Blue glass surface adaptive coating regulation and control method and system

Abstract

The invention provides a blue glass surface adaptive coating regulation and control method and system, and relates to the technical field of blue glass coating, and the method comprises the following steps: establishing a coating constraint condition; after the coating process is obtained, process analysis is carried out, and variable sensitivity and variable dependence of optimization variables are configured; after an initial parameter set is constructed based on the optimization variables, fitness analysis of the initial parameter set is carried out through a comprehensive fitness function, and optimization updating is carried out based on a fitness analysis result, variable sensitivity and variable dependence under a coating constraint condition; and when an optimization updating result meets a preset condition, stopping updating, and carrying out coating regulation and control through a final optimization result. According to the method and the device, the technical problem of relatively large fluctuation of the film layer quality caused by inaccurate control of the process parameters in the prior art can be solved, and the overall stability of the film layer quality is improved by setting the constraint conditions and optimizing the process parameters in combination with the comprehensive fitness function.

Description

Technical Field

[0001] This application relates to the technical field of blue glass coating, and particularly to a method and system for adaptively controlling the coating on the surface of blue glass. Background Art

[0002] Blue glass coating generally refers to adding a blue light filtering layer or a reflective layer on the glass surface through coating technology, so that the glass presents a blue appearance or has specific functions. By selecting different coating materials and controlling the coating process parameters, the performance of blue glass such as light transmittance, reflectivity, and ultraviolet resistance can be adjusted. However, most of the current coating processes rely on empirical adjustments and manual interventions, and it is difficult to monitor and precisely control multiple process parameters that affect the film quality in real time during the production process. Especially when facing equipment aging or environmental changes, adaptive adjustments cannot be made in a timely manner, resulting in inaccurate control of process parameters and frequent problems of large fluctuations in film quality during the production process, thus affecting the overall performance of blue glass.

[0003] In summary, there is a technical problem in the prior art that due to inaccurate control of process parameters, the film quality fluctuates greatly. Summary of the Invention

[0004] The purpose of this application is to provide a method and system for adaptively controlling the coating on the surface of blue glass to solve the technical problem in the prior art that due to inaccurate control of process parameters, the film quality fluctuates greatly.

[0005] In view of the above problems, this application provides a method and system for adaptively controlling the coating on the surface of blue glass.

[0006] In the first aspect, this application provides a method for adaptively controlling the coating on the surface of blue glass. The method for adaptively controlling the coating on the surface of blue glass is implemented through a system for adaptively controlling the coating on the surface of blue glass. Among them, the method for adaptively controlling the coating on the surface of blue glass includes: establishing coating constraint conditions, which are constructed by collecting the ultimate constraints of the film layer and the equipment parameters of the coating equipment. The coating constraint conditions include film hardness constraints, film thickness allowable deviation constraints, and coating equipment working range constraints; after obtaining the coating process, based on the coating process, perform process analysis and configure the variable sensitivity and variable dependence of the optimization variables; after constructing an initial parameter set based on the optimization variables, perform fitness analysis of the initial parameter set through a comprehensive fitness function, and perform optimization update under the coating constraint conditions based on the fitness analysis results, variable sensitivity, and variable dependence; when the optimization update result meets the preset conditions, stop the update and perform coating control through the final optimization result.

[0007] In a second aspect, the present application also provides a blue glass surface adaptive coating regulation system for implementing the blue glass surface adaptive coating regulation method as described in the first aspect. The blue glass surface adaptive coating regulation system includes: a constraint condition construction module for establishing coating constraint conditions, which are constructed by collecting the ultimate constraints of the film layer and the equipment parameters of the coating equipment, and the coating constraint conditions include film layer hardness constraints, film thickness allowable deviation constraints, and coating equipment working range constraints; a process analysis module for performing process analysis based on the coating process after obtaining the coating process and configuring the variable sensitivity and variable dependence of the optimization variables; a fitness analysis module for performing fitness analysis of the initial parameter set through a comprehensive fitness function after constructing the initial parameter set based on the optimization variables, and performing optimization update under the coating constraint conditions based on the fitness analysis results, variable sensitivity, and variable dependence; and a coating regulation module for stopping the update when the optimization update result meets the preset conditions and performing coating regulation through the final optimization result.

[0008] One or more technical solutions provided in the present application have at least the following technical effects or advantages: By establishing coating constraint conditions, which are constructed by collecting the ultimate constraints of the film layer and the equipment parameters of the coating equipment, and the coating constraint conditions include film layer hardness constraints, film thickness allowable deviation constraints, and coating equipment working range constraints; performing process analysis based on the coating process after obtaining the coating process and configuring the variable sensitivity and variable dependence of the optimization variables; performing fitness analysis of the initial parameter set through a comprehensive fitness function after constructing the initial parameter set based on the optimization variables, and performing optimization update under the coating constraint conditions based on the fitness analysis results, variable sensitivity, and variable dependence; when the optimization update result meets the preset conditions, stopping the update and performing coating regulation through the final optimization result. Blue glass is usually classified as a type of advanced inorganic non-metallic material and is a special glass. After being coated, blue glass can achieve optical functions such as sunlight control to meet different application requirements. By establishing coating constraint conditions, performing process analysis, constructing an initial parameter set according to the optimization variables, evaluating the initial parameters through a comprehensive fitness function, determining the pros and cons of the parameters, optimizing and updating the initial parameters until the result meets the preset target conditions, then stopping the update and performing coating regulation through the final optimization result, the overall stability of the film layer quality is improved, thereby enhancing the quality and long-term stability of the blue glass coating.

[0009] The above description is only an overview of the technical solution of the present application. In order to understand the technical means of the present 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 the present application more obvious and understandable, the specific embodiments of the present application are specifically given below. 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 the present application, nor is it used to limit the scope of the present application. Other features of the present application will become easily understood through the following specification. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions in the present 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 in the following description are only exemplary, and for those of ordinary skill in the art, without creative efforts, other drawings can also be obtained according to the provided drawings.

