Adhesive tape production line intelligent monitoring system and method based on Internet of Things

Abstract

The invention discloses an intelligent monitoring system and method for an adhesive tape production line based on the Internet of Things. The method comprises the following steps that S1, data of the adhesive coating thickness, the base material tension and the winding uniformity in the adhesive tape production process are collected in real time through a sensor module deployed on the adhesive tape production line; s2, transmitting the collected data to a central control system through an Internet of Things communication module; s3, the central control system conducts real-time analysis on gluing thickness fluctuation, tension distribution and winding uniformity on the basis of the collected data, and corresponding adjusting instructions are generated; and S4, the adjusting instruction is sent to the gluing equipment, the tension control device and the winding mechanism through the control module, and equipment parameters are dynamically adjusted.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet of Things, and in particular to an intelligent monitoring system and method for an adhesive tape production line based on the Internet of Things. Background Art

[0002] Adhesive tape production lines play an important role in modern industrial production, and their operating efficiency and product quality directly affect the market competitiveness of enterprises. However, traditional adhesive tape production lines mostly rely on manual inspections and simple automated control, and have problems such as large monitoring blind spots, slow response speeds, and untimely fault detection. They are unable to meet the current needs for efficient and precise production, and have restricted the development of the industry.

[0003] In the tape production process, the uniformity of the glue coating thickness is a key technical difficulty. The glue coating head is prone to slight jitters during mechanical operation, resulting in fluctuations in the thickness of the glue layer, which affects the adhesion performance of the tape. Most of the existing thickness detection methods use offline methods, which cannot obtain thickness data in real time and make timely adjustments. In addition, the tension control of the substrate roll directly determines the tightness and uniformity of the winding, but the traditional tension control system has a slow response speed, which often leads to problems of excessive or insufficient tension, affecting production quality. The lack of dynamic adjustment capabilities in the winding link also further aggravates the phenomenon of inconsistent tightness on both sides and increases the scrap rate. Therefore, it is necessary to design an efficient and accurate intelligent monitoring system and method for tape production lines based on the Internet of Things. Summary of the invention

[0004] The purpose of the present invention is to provide an intelligent monitoring system and method for a tape production line based on the Internet of Things to solve the problems raised in the above background technology.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: an intelligent monitoring method for a tape production line based on the Internet of Things, comprising the following steps:

[0006] Step S1: The sensor module deployed in the tape production line collects data on the adhesive coating thickness, substrate tension and winding uniformity in real time during the tape production process;

[0007] Step S2: Transmitting the collected data to the central control system through the Internet of Things communication module;

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

[0009] Step S4: Send adjustment instructions to the gluing equipment, tension control device and winding mechanism through the control module to dynamically adjust equipment parameters.

[0010] According to the above technical solution, step S1 is specifically as follows:

[0011] Multi-point tension sensors are arranged in the unwinding, gluing and rewinding stages of the tape production line. The tension sensors are used to sense the tension of the substrate at various positions in real time and generate corresponding tension signals. The tension sensors collect the tension values ​​T i Converted into electrical signals and transmitted to the data processing module;

[0012] A laser thickness sensor is installed at the exit of the glue coating device to detect the glue coating thickness on the tape surface in real time and realize 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, obtain the offset and clamping force values ​​on both sides, and form real-time comparison data.

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

[0015] Step S311: Real-time acquisition of the tension distribution T of the monitoring substrate i , retrieve the glue release amount Q per unit time monitored by the built-in flow sensor of the glue coating head, and the substrate speed V recorded by the encoder b ;

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

[0017] Step S313: Predict the glue coating thickness H using the tension change rate t The multivariate regression model is trained by formula (2);

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

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

[0020] Step S316: Continuously monitor the glue coating thickness H after generating the adjustment instruction t , compare the deviation between the actual value and the target value, if the deviation exceeds the set threshold, optimize and adjust the parameters Q and V again b , until the glue coating thickness tends to be stable;

[0021] in,

[0022] ΔT i =T i -T i-1 Formula (1);

[0023] Ha =H t +k1·ΔT i Formula (2);

[0024] Q 调 =Q+k Q ΔT i Formula (3);

[0025] V b调 =V b -k V ΔT i Formula (4);

[0026] Where, ΔT i is the rate of change of tension, T i is the tension at the current moment, T i-1 T i The tension at the previous moment, H a is the glue thickness value adjusted in real time based on the tension change, k1 is the linkage coefficient, which is used to quantify the tension change ΔT i The influence degree on the coating thickness, Q 调 is the output flow of the glue coating head after adjustment, k Q The linkage factor for adjusting the amount of glue is calibrated through experiments to find its optimal value, V b调 is the adjusted substrate transport speed, k V A proportional factor for speed adjustment to balance the effect of tension changes on substrate speed.

