Adhesive tape forming equipment control method and system based on adhesive performance analysis

Abstract

The invention relates to the technical field of equipment control, in particular to an adhesive tape forming equipment control method and system based on adhesive performance analysis, and the method comprises the steps: establishing a viscosity control model, a drying temperature and time adjustment algorithm and a tension adjustment algorithm; a data model and a control algorithm are incorporated into control logic of the PLC, and dynamic adjustment data of coating speed and pressure are output through a viscosity control model; outputting dynamic adjustment data of the drying temperature and time through a drying temperature and time adjustment algorithm; outputting dynamic adjustment data of the tension of the base material through a tension adjustment algorithm; adjusting each piece of dynamic adjustment data through different weights; and outputting a control value according to the adjustment result. By introducing the viscosity, solid content, rheological property and other characteristics of the adhesive into automatic control, the problem of production fluctuation caused by neglecting material characteristics in a traditional control method is solved, and the precision and consistency of the adhesive tape forming process are remarkably improved through sensor feedback, model control and PLC integration.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment control, and in particular to a method and system for controlling tape forming equipment based on adhesive performance analysis. Background Art

[0002] In the tape production process, a more common control method is PLC control, which controls the execution of each step through programming and has high reliability and flexibility. The PLC control system can accurately control multiple parameters such as temperature, pressure, speed, tension, etc., and can easily communicate with sensors and actuators to achieve automated production. It has the advantages of high reliability, easy maintenance, and adaptability to complex automated production needs.

[0003] However, although the PLC system performs well in managing these production parameters, the properties of the adhesive also have a significant impact on these parameters. Specifically, factors such as the viscosity, solid content, chemical reaction characteristics and rheological properties of the adhesive will have a combined impact on the coating speed, pressure, drying temperature, time and tension control. Current controls often ignore these properties, which may lead to inconsistent product quality and reduced production efficiency. Therefore, in the PLC control process, how to fully consider the properties of the adhesive and incorporate them into the control logic is a way to improve product quality and production efficiency.

[0004] The information disclosed in this background technology section is only intended to deepen the understanding of the overall background technology of the present invention, and should not be regarded as acknowledging or suggesting in any form that the information constitutes the prior art already known to those skilled in the art. Summary of the invention

[0005] The present invention provides a method and system for controlling a tape forming device based on adhesive performance analysis, thereby effectively solving the problems pointed out in the background technology.

[0006] In order to achieve the above object, the technical solution adopted by the present invention is:

[0007] A control method for a tape forming device based on adhesive performance analysis, wherein the tape forming device includes a tension sensor;

[0008] Control methods include:

[0009] Arrange viscosity sensor and solid content sensor for adhesive;

[0010] Establish data models and control algorithms, including viscosity control model, drying temperature and time adjustment algorithm and tension adjustment algorithm;

[0011] Incorporating the data model and control algorithm into the control logic of the PLC, including: outputting dynamic adjustment data of coating speed and pressure based on feedback data of the viscosity sensor through the viscosity control model; outputting dynamic adjustment data of drying temperature and time based on feedback data of the solid content sensor and curing characteristics of the adhesive through the drying temperature and time adjustment algorithm; and outputting dynamic adjustment data of substrate tension based on rheological characteristics of the adhesive and feedback data of the tension sensor through the tension adjustment algorithm;

[0012] Adjusting each of the dynamic adjustment data using different weights;

[0013] The control value is output according to the adjustment result.

[0014] Furthermore, each of the dynamic adjustment data is adjusted by different weights, including:

[0015] For each process parameter, compare the dynamic adjustment data collected twice before and after, and calculate the change range;

[0016] According to the variation range, weights are assigned to each parameter in a positive correlation to obtain initial weight values;

[0017] The initial weight value is adjusted according to process requirements to obtain a weight adjustment value.

[0018] Furthermore, the initial weight value is adjusted according to the process requirements to obtain the weight adjustment value, including:

[0019] Defining the priority of each process parameter according to process requirements and expressing it by a numerical value;

[0020] Setting target constraint values ​​for the process parameters;

[0021] The weight adjustment coefficient is calculated according to the priority and target constraint value of each process parameter, and the formula is:

[0022]

[0023] Among them, P i is the priority value of the process parameter;

[0024] V i is the current value of the process parameter;

[0025] C i is the target constraint value of the process parameter;

[0026] α i is the weight adjustment coefficient;

[0027] The product of the weight adjustment coefficient and the initial weight value is used as the weight adjustment value.

