Intelligent cutting control method for doubling thread winding equipment

Abstract

The invention discloses an intelligent cutting control method for doubling thread winding equipment, and relates to the technical field of intelligent control. The method comprises the steps that doubling thread winding equipment receives automatically issued doubling thread parameters through an embedded Internet of Things transmission assembly; the doubling thread winding equipment executes doubling thread winding based on the doubling thread parameters and synchronously collects winding state data in real time through the sensor network; the collected winding state data is transmitted back to a cutting decision node in real time, a winding state data string is generated for winding metering, cutting decision is made based on a winding metering result, and the cutting opportunity, the cutting speed and the cutting tension are output as a cutting control parameter set; and the cutting control parameter set is transmitted to doubling thread winding equipment to execute cutting control. The technical problems that in the prior art, spinning yarn winding equipment is insufficient in cutting control precision and cannot be adjusted in real time according to the actual situation are 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.

Description

Technical Field

[0001] The present invention relates to the field of intelligent control technology, and in particular to an intelligent cutting control method for a spinning winding device. Background Art

[0002] The spinning winding process usually involves winding textile raw materials (such as yarn and fiber) into rolls through a winder, and the cutting operation is one of the important steps that cannot be ignored. Traditional spinning winding equipment often relies on preset programs and fixed parameters to operate when performing cutting, and cannot flexibly adjust the timing, speed and tension of cutting according to the real-time winding status. Especially during the production process, factors such as yarn quality, tension changes, and winding speed may change at any time, which makes it difficult for traditional winding equipment to cope with complex production environments, resulting in insufficient cutting control accuracy and even affecting product consistency and quality. Summary of the invention

[0003] The present application provides an intelligent cutting control method for a spinning winding device, which solves the technical problem in the prior art that the cutting control accuracy of the spinning winding device is insufficient and cannot be adjusted in real time according to actual conditions.

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

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages: First, the spinning winding device receives automatically issued spinning parameters through the embedded Internet of Things transmission component, where the spinning parameters include spinning intrinsic parameters and spinning winding parameters. Next, the spinning winding device performs spinning winding based on the spinning parameters, and synchronously collects winding status data in real time through the sensor network. Then, through the Internet of Things transmission component, the collected winding status data is transmitted back to the cutting decision node in real time, and a winding status data string is generated for winding metering, and a cutting decision is made based on the winding metering result, and the cutting timing, 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 that the cutting control accuracy of the spinning winding device in the prior art is insufficient and 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 sensor networks, the technical effect of improving the cutting control accuracy is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0008] Figure 1 A schematic flow chart of an intelligent cutting control method for a spinning winding device provided in an embodiment of the present application; Figure 2 A schematic diagram of a flow chart for generating a winding status data string for winding metering in an intelligent cutting control method for a spinning winding device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0009] The present application solves the technical problem in the prior art that the cutting control accuracy of the spinning winding equipment is insufficient and cannot be adjusted in real time according to actual conditions by providing an intelligent cutting control method for the spinning winding equipment.

[0010] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0011] It should be noted that the terms "including" and "having" are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules that are not explicitly listed or are inherent to these processes, methods, products or devices.

[0012] Examples, such as Figure 1 As shown, an embodiment of the present application provides an intelligent cutting control method for a spinning winding device, wherein the method includes: The spinning winding device receives automatically transmitted spinning parameters through an embedded Internet of Things transmission component, wherein the spinning parameters include spinning intrinsic parameters and spinning winding parameters.

[0013] The spinning winding equipment can receive the spinning parameters automatically sent in real time through the embedded IoT transmission components; these spinning parameters are divided into two categories: one is the intrinsic parameters of the spinning, which include various data related to the physical properties of the spinning itself, such as the thickness, strength, elasticity, diameter, etc. of the yarn; the other is the spinning winding parameters, which involve specific control requirements in the winding process, such as winding speed, tension, reel load and material stretch. The spinning winding equipment receives the spinning parameters automatically sent from the outside through the embedded IoT components. These parameters include the physical properties of the spinning and the specific operating parameters that need to be controlled during the winding process. The equipment performs intelligent operations based on these parameters.

[0014] The yarn winding device executes yarn winding based on the yarn parameters and synchronously collects the winding status data in real time through the sensor network.