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

[0028] Step S321: Real-time acquisition of multiple sets of tension sensor data T1, T2, ..., T2 arranged in the stretching area of ​​the substrate n ;

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

[0030] Step S323: Calculate the pressing force F of the dynamic tension adjustment roller by formula (6): i , to reduce the overall deviation;

[0031] in,

[0032]

[0033] Where, ΔT 整 is an index of tension imbalance. is the mean tension, F iis the pressing force of the i-th tension adjustment roller, F0 is the basic pressing force, which is the default pressing force value preset by the system, k2 is the adjustment coefficient, = ΔT 整 To T i The partial derivative of .

[0034] According to the above technical solution, the method for analyzing and adjusting the winding uniformity in step S3 includes:

[0035] Step S331: Set the glue coating thickness H t , tension distribution T i and substrate speed V b The data is used as input to optimize the winding parameters in advance;

[0036] Step S332: Based on the autoregressive model, the winding tightness S is predicted by formula (7);

[0037] Step S333: according to the predicted winding tightness S, the speed adjustment amount Δv of the motors on both sides of the winding device is calculated by formula (8), and the speeds of the motors on both sides are synchronously adjusted to make the tightness on the left and right sides consistent;

[0038] 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, dynamically update Δv and readjust the motor speed to ensure the uniformity of winding;

[0039] in,

[0040]

[0041] Δv = k·(S-S0);

[0042] Where S is the predicted winding tightness, is the average value of tension distribution, α, β, γ are control parameters, k is the proportional coefficient, and S0 is the actual measured winding tightness value.

[0043] An intelligent monitoring system for a tape production line based on the Internet of Things, the intelligent monitoring system for the tape production line comprises a sensor module, an Internet of Things communication module, a central control system and a control module;

[0044] The sensor module is deployed in the unwinding, gluing and rewinding stages of the tape production line, and can collect data on the glue coating thickness, substrate tension and rewinding uniformity during the tape production process in real time;

[0045] The Internet of Things communication module is used to transmit the collected data to the central control system;

[0046] The central control system is used to perform real-time analysis on the fluctuation of glue coating thickness, tension distribution and winding uniformity based on the collected data, and generate corresponding adjustment instructions;

[0047] The control module is used to receive adjustment instructions sent by the central control system, and control the gluing equipment, the tension control device and the winding mechanism to dynamically adjust the equipment parameters.

[0048] 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;

[0049] The multi-point tension sensor is arranged in the unwinding, gluing and rewinding stages of the tape production line to sense the tension at each position of the substrate and generate a tension signal;

[0050] The laser thickness sensor is installed at the exit of the glue coating device and is used to detect the glue coating thickness on the tape surface in real time;

[0051] The displacement sensor and the pressure sensor are arranged on both sides of the winding device, and are used to monitor the displacement and the pressing force during the winding process respectively.

[0052] According to the above technical solution, the central control system includes a data processing module, a gluing adjustment module, a tension adjustment module and a winding adjustment module;

[0053] The data processing module analyzes the data collected by the sensor module;

[0054] The glue coating adjustment module generates a glue coating thickness adjustment instruction based on the tension distribution, glue coating thickness and substrate speed collected in real time;

[0055] The tension adjustment module generates a pressing force adjustment instruction for the tension adjustment roller according to the tension distribution data;

[0056] The winding adjustment module generates a motor speed adjustment instruction by analyzing the winding uniformity data.

[0057] According to the above technical solution, the central control system optimizes and adjusts the output flow of the glue coating head and the substrate transmission speed in real time by combining the glue coating thickness, tension distribution and substrate speed data through a pre-trained multivariate regression model.

