A method for controlling the glue coating of an aqueous pressure-sensitive adhesive coating and compounding device

Abstract

The present application provides a method for controlling the coating of adhesive liquid of an aqueous pressure-sensitive adhesive coating and composite device, which relates to the technical field of coating control and includes: constructing a substrate parameter grid and merging to obtain a merged substrate grid; performing primary coating on each substrate parameter grid to obtain first coating data of the merged substrate grid; performing deviation analysis of the first coating reference value on the first coating data to generate a first coating deviation coefficient for each substrate parameter grid; performing state evaluation on the merged substrate grid to generate a first coating state result; arranging the priorities of the substrate parameter grids to obtain a second coating grid queue; and performing secondary coating on the second coating grid queue to obtain second coating data. Through the present application, the technical problem in the prior art of inaccurate coating process control due to the flow and drying control of the adhesive liquid can be solved, the technical goal of precisely controlling the adhesive liquid coating process can be achieved, and the technical effect of improving coating uniformity and ensuring consistent coating thickness can be achieved.

Description

Technical Field

[0001] The present application relates to the technical field of coating control, and particularly to a method for controlling the coating of adhesive liquid of an aqueous pressure-sensitive adhesive coating and laminating device. Background Art

[0002] In the medical industry, the aqueous pressure-sensitive adhesive coating technology is widely used in the manufacture of medical tapes, patches and other products. However, there are some obvious defects in the existing coating technologies. First, due to the limited accuracy of the coating equipment, the coating of the adhesive liquid is uneven, which affects the product quality. Second, the control of the flow and drying of the adhesive liquid is not precise enough, and problems such as inconsistent coating thickness and poor drying effect are likely to occur. In addition, the influence of environmental factors such as temperature and humidity on the coating effect during the coating process has not been fully considered, resulting in unstable production processes. In order to overcome these defects, more advanced coating control methods need to be developed to improve the coating uniformity and stability, and ensure the product quality and production efficiency.

[0003] In summary, there is a technical problem in the prior art that due to the inaccurate control of the coating process of the flow and drying of the adhesive liquid, the coating is uneven, the thickness is inconsistent and the drying effect is poor during multiple adhesive liquid coating processes, which further affects the product quality and production efficiency. Summary of the Invention

[0004] The purpose of the present application is to provide a method for controlling the coating of adhesive liquid of an aqueous pressure-sensitive adhesive coating and laminating device, so as to solve the technical problem in the prior art that due to the inaccurate control of the coating process of the flow and drying of the adhesive liquid, the coating is uneven, the thickness is inconsistent and the drying effect is poor during multiple adhesive liquid coating processes, which further affects the product quality and production efficiency.

[0005] In view of the above problems, the present application provides a method for controlling the coating of adhesive liquid of an aqueous pressure-sensitive adhesive coating and laminating device.

[0006] The present application provides a method for controlling the coating of adhesive liquid of an aqueous pressure-sensitive adhesive coating and laminating device, including: obtaining the substrate information and coating position information of a medical substrate to construct a substrate parameter grid, and merging the substrate parameter grids to obtain a substrate merged grid; performing a first coating on the medical substrate according to a first coating reference value for each substrate parameter grid to obtain first coating data of the substrate merged grid; performing deviation analysis of the first coating reference value on the first coating data to generate a first coating deviation coefficient for each substrate parameter grid; performing a state evaluation on the substrate merged grid based on a first adhesive liquid state threshold to generate a first coating state result; arranging the priority of the substrate parameter grids according to the first coating state result and the first coating deviation coefficient to obtain a second coating grid queue; calculating expected second coating data and performing a second coating on the second coating grid queue to obtain second coating data.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0008] By obtaining the substrate information and coating position information of the medical substrate to construct a substrate parameter grid, and merging the substrate parameter grids to obtain a substrate merged grid; performing a first coating on the medical substrate according to each substrate parameter grid with a first coating reference value to obtain the first coating data of the substrate merged grid; performing a deviation analysis of the first coating reference value on the first coating data to generate a first coating deviation coefficient for each substrate parameter grid; performing a state evaluation on the substrate merged grid based on a first glue liquid state threshold to generate a first coating state result; arranging the priorities of the substrate parameter grids according to the first coating state result and the first coating deviation coefficient to obtain a second coating grid queue; calculating the expected second coating data and performing a second coating on the second coating grid queue to obtain the second coating data, the technical goal of precisely controlling the glue liquid coating process is achieved, and the technical effects of improving coating uniformity and ensuring consistent coating thickness are achieved.