[0015] After receiving the spinning parameters, the spinning winding equipment will perform the spinning winding operation according to these parameters; the equipment adjusts the speed, tension and other control variables in the winding process according to the intrinsic parameters of the spinning and the winding parameters to ensure the accuracy and stability of the winding process; at the same time, the equipment also collects the winding status data in real time through the embedded sensor network during the winding process. These winding status data may include key indicators such as tension changes, winding speed, and yarn deformation during the winding process. With the support of the sensor network, the equipment can continuously monitor and optimize the winding process in a dynamically changing production environment to ensure that the winding quality meets the predetermined requirements.

[0016] Furthermore, the winding state data includes winding speed, winding tension and winding material quality. The spinning winding device performs spinning winding based on the spinning parameters and synchronously collects the winding state data in real time through the sensor network, including: The sensor network includes a rotational sensor, a tension sensor and a pressure sensor; the rotational sensor is used to monitor and record the rotational data of the winding shaft in real time, and the winding speed is calculated based on the rotational data; the tension sensor is used to monitor and record the tension in the winding process of the spinning thread 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 disk shaft in real time, and the quality of the coil is calculated by combining the quality of the winding disk and the pressure data; the winding speed, the winding tension and the quality of the coil are output as the winding status data.

[0017] The winding status data includes winding speed, winding tension and coil quality. These key parameters 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 rotary sensor, a tension sensor and a pressure sensor, which work together to obtain comprehensive winding status information. Among them, the rotary sensor is responsible for real-time monitoring and recording the rotary data of the winding shaft (such as the rotary data of the shaft of the winding disk and the tension wheel); by analyzing these rotary data, the system can accurately calculate the actual speed of the winding, thereby ensuring that the winding process meets the predetermined operating requirements. The tension sensor is used to monitor the tension changes of the spinning during the winding process, and record and output the winding tension data in real time; the tension sensor includes a wheel tension sensor, a cantilever beam sensor, and a bearing sensor. Among them, the wheel tension sensor: the yarn bypasses the sensor wheel and directly measures the tension of the yarn, which is suitable for dynamic real-time measurement; the cantilever beam sensor: installed on the yarn guide rod or the tension control device, and measures the tension through the change of displacement or force; the bearing sensor: installed on the winding shaft, measures the force of the bearing to indirectly obtain the tension. The pressure sensor monitors and records the pressure data of the winding reel shaft in real time, and calculates the coil quality by combining the quality and pressure data of the winding reel. The coil quality is an important criterion for measuring the quality of winding, which is directly related to the quality of the final product and production efficiency. Through the synergy of these sensors, the equipment can output three key winding status data: winding speed, winding tension and coil quality, providing an accurate basis for subsequent cutting decisions.

[0018] Through the Internet of Things transmission component, the collected winding status data is transmitted back to the cutting decision node in real time, a winding status data string is generated for winding measurement, and a cutting decision is made based on the winding measurement result, and the cutting timing, cutting speed and cutting tension are output as the cutting control parameter set.

[0019] During the data collection phase, the spinning winding equipment transmits the real-time collected winding status 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 status data and generating a winding status data string for winding metering. The purpose of winding metering is to monitor the progress, quality and other key parameters of winding according to real-time data to ensure that the winding process is carried out according to predetermined standards. The winding status data string includes data such as winding speed, winding tension and coil quality. Through these data, the cutting decision node can comprehensively evaluate the state of the winding process.

[0020] Based on the winding metering results, the cutting decision node makes cutting decisions, mainly to determine the appropriate cutting timing, cutting speed, cutting tension and other control parameters. These decisions ensure the synchronization of the cutting operation with the winding process, avoiding unstable product quality or equipment damage due to inappropriate cutting timing or incorrect cutting parameters. Ultimately, the system will output a cutting control parameter set based on these decisions, including parameters such as cutting timing, cutting speed and cutting tension, and transmit it back to the spinning winding equipment. The spinning winding equipment performs the cutting operation according to the transmitted cutting control parameter set, thereby realizing the automation and precise control of the entire winding and cutting process.