[0011] Figure 1 It is a schematic flow chart of the method for regulating the surface adaptive coating of blue glass in the present application; Figure 2 It is a schematic structural diagram of the system for regulating the surface adaptive coating of blue glass in the present application.

[0012] Description of reference numerals: Constraint condition construction module 11, process analysis module 12, fitness analysis module 13, coating regulation module 14. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0013] By providing a method and system for regulating the surface adaptive coating of blue glass, the present application solves the technical problem in the prior art that due to inaccurate control of process parameters, the film layer quality fluctuates greatly. By establishing coating constraints, conducting process analysis, constructing an initial parameter set according to the optimization variables, and evaluating the initial parameters through a comprehensive fitness function to determine the advantages and disadvantages of the parameters, optimizing and updating the initial parameters until the results meet the preset target conditions, and then stopping the update and conducting coating regulation through the final optimization results, the overall stability of the film layer quality is improved, thereby enhancing the quality and long-term stability of the blue glass coating.

[0014] Next, the technical solutions in the present application will be clearly and completely described with reference to the 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 exemplary 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 of them.

[0015] Example 1, please refer to the appendix Figure 1 , this application provides a method for regulating the surface adaptable coating of blue glass. Among them, the method for regulating the surface adaptable coating of blue glass is applied to a surface adaptable coating regulation system for blue glass. The method for regulating the surface adaptable coating of blue glass specifically includes the following steps: S100: Establish coating constraints. The coating constraints are constructed by collecting the ultimate constraints of the film layer and the equipment parameters of the coating equipment. The coating constraints include film layer hardness constraints, film thickness allowable deviation constraints, and coating equipment working range constraints.

[0016] Specifically, through experimental tests, collect the ultimate constraint data of the film layer, that is, the maximum or minimum physical or chemical conditions that the film layer can withstand in a specific application, such as the upper and lower limits of the hardness, thickness, durability, etc. of the film layer. The ultimate constraints of the film layer ensure that the film layer meets the design requirements while not exceeding the limits of its physical and chemical properties. For example, measure the hardness of the film layer with a hardness tester and measure the thickness of the film layer with an ellipsometer or a step profiler. At the same time, collect the equipment parameters of the coating equipment, that is, various operating parameters of the equipment used for coating. These parameters can be adjusted or fixed, usually obtained from the equipment manual or the equipment control system, such as temperature, deposition rate, vacuum degree, atmosphere composition, working voltage, etc.

[0017] According to the collected data, construct coating constraints, that is, the limiting conditions that must be observed during the coating process to ensure the film layer quality and the normal operation of the equipment. According to the ultimate constraints of the film layer hardness, formulate the control range of the film layer hardness. If the hardness is too low, the film layer may wear during long-term use; if the hardness is too high, the film layer may become brittle, resulting in the glass being fragile. According to the ultimate constraints of the film layer thickness, formulate the film thickness allowable deviation constraints to obtain the maximum allowable deviation between the actual thickness and the target thickness of the film layer, ensuring the precise control of the film layer thickness. According to the equipment parameters of the coating equipment, formulate the coating equipment working range constraints, that is, the allowable range of parameters when the coating equipment is operating, such as temperature range, pressure range, etc., to ensure the safety and normal operation of the equipment. By collecting the ultimate constraints of the film layer and the equipment parameters of the coating equipment, construct coating constraints to ensure that the film layer performance meets the predetermined quality standards during the coating process and at the same time ensure the stable operation of the coating equipment.

[0018] S200: After obtaining the coating process, perform process analysis based on the coating process, and configure the variable sensitivity and variable dependence of the optimization variables.

[0019] Specifically, through experimental data or existing process standards, obtain the coating process flow, including the selected coating materials, the setting of coating parameters (such as temperature, pressure, gas flow rate, etc.), the operation of coating equipment, and basic requirements such as film thickness. The coating process refers to the specific coating process, including the selected materials, equipment, operation procedures, etc., to ensure that the film layer performance meets the expected goals. After obtaining the coating process, conduct a detailed process analysis on the coating process to evaluate the influence of various process parameters on the film layer performance. Process analysis refers to a detailed study of various operations, parameters in the coating process and their influence on the final film layer quality, aiming to identify the key factors affecting the film layer quality and how these factors interact with each other.

[0020] According to the results of the process analysis, select the variables that have a significant impact on the coating quality to form the optimization variables. The optimization variables are adjustable parameters that can significantly affect the film layer quality, film thickness, hardness and other targets. By adjusting the optimization variables, the best process conditions can be found. Use experimental design or numerical simulation methods to adjust the values of each optimization variable, observe the changes in the film layer quality, and measure the hardness, film thickness, optical properties, etc. of the film layer under each combination. According to the experimental results, calculate the variable sensitivity of each optimization variable through local sensitivity analysis or global sensitivity analysis. Local sensitivity analysis is to analyze the influence of a single variable (such as deposition rate, temperature, etc.) on the change of the objective function (such as film layer hardness, film thickness, etc.) when it changes near a specific point, focusing on the immediate response of the variable change in a small range to the result, usually calculated through partial derivatives. Global sensitivity analysis is to evaluate the influence of each variable on the process result by analyzing the changes in the entire variable range, taking into account the interaction between variables, and is applicable to the situation where the variable changes greatly or there are multi-variable interactions.

[0021] Make small changes to the optimization variables near a specific point, evaluate the sensitivity of each variable by measuring or calculating the change of the objective function (such as film layer hardness, film thickness), and obtain the local sensitivity of the variable through partial derivative calculation. By changing the values of the optimization variables within their entire range, evaluate the comprehensive influence of these variables on the film layer quality, calculate the variance contribution of each variable and its interaction to the film layer hardness, evaluate which variables have the greatest influence on the film layer hardness, and obtain the global sensitivity. According to the results of the sensitivity analysis, calculate the sensitivity coefficient of each variable, that is, the influence of the unit change of each variable on the objective function.