[0028] Furthermore, the drying temperature and time adjustment algorithm adopts a multi-input LSTM model, including:

[0029] The first input layer inputs the feedback data of the solid content sensor;

[0030] A second input layer, inputting the curing characteristics of the adhesive;

[0031] The first fusion layer fuses the time series information of solid content with the information of adhesive curing characteristics, connecting the outputs of the two parts;

[0032] A first fully connected layer predicts the drying temperature and time according to the output;

[0033] The first output layer outputs the predicted value.

[0034] Furthermore, the time series information of solid content is integrated with the information of adhesive curing characteristics, including:

[0035] Performing LSTM processing on the time series information of the solid content, and performing full connection layer processing on the information of the curing characteristics of the adhesive, and outputting two vectors of the same dimension respectively;

[0036] Multiply the two vectors element by element and output the fused composite vector;

[0037] Performing dimension judgment on the comprehensive vector and adjusting the dimension of the comprehensive vector according to the judgment result includes:

[0038] Analyzing the correlation matrix between the dimensions of the comprehensive vector;

[0039] Setting a correlation threshold for the correlation matrix to screen highly correlated dimensions;

[0040] Identify the actual proportion of high-correlation dimensions to the total dimensions and set a proportion threshold. When the actual proportion is greater than the proportion threshold, merge the dimensions whose correlation is higher than the correlation threshold. When the actual proportion is less than the proportion threshold, perform a dimension upgrade operation, otherwise maintain the existing dimension.

[0041] Furthermore, the correlation threshold is set based on the precision requirement of the process and is positively correlated with the precision requirement.

[0042] Furthermore, the tension adjustment algorithm adopts the same model structure as the drying temperature and time adjustment algorithm.

[0043] Furthermore, the viscosity control model is a PID control model.

[0044] Furthermore, the tape forming device also includes a temperature and humidity sensor, and the data model and control algorithm also include an environmental adaptability control model;

[0045] Incorporating the data model and control algorithm into the control logic of the PLC also includes: outputting dynamic adjustment data of the ventilation volume of the drying equipment based on the feedback data of the temperature and humidity sensor through the environmental adaptability control model.

[0046] A control system for a tape forming device based on adhesive performance analysis, wherein the tape forming device includes a tension sensor;

[0047] The control system includes:

[0048] A sensor unit, including a viscosity sensor and a solid content sensor;

[0049] Data model and control algorithm unit, including viscosity control model, drying temperature and time adjustment algorithm, tension adjustment algorithm and environmental adaptability control model;

[0050] A PLC control unit integrates the data model and the control algorithm unit, and outputs dynamic adjustment data of coating speed and pressure based on feedback data of the viscosity sensor through the viscosity control model; outputs dynamic adjustment data of drying temperature and time based on feedback data of the solid content sensor and curing characteristics of the adhesive through the drying temperature and time adjustment algorithm; and outputs dynamic adjustment data of substrate tension based on rheological characteristics of the adhesive and feedback data of the tension sensor through the tension adjustment algorithm;

[0051] A weight adjustment unit, which assigns different weights to each of the dynamic adjustment data;

[0052] The output unit outputs a control value according to the weight adjustment result of each of the dynamic adjustment numbers.

[0053] The technical solution of the present invention can achieve the following technical effects:

[0054] The present invention introduces the viscosity, solid content, rheological properties and other characteristics of the adhesive into automated control, thereby solving the problem of production fluctuations caused by traditional control methods ignoring material properties. Through sensor feedback, control model and PLC integration, the solution can dynamically adjust key production parameters such as coating speed, pressure, drying temperature, time and tension, significantly improving the accuracy and consistency of the tape forming process. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0056] Figure 1 It is a flow chart of a control method of a tape forming equipment based on adhesive performance analysis;

[0057] Figure 2 A flow chart for adjusting each dynamic adjustment data by using different weights;

[0058] Figure 3 Flowchart for obtaining weight adjustment values;

[0059] Figure 4 Flowchart for fusing time series information of solid content with information of adhesive curing characteristics. DETAILED DESCRIPTION

[0060] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0061] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items.