[0021] Furthermore, if Figure 2 As shown, the collected winding state data is transmitted back to the cutting decision node in real time through the Internet of Things transmission component, and a winding state data string is generated for winding measurement, including: Receive the winding speed, winding tension and coil quality transmitted back in real time, and store them in the corresponding winding speed data string, winding tension data string and coil quality data string according to the timestamp; smooth the winding speed data string, and perform cumulative calculation on the smoothed winding speed data string based on the integral method to obtain the cumulative winding length; perform fitting analysis based on the coil quality data string, and calculate and obtain the intuitive winding length according to the fitting analysis result; compare and obtain the difference length between the cumulative winding length and the intuitive winding length, and if the difference length is less than or equal to the preset difference control limit, output the average of the cumulative winding length and the intuitive winding length as the winding measurement result.

[0022] The real-time winding status data transmitted back include winding speed, winding tension and coil material quality. Each set of data will be stored according to its corresponding timestamp, and saved in the winding speed data string, winding tension data string and coil material quality data string respectively, to ensure that each data can accurately correspond to its collection time and provide a complete information basis for subsequent analysis. Next, the winding speed data string is smoothed to reduce mutations caused by equipment or external interference, making the data more stable. Then, based on the integral method, the smoothed winding speed data string is accumulated and calculated to obtain the cumulative winding length. The cumulative winding length refers to the accumulated length of the spinning line during the winding process. At the same time, based on the coil quality 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, collects historical coil quality data, analyzes the trend of these historical data, and identifies the relationship between coil quality and winding length; through mathematical modeling or statistical analysis of historical data, the system extracts the correlation pattern between coil quality change and winding length; combined with the current coil quality data collected in real time, it is compared and integrated with the historical trend to accurately reflect the quality change under the current production conditions; through fitting analysis of current data, the system generates a mathematical model that adapts to the existing production environment, and calculates the intuitive winding length based on it. The intuitive winding length reflects the actual accumulated 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 mean of the cumulative winding length and the intuitive winding length is output as the final winding measurement result. This measurement result is used to determine whether the current winding process meets the expected requirements and ensure the accuracy of winding before cutting operation.

[0023] Furthermore, 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, and also includes: If the difference length is greater than the preset difference control limit, the relative elongation coefficient string is calculated based on the winding tension data string and the spinning intrinsic parameters; the relative elongation coefficient string is accumulated based on the integral method to obtain the deformation compensation coefficient, and the cumulative winding length is deformed and corrected according to the deformation compensation coefficient to obtain the first corrected winding length; combining the winding tension data string and the coil quality data string, the common mutation point is extracted to determine it as the slip node, and the slip correction is performed according to the mutation amplitude and mutation duration of the slip node to obtain the second corrected winding length; the cumulative winding length is updated based on the second corrected winding length.

[0024] The system will compare the cumulative winding length and the intuitive winding length. If the difference between them is greater than the preset difference control limit, the system will continue to perform correction calculations. According to the winding tension data string collected during the winding process and the intrinsic parameters of the spinning line, the relative elongation coefficient string is calculated. The relative elongation coefficient string reflects the relative elongation of the spinning line under different tension conditions, providing a basis for subsequent deformation correction. The specific calculation formula is: ,in, is the relative elongation coefficient at the i-th time point, is the tension value in the winding tension data string, E is the elastic modulus of the spinning line, is the original length of the yarn. Then, based on the integral method, the relative elongation coefficient string is accumulated and calculated to obtain the deformation compensation coefficient. The deformation compensation coefficient reflects the cumulative deformation degree of the spinning line during the winding process, and takes into account the influence of factors such as tension change and yarn elongation on the winding length; according to this deformation compensation coefficient, the system performs deformation correction on the cumulative winding length to obtain the first corrected winding length. The first corrected winding length is more in line with the actual situation. Specifically, the first corrected winding length = cumulative winding length × (1 + deformation compensation coefficient). In addition, the system will also analyze the winding tension data string and the coil quality data string to find the mutation points therein. The mutation point refers to a sharp change in the data at a certain moment, which usually represents the occurrence of some abnormal situation or key event. By comparing the two data strings, the system identifies the time points when the mutations occur at the same time, that is, the common mutation points, and regards these common mutation points as slip nodes. The slip nodes refer to the slippage of the yarn due to uneven tension or other factors during the winding process. This slippage will cause fluctuations in the yarn winding quality. The severity of the slippage is evaluated by analyzing the mutation amplitude and mutation duration of the slip nodes, that is, the size and duration of the mutation. Slip nodes with large mutation amplitudes and long durations usually indicate more serious slippage, which may lead to larger quality fluctuations. According to the analysis results of these slip nodes, the system performs slip correction. By adjusting the winding tension, speed and other control parameters, the system can effectively reduce or eliminate slippage, ensure the stability and consistency of the winding process, and finally obtain the second corrected winding length to further improve the accuracy of the winding length. Based on the second corrected winding length, the system will update the cumulative winding length, thereby achieving more precise control of the winding process, ensuring the consistency between the winding data and the actual winding length, and improving the accuracy and stability of cutting control.

