winding equipment performs spinning and winding based on the spinning parameters, and simultaneously collects winding status data in real time through a sensor network; The collected winding status data is transmitted back to the cutting decision node in real time through the Internet of Things transmission component. A winding status data string is generated for winding measurement, and a cutting decision is made based on the winding measurement result. The cutting timing, cutting speed and cutting tension are output as a set of cutting control parameters. The cutting control parameter set is transmitted to the yarn winding equipment to execute cutting control. The winding status data includes winding speed, winding tension, and roll weight. The yarn winding equipment performs yarn winding based on the yarn parameters and simultaneously collects the winding status data in real time through a sensor network, including: The sensor network includes a resolver sensor, a tension sensor, and a pressure sensor; The refractive index sensor monitors and records the refractive index data of the take-up shaft in real time, and calculates and obtains the take-up speed based on the refractive index data. The tension sensor monitors and records the tension during the yarn winding process in real time, and outputs the winding tension. The pressure sensor monitors and records the pressure data of the winding reel shaft in real time, and calculates the mass of the roll material by combining the mass of the winding reel with the pressure data. The winding speed, winding tension, and roll mass are output as the winding status data; Specifically, the collected winding status data is transmitted back to the cutting decision node in real time via an IoT transmission component, generating a winding status data string for winding measurement, including: Receive the real-time transmitted winding speed, winding tension, and roll mass, and store them in the corresponding winding speed data string, winding tension data string, and roll mass data string according to the timestamp; The winding speed data string is smoothed, and the smoothed winding speed data string is accumulated based on the integration method to obtain the cumulative winding length; A fitting analysis is performed based on the coil quality data string, and the intuitive winding length is calculated based on the fitting analysis results. The difference between the cumulative winding length and the intuitive winding length is compared. If the difference length is less than or equal to a preset difference control limit, the average of the cumulative winding length and the intuitive winding length is output as the winding measurement result. The system includes, via an IoT transmission component, real-time transmission of the collected winding status data to the cutting decision node to generate a winding status data string for winding measurement; and further includes: If the difference length is greater than the preset difference control limit, then the relative elongation coefficient string is calculated and obtained based on the winding tension data string and the yarn intrinsic parameters; The relative elongation coefficient string is accumulated and calculated using an integral method to obtain a deformation compensation coefficient. The accumulated winding length is then deformed and corrected according to the deformation compensation coefficient to obtain a first corrected winding length. Combining the winding tension data string and the roll material quality data string, common abrupt change points are extracted and identified as slippage nodes. Slippage correction is performed based on the abrupt change amplitude and duration of the slippage node to obtain the second corrected winding length. The cumulative winding length is updated based on the second corrected winding length.

2. The intelligent cutting control method for a yarn winding device as described in claim 1, characterized in that, Cutting decisions are made based on the winding measurement results, and the output cutting timing, cutting speed, and cutting tension are a set of cutting control parameters, including: The winding measurement result is compared with the rated yarn length, and a decision trigger judgment is made in combination with the preset cutting window length; If the difference between the winding measurement result and the rated yarn length is less than or equal to the cutting window length, then the standard cutting tension and standard cutting speed are determined based on the intrinsic parameters of the yarn. Obtain the timestamp corresponding to the winding measurement result, and determine the cutting timing in combination with the preset cutting window length; The standard cutting tension and the standard cutting speed are output as the cutting speed and the cutting tension, and combined with the cutting timing, the cutting control parameter set is generated.

3. The intelligent cutting control method for a yarn winding device as described in claim 2, characterized in that, The method further includes: Obtain historical cutting data from the yarn winding equipment; Based on the historical cutting data, a central value analysis was performed to obtain the typical cutting tension and typical cutting speed of the yarn winding equipment. Determine the historical timestamp corresponding to the typical cutting tension, and perform extended analysis in conjunction with the standard cutting tension to obtain the predicted cutting tension; Determine the historical timestamp corresponding to the typical cutting speed, and perform extended analysis in conjunction with the standard cutting speed to obtain the predicted cutting speed; Based on the predicted cutting tension and the predicted cutting speed, the cutting control parameter set is constructed.

4. The intelligent cutting control method for a yarn winding device as described in claim 1, characterized in that, The method further includes: When performing cutting control, cutting execution data is collected synchronously, including tool status data and motor status data; Tool anomaly detection is performed based on the tool state data and a preset tool state transition matrix, and motor anomaly detection is performed based on the motor state data according to preset motor state control limits. If the test results show an anomaly, an anomaly alarm message will be generated to respond to the anomaly.

5. The intelligent cutting control method for a yarn winding device as described in claim 1, characterized in that, The take-up status data string, used for take-up metering, also includes: Based on the acquired winding state data string, a winding state tensor is formed, and trend fitting and periodicity analysis are performed based on the winding state tensor. Based on the analysis results, predict the forward roll-up status data within the preset time window; By combining the forward winding status data with the winding status data string, winding measurement is performed to obtain the forward winding measurement result. If the forward winding measurement result meets the preset rated yarn length, then the cutting decision and cutting control are triggered.

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