[0009] The above description is only an overview of the technical solutions of this application. In order to be able to understand the technical means of this application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features and advantages of this application more obvious and understandable, the following specific embodiments of this application are specifically given. It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of this application, nor is it used to limit the scope of this application. Other features of this application will become easily understood through the following description. Description of the Drawings

[0010] In order to more clearly illustrate the technical solutions in this application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings described below are only exemplary, and for those of ordinary skill in the art, other drawings can be obtained according to the provided drawings without creative efforts.

[0011] Figure 1 It is a schematic flowchart of a glue liquid coating control method for an aqueous pressure-sensitive adhesive coating and composite device of this application;

[0012] Figure 2 It is a schematic flowchart of generating a first coating state result in a glue liquid coating control method for an aqueous pressure-sensitive adhesive coating and composite device of this application. Detailed Embodiments

[0013] By providing a method for controlling the coating of adhesive liquid in an aqueous pressure-sensitive adhesive coating and compounding device, the present application solves the technical problem in the prior art that due to inaccurate control of the coating process of the adhesive liquid flow and drying, the coating is uneven, the thickness is inconsistent, and the drying effect is poor during multiple adhesive liquid coating processes, further affecting the product quality and production efficiency. The technical goal of accurately controlling the adhesive liquid coating process is achieved, and the technical effect of improving the coating uniformity and ensuring the consistent coating thickness is achieved.

[0014] Next, the technical solutions in the present application will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. It should be understood that the present application is not limited by the example embodiments described herein. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present application. Additionally, it should be noted that for the sake of description, only the parts related to the present application are shown in the drawings rather than all.

[0015] Embodiment, please refer to the attached Figure 1 , the present application provides a method for controlling the coating of adhesive liquid in an aqueous pressure-sensitive adhesive coating and compounding device, specifically including:

[0016] Step 1: Obtain the substrate information and coating position information of the medical substrate to construct a substrate parameter grid, and merge the substrate parameter grids to obtain a substrate merged grid.

[0017] Specifically, obtaining the substrate information and coating position information of the medical substrate further collects all necessary data related to the coating process. The coating position information includes each position of the substrate during the coating process, and the substrate information includes the type, thickness, surface treatment, strength, water absorption, etc. of the substrate, as well as the specific data of the coating position information, such as coating speed, coating thickness, etc. The substrate information and coating position information are the basis for subsequent analysis and optimization, ensuring that the entire coating process can be accurately controlled. By comprehensively and accurately obtaining these data, a solid foundation can be laid for constructing a more refined and accurate substrate parameter grid.

[0018] Then, the construction of the substrate parameter grid is to combine the obtained substrate information and coating position information, that is, to obtain the substrate parameter grid according to the coating position information and the corresponding substrate information. Each substrate parameter grid represents a specific position and the corresponding parameter value, which can intuitively display and analyze the parameter changes at each position during the coating process, providing clear guidance for subsequent merging and optimization.

[0019] Next, merging the substrate parameter grids is to integrate the preliminarily constructed substrate parameter grids to form a comprehensive substrate merged grid, and merge the adjacent substrate parameter grids to improve the data processing efficiency.

[0020] Step 2: Perform a primary coating on the medical substrate with respect to the first coating reference value for each substrate parameter grid to obtain the first coating data of the combined substrate grid.

[0021] Specifically, performing a primary coating on the medical substrate with respect to the first coating reference value means performing an initial coating operation on each substrate parameter grid according to the set reference parameters (such as coating thickness, speed, etc.), that is, according to the first coating reference value. The first coating reference value is set according to actual needs and technical requirements to ensure the quality and consistency of the initial coating. During the primary coating process, each substrate parameter grid will be coated with a layer of adhesive solution to ensure initial coverage of the entire substrate.

[0022] Then, obtaining the first coating data of the combined substrate grid means collecting and recording the actual coating results of each substrate parameter grid after the initial coating operation. The first coating data includes various parameters such as actual coating thickness, uniformity, viscosity, and drying degree. By analyzing the first coating data, the effect and quality of the initial coating can be judged, potential problems and non-uniform areas can be identified, and it is ensured that the coating data of each substrate parameter grid is accurate, thus providing a reliable basis for subsequent process adjustment. For example, during the process of obtaining the combined substrate grid, an optical monitoring system and sensors are used to monitor and record the coating effect of each substrate parameter grid in real time. Through the optical monitoring system, the coating thickness and uniformity data of each substrate parameter grid can be obtained in real time, and the first coating data is transmitted to the control system for analysis and processing. At the same time, the sensors can monitor parameters such as environmental temperature, humidity, and drying status to ensure that the coating conditions of each substrate parameter grid are consistent.

