An intelligent monitoring system and method for an adhesive tape production line based on the Internet of Things

Abstract

The application discloses a kind of based on internet of things adhesive tape production line intelligent monitoring system and method following steps: step S1: through the sensor module deployed in adhesive tape production line, the data of glueing thickness, substrate tension and winding uniformity in adhesive tape production process are collected in real time;Step S2: the data collected are transmitted to central control system by internet of things communication module;Step S3: central control system is based on acquisition data, respectively, to glueing thickness fluctuation, tension distribution and winding uniformity are analyzed in real time, and corresponding adjustment instruction is generated;Step S4: adjustment instruction is sent to glueing equipment, tension control device and winding mechanism by control module, equipment parameters are dynamically adjusted, the present application has the characteristics of efficient, accurate.

Description

Technical Field

[0001] This invention relates to the field of Internet of Things (IoT) technology, specifically to an IoT-based intelligent monitoring system and method for tape production lines. Background Technology

[0002] Adhesive tape production lines play a vital role in modern industrial production, and their operational efficiency and product quality directly impact a company's market competitiveness. However, traditional adhesive tape production lines mostly rely on manual inspections and simple automated controls, resulting in problems such as large monitoring blind spots, slow response times, and untimely fault detection. This makes them unable to meet the current demands for efficient and precise production, thus limiting the industry's development.

[0003] In the tape production process, achieving uniform adhesive coating thickness is a key technical challenge. The coating head is prone to slight vibrations during mechanical operation, leading to fluctuations in adhesive layer thickness and affecting the tape's adhesion performance. Existing thickness detection methods are mostly offline, unable to acquire and adjust thickness data in real time. Furthermore, the tension control of the substrate roll directly determines the winding tightness and uniformity, but traditional tension control systems have slow response times, often resulting in excessive or insufficient tension, impacting production quality. Insufficient dynamic adjustment capabilities in the winding process further exacerbate the inconsistency in tension between the two sides, increasing the scrap rate. Therefore, designing an efficient and precise IoT-based intelligent monitoring system and method for tape production lines is essential. Summary of the Invention

[0004] The purpose of this invention is to provide an intelligent monitoring system and method for tape production lines based on the Internet of Things, so as to solve the problems mentioned in the background art.

[0005] To address the aforementioned technical problems, this invention provides the following technical solution: an intelligent monitoring method for an adhesive tape production line based on the Internet of Things, comprising the following steps:

[0006] Step S1: Collect data on adhesive coating thickness, substrate tension, and winding uniformity in real time during the tape production process using sensor modules deployed on the tape production line.

[0007] Step S2: Transmit the collected data to the central control system via the IoT communication module;

[0008] Step S3: Based on the collected data, the central control system performs real-time analysis on the coating thickness fluctuation, tension distribution, and winding uniformity, and generates corresponding adjustment instructions.

[0009] Step S4: The control module sends adjustment commands to the coating equipment, tension control device and winding mechanism to dynamically adjust the equipment parameters.

[0010] According to the above technical solution, step S1 specifically includes:

[0011] Multiple tension sensors are installed at various points during the unwinding, coating, and rewinding stages of the adhesive tape production line. These sensors detect the tension of the substrate at different locations in real time and generate corresponding tension signals. The tension values ​​collected by the sensors are then transmitted to the manufacturer. It is converted into an electrical signal and transmitted to the data processing module;

[0012] A laser thickness sensor is installed at the outlet of the adhesive coating device to detect the adhesive coating thickness on the tape surface in real time and to achieve non-contact real-time measurement.

[0013] Displacement sensors and pressure sensors are arranged on both sides of the winding device to monitor the offset and clamping force during the winding process, respectively, and obtain the offset and clamping force values ​​on both sides to form real-time comparison data.

[0014] According to the above technical solution, the method for generating the analysis and adjustment instructions for the coating thickness fluctuation in step S3 includes:

[0015] Step S311: Real-time acquisition of tension distribution of the monitoring substrate The system retrieves the amount of adhesive released per unit time, Q, monitored by the flow sensor built into the dispensing head, and the substrate speed recorded by the encoder. ;

[0016] Step S312: Calculate the amplitude of tension change using formula (1);

[0017] Step S313: Predict the coating thickness using the amplitude of tension change. The influence of this is used to train a multivariate regression model using formula (2);

[0018] Step S314: The central control system adjusts the output flow rate of the dispensing head in real time according to formula (3);

