A temperature control method and system for hot melt adhesive screen film production

Abstract

The application belongs to the technical field of industrial real-time control software, and particularly relates to a temperature control method for hot melt adhesive screen film production, which comprises the following steps: establishing a second-order time-delay reference model of heating power and temperature response of each temperature zone of an extruder; acquiring actual temperature in real time and comparing the actual temperature with model predicted temperature to build a prediction error sequence and acquire model fidelity; when the fidelity is lower than a threshold value, starting a parameter correction program, obtaining an optimal correction value by minimizing the square integral of prediction error, updating model parameters to form a correction model; and a controller establishing a cost function based on the correction model, rolling optimization to obtain an optimal power change sequence to realize adaptive control of the temperature of each temperature zone. The application can identify model mismatch and correct it online, maintain control accuracy and stability, and improve the quality of hot melt adhesive screen film production.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of industrial real-time control software. More particularly, the present application relates to a temperature control method and system for hot melt adhesive screen film production. BACKGROUND

[0002] In the production process of hot melt adhesive screen film, accurate and stable control of the temperature of each temperature zone of the extruder is a core link that determines the physical properties and quality uniformity of the final product. Accurate temperature directly affects the viscosity and flowability of the polymer melt, and is crucial to ensuring the bonding strength and thermal stability of the screen film product.

[0003] To achieve high-precision temperature control, some advanced production systems use model predictive control (MPC) technology. The core of this technology is to establish a mathematical model that accurately describes the dynamic characteristics of the heating system, such as a second-order plus dead-time transfer function model (SOPDT). The controller predicts the future temperature changes of the system based on this model and calculates the current optimal heating power through an optimization algorithm, thereby achieving forward-looking and refined adjustment of the temperature.

[0004] However, the existing model predictive control technology has inherent defects in the hot melt adhesive production scenario. The mathematical model it relies on is essentially a static model established through one-time system identification during initial commissioning of the equipment. Long-term continuous production process causes the polymer melt at high temperature to undergo micro-thermal degradation in the extruder screw and inner wall of the barrel, gradually forming a layer of coking adhesion layer. The coking adhesion layer continuously changes the heat transfer coefficient and thermal resistance of the system, causing slow dynamic drift of the real thermodynamic characteristics of the system. The static control model cannot capture this change in characteristics caused by the aging of the physical state of the equipment, resulting in an increasing error between the predicted temperature and the actual temperature over time, and the performance of the control system also deteriorates, unable to maintain optimal control precision and stability for a long time. SUMMARY

[0005] To solve the technical problem that the above-mentioned model predictive control technology cannot maintain optimal control precision and stability for a long time, the present application provides solutions in the following aspects.

[0006] In a first aspect, the present application provides a temperature control method for hot melt adhesive screen film production, comprising: for any one temperature zone of an extruder, constructing a reference model of the heating power and temperature response relationship of the temperature zone according to a second-order plus time delay transfer function model; taking the difference between the actual temperature in a sliding window and the predicted temperature of the reference model as a prediction error sequence; obtaining the model fidelity of the reference model based on the prediction error sequence of the reference model, which is negatively correlated with the sum of squares of the autocorrelation function of the prediction error sequence in a sliding window of a set length, and positively correlated with the square of the value of the autocorrelation function when the delay is zero; in response to the model fidelity being lower than a preset threshold, starting a parameter correction program for the corresponding temperature zone; the starting parameter correction program comprises: taking the integral of the square of the difference between the model predicted temperature after adding a correction value to the reference model parameter and the actual temperature in the past period of time as a target function, the correction value being a parameter to be solved, solving the optimal correction value by minimizing the target function, and taking the model after adding the optimal correction value to the reference model parameter as a correction model; obtaining a cost function based on the correction model, solving the minimum problem of the cost function to obtain an optimal power change sequence, and realizing adaptive control of the temperature of each temperature zone of the extruder.