[0023] Step 3: Perform a deviation analysis of the first coating reference value on the first coating data to generate the first coating deviation coefficient for each substrate parameter grid.

[0024] Specifically, performing a deviation analysis of the first coating reference value on the first coating data means comparing the first coating data after actual coating with the pre-set first coating reference value to find the difference between each substrate parameter grid and the ideal state. For example, by calculating the gap between the actual coating thickness, uniformity, etc. of each substrate parameter grid and the reference value, deviation data is generated to intuitively reflect the problems and non-uniformities existing in the coating process.

[0025] Then, the first coating deviation coefficient of each substrate parameter grid is achieved through the quantification of the deviation analysis results. The first coating deviation coefficient is an indicator describing the degree of difference between the actual coating effect and the ideal reference value, usually expressed in percentage or numerical form. Specifically, it can be obtained by calculating the ratio of the difference between the first coating data and the first coating reference value to the first coating reference value. By calculating the first coating deviation coefficient of each substrate parameter grid, the deviation situation of each grid unit can be visually displayed. The larger the deviation coefficient, the greater the gap between the actual coating effect and the ideal state, and key adjustments and optimizations are required in the subsequent coating process. On the contrary, it indicates that the gap between the actual coating effect and the ideal state is smaller.

[0026] Step Four: Based on the first adhesive liquid state threshold, conduct a state evaluation on the merged substrate grid to generate the first coating state result.

[0027] Specifically, by conducting a detailed evaluation of the actual coating state of each substrate parameter grid in the merged substrate grid, determine whether the coating process has reached the expected standard, and generate the first coating state result. That is, by calculating and analyzing the fitness values of each grid unit, generate a detailed fitness distribution map, identify the coating quality problem areas, and finally generate the first coating state result, thereby enabling subsequent coating operations.

[0028] Step Five: Arrange the priorities of the substrate parameter grids according to the first coating state result and the first coating deviation coefficient to obtain the second coating grid queue.

[0029] Specifically, after the initial coating is completed and the coating effect is analyzed, based on the actual state result of the first coating and the deviation coefficient of each grid, conduct a detailed evaluation of each substrate parameter grid. The coating state result reflects the performance of each substrate parameter grid in the initial coating, such as coating thickness, uniformity, etc., while the deviation coefficient quantifies the difference between the actual performance and the expected standard. By combining the two indicators, the specific situation of each grid can be comprehensively understood, and the second coating grid queue can be obtained, providing a scientific basis for the subsequent optimization and adjustment.

[0030] Step Six: Calculate the expected second coating data to conduct a secondary coating on the second coating grid queue to obtain the second coating data.

[0031] Specifically, after obtaining the deviation coefficient of the first coating, the specific coating parameters required for each substrate parameter grid in the second coating are determined. The expected second coating data includes coating thickness, coating speed, adhesive solution flow rate, etc., aiming to compensate for the deviations existing in the first coating and make the second coating more uniform and meet the standards. For example, for grids with large deviations, it is necessary to increase or decrease the coating amount, adjust the speed, etc. to achieve the expected effect. Then, the actual secondary coating operation is carried out on each substrate parameter grid according to the pre-arranged priority order.

[0032] The adhesive solution coating control method of the aqueous pressure-sensitive adhesive coating and compounding equipment can achieve the technical goal of precisely controlling the adhesive solution coating process and achieve the technical effects of improving coating uniformity and ensuring consistent coating thickness.

[0033] Furthermore, as Figure 2 shown, this application also includes:

[0034] Extract the upper and lower limits of the adhesive solution state according to the key parameters of the adhesive solution to form the first adhesive solution state threshold; extract the first adhesive solution state from the first adhesive solution state threshold; use the first adhesive solution state as the initial particle and move it in the first adhesive solution state threshold according to a preset step length to obtain iterative particles; perform state evaluation on the iterative particles based on the state function. If the state fitness of any iterative particle is higher than that of the previous iterative particle, retain and update until an iterative stagnation result is obtained; take the iterative result obtained by performing iteration on the iterative stagnation result as the first adhesive solution state. If the substrate merging grid reaches the first adhesive solution state, the state evaluation is qualified, and the first coating state result is generated.