[0058] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: the present invention, by combining the Internet of Things technology, realizes real-time monitoring and dynamic adjustment of the adhesive thickness, substrate tension and winding uniformity in the tape production process, significantly improving the automation and intelligence level of the production line. Moreover, the three links are coordinated and linked, which not only solves the limitation of single parameter adjustment, but also significantly reduces the scrap rate and improves production efficiency and product quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention.

[0060] In the attached picture:

[0061] Figure 1 It is a schematic flow chart of the method of the present invention;

[0062] Figure 2 It is a schematic diagram of the system module composition of the present invention. DETAILED DESCRIPTION

[0063] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0064] Embodiment 1

[0065] See also Figure 1 The present invention provides a technical solution: an intelligent monitoring method for a tape production line based on the Internet of Things, comprising the following steps:

[0066] Step S1: The sensor module deployed in the tape production line collects data on the adhesive coating thickness, substrate tension and winding uniformity in real time during the tape production process;

[0067] Step S2: Transmitting the collected data to the central control system through the Internet of Things communication module;

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

[0069] Step S4: Send adjustment instructions to the gluing equipment, tension control device and winding mechanism through the control module to dynamically adjust equipment parameters.

[0070] Step S1 is specifically as follows:

[0071] Multi-point tension sensors are arranged in the unwinding, gluing and rewinding stages of the tape production line. The tension sensors are used to sense the tension of the substrate at various positions in real time and generate corresponding tension signals. The tension sensors collect the tension values ​​T iConverted into electrical signals and transmitted to the data processing module; the existing technology often monitors the tension at a single point, which makes it difficult to achieve overall optimization. Therefore, multi-point tension sensors are arranged to generate a tension distribution map through real-time feedback from each sensor to analyze the tension change trend;

[0072] A laser thickness sensor is installed at the exit of the glue coating device to detect the glue coating thickness on the tape surface in real time and realize 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, obtain the offset and clamping force values ​​on both sides, and form real-time comparison data.

[0074] The method for analyzing the fluctuation of the coating thickness and generating the adjustment instruction in step S3 includes:

[0075] Step S311: Real-time acquisition of the tension distribution T of the monitoring substrate i , retrieve the glue release amount Q per unit time monitored by the built-in flow sensor of the glue coating head, and the substrate speed V recorded by the encoder b ;

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

[0077] Step S313: Predict the glue coating thickness H using the tension change rate t The multivariate regression model is trained by formula (2);

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

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

[0080] Step S316: Continuously monitor the glue coating thickness H after generating the adjustment instruction t , compare the deviation between the actual value and the target value, if the deviation exceeds the set threshold, optimize and adjust the parameters Q and V again b , until the glue coating thickness tends to be stable;

[0081] in,

[0082] ΔT i =T i -T i-1 Formula (1);

[0083] H a =H t +k1·ΔT i Formula (2);

[0084] Q 调 =Q+k Q ΔT i Formula (3);

[0085] V b调 =V b -k V ΔT i Formula (4);

[0086] Where, ΔT i is the rate of change of tension, T i is the tension at the current moment, T i-1 T i The tension at the previous moment, H a is the glue thickness value adjusted in real time based on the tension change, k1 is the linkage coefficient, which is used to quantify the tension change ΔT i The influence degree on the coating thickness, Q 调 is the output flow of the glue coating head after adjustment, k Q The linkage factor for adjusting the amount of glue is calibrated through experiments to find its optimal value, V b调 is the adjusted substrate transport speed, k V It is a proportional factor for speed adjustment, which is used to balance the effect of tension changes on substrate speed. During the tape production process, changes in substrate tension will cause changes in the surface state of the substrate. For example, different degrees of stretching will lead to uneven glue thickness. By acquiring tension distribution data in real time, the glue thickness is dynamically adjusted to match the changing trend of tension, effectively reducing glue fluctuations and improving glue consistency and accuracy. The formula-driven adjustment mechanism can quickly respond to tension changes and avoid glue thickness deviations caused by delayed adjustment in traditional methods.