[0007] The present application can continuously adapt to the actual physical process by calculating the prediction error of the reference model in real time and monitoring its fidelity, automatically correcting the model parameters when model mismatch is detected, so that the controller can always perform prediction and optimization control based on the latest thermodynamic characteristics, thereby reducing steady-state error, suppressing temperature oscillation, and improving long-term stability and product consistency of temperature zone control.

[0008] Preferably, the reference model of the heating power and temperature response relationship of the temperature zone comprises: applying a pseudo-random binary sequence excitation signal to the heater of each temperature zone; collecting temperature response data under the action of the excitation signal; and obtaining a second-order plus time delay transfer function as the reference model by using a system identification method based on the excitation signal and the temperature response data.

[0009] The present application can fully stimulate the dynamic characteristics of the system in a limited time by applying a pseudo-random binary sequence excitation signal to the temperature zone in the initial state of the device, collecting the temperature response of the system, and establishing a second-order plus time delay transfer function model by using a system identification method, and can obtain parameters describing thermal inertia and delay characteristics at a relatively small experimental cost. Since the constructed SOPDT model considers both accuracy and computability, it can accurately reflect the heat transfer characteristics of the temperature zone of the extruder, and also provides a differentiable mathematical structure for subsequent online correction and control optimization, thereby improving the adaptability and response sensitivity of the control system under different operating conditions.

[0010] Preferably, the difference between the actual temperature within the sliding window and the predicted temperature of the benchmark model as the prediction error sequence comprises: within a preset time window, the prediction error sequence of the benchmark model is obtained by subtracting the actual temperature measured by the sensor from the predicted temperature calculated based on the benchmark model and the historical heating power.

[0011] Preferably, the model fidelity satisfies the expression: ; wherein, denotes the time index; denotes the model fidelity of the benchmark model at the time instant; denotes the prediction error of the benchmark model at the time instant ; denotes the prediction error sequence within a sliding window with a length of ; denotes the value of the autocorrelation function at a delay of ; denotes the delay index; is the length of the preset sliding window.

[0012] The present application realizes online evaluation of the precision of the benchmark model by obtaining the model fidelity based on the autocorrelation characteristics of the prediction error sequence. The model fidelity comprehensively reflects the proportion of the autocorrelation sum of error sequences within a sliding time window and the zero-delay autocorrelation, and can reflect the time correlation component caused by systematic deviation in model errors. When the model deviates from the actual process, the index automatically decreases, triggering the parameter correction program. The present application can timely discover model mismatch without interrupting production, realize early identification of dynamic performance degradation, and thus ensure that the controller always operates based on a high-fidelity system model, stabilizing long-term control precision.

[0013] Preferably, the preset threshold is set to 0.85.

[0014] Preferably, the optimal correction value satisfies the expression: ; wherein, denotes the time index; denotes each time instant within the time interval from the time instant to the time instant ; denotes the optimal correction value, is the correction value of the initial static gain of the system; is the correction value of the first initial time constant; is the correction value of the second initial time constant; denotes the optimal correction value of the first initial time constant, denotes the optimal correction value of the second initial time constant, represents the correction value that optimizes the initial static gain of the system; represents the correction value that minimizes the target function; represents the length of the time window; represents the actual temperature value measured by the sensor at time ; represents the actual temperature value measured by the sensor at time ; represents the temperature prediction value after the reference model adds the correction value

[0015] After detecting the model mismatch, the present application automatically calculates the optimal correction value by minimizing the target function that integrates the square of the difference between the corrected model prediction temperature and the actual temperature in the past period of time. This optimization process, which is guided by the minimum error, makes slight adjustments to the static gain and time constant of the system, so that the corrected model re-matches the actual thermal dynamic characteristics under the current working condition. Through this method, the model adaptive update can be realized without re-identifying the system, avoiding production interruption, reducing temperature control lag, keeping the model prediction consistent with the real response, and improving the tolerance of the controller to thermal characteristic drift and temperature tracking accuracy.