[0062] Embodiment 1

[0063] like Figure 1 As shown, a control method for a tape forming device based on adhesive performance analysis, the tape forming device includes a tension sensor; the control method includes:

[0064] S1: Arrange viscosity sensors and solid content sensors for adhesives; the viscosity sensor is used to detect the rheological properties of the adhesive in real time, especially the viscosity changes under different temperature and pressure conditions, and the solid content sensor is used to measure the proportion of solid matter in the adhesive. Through accurate sensor feedback data, the state changes of the adhesive can be understood in real time, providing accurate basic data for the adjustment of the subsequent control algorithm;

[0065] S2: Establish data models and control algorithms, including viscosity control model, drying temperature and time adjustment algorithm and tension adjustment algorithm. The establishment of models and algorithms can realize automatic adjustment of key parameters, making the production process more accurate and flexible, and can be dynamically adjusted according to the characteristics of the adhesive;

[0066] S3: Incorporate data models and control algorithms into the control logic of PLC, including: through the viscosity control model, based on the feedback data of the viscosity sensor, output the dynamic adjustment data of the coating speed and pressure; through the drying temperature and time adjustment algorithm, based on the feedback data of the solid content sensor and the curing characteristics of the adhesive, output the dynamic adjustment data of the drying temperature and time, the specific curing characteristics include curing time, curing temperature, viscosity change, moisture sensitivity, shear strength, thermal expansion coefficient and cross-linking density, etc.; and, through the tension adjustment algorithm, based on the rheological properties of the adhesive and the feedback data of the tension sensor, output the dynamic adjustment data of the substrate tension, the rheological properties of the adhesive include shear thinning, shear thickening, thixotropy, viscoelasticity, etc.; the PLC system can realize full automatic control of the entire production process through the integration of real-time processing and control logic, reduce manual intervention, and improve production efficiency;

[0067] S4: Adjust each dynamic adjustment data by different weights. Through weight adjustment, the system can flexibly adjust production parameters according to actual needs, thereby improving control accuracy and response speed, and avoiding over-adjustment or under-adjustment of a certain parameter;

[0068] S5: Output the control value according to the adjustment result to achieve fine control of the tape forming process.

[0069] This embodiment solves the problem of production fluctuations caused by traditional control methods ignoring material properties by introducing adhesive properties such as viscosity, solid content, and rheological properties into automated control. Through sensor feedback, control model and PLC integration, the solution can dynamically adjust key production parameters such as coating speed, pressure, drying temperature, time and tension, significantly improving the accuracy and consistency of the tape forming process.

[0070] In this embodiment, the real-time data provided by the viscosity sensor enables the system to respond quickly to changes in adhesive viscosity, ensuring that the coating process is always carried out under optimal viscosity conditions, avoiding the coating quality affected by viscosity fluctuations, and optimizing the coating thickness and uniformity by linking the viscosity with the dynamic adjustment of the coating speed and pressure to ensure coating consistency. The viscosity control model can dynamically adjust the output data according to different viscosity inputs to adapt to changes in different types of adhesives. By combining the solid content and curing characteristics data, the drying temperature and time can be adjusted more accurately to prevent over-drying or under-drying. As the solid content of the adhesive changes, the system can flexibly adjust the drying temperature and time, so that the production process can adapt to different material and process requirements. Combining the rheological property data and tension data of the adhesive, the tension can be adjusted according to the flow characteristics of the adhesive under different conditions. The tension applied to the substrate affects the uniform coating of the adhesive. If the viscosity of the adhesive is too high or the fluidity is insufficient, the tension change may cause uneven coating, resulting in stretch marks or coatings of varying thicknesses. When the fluidity of the adhesive changes, the tension of the substrate must be adjusted accordingly. Specifically, adhesives with poor fluidity require greater tension to ensure their uniform distribution, while adhesives with better fluidity can use lower tension.