[0022] Variable sensitivity refers to the degree of influence of a certain variable change on the final result. That is to say, if a certain process parameter (variable) changes, how the quality of the film layer (such as hardness, film thickness, optical properties, etc.) will change accordingly. If a variable has a very large influence on the result, it is a high-sensitivity variable, otherwise it is a low-sensitivity variable. Variables with higher sensitivity should be the key adjustment objects in the optimization process.

[0023] Through variable dependency analysis, the mutual relationships among multiple variables are determined. If there is a dependency relationship between two variables, these dependencies should be considered during the optimization process to avoid adverse changes in other variables when adjusting one variable. Variable dependency refers to the mutual relationships and influences among multiple variables, that is, changing one variable may affect the changes of other variables. Analyzing variable dependencies helps to better understand the interactions among variables in the process and avoid conflicting or unachievable optimization goals during the optimization process.

[0024] By analyzing experimental data, the dependency relationships between variables are determined. That is to say, when analyzing the change of a certain variable, it is determined whether other variables are affected and whether they show a certain relationship. For example, in some cases, there may be an interaction between temperature and atmosphere composition, that is, when the temperature increases, the change of the atmosphere composition has a more significant impact on the film hardness. Through experimental data, a variable dependency model is established through a quadratic regression model to capture the interactions between variables. When performing process optimization, not only the sensitivity of individual variables but also the dependencies between variables should be considered. By adjusting the combination of these variables, better film quality can be obtained.

[0025] By identifying the key variables that affect the film quality, these variables can be accurately adjusted to ensure that the film quality reaches the optimal state. Through a comprehensive analysis of variable sensitivity and dependency, the coating process is ensured to be more controllable, thereby improving the stability of the overall process and reducing the film quality fluctuations caused by improper variable adjustment.

[0026] S300: After constructing the initial parameter set based on the optimization variables, the fitness analysis of the initial parameter set is carried out through a comprehensive fitness function, and the optimization update is carried out under the coating constraint conditions based on the fitness analysis results, variable sensitivity, and variable dependency.

[0027] Specifically, according to the determined optimization variables, a reasonable range and initial value are set for each optimization variable to construct the initial parameter set. The comprehensive fitness function is used to perform the fitness analysis of the initial parameter set to obtain the fitness analysis results. The comprehensive fitness function is a mathematical function used to evaluate the performance of the coating process, comprehensively considering multiple indicators such as film thickness, film adhesion, and optical properties. According to the fitness analysis results, an independent fitness set is obtained therefrom, including film thickness fitness, film adhesion fitness, and optical property fitness.

[0028] Trigger analysis is performed on independent fitness sets. If the fitness evaluation of certain parameter combinations is poor and the changed parameters have a greater impact on the film quality, the adjustment of the joint gene is triggered according to this evaluation mechanism. For example, the uneven film thickness of a certain combination may be due to the uncoordinated changes in temperature and gas flow, which can be solved by adjusting the joint gene of these two variables in a linked manner.

[0029] According to variable dependency, analyze the dependency relationship between variables and determine which variables need to be adjusted together. According to the results of trigger analysis, determine which variable combinations should be adjusted in conjunction; match to obtain joint combination values; for those that are not matched, determine independent combination values. Combine and screen the joint combination values ​​and independent combination values ​​to select the optimal combination and configure it as a joint gene. The joint gene will be used as an optimization target and updated in the next round of optimization. According to the variable sensitivity analysis, determine the benchmark adjustment amplitude. For the film thickness with high sensitivity, a larger adjustment amplitude can be set.

[0030] The target parameter in the initial parameter set is selected as the optimization target, and the other parameters in the initial parameter set are updated using the benchmark adjustment amplitude. Then, adaptive mutation updates of the target parameters (for example, fine-tuning of the film thickness) are performed, and cross-mutation is performed. Through these mutation and cross-mutation processes, an updated parameter set is established for the next round of optimization updates. After each round of optimization updates, the new parameter set will be subjected to fitness analysis again to evaluate whether the quality of the film layer has been improved. If the comprehensive fitness value is further improved, it means that the optimization process is effective.

[0031] Ensure that the entire optimization process meets the coating constraints. Coating constraints refer to the restrictions that must be met when optimizing coating, including the hardness of the film layer, film thickness deviation, equipment operating range, etc. Optimal update refers to the process of improving the results by adjusting the parameter combination during the optimization process. After each update, the optimization algorithm will re-evaluate the fitness based on the new parameter values, and gradually find an optimal parameter set that meets the preset goals. By performing optimal updates based on the fitness analysis results and variable sensitivity and variable dependency, the coating parameters are optimized and the coating quality and efficiency are improved. At the same time, by considering the dependency between variables, the impact of each variable on the coating quality is fully understood, so that the parameters can be adjusted more accurately.

[0032] S400: When the optimization update result meets the preset conditions, the update is stopped, and the coating is regulated by the final optimization result.

[0033] Specifically, before starting the optimization process, a set of target performance indicators and corresponding thresholds are defined, including specific values such as film thickness, adhesion, optical properties, etc., which are used to determine whether the optimization goal is achieved. The preset conditions may include the fitness value reaching a certain threshold, the number of iterations reaching the upper limit, or other performance indicators reaching the expected goal. The optimization process aims to optimize indicators such as the film thickness, adhesion, and optical properties by evaluating and adjusting parameters multiple times. In each round of optimization update, the fitness function calculates the comprehensive fitness value based on the performance of the current parameter set. When the optimization update result meets the preset conditions, the update process stops. For example, when the comprehensive fitness value reaches or approaches the preset goal, it indicates that the quality of the film has met the requirements; or when the predetermined maximum number of optimization iterations is reached but the fitness value fails to reach the goal, the update will automatically stop and the current best result will be given; or if the fitness value changes little in multiple rounds of iteration, it is considered that the convergence state has been reached and the update stops.

[0034] After stopping the update, the final optimization result will be used as the control parameter for the coating process to regulate the coating in actual production. According to the final optimization result, the working parameters of the coating equipment are adjusted, such as the working temperature, gas flow rate, deposition rate, etc. of the coating machine. The final result obtained through optimization updates ensures that all performance indicators in the coating process can meet the design requirements, ensuring the best performance of the coating process in terms of film thickness, adhesion, optical properties, etc., thereby achieving the purpose of improving product quality and production efficiency.