[0016] Preferably, the length of the time window is set to 3 times the system settling time.

[0017] Preferably, the cost function satisfies the expression: ; wherein, represents the cost function; is the temperature set value in the th control cycle; is the temperature prediction value in the th control cycle based on the corrected model; is the change amount of the heating power in the th control cycle relative to the previous cycle; is the prediction time domain; is the control time domain; is the weight coefficient.

[0018] In the model prediction control stage, the present application obtains the cost function containing the temperature deviation term and the control change term, and solves the future control sequence by optimizing its minimization, so that the controller can achieve a balance between fast response and smooth control: the former ensures that the temperature can quickly track the set value, and the latter suppresses the sharp fluctuations of the heating power. By using the cost function in the rolling optimization framework, the control output remains smooth in dynamic changes, reducing temperature overshoot and actuator wear, improving the overall response quality and energy efficiency stability of the system, and making the extruder temperature control process more reliable.

[0019] Preferably, the weight coefficient is set to .

[0020] In a second aspect, the present application provides a temperature control system for hot melt adhesive mesh film production, comprising a processor and a memory, wherein the memory stores computer program instructions which, when executed by the processor, implement the above-mentioned temperature control method for hot melt adhesive mesh film production.

[0021] By adopting the above technical solution, the above-mentioned temperature control method for hot melt adhesive mesh film production is generated into a computer program and stored in the memory to be loaded and executed by the processor, so that a terminal device is made according to the memory and the processor, and use is facilitated.

[0022] The present application has the following advantages:

[0023] The present application obtains a temperature control system that can be self-adaptively adjusted, monitors model prediction accuracy in real time in a continuous production process, and dynamically corrects model parameters according to model fidelity, so that the controller always makes prediction and optimization based on the latest system characteristics, deals with the problem of thermodynamic drift caused by thermal degradation, fouling or aging of the extruder in long-term operation, reduces the risk of temperature disorder, and enables each temperature zone to maintain stability and consistency of the set temperature after long-time operation, thereby improving the repeatability of product quality.

[0024] The present application adopts a pseudo-random binary sequence excitation and system identification method to establish an initial reference model, which ensures identification efficiency while retaining an accurate description of the inertia and delay characteristics of the second-order plus time delay model, facilitates subsequent online fine tuning through parameter correction, and does not need to perform complex system identification again; by combining model fidelity and the optimization criterion of minimizing error integral, the present application can automatically repair model deviation without affecting production, so that the model continuously reflects the real state of the equipment. This design enhances the self-adaptive ability and long-term reliability of the control system, and provides a more robust temperature control basis for hot melt adhesive mesh film production.

[0025] The present application obtains a cost function containing temperature deviation terms and control change terms in the model predictive controller, calculates future control sequences through a rolling optimization algorithm, realizes coordinated control of response speed and stability, and the dual constraints of the cost function enable the control system to maintain fast temperature tracking while avoiding dramatic fluctuations in power output, thereby improving the smoothness of control action and energy utilization efficiency. The controller can still stably maintain the set temperature of each temperature zone when facing load disturbance and material fluctuation, thereby realizing long-term accurate temperature management in hot melt adhesive mesh film production and improving overall production stability. BRIEF DESCRIPTION OF DRAWINGS

[0026] Figure 1 is a flowchart schematically showing a temperature control method for hot melt adhesive mesh film production in the present application;

[0027] Figure 2 is a schematic diagram illustrating the adaptive adjustment of heating power according to the present application. DETAILED DESCRIPTION

[0028] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of the present application.

[0029] The specific embodiments of the present application will be described in detail below with reference to the drawings.

[0030] The embodiments of the present application disclose a temperature control method for hot melt adhesive mesh film production, referring to Figure 1 , comprising steps S1-S4:

[0031] S1, for any one temperature zone of the extruder, a second-order plus time delay transfer function model is used to construct a reference model of the heating power and temperature response relationship of the temperature zone.