[0025] Furthermore, the cutting decision is made based on the winding metering result, and the cutting timing, cutting speed and cutting tension are output as the cutting control parameter set, including: Compare the winding metering result with the rated spinning length, and make a decision trigger judgment in combination with the preset cutting window length; if the difference between the winding metering result and the rated spinning length is less than or equal to the cutting window length, determine the standard cutting tension and the standard cutting speed based on the intrinsic parameters of the spinning line; obtain the timestamp corresponding to the winding metering result, and determine the cutting timing in combination with the preset cutting window length; output the standard cutting tension and the standard cutting speed as the cutting speed and the cutting tension, and generate the cutting control parameter set in combination with the cutting timing.

[0026] Specifically, the winding metering result is compared with the rated spinning length, and combined with the preset cutting window length, it is determined whether the cutting operation is triggered; the cutting window length defines an allowable error range. If the difference between the winding metering result and the rated spinning length is less than or equal to the cutting window length, it indicates that the winding is close to the target spinning length and meets the requirements of the cutting timing. Then, if the difference meets the conditions, the standard cutting tension and standard cutting speed are calculated according to the intrinsic parameters of the spinning (such as the elastic modulus and strength of the yarn). These two parameters are dynamically adjusted according to the physical properties of the spinning and the winding state, aiming to ensure that the optimal tension and speed are maintained during the cutting operation, thereby reducing errors and instability during the cutting process. Then, the system obtains the timestamp corresponding to the winding metering result, and combines the preset cutting window length to calculate the ideal cutting time. The determination of the cutting time should not only consider whether the winding length meets the requirements, but also predict the best time for cutting based on the real-time data of the winding, so as 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 based on the above parameters. This parameter set includes standard cutting tension, cutting speed and cutting timing, and is used to guide the execution of the cutting operation to ensure precise control of the entire process.

[0027] Furthermore, the method further comprises: Acquire historical cutting data of a spinning winding device; perform concentrated value analysis based on the historical cutting data to obtain typical cutting tension and typical cutting speed of the spinning winding device; determine the historical timestamp corresponding to the typical cutting tension, and perform extended analysis in combination with the standard cutting tension to obtain a predicted cutting tension; determine the historical timestamp corresponding to the typical cutting speed, and perform extended analysis in combination with the standard cutting speed to obtain a predicted cutting speed; construct the cutting control parameter set based on the predicted cutting tension and the predicted cutting speed.

[0028] Preferably, historical cutting data of the spinning winding equipment are obtained, which include key information such as tension and speed recorded in past cutting operations; then, a centralized value analysis is performed on these historical cutting data, and the typical cutting tension and typical cutting speed of the equipment are determined by analyzing the distribution and trend of the historical data. These typical parameters reflect the standard range of cutting operations of the equipment under normal operating conditions, and provide an important reference for subsequent cutting decisions; then, the historical timestamp corresponding to the typical cutting tension is determined, that is, the time nodes when these typical cutting tension values ​​appear are determined. Through these timestamps, the system can understand the best or most common cutting tensions that the equipment has ever achieved under different winding conditions; according to the value of the standard cutting tension in the current operation, an extended analysis is performed on the typical cutting tension in the historical data, and by mathematically modeling or algorithmically analyzing the relationship between the historical cutting tension and the current standard cutting tension, the system can predict the cutting tension that may occur under the current winding state, that is, the predicted cutting tension; similarly, the historical timestamp corresponding to the typical cutting speed is determined, and an extended analysis is performed in combination with the real-time standard cutting speed to obtain the predicted cutting speed, thereby 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 cutting control parameter set is constructed, including the predicted tension, speed and other control elements.