[0035] Specifically, extracting the upper and lower limits of the adhesive solution state according to the key parameters of the adhesive solution forms the first adhesive solution state threshold and determines the acceptable range of the adhesive solution state. For example, the key parameters include viscosity, dryness, etc., which are set based on experimental data and process requirements. The upper limit represents the maximum acceptable value of the key parameters of the adhesive solution, and the lower limit represents the minimum acceptable value. By determining the upper and lower limits and forming the first adhesive solution state threshold, it can ensure that the adhesive solution maintains the best state during the coating process, thereby achieving an ideal coating effect.

[0036] Then, extracting the first adhesive solution state from the first adhesive solution state threshold means that after determining the upper and lower limits of the key parameters of the adhesive solution, a specific state within its acceptable range of the key parameters of the adhesive solution is selected as the initial state for subsequent optimization and adjustment starting from the first adhesive solution state. The selection of the first adhesive solution state is usually based on empirical data and preliminary experimental results in actual production to ensure its high adaptability and feasibility.

[0037] Next, take the first glue liquid state as the initial particle, move it within the first glue liquid state threshold according to a preset step size to obtain iterative particles, and perform parameter adjustment and optimization. Based on the initial state, generate multiple new iterative particles by moving in the parameter space according to the preset step size. The iterative particles represent different glue liquid states, aiming to explore the entire glue liquid state space to find the optimal glue liquid state combination for the second coating.

[0038] Next, perform a state evaluation of the iterative particles based on the state function. If the state fitness of any iterative particle is higher than the state fitness of the previous generation of particles, retain and update until an iterative stagnation result is obtained. Define a state function to evaluate the fitness of each iterative particle. The state function is usually based on evaluation indicators of the actual coating effect, such as coating thickness, viscosity, etc. If the fitness of a certain iterative particle is higher than the fitness of the previous iterative particle, update it as the new optimal particle and continue the iteration until the fitness no longer improves significantly, that is, reach the iterative stagnation state.

[0039] Next, take the iterative result obtained by performing iteration on the iterative stagnation result as the first glue liquid state. If the substrate merged grid reaches the first glue liquid state, the state evaluation is qualified, and the first coating state result is generated. Finally, through multiple iterations and optimizations, determine an optimal glue liquid state, which is the first glue liquid state. Then, during the actual coating process, if the coating effect of each substrate parameter grid in the substrate merged grid can reach or approach the first glue liquid state, it means that the coating process meets the expectations, the state evaluation is qualified, and finally the first coating state result is generated to ensure the coating quality and consistency.

[0040] Extract the upper and lower limits of the key parameters of the glue liquid to form the first glue liquid state threshold, extract the initial glue liquid state from it as the initial particle, use an optimization algorithm for iteration, evaluate and update the iterative particles based on the state function until an iterative stagnation result is obtained. Take the iterative result as the first glue liquid state, and verify whether the substrate merged grid reaches this state during the actual coating process, so as to ensure the uniformity and quality of the coating process, and effectively improve the stability and reliability of the coating process.

[0041] Furthermore, this application also includes:

[0042] The state function is: Where SD fit is the surface dryness fitness, SD actual is the actual surface dryness, SD std is the standard surface dryness, V fic is the viscosity fitness, V actual is the actual viscosity, V std is the standard viscosity, SFfit is the flatness fitness, SF actual is the actual flatness, SF std is the standard flatness, DT fit is the drying time fitness, DT actual is the actual drying time, DT std is the standard drying time, T fit is the temperature fitness, T actual is the actual temperature, T std is the standard temperature, H fit is the humidity fitness, H actual is the actual humidity, H std is the standard humidity, Total fit is the status fitness, SD fit , V fit , SF fit , DT fit , T fit , H fit and Total fit The value ranges of and are [0, 1]. ω1, ω2, ω3, ω4, ω5 and ω6 are the weights of SD fit , V fit , SF fit , DT fit , T fit , H fit respectively.