[0019] Step S315: The substrate speed is adjusted according to formula (4) to dynamically correct the substrate transmission speed;

[0020] Step S316: After generating the adjustment command, continuously monitor the adhesive coating thickness. The deviation between the actual value and the target value is compared. If the deviation exceeds the set threshold, the parameters Q and Q are optimized and adjusted again. Continue until the adhesive coating thickness stabilizes;

[0021] in,

[0022] Formula (1);

[0023] Formula (2);

[0024] Formula (3);

[0025] Formula (4);

[0026] In the formula, The rate of change of tension, For the tension at the present moment, for The tension of the previous moment, This is the coating thickness value adjusted in real time based on tension changes. This is the linkage coefficient, used to quantify tension changes. The degree of influence on the thickness of the adhesive coating The adjusted output flow rate of the dispensing head. To determine the optimal value of the linkage factor for adjusting the amount of adhesive, an experiment was conducted. For the adjusted substrate transport speed, This is a scaling factor for speed adjustment, used to balance the effect of tension changes on the substrate speed.

[0027] According to the above technical solution, the method for generating the tension distribution adjustment command in step S3 includes:

[0028] Step S321: Real-time acquisition of data from multiple tension sensors in the tensile region of the substrate. ;

[0029] Step S322: Calculate the overall tension deviation using formula (5);

[0030] Step S323: Calculate the clamping force of the dynamically adjusted tension regulating roller using formula (6). To reduce overall deviation;

[0031] in,

[0032] Formula (5);

[0033] Formula (6);

[0034] In the formula, As an indicator of tension imbalance, The average tension value Let i be the clamping force of the i-th tension adjusting roller. The base clamping force is the system's preset default clamping force value. To adjust the coefficient, for right The partial derivatives of .

[0035] According to the above technical solution, the method for analyzing and adjusting the winding uniformity in step S3 includes:

[0036] Step S331: Adjust the adhesive coating thickness Tension distribution and substrate speed Data is used as input to optimize take-up parameters in advance;

[0037] Step S332: Based on the autoregressive model, predict the winding tightness S using formula (7);

[0038] Step S333: Based on the predicted winding tension S, calculate the speed adjustment of the motors on both sides of the winding equipment using formula (8). Adjust the speed of the motors on both sides synchronously to ensure that the tension on both sides is consistent;

[0039] Step S334: Monitor the actual tightness of the left and right sides in real time and compare it with the predicted value. If the deviation exceeds the set threshold, update dynamically. And readjust the motor speed to ensure uniform winding;

[0040] in,

[0041] Formula (7);

[0042] Formula (8);

[0043] In the formula, For the predicted winding tightness, This represents the average value of the tension distribution. Here, k is the control parameter, and k is the proportional coefficient. This is the actual measured winding tightness value.

[0044] An intelligent monitoring system for a tape production line based on the Internet of Things (IoT) includes a sensor module, an IoT communication module, a central control system, and a control module.

[0045] The sensor module is deployed in the unwinding, coating, and winding stages of the tape production line, and can collect data on coating thickness, substrate tension, and winding uniformity in real time during the tape production process.

[0046] The IoT communication module is used to transmit the collected data to the central control system;

[0047] The central control system is used to perform real-time analysis of coating thickness fluctuations, tension distribution, and winding uniformity based on the collected data, and to generate corresponding adjustment instructions.

[0048] The control module is used to receive adjustment instructions sent by the central control system and control the coating equipment, tension control device and winding mechanism to dynamically adjust the equipment parameters.

[0049] According to the above technical solution, the sensor module includes a multi-point tension sensor, a laser thickness sensor, a displacement sensor, and a pressure sensor;

[0050] The multi-point tension sensor is arranged in the unwinding, coating and rewinding stages of the tape production line to sense the tension at various positions of the substrate and generate tension signals.

[0051] The laser thickness sensor is installed at the outlet of the adhesive coating device to detect the adhesive coating thickness on the tape surface in real time.

[0052] The displacement sensor and pressure sensor are arranged on both sides of the winding device to monitor the offset and clamping force during the winding process, respectively.

[0053] According to the above technical solution, the central control system includes a data processing module, an adhesive adjustment module, a tension adjustment module, and a winding adjustment module;

[0054] The data processing module parses the data collected by the sensor module;

[0055] The adhesive coating adjustment module generates an adhesive coating thickness adjustment command based on real-time collected tension distribution, adhesive coating thickness, and substrate speed.