[0032] It should be noted that the production process of the hot melt adhesive mesh film is completed in the extruder, and accurate temperature control of the extruder is a core link to ensure the product quality of the hot melt adhesive mesh film. The temperature directly affects the viscosity and flowability of the polymer melt, and further determines the uniformity, bonding strength and thermal stability of the final hot melt adhesive mesh film product.

[0033] In order to achieve the goal of accurate adaptive control of the temperature of the extruder, the present application establishes a reference model for each temperature zone of the extruder before the extruder starts producing the hot melt adhesive mesh film, which preliminarily mathematically describes the thermodynamic behavior of the temperature zone and is the basis for subsequent online adaptive adjustment.

[0034] Specifically, when the extruder is in a clean and stable initial state, a temperature zone is selected as a target object; a predetermined form of excitation signal, such as a Pseudorandom Binary Sequence (PRBS) signal, is applied to the heater of the temperature zone, which can fully excite the dynamic characteristics of the system in a short time; at the same time, a high-precision temperature sensor, such as a PT100 thermocouple, is used to collect the temperature response sequence of the temperature zone under the action of the excitation signal and the stable time of the temperature zone; a second-order plus time delay transfer function model capable of describing the dynamic characteristics of the system is fitted according to the input excitation signal sequence and the output temperature response sequence by using system identification theory, such as ARX model identification method based on least squares method, as a reference model for the subsequent steps, which is referred to as a reference model.

[0035] Specifically, the second-order plus time delay transfer function model satisfies the expression:

[0036]

[0037] wherein, represents a Laplace operator, represents a second-order plus time delay transfer function model under the Laplace operator, represents an initial static gain of the system, and respectively represent a first initial time constant and a second initial time constant, the first and second initial time constants of the fitted model are known; represents a pure time delay of the system.

[0038] Transfer function Describes how the change in heating power causes the change in temperature of the temperature zone in the initial state: static gain The greater, the greater the temperature change caused by the same power change; the first initial time constant and the second initial time constant, the slower the system temperature change, the greater the inertia; the pure time delay The greater, the more sluggish the system response, and the initial model obtained provides a benchmark for subsequent online monitoring and adaptive adjustment.

[0039] S2, for any one temperature zone, the difference between the actual temperature in the sliding window and the predicted temperature of the benchmark model is taken as a prediction error sequence; the model fidelity of the benchmark model is obtained based on the prediction error sequence of the benchmark model; in response to the model fidelity being lower than a preset threshold, a parameter correction program of the corresponding temperature zone is started.

[0040] Since the prediction error of an accurate model should behave as random, uncorrelated white noise; and when the model is mismatched with the actual physical process, its prediction error sequence in a sliding window will show obvious temporal correlation. Therefore, the present application needs an index to continuously evaluate the accuracy of the benchmark model online.

[0041] Specifically, for each temperature zone, the model fidelity of the temperature zone satisfies the expression:

[0042]

[0043] wherein, represents a time index; represents the model fidelity of the benchmark model at the moment; represents the prediction error of the benchmark model at the moment, and the calculation method is , Indicates that the sensor is in The actual temperature measured at all times. It was calculated from the baseline model and historical heating power. The predicted temperature at any given time, and the prediction errors at all times within a preset sliding window constitute a prediction error sequence. By solving the problem The difference equations are obtained by conversion using standard discretization techniques such as the zero-order preservation method; Represents the prediction error sequence In a length of The autocorrelation function within the sliding window with a delay of The value at time; This represents the autocorrelation value when the delay is 0. Indicates a lazy index; The preset length of the sliding window.

[0044] The value range is from 5 control cycles to 15 control cycles. If... If the value is too small, the slow dynamic correlation of the error cannot be fully captured; if If the value is too large, it will introduce unnecessary computational noise and increase the computational burden. In this embodiment, the preset sliding window length of 10 control cycles can well balance the detection sensitivity and computational stability.