[0035] Furthermore, step S300 of the present application includes: The comprehensive fitness function is as follows: ; where represents the fitness value, is the weight of the film thickness, represents the film thickness fitness function, , represents the total number of measurement points, represents any one measurement point, represents the th thickness value of the measurement point, represents the average film thickness, is the weight of the film adhesion, represents the film adhesion fitness function, , represents the film adhesion test value, represents the best adhesion value, is the weight of the optical property, represents the optical property fitness function, , is the light transmittance weight, is the light transmittance, is the reflection weight, is the reflectivity.

[0036] Specifically, the comprehensive fitness function is used to quantify the comprehensive effect among multiple optimization goals, enabling the comprehensive consideration of different film layer characteristics (such as film thickness, adhesion, optical properties, etc.), and adjusting the process parameters according to these characteristics to achieve the best effect. Using a film thickness measuring instrument, measure the film thickness at multiple points (such as the four corners, center, etc. of the film layer), calculate the average film thickness, and according to the formula calculate the difference between the film thickness at multiple points and the average thickness to obtain the film thickness fitness function, where represents the total number of measurement points, represents any one measurement point, represents the thickness value of the th measurement point,

[0037] Test the film layer adhesion through an adhesion testing instrument, and preset a best adhesion value based on experience or literature data. By measuring the film layer adhesion and comparing it with the best adhesion value, according to the formula calculate the film layer adhesion fitness function, where represents the measured value of the film layer adhesion, represents the best adhesion value. The closer the measured value of the film layer adhesion is to the best adhesion value, that is, the closer the film layer adhesion fitness function is to 1, the better the film layer adhesion.

[0038] Measure the transmittance T and reflectivity R of the film layer through an optical measuring instrument. The transmittance is the proportion of light allowed to pass through the film layer, and the reflectivity is the proportion of light reflected by the film layer. According to the formula calculate the optical performance fitness function, where is the transmittance weight, is the transmittance, is the reflection weight, is the reflectivity. For example, assume the transmittance is 80%, the reflectivity is 10%, the transmittance weight is 0.7, and the reflection weight is 0.3, and calculate the optical performance fitness to be 59%.

[0039] is the film thickness weight, represents the film layer adhesion weight, represents the optical performance weight, indicating their influence degree on the final fitness. represents the film thickness fitness function, represents the film layer adhesion fitness function, represents the optical performance fitness function, according to the formula The comprehensive fitness function is calculated to represent the overall effect of the current process parameter combination on film thickness, adhesion and optical properties.

[0040] If the fitness evaluation of some parameter combinations is poor, and the changed parameters have a greater impact on the quality of the membrane layer, the adjustment of the joint gene is triggered according to this evaluation mechanism. For example, the uneven film thickness of a certain combination may be due to the uncoordinated changes in temperature and gas flow, which can be solved by adjusting the joint gene of these two variables in a linked manner. Through multiple experiments and calculations, the parameters are gradually adjusted until a satisfactory fitness value is reached. The fitness analysis of the initial parameter set is performed through the comprehensive fitness function, the performance of each parameter combination is quantified, and compared, and the membrane quality is comprehensively evaluated and optimized.

[0041] Furthermore, the present application also includes the following steps: The fitness analysis result is parsed to obtain an independent fitness set, wherein the independent fitness set includes film thickness fitness, film adhesion fitness, and optical performance fitness; fitness trigger analysis is performed on the independent fitness set to establish a trigger analysis result; variable linkage combination is performed according to the trigger analysis result to configure a joint gene, and the next round of optimization update is performed based on the joint gene.

[0042] Specifically, the fitness analysis of the initial parameter set was performed through a comprehensive fitness function, and the fitness analysis results were obtained. The overall fitness value was split into independent fitness sets, each of which reflects different quality indicators of the film layer, including film thickness fitness, film adhesion fitness, and optical performance fitness. The film thickness fitness measures whether the thickness distribution of the film layer is uniform. If the film thickness is too thin or too thick, it will affect the performance of the film layer, resulting in a lower fitness value; the film adhesion fitness reflects the adhesion strength between the film layer and the substrate. Too low adhesion may cause the film layer to fall off, thereby affecting its performance; the optical performance fitness is used to evaluate the optical performance of the film layer. The higher the fitness value, the better the optical performance of the film layer.

[0043] By observing the changes in each fitness value, perform fitness trigger analysis on the independent fitness set to determine which metrics reach the trigger point. By setting thresholds for each independent fitness, determine when this fitness will trigger adjustments to other variables. Analyze whether each fitness value is below its threshold and record the parameter combinations that need to be adjusted. Based on the analysis results, establish a trigger record indicating which parameter combinations fail to meet the criteria. For example, assume the independent fitness set includes a film thickness fitness of 0.012, an adhesion fitness of 0.85, and an optical property fitness of 0.68, with corresponding temperature of 320 °C and gas flow rate of 60 sccm. Set the film thickness fitness trigger point to be greater than 0.015, the film adhesion fitness trigger point to be equal to 1, and the optical property fitness trigger point to be less than 0.65. The film thickness fitness is below the threshold of 0.015, indicating uneven film thickness. Adjust the temperature and gas flow rate, lower the temperature to 310 °C, and at the same time adjust the gas flow rate to 55 sccm; the adhesion fitness is low, which may be caused by substrate cleanliness and gas flow rate.

[0044] According to the trigger analysis results, perform variable linkage combination. Based on the dependency relationships between different parameters, select those variables that have a significant impact on the film quality for linkage adjustment. Variable linkage combination refers to determining which variables need to be jointly adjusted according to the mutual influence between variables during the optimization process. For example, if the trigger analysis results show that temperature and gas flow rate have a strong impact on the film thickness, it can be decided to jointly adjust these two variables.

[0045] According to the results of the variable linkage combination, select appropriate parameter combinations as joint genes. The joint genes will be used as one of the initial solutions in the genetic algorithm and participate in the next round of optimization update. A joint gene refers to a combination of multiple related parameters in the genetic algorithm. Through the combination and optimization of genes, an optimal parameter set can be found, thereby achieving the optimal film performance.