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

[0088] Step S321: Real-time acquisition of multiple sets of tension sensor data T1, T2, ..., T2 arranged in the stretching area of ​​the substrate n ;

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

[0090] Step S323: Calculate the pressing force Fi of the tension adjustment roller dynamically by formula (6) to reduce the overall deviation;

[0091] in,

[0092]

[0093] Where, ΔT 整is an index of tension imbalance. is the mean tension, F i is the pressing force of the i-th tension adjustment roller, F0 is the basic pressing force, which is the default pressing force value preset by the system, k2 is the adjustment coefficient, = ΔT 整 To T i The partial derivative of ; predict the potential impact of tensile tension on winding tightness in advance, balance the tension distribution by optimizing the pressing force, ensure the uniformity of substrate tension, and realize the linkage regulation of tensile tension distribution and winding.

[0094] The method for analyzing and adjusting the winding uniformity in step S3 includes:

[0095] Step S331: Set the glue coating thickness H t , tension distribution T i and substrate speed V b The data is used as input to optimize the winding parameters in advance;

[0096] Step S332: Based on the autoregressive model, the winding tightness S is predicted by formula (7);

[0097] Step S333: according to the predicted winding tightness S, the speed adjustment amount Δv of the motors on both sides of the winding device is calculated by formula (8), and the speeds of the motors on both sides are synchronously adjusted to make the tightness on the left and right sides consistent;

[0098] 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, dynamically update Δv and readjust the motor speed to ensure the uniformity of winding;

[0099] in,

[0100]

[0101] Δv = k·(S-S0);

[0102] Where S is the predicted winding tightness, is the average value of tension distribution, α, β, γ are control parameters, k is the proportional coefficient, and S0 is the actual measured winding tightness value;

[0103] Aiming at the problems of uneven coating thickness, substrate tension fluctuation and poor winding uniformity in existing tape production, the precise control and high automation of the production process are achieved through multi-dimensional intelligent monitoring and dynamic adjustment, which has significant technological advancement and practical application value.

[0104] For example, for the problem of glue thickness fluctuation, the output flow of the glue coating head and the substrate transmission speed can be optimized in real time by using the tension change rate, glue thickness and substrate speed data, combined with a multivariate regression model and a dynamic adjustment algorithm, to ensure high precision and consistency of the glue thickness; for tension distribution, the present invention uses the tension data collected by a multi-point tension sensor, generates adjustment instructions using a tension deviation calculation formula, dynamically optimizes the pressing force of the tension adjustment roller, and significantly improves tension uniformity; and in terms of winding uniformity, by introducing an autoregressive model to predict the tightness and dynamically adjusting the speed of the winding motor, the winding quality can be accurately controlled to ensure the consistency of the final product;

[0105] The adjustment instruction generation mechanism of the present invention is highly intelligent. Its parameter optimization method based on mathematical models and experimental calibration can quickly respond to fluctuations in the production process, dynamically adjust equipment operating parameters, and effectively improve production efficiency and product quality. Through the linkage control of the three key dimensions of gluing, tension and winding, the present invention realizes the coordinated optimization of the overall performance of the production line, overcomes the limitations of single parameter adjustment, and greatly improves the intelligence level and stability of the production line.

[0106] Embodiment 2

[0107] See also Figure 2 , the present invention also provides an intelligent monitoring system for a tape production line based on the Internet of Things, the intelligent monitoring system for a tape production line includes a sensor module, an Internet of Things communication module, a central control system and a control module;

[0108] The sensor module is deployed in the unwinding, gluing and rewinding stages of the tape production line, and can collect data on the glue thickness, substrate tension and rewinding uniformity during the tape production process in real time;

[0109] The IoT communication module is used to transmit the collected data to the central control system;

[0110] The central control system is used to analyze the fluctuation of glue coating thickness, tension distribution and winding uniformity in real time based on the collected data, and generate corresponding adjustment instructions;

[0111] The control module is used to receive adjustment instructions sent by the central control system, and control the gluing equipment, tension control device and winding mechanism to dynamically adjust equipment parameters.

[0112] The sensor module includes a multi-point tension sensor, a laser thickness sensor, a displacement sensor and a pressure sensor;

[0113] Multi-point tension sensors are arranged in the unwinding, gluing and rewinding stages of the tape production line to sense the tension at various positions of the substrate and generate tension signals;

[0114] The laser thickness sensor is installed at the exit of the glue coating device to detect the glue coating thickness on the tape surface in real time;

[0115] The displacement sensor and pressure sensor are arranged on both sides of the winding device, and are used to monitor the offset and pressing force during the winding process respectively.