[0071] In the tape production process, multiple parameters such as coating speed, pressure, drying temperature, time and tension influence each other. After independent output by the above algorithms, different weights are assigned to meet process priorities, so that the system can highlight the priority adjustment parameters according to process requirements, thereby smoothing the changes between different parameters during dynamic adjustment and reducing excessive interference to the system. This balancing mechanism ensures that the system remains stable in the face of complex production changes and avoids drastic fluctuations in parameters. Through comprehensive adjustments, the system can quickly respond to the linkage changes of multiple variables under complex working conditions and ensure the stability and consistency of the entire production process.

[0072] As a preferred embodiment of the above, Figure 2 As shown, each dynamically adjusted data is adjusted by different weights, including:

[0073] S41: For each process parameter, compare the dynamic adjustment data collected twice before and after, and calculate the change range;

[0074] S42: assigning weights to each parameter in a positive correlation according to the change amplitude to obtain an initial weight value;

[0075] S43: Adjust the initial weight value according to the process requirements to obtain the weight adjustment value.

[0076] In this preferred solution, the first weight adjustment automatically assigns weights according to the range of change of each process parameter, so that the system can dynamically adapt to the changes of various parameters in the production process. Parameters with larger changes have higher initial weights, indicating that these parameters have a greater impact on the production process. The system will give priority to adjusting these parameters to ensure that the system can flexibly and efficiently respond to sudden changes in production and ensure the priority of adjusting the most important parameters.

[0077] The second weight adjustment is based on specific process requirements. This process demand-oriented weight adjustment greatly enhances the flexibility of the system, enabling it to adapt to different production scenarios and product requirements. The second weight adjustment allows the system to have greater flexibility in balancing production accuracy and efficiency; for example, for high-precision production, weight adjustment can ensure more precise control of key parameters such as coating thickness and tension, while for fast production, the weight can be adjusted to prioritize production speed.

[0078] In summary, through two adjustments, the system achieves priority control of key parameters, avoids unnecessary frequent adjustments, and improves resource utilization efficiency. The first adjustment ensures that resources are concentrated on the parameters with the greatest changes, while the second adjustment ensures that process requirements are met first. The system can maximize output under limited resources; the two-layer adjustment mechanism not only ensures the rationality of parameter adjustment, but also enhances the stability and robustness of the system in complex environments.

[0079] As a preferred embodiment of the above, Figure 3 As shown, the initial weight value is adjusted according to the process requirements to obtain the weight adjustment value, including:

[0080] A1: Define the priority of each process parameter according to the process requirements and express it through a numerical value; for example, each parameter such as coating thickness, drying temperature, pressure and tension has its priority, which determines which parameters are more critical in the production process. The priority value P i It can be set according to process requirements, such as product accuracy, production speed, product stability and other indicators;

[0081] A2: Set target constraint values ​​for process parameters as the ideal state pursued by the system in the actual production process;

[0082] A3: Calculate the weight adjustment coefficient based on the priority and target constraint value of each process parameter. The formula is:

[0083]

[0084] Among them, P i is the priority value of the process parameter;

[0085] Vi is the current value of the process parameter;

[0086] C i is the target constraint value of the process parameter;

[0087] α i is the weight adjustment coefficient;

[0088] The first part of the above formula is the priority weighting, and the part in brackets corrects the deviation between the current value and the target value. The formula combines the priority of the parameter and the actual deviation, so that the system can flexibly adjust different parameters. For example, parameters with higher priority and larger deviation will receive higher weights, thereby obtaining more adjustment resources and quickly adjusting them to the appropriate range. By considering the deviation, the system can dynamically increase the control of parameters with larger deviations to ensure that the system can respond in time when facing large fluctuations and maintain the accuracy and stability of production; this formula ensures that the system can adapt to different process requirements and actual conditions, taking into account the actual fluctuations in current production, and adjusting weights according to priority allocation, making the entire production process smoother and more efficient;

[0089] A4: The product of the weight adjustment coefficient and the initial weight value is taken as the weight adjustment value.

[0090] Among them, the drying temperature and time adjustment algorithm adopts a multi-input LSTM model, including: the first input layer, which inputs the feedback data of the solid content sensor; the second input layer, which inputs the curing characteristics of the adhesive; the first fusion layer, which fuses the time series information of the solid content with the information of the curing characteristics of the adhesive, and connects the outputs of the two parts; the first fully connected layer, which predicts the drying temperature and time based on the output; the first output layer, which outputs the predicted value.