[0056] The tension adjustment module generates a clamping force adjustment command for the tension adjustment roller based on the tension distribution data;

[0057] The winding adjustment module generates motor speed adjustment commands by analyzing winding uniformity data.

[0058] According to the above technical solution, the central control system uses a pre-trained multivariate regression model, combined with data on adhesive thickness, tension distribution, and substrate speed, to optimize and adjust the output flow rate of the adhesive applicator and the substrate transmission speed in real time.

[0059] Compared with existing technologies, the beneficial effects achieved by this invention are as follows: By combining Internet of Things (IoT) technology, this invention enables real-time monitoring and dynamic adjustment of adhesive coating thickness, substrate tension, and winding uniformity during the tape production process, significantly improving the automation and intelligence level of the production line. Furthermore, the coordinated operation of these three stages not only overcomes the limitations of adjusting a single parameter but also significantly reduces the scrap rate, improving production efficiency and product quality. Attached Figure Description

[0060] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0061] Figure 1 This is a schematic diagram of the method flow of the present invention;

[0062] Figure 2 This is a schematic diagram of the system module composition of the present invention. Detailed Implementation

[0063] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0064] Example 1

[0065] Please see Figure 1 This invention provides a technical solution: an intelligent monitoring method for an adhesive tape production line based on the Internet of Things, comprising the following steps:

[0066] Step S1: Collect data on adhesive coating thickness, substrate tension, and winding uniformity in real time during the tape production process using sensor modules deployed on the tape production line.

[0067] Step S2: Transmit the collected data to the central control system via the IoT communication module;

[0068] Step S3: Based on the collected data, the central control system performs real-time analysis on the coating thickness fluctuation, tension distribution, and winding uniformity, and generates corresponding adjustment instructions.

[0069] Step S4: The control module sends adjustment commands to the coating equipment, tension control device and winding mechanism to dynamically adjust the equipment parameters.

[0070] Step S1 is as follows:

[0071] Multiple tension sensors are installed at various points during the unwinding, coating, and rewinding stages of the adhesive tape production line. These sensors detect the tension of the substrate at different locations in real time and generate corresponding tension signals. The tension values ​​collected by the sensors are then transmitted to the manufacturer. The tension is converted into an electrical signal and transmitted to the data processing module. Existing technologies often monitor tension at a single point, making it difficult to achieve overall optimization. Therefore, multiple tension sensors are deployed, and a tension distribution map is generated through the real-time feedback of each sensor to analyze the trend of tension changes.

[0072] A laser thickness sensor is installed at the outlet of the adhesive coating device to detect the adhesive coating thickness on the tape surface in real time and to achieve non-contact real-time measurement.

[0073] Displacement sensors and pressure sensors are arranged on both sides of the winding device to monitor the offset and clamping force during the winding process, respectively, and obtain the offset and clamping force values ​​on both sides to form real-time comparison data.

[0074] The method for generating instructions to analyze and adjust the coating thickness fluctuation in step S3 includes:

[0075] Step S311: Real-time acquisition of tension distribution of the monitoring substrate The system retrieves the amount of adhesive released per unit time, Q, monitored by the flow sensor built into the dispensing head, and the substrate speed recorded by the encoder. ;

[0076] Step S312: Calculate the tension change amplitude using formula (1) to reflect the tensile change trend of the substrate in the current time period;

[0077] Step S313: Predict the coating thickness using the amplitude of tension change. The influence of this is used to train a multivariate regression model using formula (2);

[0078] Step S314: The central control system adjusts the output flow rate of the dispensing head in real time according to formula (3);

[0079] Step S315: The substrate speed is adjusted according to formula (4) to dynamically correct the substrate transmission speed;

[0080] Step S316: After generating the adjustment command, continuously monitor the adhesive coating thickness. The deviation between the actual value and the target value is compared. If the deviation exceeds the set threshold, the parameters Q and Q are optimized and adjusted again. Continue until the adhesive coating thickness stabilizes;

[0081] in,

[0082] Formula (1);

[0083] Formula (2);

[0084] Formula (3);

[0085] Formula (4);

[0086] In the formula, The rate of change of tension, For the tension at the present moment, for The tension of the previous moment, This is the coating thickness value adjusted in real time based on tension changes. This is the linkage coefficient, used to quantify tension changes. The degree of influence on the thickness of the adhesive coating The adjusted output flow rate of the dispensing head. To determine the optimal value of the linkage factor for adjusting the amount of adhesive, an experiment was conducted. For the adjusted substrate transport speed, This is a scaling factor for speed adjustment, used to balance the impact of tension changes on the substrate speed. During tape production, changes in substrate tension can cause changes in the substrate surface condition. For example, different degrees of stretching can lead to uneven adhesive thickness. By acquiring tension distribution data in real time, the adhesive thickness can be dynamically adjusted to match the trend of tension changes, effectively reducing adhesive fluctuations and improving adhesive consistency and accuracy. Furthermore, through a formula-driven adjustment mechanism, it can quickly respond to tension changes, avoiding adhesive thickness deviations caused by lag adjustments in traditional methods.