[0046] Use the obtained joint genes for the next round of optimization update. Determine the adjustment amplitude according to the variable sensitivity. Based on the fitness analysis results, determine the target parameters from the initial parameter set. Generate new parameter combinations through genetic operations such as crossover and mutation to further optimize the film quality. Based on the joint genes, the adjusted parameter values will be used for the next round of optimization. Through fitness trigger analysis and variable linkage combination, optimize the film thickness, adhesion, and optical properties to ensure that the film quality meets the predetermined requirements, determine the best coating parameter combination, and improve the coating quality and efficiency.

[0047] Furthermore, this application also includes the following steps: Obtain a set of variable combination pairs according to the variable dependency screening; perform linkage combination matching on the set of variable combination pairs according to the trigger analysis result, and establish a combined combination value according to the linkage combination matching result; establish an independent combination value for the variable combination pairs that are not matched in the set of variable combination pairs; use the combined combination value and the independent combination value for combination screening, and configure combined genes according to the combination screening result.

[0048] Specifically, according to the variable dependencies of the variables to be optimized, determine which variables need to be adjusted together, and determine variable combination pairs according to the dependency strength. Construct a set of variable combination pairs based on the determined variable combination pairs, including all variable pairs that need to be adjusted together. For example, according to the variable dependency relationship, variable influence pairs are screened out. The correlation coefficient between pressure and deposition rate is 0.78, the correlation coefficient between substrate cleanliness and adhesion is 0.50, and the correlation coefficient between temperature and gas flow rate is 0.85.

[0049] According to the trigger analysis result, perform linkage combination matching on the variables in the set of variable combination pairs to determine which variable combinations should be adjusted together to optimize the process. Linkage combination matching means that according to the result of trigger analysis, the variable combinations are adjusted so that they match within the optimal working range. Establish rules for linkage combination matching. If the fitness of a certain variable combination pair is poor (such as a large film thickness deviation) and the dependence between variables is high, then perform linkage optimization; if the fitness of a certain variable combination pair is good, then no adjustment is made; for combinations where the influence of a variable on other variables is small but still need to be optimized, optimize them separately and do not participate in the linkage. For example, the current fitness between temperature and gas flow rate is 0.68, and the set target fitness is 0.75. Therefore, the variable combination of temperature and gas flow rate needs to be optimized.

[0050] Establish a combined combination value according to the linkage combination matching result. That is to say, if a certain variable triggers the optimization condition, then the variables that depend on it will also be taken into account to form a combined combination value. For the variable combination pairs that are not matched in the set of variable combination pairs, establish an independent combination value, which represents variable combinations that can be considered independently during the optimization process. If some variable combinations are not identified by the trigger analysis as objects that need linkage optimization, they will be adjusted separately and form independent combination values. For example, if a certain variable (such as substrate rotation speed) has little influence on other variables, its parameters can be optimized separately without affecting the overall process.

[0051] Combination screening is carried out using combined combination values and independent combination values, the fitness of each combination is calculated, and crossover, selection, and mutation are used for combination screening. Combination screening refers to screening out the optimal set of parameter combinations from multiple variable combination schemes so that the optimization objectives (such as film thickness uniformity, adhesion, and optical properties) reach the best. Combinations with higher fitness are preferentially selected, and some variables are exchanged between different combinations, and some parameters are randomly adjusted to increase diversity. After crossover, selection, and mutation, multiple sets of candidate parameters are obtained, and the one with the highest fitness is selected as the combined gene. The combined gene refers to the optimal parameter set formed to ensure the optimal combination of variables during the optimization process. The role of the combined gene is to simulate the biological genetic mechanism, enabling the variables to continuously evolve during the optimization process and finally obtaining the optimal solution. The optimal variable combination value is applied to the optimization algorithm for the next round of update.

[0052] For example, during the film coating process, the substrate temperature T, sputtering power P, and gas flow rate F are interrelated variables, and their combination needs to meet certain process conditions and form an optimal set of parameters after optimization. The substrate rotation speed R has a relatively small impact on the film thickness, so it can be optimized independently. After crossover, selection, and mutation, multiple sets of candidate parameters are obtained as follows: For combination 1, the sputtering power is 150 W, the temperature is 290 °C, the gas flow rate is 15 sscm, the substrate rotation speed is 25 rpm, and the fitness is 0.89; for combination 2, the sputtering power is 155 W, the temperature is 292 °C, the gas flow rate is 17 sscm, the substrate rotation speed is 30 rpm, and the fitness is 0.92; for the sputtering power of 158 W, the temperature is 295 °C, the gas flow rate is 18 sscm, and the substrate rotation speed is 35 rpm, the fitness is 0.91. The fitness of combination 2 is the highest, so it is used as the combined gene.

[0053] By the linkage combination matching and combination screening of the variable combination for the set, considering the interaction between variables and taking these variables into account simultaneously during the optimization process, it helps to determine the best film coating parameter combination, improve the accuracy of process parameter optimization, and thus improve the film coating quality and efficiency.

[0054] Furthermore, the present application further includes the following steps: Configuring a reference adjustment amplitude based on the variable sensitivity; selecting a target parameter from the initial parameter set according to the fitness analysis result, taking the target parameter as the optimization target, and updating the remaining parameters in the initial parameter set based on the reference adjustment amplitude; performing adaptive mutation update of the target parameter, and after cross-mutating all the update results, establishing an updated parameter set to complete one round of optimization update.

[0055] Specifically, according to the variable sensitivity, the reference adjustment amplitude is determined, that is, the step size for adjusting parameters during the optimization process. For variables with higher sensitivity, a larger adjustment amplitude is set so that these parameters can be adjusted more significantly during the optimization process; while for variables with lower sensitivity, a smaller adjustment amplitude is set. According to the fitness analysis results, the parameter that has the greatest impact on the process result is selected as the target parameter, that is, the parameter to be mainly adjusted. According to the reference adjustment amplitude, the remaining parameters in the initial parameter set are updated, and the parameter values are adjusted according to the sensitivity and reference adjustment amplitude of each parameter to better optimize the target parameter. For example, if the target parameter is temperature, parameters such as gas flow rate and deposition rate are adjusted according to the reference adjustment amplitude.