[0091] In this preferred solution, by using the LSTM model to process the time series data of solid content, the system can identify the historical trend of solid content and predict future changes. After combining the curing characteristics of the adhesive, the system can more accurately predict the required drying temperature and time. The time series advantage of LSTM enables the model to adapt to the changes of different batches of materials to ensure the stability of production. Through the multi-input structure, the system can dynamically capture multi-dimensional data, not only considering the solid content under the current working conditions, but also considering the curing behavior of the adhesive. Combined with these data, the system can automatically adjust the temperature and time of the drying equipment, reduce the error of manual adjustment, and improve the overall level of automation of production.

[0092] As a preferred embodiment of the above, Figure 4 As shown in the figure, the time series information of solid content is integrated with the information of adhesive curing characteristics, including:

[0093] B1: Perform LSTM processing on the time series information of solid content, extract the dynamic features in the time series, capture the law of solid content changing over time during the production process, and perform full connection layer processing on the information of adhesive curing characteristics, extract the features reflecting its physical and chemical properties in the curing characteristics, and output two vectors of the same dimension respectively, which provide a basis for subsequent fusion operations, specifically the first vector and the second vector shown in the figure;

[0094] B2: Multiply the two vectors element by element and output the fused comprehensive vector; the element-by-element multiplication method can maintain the data interaction in each dimension, organically combine the two input features, and combine the time variation information of the solid content and the static information of the curing characteristics of the adhesive in each feature dimension, so as to obtain a more expressive comprehensive feature vector;

[0095] B3: Determine the dimension of the comprehensive vector and adjust the dimension of the comprehensive vector according to the determination result, including:

[0096] B31: Analyze the correlation matrix between the dimensions of the comprehensive vector;

[0097] B32: Set the correlation threshold for the correlation matrix to screen the highly correlated dimensions to avoid the interference of redundant data on the model;

[0098] B33: Identify the actual proportion of high-correlation dimensions in the total dimensions and set a proportion threshold. When the actual proportion is greater than the proportion threshold, merge the dimensions whose correlation is higher than the correlation threshold. When the actual proportion is less than the proportion threshold, use the dimension upgrade operation, otherwise maintain the existing dimension.

[0099] By calculating the proportion of high-correlation dimensions in the total dimensions, it is decided what kind of adjustment to make to the dimensions of the comprehensive vector; if the proportion of high-correlation dimensions is greater than the set threshold, it means that there is a lot of redundancy in the fused feature dimensions, and the system will merge these high-correlation dimensions to reduce the dimensions and improve the efficiency of the model; if the proportion of high-correlation dimensions is less than the threshold, the system will increase the representation ability of the features through dimensionality increase operations to ensure the generalization and expressiveness of the model; for example, new features can be generated based on the combination of original features, and the combination can be the interaction, product, difference, ratio between different features; or based on the feature expansion of time series, the feature dimensions can be increased to capture more time dependencies and trends, etc.

[0100] In this preferred solution, the time series information of solid content is processed by LSTM, and the system can capture the change pattern of solid content over time in the entire production process and obtain dynamic features. At the same time, the curing characteristics of the adhesive are processed by the fully connected layer, and the inherent static physical and chemical characteristics of the adhesive can be extracted. The dynamic features are combined with the static features through the element-by-element multiplication fusion method to ensure that the system can organically integrate the two types of data in each dimension and obtain a more expressive comprehensive feature vector. This feature enables the system to not only dynamically track the changes in solid content during the production process, but also ensure that the characteristics of the adhesive are accurately considered in the control process, thereby improving the accuracy and control level of the production process.

[0101] As a preferred embodiment of the above, the correlation threshold is set based on the precision requirement of the process and is positively correlated with the precision requirement. The correlation threshold is used to identify which feature dimensions are redundant or have a strong correlation with other features. Setting a high correlation threshold means that the system only retains those independent features that have a greater impact on the results and removes redundant features. When the process has higher precision requirements, the system needs to more accurately capture the features that have the greatest impact on the production process and ensure that the model can use this information to make more precise control adjustments. Therefore, higher precision requirements mean that features need to be screened more strictly, and only features that contribute significantly to the model are retained.