[0043] Specifically, the state function is used to comprehensively evaluate the state of the adhesive solution. The state function Total fit has a value range between 0 and 1. The closer the value is to 1, the more suitable the state of the adhesive solution is for the current coating requirements. Conversely, the farther it is. Among them, the surface dryness fitness is calculated by comparing the actual surface dryness and the standard surface dryness, ensuring that when the actual surface dryness is close to the standard surface dryness, the fitness is close to 1, and vice versa. Similarly, when the actual viscosity is close to the standard viscosity, the fitness is high, and vice versa. The closer the actual flatness is to the standard value, the higher the fitness, and vice versa. When the actual drying time is close to the standard drying time, the fitness is high, and vice versa. When the actual temperature is close to the standard temperature, the fitness is high, and vice versa. When the actual humidity is close to the standard humidity, the fitness is high, and vice versa.

[0044] Finally, each fitness is weighted and summed according to the weights to obtain the comprehensive state fitness, which is used to evaluate the overall state of the adhesive solution. If the state fitness of a certain iterative particle is higher than that of the previous iterative particle, it is retained and updated until the iteration stagnates, determining the final optimal state of the adhesive solution, ensuring the continuous optimization of the adhesive solution state until the optimal conditions are reached.

[0045] Furthermore, this application also includes:

[0046] Perform local optimal escape of the iterative particles with a preset local step size according to the iterative stagnation result, and continue to perform iteration according to the local optimal escape result; if the state fitness of any local particle is higher than the state fitness of the local particle in the previous iteration, retain and update until a local iterative stagnation result is obtained; integrate the local iterative stagnation result to obtain an iterative result.

[0047] Specifically, performing local optimal escape of the iterative particles with a preset local step size according to the iterative stagnation result means that after reaching a stagnation state in the preliminary iteration process, explore near the current parameter space by setting a preset local step size to find a better local solution. Local optimal escape is to avoid the key parameters of the glue solution falling into a local optimal solution. The key parameters of the glue solution explore new possibilities for each key parameter of the glue solution by moving and adjusting within a small range near the solution of each current key parameter of the glue solution. Specifically, the size and direction of the preset step size can be determined by random generation or setting, so as to generate new iterative particles and enter a new parameter space for optimization.

[0048] Then, continuing to perform iteration according to the local optimal escape result means continuing iterative calculation and optimization on the basis of local optimal escape. By evaluating the state fitness of the new iterative particles, if a better solution is found within the local range for the new iterative particles, continue to perform further optimization and adjustment on the basis of the new solution. Keep repeating until no better solution can be found, that is, a new local iterative stagnation state is reached.

[0049] Next, if the state fitness of any local particle is higher than the state fitness of the local particle in the previous iteration, retain and update until a local iterative stagnation result is obtained. That is, in each local optimal escape and iteration process, if the state fitness of a new particle is higher than that of the previous particle, update the current optimal solution and continue to perform iteration until the fitness of all particles no longer increases significantly, reaching a local iterative stagnation result, ensuring that the optimal solution within the current parameter space can be found in each iteration.

[0050] Next, integrating the local iterative stagnation result to obtain an iterative result means that after all local iterative processes are completed, integrate the optimal results of each local iterative stagnation to form a comprehensive iterative result. The iterative result represents the optimal glue solution state parameters of each key parameter of the glue solution found within the glue solution state after a whole coating. By integrating each local optimal result, it can be ensured that the glue solution state setting in the whole coating process reaches the global optimum, so as to achieve the best glue solution coating effect, which is used for secondary coating when in the best glue solution coating state, to prevent the glue solution after the first coating from not adhering to the glue solution of the second coating or the glue solution of the first coating flowing, resulting in the inability to perform the second coating.

[0051] Escape from the local optimum through the preliminary iteration stagnation result, continue to explore and optimize under the preset step size, find the local optimum solution, and update the optimum solution through continuous iteration and evaluation until local iteration stagnation is reached. Finally, integrate all the local iteration stagnation results to obtain the global iterative optimum result, and ensure that all parameters in the glue coating process reach the best state through systematic exploration and optimization, providing high-quality and consistent coating effects.

[0052] Furthermore, this application also includes:

[0053] Arrange the substrate parameter grid in a high-priority order according to the first coating state result to obtain the second coating grid high-priority queue; arrange the substrate parameter grid in a low-priority order according to the first coating deviation coefficient to obtain the second coating grid low-priority queue; extract the same-position substrate parameter grids from the second coating grid high-priority queue and the second coating grid low-priority queue, and fix the same-position substrate parameter grids in the second coating grid queue; extract the different-position substrate parameter grids from the second coating grid high-priority queue and the second coating grid low-priority queue, and fix the different-position substrate parameter grids in the second coating grid queue according to the different-position evaluation result; perform the fixation of the same-position substrate parameter grids and the different-position substrate parameter grids to obtain the second coating grid queue.