[0056] Adaptive mutation is performed on the target parameter, and the mutation amplitude is dynamically adjusted according to the current state and optimization progress of the target parameter. Adaptive mutation update is an optimization strategy that makes the adjustment amplitude of the parameter adapt to the optimization process. For example, if the performance of the target parameter was good in the previous round of optimization, its update amplitude can be reduced to avoid over-optimization; conversely, if the performance is poor, its update amplitude can be increased to accelerate the optimization speed.

[0057] Cross mutation is performed on all the update results, and the update results of different parameters are combined to generate new parameter combinations. Crossover means exchanging part of the information between two sets of parameters (parent generations) to generate new parameter combinations. The purpose of crossover is to combine two excellent solutions to generate potentially better solutions. Mutation means making a small-range random adjustment to a solution to increase the diversity of the search space and avoid local optimal solutions. The specific process is to select multiple optimized parameter sets as parent generations for crossover. After the crossover operation, the parameters generated by the crossover are mutated to increase the diversity of the solutions. The mutation range is adjusted according to the adaptive mutation strategy to ensure that the mutation does not deviate from the reasonable range.

[0058] After cross mutation, a new set of parameter combinations is obtained, which includes the optimized solutions, including the new target parameter values (such as temperature, gas flow rate, power, etc.), and the parameters adjusted after mutation. The results after cross mutation are combined to form a new parameter set, that is, the updated parameter set, completing one round of update optimization and being used for the next round of optimization process. The new parameter set will bring higher film quality and make the film performance gradually approach the preset target.

[0059] Through the combination of adaptive mutation and cross mutation, the quality of the film has been significantly improved, and the indicators such as film thickness uniformity, adhesion, and optical properties have been optimized. The combination of the mutation amplitude and the crossover operation not only accelerates the optimization process but also ensures diversity and avoids falling into local optimal solutions.

[0060] Furthermore, step S400 of this application includes: Perform coating monitoring to establish a monitoring data set; conduct matching analysis of the actual coating effect and the optimization result based on the monitoring data set to establish a matching error; establish a backtracking identifier according to the matching error, and use the backtracking identifier to perform backtracking self-inspection of the coating process to establish a backtracking self-inspection result; perform coating process control compensation based on the backtracking self-inspection result.

[0061] Specifically, during the coating process, key parameters and performance indicators are monitored in real time or regularly, and data during the coating process is collected through various sensors and measuring instruments to obtain a monitoring data set, that is, various data during the coating process, usually including data sets of indicators such as the thickness, adhesion, light transmittance, and reflectivity of the film layer. Through coating monitoring, the changes in the film layer are continuously tracked, and parameters related to the film layer quality are recorded.

[0062] Compare the actual results in the monitoring data set with the optimized optimization results to evaluate the gap between the two. The matching error includes thickness error (the gap between the actual thickness and the target thickness), adhesion error (the gap between the actual adhesion and the target adhesion), optical performance error (the gap between the light transmittance and reflectivity and the target values), etc.

[0063] According to the matching error, establish a backtracking identifier for data with large errors, indicating which links or parameters have problems. The backtracking identifier refers to an identifier generated during the coating process based on the matching error analysis, and is used to backtrack key parameters and steps in the process. Through the backtracking identifier, perform backtracking self-inspection of the coating process, backtrack to specific coating steps or parameter settings, and check whether there are process link or control parameter deviations, including checking equipment status, operation steps, environmental conditions, etc., resulting in the film layer quality not meeting expectations. According to the problems and their causes determined by the backtracking self-inspection, generate a backtracking self-inspection result. For example, if there is an error in the film layer thickness compared with the expectation, the backtracking analysis will show that it is caused by parameter deviations such as gas flow rate, temperature, or deposition rate.

[0064] According to the backtracking self-inspection result, adjust the process parameters that affect the film layer quality, such as changing the deposition rate, adjusting the temperature, etc., to optimize the coating effect. Through coating process control compensation, after discovering process errors, take measures for adjustment or compensation to ensure that the film layer quality in subsequent coating processes meets the expected goals. Through the matching analysis of the actual coating effect and the optimization result, any process deviations can be discovered and corrected, and the root cause of potential problems can be determined, thereby optimizing control parameters and ensuring the stability of the film layer quality.

[0065] Furthermore, the present application further includes the following steps: Establish a self-inspection cycle, perform self-inspection of the coating effect during the self-inspection cycle to establish a cycle deviation; generate a cycle compensation using the cycle deviation, and perform self-optimization management of the coating process based on the cycle compensation.

[0066] Specifically, according to the stability of the coating process and the length of the production cycle, the self-inspection cycle, that is, the time interval of self-inspection, is determined for regularly checking the coating effect. At the end of each cycle, a process inspection is carried out to ensure that the film layer quality meets the expected goals. During each self-inspection cycle, key parameters and performance index data in the coating process are collected using on-line detection equipment, various sensors (such as thickness gauges, adhesion testers, optical sensors, etc.), compared with the expected values, and the cycle deviation is calculated. The cycle deviation generally refers to the differences between parameters such as the thickness, adhesion, and optical properties of the film layer and the target values.

[0067] Based on the analysis results of the cycle deviation, cycle compensation is generated to adjust the coating process to make up for the deviation and make the quality of subsequent film layers closer to the target, including adjusting process parameters (such as deposition rate, temperature, pressure) or equipment maintenance (such as cleaning, calibration). By regularly monitoring the cycle deviation and dynamically adjusting the key parameters (such as temperature, gas flow rate, pressure, etc.) in the coating process, process self-optimization is achieved to ensure that the film layer quality continuously meets the requirements.