[0087] The method for generating the tension distribution adjustment command in step S3 includes:

[0088] Step S321: Real-time acquisition of data from multiple tension sensors in the tensile region of the substrate. ;

[0089] Step S322: Calculate the overall tension deviation using formula (5);

[0090] Step S323: Calculate the clamping force of the dynamically adjusted tension regulating roller using formula (6). To reduce overall deviation;

[0091] in,

[0092] Formula (5);

[0093] Formula (6);

[0094] In the formula, As an indicator of tension imbalance, The average tension value Let i be the clamping force of the i-th tension adjusting roller. The base clamping force is the system's preset default clamping force value. To adjust the coefficient, for right The partial derivatives of the tensile tension are used to predict the potential impact of tensile tension on winding tightness in advance. By optimizing the tension distribution of the clamping force, the uniformity of the substrate tension is ensured, and the tension distribution and winding are linked and controlled.

[0095] The analysis and adjustment methods for winding uniformity in step S3 include:

[0096] Step S331: Adjust the adhesive coating thickness Tension distribution and substrate speed Data is used as input to optimize take-up parameters in advance;

[0097] Step S332: Based on the autoregressive model, predict the winding tightness S using formula (7);

[0098] Step S333: Based on the predicted winding tension S, calculate the speed adjustment of the motors on both sides of the winding equipment using formula (8). Adjust the speed of the motors on both sides synchronously to ensure that the tension on both sides is consistent;

[0099] Step S334: Monitor the actual tightness of the left and right sides in real time and compare it with the predicted value. If the deviation exceeds the set threshold, update dynamically. And readjust the motor speed to ensure uniform winding;

[0100] in,

[0101] Formula (7);

[0102] Formula (8);

[0103] In the formula, For the predicted winding tightness, This represents the average value of the tension distribution. Here, k is the control parameter, and k is the proportional coefficient. This is the actual measured value of the winding tightness;

[0104] To address issues such as uneven adhesive coating thickness, substrate tension fluctuations, and poor winding uniformity in existing tape production, this paper achieves precise control and high automation of the production process through multi-dimensional intelligent monitoring and dynamic adjustment, demonstrating significant technological advancement and practical application value.

[0105] For example, regarding the issue of coating thickness fluctuation, by utilizing tension change amplitude, coating thickness, and substrate speed data, combined with a multivariate regression model and dynamic adjustment algorithm, the output flow rate of the coating head and the substrate transmission speed can be optimized in real time, ensuring high precision and consistency of coating thickness. Regarding tension distribution, this invention uses tension data collected by multi-point tension sensors and generates adjustment commands using a tension deviation calculation formula to dynamically optimize the clamping force of the tension adjusting roller, significantly improving tension uniformity. As for winding uniformity, by introducing an autoregressive model to predict tightness and dynamically adjusting the speed of the winding motor, the winding quality can be precisely controlled, ensuring the consistency of the final product.

[0106] The adjustment command generation mechanism of this invention is highly intelligent. Based on mathematical models and experimentally calibrated parameter optimization methods, it can quickly respond to fluctuations that occur during production, dynamically adjust equipment operating parameters, and effectively improve production efficiency and product quality. Through the coordinated control of three key dimensions—adhesive application, tension, and winding—this invention achieves synergistic optimization of the overall performance of the production line, overcomes the limitations of single parameter adjustment, and significantly improves the intelligence level and stability of the production line.

[0107] Example 2

[0108] Please see Figure 2 The present invention also provides an intelligent monitoring system for tape production line based on the Internet of Things. The intelligent monitoring system for tape production line includes a sensor module, an Internet of Things communication module, a central control system, and a control module.

[0109] The sensor module is deployed in the unwinding, coating, and winding stages of the tape production line, and can collect data on coating thickness, substrate tension, and winding uniformity in real time during the tape production process.