[0054] Specifically, arrange the substrate parameter grid in a high-priority order according to the first coating state result to obtain the second coating grid high-priority queue. Based on the initial coating result, arrange the substrate parameter grids with a coating state suitable for rapid second coating after the first coating, that is, the substrate parameter grids with higher fitness, in a high-priority order, indicating that the substrate parameter grids have reached or are close to a state suitable for rapid second coating after the first coating. Therefore, higher quality standards can be maintained in subsequent coatings.

[0055] Then, arrange the substrate parameter grid in a low-priority order according to the first coating deviation coefficient to obtain the second coating grid low-priority queue. Arrange the substrate parameter grids with a large difference between the coating effect after the first coating and the requirement benchmark of the first coating, that is, the substrate parameter grids with a large first coating deviation coefficient, in a low-priority order, indicating a large gap from the ideal state in the first coating. Therefore, key adjustments and optimizations are required in subsequent coatings, but the priority is still lower than the high-priority arrangement method where the glue state allows for immediate second coating. Through the low-priority arrangement, it can be ensured that problem areas are preferentially addressed during the second coating process to improve the overall coating quality.

[0056] Then, substrate parameter grids of the same order are respectively extracted from the high-priority queue and the low-priority queue, and the substrate parameter grids of the same order are fixed in the second coating grid queue to be subjected to the second coating.

[0057] Next, substrate parameter grids of different orders are respectively extracted from the high-priority queue and the low-priority queue and fixed according to their specific evaluation results. Through the arrangement of different orders, it can be ensured that the states and requirements of each substrate parameter grid are fully considered, and targeted adjustments and optimizations are carried out during the second coating process.

[0058] Through the coordinated arrangement of high priority and low priority, it is ensured that the coating quality can be effectively improved during the second coating process. By performing the fixing step, a coating scheme with a reasonable structure and clear priorities can be formed, providing strong guidance for the second coating.

[0059] Furthermore, the present application further includes:

[0060] Performing high-level different-order and low-level different-order extractions on the high-priority queue and the low-priority queue of the second coating grid, calculating the different-order difference between the high-level different order and the low-level different order; determining whether the different-order difference meets the order difference of the different-order arrangement threshold; if it meets, fixing the different-order substrate parameter grid in the second coating grid queue based on the priority order in the high-level different order and the low-level different order, and obtaining the first evaluation result of the different-order substrate parameter grid and adding it to the different-order evaluation result.

[0061] Specifically, substrate parameter grids in different orders are respectively extracted from the coating grids in the high-priority queue and the low-priority queue of the second coating grid. The high-level different order represents the order in the high-priority queue of the second coating grid, while the low-level different order represents the order in the low-priority queue of the second coating grid.

[0062] Next, calculate the different-order difference between the high-level different order and the low-level different order, and perform a difference calculation on the specific parameter values of the high-level different order and the low-level different order.

[0063] Next, determine whether the difference in cross-position satisfies the positional difference threshold for cross-position arrangement. Based on the preset cross-position arrangement threshold, determine whether the calculated cross-position difference is within the grid range of the substrate parameters that urgently need to be coated due to the superposition of two reasons: the better state after the first coating and the larger error after the first coating, which urgently requires the second coating to be carried out preferentially. If the cross-position difference is less than or equal to the cross-position arrangement threshold, fix the cross-position substrate parameter grid in the second coating grid queue based on the priority position among the high-level cross-position and the low-level cross-position, and add the first evaluation result of the cross-position substrate parameter grid to the cross-position evaluation result. Among them, the first evaluation result includes that the substrate parameter grid is the grid of the substrate parameters that urgently need to be coated due to the superposition of two reasons: the better state after the first coating and the larger error after the first coating, which urgently requires the second coating to be carried out preferentially.

[0064] By extracting the high-level cross-position and the low-level cross-position in the second coating grid high-priority queue and the second coating grid low-priority queue, calculating the cross-position difference, judging whether it satisfies the positional difference threshold for cross-position arrangement, and fixing based on the priority position, the priority position in the coating process can be further optimized, generating the second coating grid queue, providing scientific and reasonable guidance for the subsequent coating process.