[0068] The specific implementation process is as follows: According to the cycle deviation within each self-inspection cycle, cycle compensation is generated by adjusting the key process parameters. For example, if the film layer thickness deviation is large, the gas flow rate or temperature may need to be adjusted; if the light transmittance deviation is large, the material evaporation rate or reflectivity may need to be adjusted. After the implementation of the cycle compensation, the next round of production is carried out based on the process parameters adjusted by the compensation. After the end of each cycle, the adjustment of the process parameters takes effect in real time and is used in the subsequent coating production. Through continuous self-inspection and cycle compensation, self-optimization management of the coating process is achieved, making the production process gradually stable, continuously improving the film layer quality, and being able to cope with the impacts brought by changes such as equipment status and raw material differences.

[0069] Periodic self-inspection and deviation analysis can timely detect process problems, reduce the fluctuations in the film layer quality caused by external factors (such as equipment aging, material batch changes), and thus maintain stable production quality. Through the implementation of the self-inspection cycle and cycle compensation mechanism, continuous optimization of the coating process can be achieved, improving the stability of the film layer quality, reducing abnormal fluctuations in the production process, ensuring that the film layer quality continuously meets the standards, and improving production efficiency.

[0070] In summary, the blue glass surface adaptability coating regulation method provided by this application has the following technical effects: By establishing coating constraints, the coating constraints are constructed by collecting the ultimate constraints of the film layer and the equipment parameters of the coating equipment. The coating constraints include film hardness constraints, allowable film thickness deviation constraints, and coating equipment working range constraints. After obtaining the coating process, process analysis is carried out based on the coating process, and the variable sensitivity and variable dependence of the optimization variables are configured. After constructing the initial parameter set based on the optimization variables, fitness analysis of the initial parameter set is carried out through the comprehensive fitness function, and optimization update is carried out under the coating constraints based on the fitness analysis results, variable sensitivity, and variable dependence. When the optimization update result meets the preset conditions, the update is stopped, and coating regulation is carried out through the final optimization result. Blue glass is usually classified as a kind of advanced inorganic non-metallic material, which is a special glass. After coating treatment, blue glass can achieve optical functions such as sunlight control to meet different application requirements. By establishing coating constraints, carrying out process analysis, constructing an initial parameter set according to the optimization variables, evaluating the initial parameters through the comprehensive fitness function, determining the pros and cons of the parameters, optimizing and updating the initial parameters until the results meet the preset target conditions, then stopping the update and carrying out coating regulation through the final optimization result, the overall stability of the film layer quality is improved, thereby enhancing the quality and long-term stability of blue glass coating.

[0071] Embodiment 2. Based on the same inventive concept as the blue glass surface adaptive coating regulation method in the foregoing Embodiment 1, the present application also provides a blue glass surface adaptive coating regulation system. Please refer to the appendix Figure 2 , the blue glass surface adaptive coating regulation system includes: A constraint condition construction module 11, configured to establish coating constraints. The coating constraints are constructed by collecting the ultimate constraints of the film layer and the equipment parameters of the coating equipment. The coating constraints include film hardness constraints, allowable film thickness deviation constraints, and coating equipment working range constraints; a process analysis module 12, configured to carry out process analysis based on the coating process after obtaining the coating process, and configure the variable sensitivity and variable dependence of the optimization variables; a fitness analysis module 13, configured to carry out fitness analysis of the initial parameter set through the comprehensive fitness function after constructing the initial parameter set based on the optimization variables, and carry out optimization update under the coating constraints based on the fitness analysis results, variable sensitivity, and variable dependence; a coating regulation module 14, configured to stop the update when the optimization update result meets the preset conditions, and carry out coating regulation through the final optimization result.

[0072] Further, the fitness analysis module 13 in the blue glass surface adaptive coating regulation system is further configured to: The comprehensive fitness function is as follows: ; where represents the fitness value, is the film thickness weight, Characterize the fitness function of the film layer thickness , Characterize the total number of measurement points Characterize any one measurement point Characterize the thickness value of the measurement point Characterize the average film layer thickness Characterize the adhesion weight of the film layer Characterize the fitness function of the film layer adhesion , Characterize the measured value of the film layer adhesion Characterize the optimal adhesion value Characterize the optical performance weight Characterize the fitness function of the optical performance , is the light transmittance weight is the light transmittance is the reflection weight is the reflectance

[0073] Furthermore, the fitness analysis module 13 in the adaptive coating regulation system for the blue glass surface is further configured to: Analyze the fitness analysis result to obtain an independent fitness set, where the independent fitness set includes the film layer thickness fitness, the film layer adhesion fitness, and the optical performance fitness; perform fitness trigger analysis on the independent fitness set to establish a trigger analysis result; perform variable linkage combination according to the trigger analysis result to configure a combined gene, and perform the next round of optimization update based on the combined gene

[0074] Furthermore, the fitness analysis module 13 in the adaptive coating regulation system for the blue glass surface is further configured to: Obtain a set of variable combination pairs according to the variable dependency screening; perform linkage combination matching on the set of variable combination pairs according to the trigger analysis result, and establish a combined combination value according to the linkage combination matching result; establish an independent combination value for the variable combination pairs that are not matched in the set of variable combination pairs; perform combination screening using the combined combination value and the independent combination value, and configure a combined gene according to the combination screening result

[0075] Furthermore, the fitness analysis module 13 in the adaptive coating regulation system for the blue glass surface is further configured to: Configure the benchmark adjustment amplitude based on the variable sensitivity; select the target parameter in the initial parameter set according to the fitness analysis result, use the target parameter as the optimization target, and update the remaining parameters in the initial parameter set based on the benchmark adjustment amplitude; perform adaptive mutation update of the target parameter, and after cross-mutating all the update results, establish an updated parameter set to complete one round of optimization update.

[0076] Further, the coating regulation module 14 in the blue glass surface adaptive coating regulation system is further configured to: Execute coating monitoring to establish a monitoring data set; perform matching analysis of the actual coating effect and the optimization result based on the monitoring data set to establish a matching error; establish a backtracking identifier according to the matching error, and use the backtracking identifier to perform backtracking self-inspection of the coating process to establish a backtracking self-inspection result; perform coating process control compensation based on the backtracking self-inspection result.