[0110] The Internet of Things (IoT) communication module is used to transmit the collected data to the central control system;

[0111] The central control system is used to perform real-time analysis of coating thickness fluctuations, tension distribution, and winding uniformity based on the collected data, and to generate corresponding adjustment instructions.

[0112] The control module is used to receive adjustment commands sent by the central control system and control the coating equipment, tension control device and winding mechanism to dynamically adjust the equipment parameters.

[0113] The sensor module includes a multi-point tension sensor, a laser thickness sensor, a displacement sensor, and a pressure sensor;

[0114] Multi-point tension sensors are deployed in the unwinding, coating, and rewinding stages of the tape production line to sense the tension at various locations on the substrate and generate tension signals.

[0115] A laser thickness sensor is installed at the outlet of the adhesive coating device to detect the adhesive coating thickness on the tape surface in real time.

[0116] Displacement and pressure sensors are arranged on both sides of the winding device to monitor the offset and clamping force during the winding process, respectively.

[0117] The central control system includes a data processing module, an adhesive application adjustment module, a tension adjustment module, and a winding adjustment module;

[0118] The data processing module parses the data collected by the sensor module;

[0119] The adhesive application adjustment module generates adhesive thickness adjustment commands based on real-time collected tension distribution, adhesive thickness, and substrate speed.

[0120] The tension adjustment module generates a clamping force adjustment command for the tension adjustment roller based on the tension distribution data;

[0121] The winding adjustment module generates motor speed adjustment commands by analyzing winding uniformity data.

[0122] The central control system uses a pre-trained multivariate regression model, combined with data on adhesive thickness, tension distribution, and substrate speed, to optimize and adjust the output flow rate of the adhesive applicator and the substrate transmission speed in real time.

[0123] Specifically, by deploying various sensor modules during the unwinding, coating, and winding stages, including multi-point tension sensors, laser thickness sensors, displacement sensors, and pressure sensors, this invention can collect key parameters such as coating thickness, substrate tension, and winding uniformity in real time during the production process, ensuring the comprehensiveness and real-time nature of data acquisition. Simultaneously, the IoT communication module efficiently transmits the collected data to the central control system, providing a data foundation for real-time analysis and dynamic adjustments.

[0124] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0125] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A smart monitoring method for an adhesive tape production line based on the Internet of Things, characterized in that: The IoT-based intelligent monitoring method for tape production lines includes the following steps: Step S1: Collect data on adhesive coating thickness, substrate tension, and winding uniformity in real time during the tape production process using sensor modules deployed on the tape production line. Step S2: Transmit the collected data to the central control system via the IoT communication module; Step S3: Based on the collected data, the central control system performs real-time analysis on the coating thickness fluctuation, tension distribution, and winding uniformity, and generates corresponding adjustment instructions. Step S4: The control module sends adjustment commands to the coating equipment, tension control device, and winding mechanism to dynamically adjust the equipment parameters; The method for generating instructions to analyze and adjust the coating thickness fluctuation in step S3 includes: Step S311: Real-time acquisition of tension distribution of the monitoring substrate The system retrieves the amount of adhesive released per unit time, Q, monitored by the flow sensor built into the dispensing head, and the substrate speed recorded by the encoder. ; Step S312: Calculate the amplitude of tension change using formula (1); Step S313: Predict the coating thickness using the amplitude of tension change. The influence of this is used to train a multivariate regression model using formula (2); Step S314: The central control system adjusts the output flow rate of the dispensing head in real time according to formula (3); Step S315: The substrate speed is adjusted according to formula (4) to dynamically correct the substrate transmission speed; Step S316: After generating the adjustment command, continuously monitor the adhesive coating thickness. The deviation between the actual value and the target value is compared. If the deviation exceeds the set threshold, the parameters Q and Q are optimized and adjusted again. Continue until the adhesive coating thickness stabilizes; in, Official (1); Official (2); Official (3); Official (4); In the formula, The magnitude of the tension change. For the tension at the present moment, for The tension of the previous moment, This is the coating thickness value adjusted in real time based on tension changes. This is the linkage coefficient, used to quantify tension changes. The degree of influence on the thickness of the adhesive coating The adjusted output flow rate of the dispensing head. To determine the optimal value of the linkage factor for adjusting the amount of adhesive, an experiment was conducted. For the adjusted substrate transport speed, This is a scaling factor for speed adjustment, used to balance the effect of tension changes on the substrate speed.