[0029] The cutting control parameter set is transmitted to the yarn winding device to execute cutting control.

[0030] The calculated cutting control parameter set is transmitted to the spinning winding equipment in real time through the IoT transmission component. The cutting control parameter set contains the latest winding data and cutting requirements. After receiving the control command, the equipment performs the cutting operation according to these parameters. By transmitting the parameter set, the system ensures that the equipment can perform the cutting task 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 realizes dynamic control of the spinning winding process, enabling the equipment to adjust according to the real-time situation during the winding process, ensuring that each cutting is completed under the best conditions, improving production efficiency and ensuring product quality.

[0031] Furthermore, the method further comprises: When executing cutting control, cutting execution data is synchronously collected, wherein the cutting execution data includes tool status data and motor status data; tool abnormality detection is performed based on the tool status data and a preset tool status migration matrix, and motor abnormality detection is performed based on the motor status data according to the preset motor status control limit; if the detection result shows that an abnormality exists, an abnormal alarm message is generated for abnormal response.

[0032] When executing cutting control, the system will synchronously collect cutting execution data, which includes tool status data and motor status data. By real-time monitoring of the status of the tool and motor, the system can timely understand the operation of the equipment during the cutting process. Specifically, the working status, wear, vibration and other indicators of the tool are collected, and abnormal detection is performed according to the preset tool state migration 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 parameters such as motor speed, load, current, etc., and performs motor abnormality detection based on the preset motor state control limit; when the motor state data exceeds the preset range, the system determines that the motor has abnormalities, such as overload or overheating; if the test results show that the tool or motor is abnormal, the system will generate 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 failures. 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.

[0033] Furthermore, the winding state data string for winding measurement also includes: 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 within a preset time window is predicted; the winding metering is performed in combination with the forward-looking winding state data and the winding state data string to obtain the forward-looking winding metering result; if the forward-looking winding metering result meets the preset rated spinning length, the cutting decision and cutting control are triggered.

[0034] Specifically, based on the acquired winding state data string, the system forms a winding state tensor, which contains winding state data of multiple dimensions at different time points, such as winding speed, tension and coil quality, etc.; then, the system performs trend fitting and periodic analysis on the winding state tensor. Through these analyses, the system can identify the long-term change trend and periodic fluctuation in the winding process, providing a basis for subsequent predictions; next, based on the results of trend fitting and periodic analysis, the system predicts the forward-looking winding state data within the preset time window. These forward-looking data provide the system with the expected performance of the winding state in the future, which helps to make control decisions in advance; the forward-looking winding state data is combined with the existing winding state data string to perform winding metering, thereby obtaining the forward-looking winding metering result; finally, the system will determine whether the forward-looking winding metering result meets the preset rated spinning length. If the conditions are met, the system will trigger the cutting decision and cutting control. Through this method, the system can predict the progress of winding based on forward-looking data before the winding process is completely completed, so as to start the cutting operation in time, ensure the high synchronization and accuracy of the winding and cutting processes, and further improve production efficiency and product quality.

[0035] In summary, the embodiments of the present application have at least the following technical effects: First, the spinning winding device receives automatically issued spinning parameters through the embedded Internet of Things transmission component, where the spinning parameters include spinning intrinsic parameters and spinning winding parameters. Next, the spinning winding device performs spinning winding based on the spinning parameters, and synchronously collects winding status data in real time through the sensor network. Then, through the Internet of Things transmission component, the collected winding status data is transmitted back to the cutting decision node in real time, and a winding status data string is generated for winding metering, and a cutting decision is made based on the winding metering result, and the cutting timing, 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 that the cutting control accuracy of the spinning winding device in the prior art is insufficient and 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 sensor networks, the technical effect of improving the cutting control accuracy is achieved.

[0036] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0037] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

[0038] This specification and drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may 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 fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. An intelligent cutting control method for a spinning winding device, characterized in that: The method comprises: The spinning winding device receives automatically issued spinning parameters through the embedded Internet of Things transmission component, wherein the spinning parameters include spinning intrinsic parameters and spinning winding parameters; The spinning winding device performs spinning winding based on the spinning parameters and synchronously collects winding status data in real time through a sensor network; 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 a cutting decision is made based on the winding measurement result, and the cutting timing, cutting speed and cutting tension are output as a cutting control parameter set; The cutting control parameter set is transmitted to the yarn winding device to execute cutting control.