[0045] Prediction error The autocorrelation value at a delay of 0 is numerically equal to the variance of the signal within that time window, representing the overall fluctuation range of the prediction error. The square is used to unify the dimensions of the numerator and denominator. The error of the current benchmark model was evaluated. The degree of linear correlation between the errors before each control cycle, assuming the baseline model is perfectly accurate, then all prediction uncertainties should stem from unpredictable random measurement noise; in this case, the prediction error... It appears as white noise, and the autocorrelation value of white noise... Both tend to approach 0; conversely, if the baseline model is inaccurate, for example, when the temperature rises, an inaccurate baseline model may continuously overestimate or underestimate the temperature, leading to prediction errors at the current moment. Prediction error compared to previous time points There is a clear correlation between them. The value will deviate significantly from 0; The autocorrelation values ​​under all non-zero delays are summed to form an overall measure of the degree of non-white noise in the prediction error sequence; the ratio in parentheses The proportion of the total energy represented by the prediction error that is contributed by the time correlation or systematic bias.

[0046] When the benchmark model matches the real process well, the molecule approaches 0, and the model fidelity approaches 1; when the benchmark model mismatches the real process more and more, the molecule increases significantly, and the model fidelity decreases. The lower the value of the molecule, the stronger the time correlation of the prediction error sequence, and the more serious the deviation of the model from the real process. Further, when the molecule is lower than a preset threshold, for example, 0.85, the system determines that the current benchmark model has been significantly mismatched, and triggers the next step of model parameter correction.

[0047] Further, when the molecule is lower than a preset threshold, for example, 0.85, the system determines that the current benchmark model has been significantly mismatched, and triggers the next step of model parameter correction. S3, the integral of the square of the difference between the temperature predicted by the model after the benchmark model parameters are added to the correction value and the actual temperature in the past period of time is taken as the objective function, the correction value is the parameter to be solved, the optimal correction value is solved by minimizing the objective function, and the model after the benchmark model parameters are added to the optimal correction value is taken as the corrected model.

[0048] It should be noted that after detecting the model mismatch, the present application fine-tunes the parameters of the benchmark model

[0049] without interrupting production and performing large-scale system identification, so that the benchmark model matches the current physical system again. Device fouling is a slow accumulation process, mainly affecting the heat exchange efficiency and thermal inertia of the system, which is mainly reflected in the slow drift of the static gain

[0050] and time constant of the model, and the present application finds the optimal correction value of and to restore the model accuracy. Specifically, for each temperature zone, the optimal correction value satisfies the expression:

[0051]

[0052] ;

[0053] wherein, indicates the time index; indicates each time from time to time ; indicates the optimal correction value, is the correction value of the initial static gain of the system; and ​​It is the correction value of the first initial time constant; It is the correction value of the second initial time constant; This represents the optimal correction value for the first initial time constant. This represents the optimal correction value for the second initial time constant. This represents the optimal correction value for the initial static gain of the system; This indicates the search for a correction value that minimizes the objective function. Indicates the length of the time window; Indicates at time The actual temperature value measured by the sensor; Indicates at time Add correction values ​​to the baseline model The predicted temperature value.

[0054] More specifically, For the system settling time This embodiment uses [a certain number of times]. For stable time The time window length is set to ensure sufficient data to accurately assess the effect of parameter correction, while avoiding interference from outdated data on the current state. Implementers can choose the appropriate time window length based on actual needs.

[0055] Furthermore, the modified model takes the following form:

[0056]

[0057] in, This represents the modified model under the Laplace operator. This represents the initial static gain of the system; and These represent the first initial time constant and the second initial time constant, respectively. This represents the pure time delay of the system; Represents the Laplace operator; This represents the correction value for the first initial time constant. This represents the correction value for the second initial time constant. This represents the correction value for the initial static gain of the system.