2. The intelligent monitoring method for a tape production line based on the Internet of Things according to claim 1, characterized in that: Step S1 specifically involves: Multiple tension sensors are installed at various points during the unwinding, coating, and rewinding stages of the adhesive tape production line. These sensors detect the tension of the substrate at different locations in real time and generate corresponding tension signals. The tension values ​​collected by the sensors are then transmitted to the manufacturer. It is converted into an electrical signal and transmitted to the data processing module; A laser thickness sensor is installed at the outlet of the adhesive coating device to detect the adhesive coating thickness on the tape surface in real time and to achieve non-contact real-time measurement. Displacement sensors and pressure sensors are arranged on both sides of the winding device to monitor the offset and clamping force during the winding process, respectively, and obtain the offset and clamping force values ​​on both sides to form real-time comparison data.

3. The intelligent monitoring method for a tape production line based on the Internet of Things according to claim 1, characterized in that: The method for generating the tension distribution adjustment command in step S3 includes: Step S321: Real-time acquisition of data from multiple tension sensors in the tensile region of the substrate. ; Step S322: Calculate the overall tension deviation using formula (5); Step S323: Calculate the clamping force of the dynamically adjusted tension regulating roller using formula (6). To reduce overall deviation; in, Official (5); Official (6); In the formula, As an indicator of tension imbalance, The average tension value Let i be the clamping force of the i-th tension adjusting roller. The base clamping force is the system's preset default clamping force value. To adjust the coefficient, for right The partial derivatives of .

4. The intelligent monitoring method for a tape production line based on the Internet of Things according to claim 3, characterized in that: The method for analyzing and adjusting the winding uniformity in step S3 includes: Step S331: Adjust the adhesive coating thickness Tension distribution and substrate speed Data is used as input to optimize take-up parameters in advance; Step S332: Based on the autoregressive model, predict the winding tightness S using formula (7); Step S333: Based on the predicted winding tension S, calculate the speed adjustment of the motors on both sides of the winding equipment using formula (8). Adjust the speed of the motors on both sides synchronously to ensure that the tension on both sides is consistent; Step S334: Monitor the actual tightness of the left and right sides in real time and compare it with the predicted value. If the deviation exceeds the set threshold, update dynamically. And readjust the motor speed to ensure uniform winding; in, Official (7); Official (8); In the formula, For the predicted winding tightness, This represents the average value of the tension distribution. Here, k is the control parameter, and k is the proportional coefficient. This is the actual measured winding tightness value.

5. An intelligent monitoring system for a tape production line based on the Internet of Things, characterized in that: The intelligent monitoring system for the tape production line includes a sensor module, an IoT communication module, a central control system, and a control module. The sensor module is deployed in the unwinding, coating, and winding stages of the tape production line, and can collect data on coating thickness, substrate tension, and winding uniformity in real time during the tape production process. The IoT communication module is used to transmit the collected data to the central control system; The central control system is used to perform real-time analysis of coating thickness fluctuation, tension distribution, and winding uniformity based on the collected data, and generate corresponding adjustment instructions. The central control system uses a pre-trained multivariate regression model, combined with coating thickness, tension distribution, and substrate speed data, to optimize and adjust the coating head output flow and substrate transmission speed in real time. The control module is used to receive adjustment instructions sent by the central control system and control the coating equipment, tension control device and winding mechanism to dynamically adjust the equipment parameters.

6. The intelligent monitoring system for a tape production line based on the Internet of Things according to claim 5, characterized in that: The sensor module includes a multi-point tension sensor, a laser thickness sensor, a displacement sensor, and a pressure sensor; The multi-point tension sensor is arranged in the unwinding, coating and rewinding stages of the tape production line to sense the tension at various positions of the substrate and generate tension signals. The laser thickness sensor is installed at the outlet of the adhesive coating device to detect the adhesive coating thickness on the tape surface in real time. The displacement sensor and pressure sensor are arranged on both sides of the winding device to monitor the offset and clamping force during the winding process, respectively.

7. The intelligent monitoring system for a tape production line based on the Internet of Things according to claim 5, characterized in that: The central control system includes a data processing module, an adhesive application adjustment module, a tension adjustment module, and a winding adjustment module; The data processing module parses the data collected by the sensor module; The adhesive coating adjustment module generates an adhesive coating thickness adjustment command based on real-time collected tension distribution, adhesive coating thickness, and substrate speed. The tension adjustment module generates a clamping force adjustment command for the tension adjustment roller based on the tension distribution data; The winding adjustment module generates motor speed adjustment commands by analyzing winding uniformity data.

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