[0065] Furthermore, this application also includes:

[0066] If it does not meet the requirement, fix the cross-position substrate parameter grid in the second coating grid queue based on the high-level cross-position, and add the second evaluation result of the cross-position substrate parameter grid to the cross-position evaluation result.

[0067] Specifically, in the case where the cross-position difference does not meet the cross-position arrangement threshold, directly use the position of the high-level cross-position to fix the second coating grid queue. The high-level cross-position represents the substrate parameter grid with a better state of the glue liquid after the first coating and is suitable for quickly carrying out the second coating. Therefore, even if the cross-position difference is large, using the parameters of the high-level cross-position can still optimize the coating effect to a certain extent. After fixing, evaluate the cross-position substrate parameter grid to generate the second evaluation result. Among them, the second evaluation result includes that only one of the two reasons for the substrate parameter grid requires priority arrangement, but still arranges according to the high-priority reason.

[0068] By extracting the high-level and low-level cross-positions of the cross-position substrate parameter grid and calculating the cross-position difference, if the difference does not meet the preset threshold, use the parameters of the high-level cross-position for fixing and generate the second evaluation result, ensuring that even in the case of a large cross-position difference, the coating effect can still be optimized by selecting the parameters of the high-level cross-position. By integrating the evaluation results, detailed guidance can be provided for the second coating, further improving the coating quality and consistency.

[0069] Further, this application also includes:

[0070] Performing deviation analysis based on the first coating deviation coefficient and the second coating reference value to obtain the expected second coating data; performing secondary coating on the second coating grid queue based on the expected second coating data to obtain the second coating data.

[0071] Specifically, after the first coating is completed, the difference between the actual coating situation of the first coating of each substrate parameter grid and the second set coating reference value is calculated, indicating that the difference between the actual coating situation of the first coating and the second set coating reference value needs to be compensated during the second coating. Then, using the expected second coating data, secondary coating is performed on the substrate parameter grids in the second coating grid queue to obtain the second coating data, ensuring that the second coating meets the standards.

[0072] Through detailed deviation analysis and precise parameter adjustment, the effect of the secondary coating is ensured, and the quality and consistency of the coating process are improved.

[0073] In summary, the glue coating control method of an aqueous pressure-sensitive adhesive coating and composite device provided by this application has the following technical effects:

[0074] By obtaining the substrate information and coating position information of the medical substrate to construct substrate parameter grids, merging the substrate parameter grids to obtain a substrate merged grid; performing primary coating on the medical substrate for each substrate parameter grid with the first coating reference value to obtain the first coating data of the substrate merged grid; performing deviation analysis of the first coating reference value on the first coating data to generate the first coating deviation coefficient of each substrate parameter grid; performing state evaluation on the substrate merged grid based on the first glue state threshold to generate the first coating state result; arranging the priority of the substrate parameter grids according to the first coating state result and the first coating deviation coefficient to obtain the second coating grid queue; calculating the expected second coating data to perform secondary coating on the second coating grid queue to obtain the second coating data, achieving the technical goal of precisely controlling the glue coating process and achieving the technical effects of improving coating uniformity and ensuring consistent coating thickness.

[0075] The above description of the disclosed embodiments enables those skilled in the art to implement or use this application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application will not be limited to the embodiments shown herein, but will be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0076] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Thus, if these modifications and variations of this application fall within the scope of this application and its equivalent technologies, this application also intends to include these modifications and variations.

Claims

1. A method for controlling the coating of adhesive solution of an aqueous pressure-sensitive adhesive coating and compounding device, characterized in that, Including: Obtain the substrate information and coating position information of the medical substrate to construct a substrate parameter grid, and merge the substrate parameter grids to obtain a substrate merged grid; Perform a first coating on the medical substrate according to each substrate parameter grid with a first coating reference value to obtain first coating data of the substrate merged grid; Perform deviation analysis of the first coating reference value on the first coating data to generate a first coating deviation coefficient for each substrate parameter grid; Perform a state evaluation on the substrate merged grid based on a first glue state threshold to generate a first coating state result; Arrange the priorities of the substrate parameter grids according to the first coating state result and the first coating deviation coefficient to obtain a second coating grid queue; Calculate the expected second coating data and perform a second coating on the second coating grid queue to obtain second coating data; The performing a state evaluation on the substrate merged grid based on a first glue state threshold to generate a first coating state result includes: Extract the upper and lower limits of the glue state according to the key parameters of the glue to form a first glue state threshold; Extract a first glue state from the first glue state threshold; Use the first glue state as an initial particle and move it in the first glue state threshold at a preset step length to obtain an iterative particle; Perform a state evaluation on the iterative particle based on a state function. If the state fitness of any iterative particle is higher than that of the previous iterative particle, retain and update until an iterative stagnation result is obtained; Use the iterative result obtained by performing iteration on the iterative stagnation result as the first glue state. If the substrate merged grid reaches the first glue state, the state evaluation is qualified, and the first coating state result is generated.