[0116] The central control system includes a data processing module, a gluing adjustment module, a tension adjustment module and a winding adjustment module;

[0117] The data processing module analyzes the data collected by the sensor module;

[0118] The glue coating adjustment module generates glue coating thickness adjustment instructions based on the real-time collected tension distribution, glue coating thickness and substrate speed;

[0119] The tension adjustment module generates a pressing force adjustment instruction for the tension adjustment roller according to the tension distribution data;

[0120] The winding adjustment module generates motor speed adjustment instructions by analyzing the winding uniformity data.

[0121] The central control system uses a pre-trained multivariate regression model, combined with glue coating thickness, tension distribution and substrate speed data, to optimize and adjust the glue coating head output flow and substrate transmission speed in real time;

[0122] Specifically, by arranging a variety of sensor modules in the unwinding, gluing and rewinding stages, including multi-point tension sensors, laser thickness sensors, displacement sensors and pressure sensors, the present invention can collect key parameters such as gluing thickness, substrate tension and rewinding uniformity in real time during the production process, ensuring the comprehensiveness and real-time nature of data collection. At the same time, the Internet of Things communication module efficiently transmits the collected data to the central control system, providing a data basis for real-time analysis and dynamic adjustment.

[0123] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0124] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An intelligent monitoring method for a tape production line based on the Internet of Things, characterized in that: The method for intelligent monitoring of a tape production line based on the Internet of Things comprises the following steps: Step S1: The sensor module deployed in the tape production line collects data on the adhesive coating thickness, substrate tension and winding uniformity in real time during the tape production process; Step S2: Transmitting the collected data to the central control system through the Internet of Things communication module; Step S3: The central control system performs real-time analysis on the fluctuation of the coating thickness, the tension distribution and the uniformity of the winding based on the collected data, and generates corresponding adjustment instructions; Step S4: Send adjustment instructions to the gluing equipment, tension control device and winding mechanism through the control module to dynamically adjust equipment parameters.

2. According to the method for intelligent monitoring of a tape production line based on the Internet of Things in claim 1, it is characterized in that: The step S1 is specifically as follows: Multi-point tension sensors are arranged in the unwinding, gluing and rewinding stages of the tape production line. The tension sensors are used to sense the tension of the substrate at various positions in real time and generate corresponding tension signals. The tension sensors collect the tension values ​​T i Converted into electrical signals and transmitted to the data processing module; A laser thickness sensor is installed at the exit of the glue coating device to detect the glue coating thickness on the tape surface in real time and realize 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, obtain the offset and clamping force values ​​on both sides, and form real-time comparison data.

3. According to the method of intelligent monitoring of a tape production line based on the Internet of Things in claim 1, it is characterized in that: The method for analyzing the fluctuation of the coating thickness and generating the adjustment instruction in step S3 includes: Step S311: Real-time acquisition of the tension distribution T of the monitoring substrate i , retrieve the glue release amount Q per unit time monitored by the built-in flow sensor of the glue coating head, and the substrate speed V recorded by the encoder b ; Step S312: Calculate the tension change rate using formula (1); Step S313: Predict the glue coating thickness H using the tension change rate t The multivariate regression model is trained by formula (2); Step S314: the central control system adjusts the output flow of the glue coating head in real time according to formula (3); Step S315: adjusting the substrate speed to dynamically correct the substrate transmission speed according to formula (4); Step S316: Continuously monitor the glue coating thickness H after generating the adjustment instruction t , compare the deviation between the actual value and the target value, if the deviation exceeds the set threshold, optimize and adjust the parameters Q and V again b , until the glue coating thickness tends to be stable; in, ΔT i =T i -T i-1 Formula (1); H a =H t +k1·ΔT i Formula (2); Q 调 =Q+k Q ΔT i Formula (3); V b调 =V b -k V ΔT i Formula (4); In the formula, ΔT i is the rate of change of tension, T i is the tension at the current moment, T i-1 T i The tension at the previous moment, H a is the glue thickness value adjusted in real time based on the tension change, k1 is the linkage coefficient, which is used to quantify the tension change ΔT i The influence degree on the coating thickness, Q 调 is the output flow of the glue coating head after adjustment, k Q The linkage factor for adjusting the amount of glue is calibrated through experiments to find its optimal value, V b调 is the adjusted substrate transport speed, k V A proportional factor for speed adjustment to balance the effect of tension changes on substrate speed.