[0102] As a preferred embodiment of the above, the tension adjustment algorithm adopts the same model structure as the drying temperature and time adjustment algorithm. Based on this preferred technical solution, the tension adjustment algorithm can achieve similar effects, mainly in terms of dynamically capturing the time changes of tension, making comprehensive adjustments based on the characteristics of the adhesive, preventing quality problems caused by tension fluctuations, improving the level of automation, and improving production efficiency and product consistency. This model structure can ensure more accurate tension adjustment, reduce production fluctuations, and improve the stability and intelligence level of the production process.

[0103] Specifically, the tension adjustment algorithm includes a third input layer, which inputs the feedback data of the tension sensor; a fourth input layer, which inputs the rheological properties of the adhesive; a second fusion layer, which fuses the tension time series information with the information of the rheological properties of the adhesive; a second fully connected layer, which predicts the tension adjustment value based on the fused features; and a second output layer, which outputs the predicted tension adjustment value.

[0104] As a preferred embodiment of the above, the viscosity control model is a PID control model. In viscosity control, stability is particularly important. Excessive fluctuations or overly complex models may cause adverse effects on coating quality. PID control has strong stability and reliability and is particularly suitable for fine control in a relatively stable environment.

[0105] As a preferred embodiment of the above embodiment, the tape forming equipment also includes a temperature and humidity sensor, and the data model and control algorithm also include an environmental adaptability control model; incorporating the data model and control algorithm into the control logic of the PLC also includes: through the environmental adaptability control model, based on the feedback data of the temperature and humidity sensor, outputting the dynamic adjustment data of the ventilation volume of the drying equipment, and the PID control model can also be used here.

[0106] Embodiment 2

[0107] The control system of the tape forming equipment based on the adhesive performance analysis, the tape forming equipment includes a tension sensor; the control system includes:

[0108] A sensor unit, including a viscosity sensor and a solid content sensor; a data model and control algorithm unit, including a viscosity control model, a drying temperature and time adjustment algorithm, a tension adjustment algorithm and an environmental adaptability control model; a PLC control unit, integrating the data model and the control algorithm unit, outputs dynamic adjustment data of coating speed and pressure based on feedback data of the viscosity sensor through the viscosity control model; outputs dynamic adjustment data of drying temperature and time based on feedback data of the solid content sensor and curing characteristics of the adhesive through the drying temperature and time adjustment algorithm; and outputs dynamic adjustment data of substrate tension based on rheological characteristics of the adhesive and feedback data of the tension sensor through the tension adjustment algorithm; a weight adjustment unit, assigning different weights to each dynamic adjustment data; an output unit, outputting a control value according to the weight adjustment result of each dynamic adjustment number.

[0109] The technical effects achieved by this embodiment are the same as those of the above embodiments and will not be described again here.

[0110] The above shows and describes the basic principles, main features and advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A method for controlling a tape forming device based on adhesive performance analysis, characterized in that: The tape forming equipment includes a tension sensor; Control methods include: Arrange viscosity sensor and solid content sensor for adhesive; Establish data models and control algorithms, including viscosity control model, drying temperature and time adjustment algorithm and tension adjustment algorithm; Incorporating the data model and control algorithm into the control logic of the PLC, including: outputting dynamic adjustment data of coating speed and pressure based on feedback data of the viscosity sensor through the viscosity control model; outputting dynamic adjustment data of drying temperature and time based on feedback data of the solid content sensor and curing characteristics of the adhesive through the drying temperature and time adjustment algorithm; and outputting dynamic adjustment data of substrate tension based on rheological characteristics of the adhesive and feedback data of the tension sensor through the tension adjustment algorithm; Adjusting each of the dynamic adjustment data using different weights; The control value is output according to the adjustment result.

2. The method for controlling a tape forming device based on adhesive performance analysis according to claim 1, characterized in that: The dynamically adjusted data are adjusted by different weights, including: For each process parameter, compare the dynamic adjustment data collected twice before and after, and calculate the change range; According to the variation range, weights are assigned to each parameter in a positive correlation to obtain initial weight values; The initial weight value is adjusted according to process requirements to obtain a weight adjustment value.