[0058] At any moment The difference between the actual temperature and the predicted temperature of the new model with the corrected parameter combination; the magnitude of the difference depends on the correction value. The quality of the model depends on whether the parameter values ​​are adjusted to make the new model's predictions very close to the historical true values, and the difference. The difference will be small if the error is large, and vice versa; square the difference to ensure the error is positive, and impose a larger penalty on larger errors; sum up the squared errors of all times in the most recent time period to find a set of... This minimizes the total accumulated error.

[0059] The minimization problem of this invention can be solved using standard numerical optimization algorithms. In this embodiment, the gradient descent method is used as the numerical optimization algorithm. Implementers can choose the appropriate numerical optimization algorithm based on the actual situation.

[0060] S4. Based on the modified model, obtain the cost function, solve the cost function minimization problem to obtain the optimal power change sequence, and realize adaptive control of the temperature of each temperature zone of the extruder.

[0061] It should be noted that this invention applies the modified model to the model predictive controller (...). This allows us to obtain the optimal power change sequence for each temperature zone in the next control cycle.

[0062] Specifically, in Internally, the controller updates the baseline model to a corrected model. In each control cycle, the MPC controller performs a rolling optimization, which includes: acquiring the actual temperature of the current temperature range; and based on the updated model and the current state, predicting the system's performance in the next prediction time domain using difference equations. The temperature change trajectory within the time domain; solving a cost function minimization problem to find the future control time domain. The optimal power change sequence within the period is used, and the first element of the optimal power change sequence is used as the power change amount for the next period.

[0063] More specifically, the cost function for each temperature zone is obtained by balancing tracking accuracy and control stability, and the cost function satisfies the expression:

[0064]

[0065] in, Represents the cost function; It is the first Temperature setpoint for each control cycle; The first one based on the modified model Temperature prediction value for each control cycle; It is the first The change in heating power in each control cycle relative to the previous cycle. It predicts the time domain; It controls the time domain; It is the weighting coefficient.

[0066] More specifically, the heating power of each control cycle is calculated by the MPC controller; the prediction time domain represents the number of control cycles that predict the temperature at one time, and the length of the prediction time domain should cover the main dynamic response time of the system. In this embodiment, it is set... The control time domain represents the number of control cycles for adjusting the temperature setpoint in a single calculation. much smaller To reduce computational load, in this embodiment, the following is set: One control cycle; These are weighting coefficients. for[ ], smaller A larger value implies a greater emphasis on control precision, allowing for more aggressive control actions; The value focuses more on controlling the smoothness of the input and avoiding violent actuator movements. In this embodiment, the value is set to... The implementers can choose the length of the prediction time domain, the length of the control time domain, and the weighting coefficients according to the actual situation.

[0067] The first term of the cost function penalizes the deviation between the predicted temperature and the setpoint, aiming to bring the temperature to and maintain the setpoint as quickly as possible; the second term penalizes drastic changes in the control variable, aiming to ensure the smoothness of the control process. This is achieved by minimizing... The problem is solved using a mathematical optimization algorithm. MPC can find an optimal power change sequence that balances fast response and stable control. This embodiment uses gradient descent as the mathematical optimization algorithm to solve the minimization problem. Implementers can choose the appropriate mathematical optimization algorithm based on the specific requirements.

[0068] Furthermore, the first element of the optimal power change sequence obtained by solving is sent to the heating actuator as the power change amount for the next control cycle, thereby realizing rolling optimization control of temperature.

[0069] For example, Figure 2 This is a schematic diagram of adaptive adjustment of heating power. The blue curve represents the actual temperature, and the green curve represents the heating power.

[0070] This establishes a method for temperature control in the production of hot melt adhesive film.

[0071] This invention also discloses a temperature control system for hot melt adhesive web production, including a processor and a memory. The memory stores computer program instructions, which, when executed by the processor, implement a temperature control method for hot melt adhesive web production according to the present invention.

[0072] The system also includes other components well known to those skilled in the art, such as communication buses and communication interfaces, the settings and functions of which are known in the art and will not be described in detail here.