2. The glue coating control method of an aqueous pressure-sensitive adhesive coating and compounding device as described in claim 1, characterized in that, The state function is: Among them, SD fit is the surface dryness fitness, SD actual is the actual surface dryness, SD std is the standard surface dryness, V fit is the viscosity fitness, V actual is the actual viscosity, V std is the standard viscosity, SF fit is the flatness fitness, SF actual is the actual flatness, SF std is the standard flatness, DT fit is the drying time fitness, DT actual is the actual drying time, DT std is the standard drying time, T fit is the temperature fitness, T actual is the actual temperature, T std is the standard temperature, H fit is the humidity fitness, H actual is the actual humidity, H std is the standard humidity, Total fit is the status fitness, SD fit 、V fit 、SF fit 、DT fit 、T fit 、H fit and Total fit have a value range of [0, 1], and ω1, ω2, ω3, ω4, ω5, and ω6 are the weights of SD fit 、V fit 、SF fit 、DT fit 、T fit 、H fit respectively.

3. The glue coating control method of an aqueous pressure-sensitive adhesive coating and composite device as described in claim 2, characterized in that, The using the iterative result obtained by performing iteration on the iterative stagnation result includes: Perform local optimal escape of the iterative particle under a preset local step length according to the iterative stagnation result, and continue to perform iteration according to the local optimal escape result; If the state fitness of any local particle is higher than that of the previous iterative local particle, retain and update until a local iterative stagnation result is obtained; Integrate the local iterative stagnation results to obtain an iterative result.

4. The glue coating control method of an aqueous pressure-sensitive adhesive coating and compounding device according to claim 1, characterized in that, The arranging the priorities of the substrate parameter grids according to the first coating state result and the first coating deviation coefficient to obtain a second coating grid queue includes: Arrange the substrate parameter grids with high priorities according to the first coating state result to obtain a second coating grid high-priority queue; Arrange the substrate parameter grids with low priorities according to the first coating deviation coefficient to obtain a second coating grid low-priority queue; Extract the same-position substrate parameter grids from the second coating grid high-priority queue and the second coating grid low-priority queue, and fix the same-position substrate parameter grids in the second coating grid queue; Extract the different-position substrate parameter grids from the second coating grid high-priority queue and the second coating grid low-priority queue, and fix the different-position substrate parameter grids in the second coating grid queue according to the different-position evaluation result; Performing the fixation of the in - line substrate parameter grid and the out - of - line substrate parameter grid to obtain the second coating grid queue.

5. The glue coating control method of an aqueous pressure-sensitive adhesive coating and composite device according to claim 4, characterized in that, Obtaining the out - of - line evaluation result, including: Extracting the high - level out - of - line and low - level out - of - line in the second coating grid high - priority queue and the second coating grid low - priority queue, and calculating the out - of - line difference between the high - level out - of - line and the low - level out - of - line; Judging whether the out - of - line difference meets the out - of - line arrangement threshold of the out - of - line difference; If it meets, fixing the out - of - line substrate parameter grid based on the priority out - of - line among the high - level out - of - line and the low - level out - of - line in the second coating grid queue, and adding the first evaluation result of the out - of - line substrate parameter grid to the out - of - line evaluation result.

6. The glue solution coating control method of an aqueous pressure-sensitive adhesive coating and compounding device as described in claim 5, characterized in that, If it does not meet, fixing the out - of - line substrate parameter grid based on the high - level out - of - line in the second coating grid queue, and adding the second evaluation result of the out - of - line substrate parameter grid to the out - of - line evaluation result.

7. The glue coating control method of an aqueous pressure-sensitive adhesive coating and compounding device according to claim 1, characterized in that, Performing secondary coating on the second coating grid queue with the calculated expected second coating data to obtain the second coating data, including: Obtaining the expected second coating data through deviation analysis based on the first coating deviation coefficient and the second coating reference value; Performing secondary coating on the second coating grid queue based on the expected second coating data to obtain the second coating data.

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