3. The method for controlling a tape forming device based on adhesive performance analysis according to claim 2, characterized in that: The initial weight value is adjusted according to the process requirements to obtain the weight adjustment value, including: Defining the priority of each process parameter according to process requirements and expressing it by a numerical value; Setting target constraint values ​​for the process parameters; The weight adjustment coefficient is calculated according to the priority and target constraint value of each process parameter, and the formula is: Among them, P i is the priority value of the process parameter; V i is the current value of the process parameter; C i is the target constraint value of the process parameter; α i is the weight adjustment coefficient; The product of the weight adjustment coefficient and the initial weight value is used as the weight adjustment value.

4. The method for controlling a tape forming device based on adhesive performance analysis according to claim 1, characterized in that: The drying temperature and time adjustment algorithm adopts a multi-input LSTM model, including: The first input layer inputs the feedback data of the solid content sensor; A second input layer, inputting the curing characteristics of the adhesive; The first fusion layer fuses the time series information of solid content with the information of adhesive curing characteristics, connecting the outputs of the two parts; A first fully connected layer predicts the drying temperature and time according to the output; The first output layer outputs the predicted value.

5. The method for controlling a tape forming device based on adhesive performance analysis according to claim 4, characterized in that: The time series information of solid content is integrated with the information of adhesive curing characteristics, including: Performing LSTM processing on the time series information of the solid content, and performing full connection layer processing on the information of the curing characteristics of the adhesive, and outputting two vectors of the same dimension respectively; Multiply the two vectors element by element and output the fused composite vector; Performing dimension judgment on the comprehensive vector and adjusting the dimension of the comprehensive vector according to the judgment result includes: Analyzing the correlation matrix between the dimensions of the comprehensive vector; Setting a correlation threshold for the correlation matrix to screen highly correlated dimensions; Identify the actual proportion of high-correlation dimensions to the total dimensions and set a proportion threshold. When the actual proportion is greater than the proportion threshold, merge the dimensions whose correlation is higher than the correlation threshold. When the actual proportion is less than the proportion threshold, perform a dimension upgrade operation, otherwise maintain the existing dimension.

6. The method for controlling a tape forming device based on adhesive performance analysis according to claim 5, characterized in that: The correlation threshold is set based on the precision requirement of the process and is positively correlated with the precision requirement.

7. The method for controlling a tape forming device based on adhesive performance analysis according to claim 4, characterized in that: The tension adjustment algorithm adopts the same model structure as the drying temperature and time adjustment algorithm.

8. The method for controlling a tape forming device based on adhesive performance analysis according to claim 1, characterized in that: The viscosity control model is a PID control model.

9. The method for controlling a tape forming device based on adhesive performance analysis according to claim 1, characterized in that: The adhesive tape forming device also includes a temperature and humidity sensor, and the data model and control algorithm also include an environmental adaptability control model; Incorporating the data model and control algorithm into the control logic of the PLC also includes: outputting dynamic adjustment data of the ventilation volume of the drying equipment based on the feedback data of the temperature and humidity sensor through the environmental adaptability control model.

10. The control system of adhesive tape forming equipment based on adhesive performance analysis is characterized in that: The tape forming equipment includes a tension sensor; The control system includes: A sensor unit, including a viscosity sensor and a solid content sensor; Data model and control algorithm unit, including viscosity control model, drying temperature and time adjustment algorithm, tension adjustment algorithm and environmental adaptability control model; A PLC control unit integrates the data model and the control algorithm unit, and outputs dynamic adjustment data of coating speed and pressure based on feedback data of the viscosity sensor through the viscosity control model; outputs dynamic adjustment data of drying temperature and time based on feedback data of the solid content sensor and curing characteristics of the adhesive through the drying temperature and time adjustment algorithm; and outputs dynamic adjustment data of substrate tension based on rheological characteristics of the adhesive and feedback data of the tension sensor through the tension adjustment algorithm; A weight adjustment unit, which assigns different weights to each of the dynamic adjustment data; The output unit outputs a control value according to the weight adjustment result of each of the dynamic adjustment numbers.

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