[0077] Further, the blue glass surface adaptive coating regulation system further includes a self-inspection compensation module, and the self-inspection compensation module is further configured to: Establish a self-inspection period, perform self-inspection of the coating effect during the self-inspection period to establish a period deviation; generate a period compensation using the period deviation, and perform self-optimization management of the coating process based on the period compensation.

[0078] The various embodiments in this specification are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. The Figure 1 Blue glass surface adaptive coating regulation method and specific examples in the first embodiment are equally applicable to the blue glass surface adaptive coating regulation system in this embodiment. Through the foregoing detailed description of the blue glass surface adaptive coating regulation method, those skilled in the art can clearly know the blue glass surface adaptive coating regulation system in this embodiment. Therefore, for the sake of simplicity of the specification, it will not be elaborated here. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the description of the method part.

[0079] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present 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 the present application. Therefore, the present 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.

[0080] Obviously, those skilled in the art can make various changes and modifications 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 is also intended to include these changes and modifications.

Claims

1. A method for controlling the adaptive coating of a blue glass surface, characterized in that: include: Establishing coating constraint conditions, wherein the coating constraint conditions are constructed by collecting the limit constraints of the film layer and the equipment parameters of the coating equipment, and the coating constraint conditions include the film layer hardness constraint, the film thickness allowable deviation constraint, and the coating equipment working range constraint; After obtaining the coating process, a process analysis is performed based on the coating process, and variable sensitivity and variable dependency of the optimization variables are configured; After constructing the initial parameter set based on the optimization variables, the fitness analysis of the initial parameter set is performed through the comprehensive fitness function, and the optimization update is performed under the coating constraint conditions based on the fitness analysis results and the variable sensitivity and variable dependency; When the optimization update result meets the preset conditions, the update is stopped and the coating is regulated by the final optimization result.

2. The method for controlling the adaptive coating of the blue glass surface according to claim 1, characterized in that: The comprehensive fitness function is as follows: ； in, Represents the fitness value, is the film thickness weight, Characterize the film thickness fitness function, , Characterize the total number of measurement points, Characterize any measurement point, Characterization The thickness value of each measuring point, Characterize the average film thickness, Characterize the film adhesion weight, Characterize the film adhesion fitness function, , Characterize the film adhesion test value, Characterize the best adhesion value, Characterize the optical performance weight, Characterize the optical performance fitness function, , is the transmittance weight, is the transmittance, is the reflection weight, is the reflectivity.

3. The method for controlling the adaptive coating of blue glass surface according to claim 2, characterized in that: The optimization update based on the fitness analysis results and variable sensitivity and variable dependency under the coating constraint conditions includes: Analyze the fitness analysis result to obtain an independent fitness set, wherein the independent fitness set includes film thickness fitness, film adhesion fitness, and optical performance fitness; Performing fitness trigger analysis on the independent fitness set to establish a trigger analysis result; According to the trigger analysis result, variables are linked and combined to configure joint genes, and the next round of optimization update is performed based on the joint genes.

4. The method for controlling the adaptive coating of blue glass surface according to claim 3, characterized in that: The performing variable linkage combination according to the trigger analysis result to configure the joint gene includes: Obtain a set of variable combination pairs according to the variable dependency screening; Perform linkage combination matching of a set of variable combination pairs according to the trigger analysis result, and establish a joint combination value according to the linkage combination matching result; Establishing independent combination values ​​for unmatched variable combination pairs in the variable combination pair set; The combined combination value and the independent combination value are used to perform combined screening, and the combined gene is configured according to the combined screening result.

5. The method for controlling the adaptive coating of the blue glass surface according to claim 3, characterized in that: The next round of optimization update based on the combined gene includes: configuring a benchmark adjustment amplitude based on the variable sensitivity; Selecting a target parameter in the initial parameter set according to the fitness analysis result, taking the target parameter as the optimization target, and updating the remaining parameters in the initial parameter set based on the benchmark adjustment amplitude; Perform adaptive mutation update of target parameters, cross-mutate all update results, establish update parameter set, and complete a round of optimization update.

6. The method for controlling the adaptive coating on the surface of blue glass according to claim 1, characterized in that: After the coating is regulated by the final optimization result, the following steps are included: Perform coating monitoring and establish monitoring data sets; Perform matching analysis between actual coating effect and optimization result based on the monitoring data set, and establish matching error; Establishing a retrospective mark according to the matching error, performing a retrospective self-inspection of the coating process using the retrospective mark, and establishing a retrospective self-inspection result; The coating process control compensation is performed based on the retrospective self-check result.

7. The method for controlling the adaptive coating on the surface of blue glass according to claim 1, characterized in that: Also includes: Establish a self-inspection cycle, conduct self-inspection of the coating effect during the self-inspection cycle, and establish a cycle deviation; The cycle deviation is used to generate cycle compensation, and self-optimization management of the coating process is performed based on the cycle compensation.

8. The blue glass surface adaptive coating control system is characterized by: The method for controlling the surface adaptive coating of blue glass according to any one of claims 1 to 7 is used to implement the steps of the method, wherein the surface adaptive coating of blue glass controls the surface of the blue glass, comprising: A constraint condition building module is used to establish coating constraint conditions, wherein the coating constraint conditions are built by collecting the limit constraints of the film layer and the equipment parameters of the coating equipment, and the coating constraint conditions include the constraint of the hardness of the film layer, the constraint of the allowable deviation of the film thickness, and the constraint of the working range of the coating equipment; A process analysis module, for performing process analysis based on the coating process after obtaining the coating process, and configuring variable sensitivity and variable dependency of optimization variables; The fitness analysis module is used to construct the initial parameter set based on the optimization variables, and then perform fitness analysis on the initial parameter set through a comprehensive fitness function, and perform optimization and update under the coating constraint conditions based on the fitness analysis results and variable sensitivity and variable dependency; The coating control module is used to stop updating when the optimization update result meets the preset conditions, and to perform coating control based on the final optimization result.

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