4. According to the method of intelligent monitoring of a tape production line based on the Internet of Things in claim 1, it is characterized in that: The method for generating adjustment instructions for tension distribution in step S3 includes: Step S321: Real-time acquisition of multiple sets of tension sensor data T1, T2, ..., T2 arranged in the stretching area of ​​the substrate n ; Step S322: Calculate the overall tension deviation using formula (5); Step S323: Calculate the pressing force F of the dynamic tension adjustment roller by formula (6): i , to reduce the overall deviation; in, In the formula, ΔT 整 is an index of tension imbalance. is the mean tension, F i is the pressing force of the i-th tension adjustment roller, F0 is the basic pressing force, which is the default pressing force value preset by the system, k2 is the adjustment coefficient, = ΔT 整 To T i The partial derivative of .

5. The method for intelligent monitoring of a tape production line based on the Internet of Things according to claim 4 is characterized in that: The method for analyzing and adjusting the winding uniformity in step S3 includes: Step S331: Set the glue coating thickness H t , tension distribution T i and substrate speed V b The data is used as input to optimize the winding parameters in advance; Step S332: Based on the autoregressive model, the winding tightness S is predicted by formula (7); Step S333: according to the predicted winding tightness S, the speed adjustment amount Δv of the motors on both sides of the winding device is calculated by formula (8), and the speeds of the motors on both sides are synchronously adjusted to make the tightness on the left and right sides 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, dynamically update Δv and readjust the motor speed to ensure the uniformity of winding; in, Δv = k·(S-S0); Where S is the predicted winding tightness, is the average value of the tension distribution, α, β, γ are control parameters, k is the proportional coefficient, and S0 is the actual measured winding tightness value.

6. An intelligent monitoring system for a tape production line based on the Internet of Things, characterized in that: The intelligent monitoring system for the adhesive tape production line includes a sensor module, an Internet of Things communication module, a central control system and a control module; The sensor module is deployed in the unwinding, gluing and rewinding stages of the tape production line, and can collect data on the glue coating thickness, substrate tension and rewinding uniformity during the tape production process in real time; The Internet of Things 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 on the fluctuation of glue coating thickness, tension distribution and winding uniformity based on the collected data, and generate corresponding adjustment instructions; The control module is used to receive adjustment instructions sent by the central control system, and control the gluing equipment, the tension control device and the winding mechanism to dynamically adjust the equipment parameters.

7. The intelligent monitoring system for adhesive tape production line based on Internet of Things according to claim 6 is 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, gluing and rewinding stages of the tape production line to sense the tension at each position of the substrate and generate a tension signal; The laser thickness sensor is installed at the exit of the glue coating device and is used to detect the glue coating thickness on the tape surface in real time; The displacement sensor and the pressure sensor are arranged on both sides of the winding device, and are used to monitor the displacement and the pressing force during the winding process respectively.

8. The intelligent monitoring system for adhesive tape production line based on Internet of Things according to claim 6 is characterized in that: The central control system includes a data processing module, a gluing adjustment module, a tension adjustment module and a winding adjustment module; The data processing module analyzes the data collected by the sensor module; The glue coating adjustment module generates a glue coating thickness adjustment instruction based on the tension distribution, glue coating thickness and substrate speed collected in real time; The tension adjustment module generates a pressing force adjustment instruction for the tension adjustment roller according to the tension distribution data; The winding adjustment module generates a motor speed adjustment instruction by analyzing the winding uniformity data.

9. The intelligent monitoring system for adhesive tape production line based on Internet of Things according to claim 6 is characterized in that: The central control system optimizes and adjusts the output flow of the glue coating head and the substrate transmission speed in real time through a pre-trained multivariate regression model combined with glue coating thickness, tension distribution and substrate speed data.

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