2. The intelligent cutting control method for a spinning winding device according to claim 1, characterized in that: The winding state data includes winding speed, winding tension and winding material quality. The spinning winding device performs spinning winding based on the spinning parameters and synchronously collects the winding state data in real time through the sensor network, including: The sensor network includes a rotary sensor, a tension sensor and a pressure sensor; The rotary sensor is used to monitor and record the rotary data of the winding shaft in real time, and the winding speed is calculated based on the rotary data. The tension sensor is used to monitor and record the tension during the winding process of the spinning thread 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 reel shaft in real time, and the mass of the coil is calculated and obtained by combining the mass of the winding reel with the pressure data; The winding speed, the winding tension and the coil material quality are output as the winding state data.

3. The intelligent cutting control method for a spinning winding device according to claim 2, characterized in that: The collected winding state data is transmitted back to the cutting decision node in real time through the Internet of Things transmission component, and a winding state data string is generated for winding measurement, including: Receive the winding speed, the winding tension and the coil material quality transmitted back in real time, and store them into corresponding winding speed data string, winding tension data string and coil material quality data string according to the timestamp; Smoothing the winding speed data string, and performing cumulative calculation on the smoothed winding speed data string based on an integration method to obtain a cumulative winding length; Performing a fitting analysis based on the coil material quality data string, and calculating and obtaining an intuitive coiling length according to the fitting analysis result; The difference length between the cumulative winding length and the intuitive winding length is obtained by comparison. 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.

4. The intelligent cutting control method for a spinning winding device according to claim 3, characterized in that: 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, and also includes: If the difference length is greater than the preset difference control limit, a relative elongation coefficient string is calculated based on the winding tension data string and the spinning intrinsic parameter; Based on the integral method, the relative elongation coefficient string is cumulatively calculated to obtain a deformation compensation coefficient, and the cumulative retracted length is deformed and corrected according to the deformation compensation coefficient to obtain a first corrected retracted length; Combining the winding tension data string with the coil material quality data string, extracting a common mutation point to determine as a slip node, and performing a slip correction according to the mutation amplitude and mutation duration of the slip node to obtain a second corrected winding length; The accumulated retracted length is updated based on the second modified retracted length.

5. The intelligent cutting control method for a spinning winding device according to claim 3, characterized in that: Cutting decisions are made based on the winding metering results, and the cutting timing, cutting speed and cutting tension are output as the cutting control parameter set, including: Comparing the winding measurement result with the rated spinning length, and making a decision triggering judgment in combination with a preset cutting window length; If the difference between the winding measurement result and the rated spinning length is less than or equal to the cutting window length, a standard cutting tension and a standard cutting speed are determined based on the intrinsic parameters of the spinning line; Obtaining a timestamp corresponding to the winding measurement result, and determining the cutting timing in combination with a 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.

6. The intelligent cutting control method for a spinning winding device according to claim 5, characterized in that: The method further comprises: Obtain historical cutting data of spinning winding equipment; Performing concentrated value analysis based on the historical cutting data to obtain typical cutting tension and typical cutting speed of the spinning winding device; Determine the historical timestamp corresponding to the typical cutting tension, and perform extended analysis in combination 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 combination with the standard cutting speed to obtain a predicted cutting speed; The cutting control parameter set is constructed based on the predicted cutting tension and the predicted cutting speed.

7. The intelligent cutting control method for a spinning winding device according to claim 1, characterized in that: The method further comprises: When executing cutting control, synchronously collect cutting execution data, wherein the cutting execution data includes tool state data and motor state data; Performing tool anomaly detection according to the tool state data and a preset tool state migration matrix, and performing motor anomaly detection based on the motor state data according to a preset motor state control limit; If the detection result shows an abnormality, an abnormal alarm message is generated to respond to the abnormality.

8. The intelligent cutting control method for a spinning winding device according to claim 1, characterized in that: The winding status data string is used for winding measurement, and also includes: 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 results, the forward-looking winding status data within the preset time window is predicted; Combining the forward-looking winding state data with the winding state data string to perform winding measurement, and obtaining the forward-looking winding measurement result; If the forward-looking winding metering result meets the preset rated spinning length, the cutting decision and cutting control are triggered.

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