Claims

1. A temperature control method for hot melt adhesive screen film production, characterized by, The method comprises the following steps: For any temperature zone of the extruder, a reference model of the relationship between heating power and temperature response of the temperature zone is constructed according to a second-order plus time-delay transfer function model; A difference between the actual temperature in the sliding window and the predicted temperature of the reference model is taken as a prediction error sequence; a model fidelity of the reference model is obtained based on the prediction error sequence of the reference model, the model fidelity is negatively correlated with a square sum of an autocorrelation function of the prediction error sequence in a sliding window of a set length, and positively correlated with a square of a value of the autocorrelation function when the delay is zero, and satisfies the expression: ； wherein, denotes a time index; denotes a model fidelity of the time reference model; denotes a prediction error of the reference model at a time instant; denotes a sequence of prediction errors an autocorrelation function over a sliding window of length at a delay of ; denotes the autocorrelation value at a delay of 0; denotes a delay index; is a length of the preset sliding window; In response to the model fidelity being lower than a preset threshold, a parameter correction program of the corresponding temperature zone is started; The parameter correction program comprises the following steps: taking, in a past period of time, an integral of a square of a difference between a predicted temperature of a model obtained by adding a correction value to a parameter of the reference model and an actual temperature as a target function, the correction value being a parameter to be solved, solving the optimal correction value by minimizing the target function, and taking a model obtained by adding the optimal correction value to the parameter of the reference model as a correction model; A cost function is obtained based on the correction model, and the cost function satisfies the expression: ； wherein, represents a cost function; is a temperature set value of the control cycle; is a temperature prediction value of the control cycle based on the correction model; is a change amount of the heating power of the control cycle with respect to the previous cycle; is a prediction time domain; is a control time domain; is a weight coefficient; An optimal power change sequence is obtained by solving a minimization problem of the cost function, and adaptive control of temperatures of each temperature zone of the extruder is realized.

2. The temperature control method for hot-melt adhesive screen film production according to claim 1, characterized in that, The reference model of the relationship between heating power and temperature response of the temperature zone comprises the following steps: A pseudo-random binary sequence excitation signal is applied to the heater of each temperature zone; temperature response data under the action of the excitation signal are collected; and a second-order plus time-delay transfer function is obtained by a system identification method based on the excitation signal and the temperature response data, as the reference model.

3. The temperature control method for hot melt adhesive screen film production according to claim 1, characterized in that, The difference between the actual temperature in the sliding window and the predicted temperature of the reference model is taken as the prediction error sequence, which comprises the following steps: In a preset time window, a prediction error sequence of the reference model is obtained by subtracting a predicted temperature calculated based on the reference model and historical heating power from an actual temperature measured by a sensor.

4. The temperature control method for hot-melt adhesive screen membrane production according to claim 1, characterized in that, The preset threshold is set to 0.

85.

5. The temperature control method for hot-melt adhesive screen film production according to claim 1, characterized in that, The optimal correction value satisfies the expression: ； wherein, denotes a time index; denotes each time within the time period to the time period ; denotes the optimal correction value, is the correction value of the initial static gain of the system; is the correction value of the first initial time constant; is the correction value of the second initial time constant; denotes the optimal correction value of the first initial time constant, denotes the optimal correction value of the second initial time constant, denotes the optimal correction value of the initial static gain of the system; denotes the correction value that makes the target function reach the minimum value; denotes the length of the time window; denotes the actual temperature value measured by the sensor at the time ; denotes the temperature prediction value after the reference model adds the correction value at the time ; 6. The temperature control method for hot-melt adhesive screen membrane production according to claim 5, characterized in that, The length of the time window is set to 3 times of a system stable time.

7. The temperature control method for hot-melt adhesive screen film production according to claim 1, wherein, The weight coefficient is set as .

8. A temperature control system for hot melt adhesive web film production, characterized by, The method comprises the following steps: A processor and a memory are provided, and the memory stores computer program instructions, when the computer program instructions are executed by the processor, a temperature control method for hot melt adhesive screen production according to any one of claims 1-7 is realized.

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