An optimization method and device for the self-cleaning performance of an aviation drag reduction film

Abstract

The present invention discloses a method and device for optimizing the self-cleaning performance of an aviation drag reduction film, which relates to the technical field of drag reduction films. The method includes: obtaining the self-cleaning coating optimization space of the self-cleaning coating in the aviation drag reduction film and the drag reduction layer characteristic information of the drag reduction functional layer; obtaining the first drag reduction performance influence parameter; obtaining the first pollutant cleaning scale parameter; conducting an analysis on the improvement of the drag reduction performance of the self-cleaning coating for cleaning pollution to obtain the first drag reduction performance improvement parameter; obtaining the optimal self-cleaning coating parameters as the optimization result of the self-cleaning performance of the aviation drag reduction film. It solves the technical problem that the existing self-cleaning of aviation drag reduction films overly emphasizes the cleaning ability while ignoring its potential impact on the drag reduction performance, resulting in the self-cleaning coating being unable to exert the best cleaning effect in complex environments, realizes the balance between the self-cleaning performance and the drag reduction performance, and achieves the technical effects of improving the optimization efficiency and accuracy and enhancing the stability and reliability of the aviation drag reduction film.

Description

Technical Field

[0001] The present application relates to the technical field related to drag reduction films, and in particular to a method and device for optimizing the self-cleaning performance of aviation drag reduction films. Background Art

[0002] In today's era of rapid development of the aviation industry, flight efficiency and sustainability have become key factors in promoting technological progress. Drag reduction films change the friction between the airflow and the aircraft surface and reduce drag by coating special functional coatings on the aircraft surface. As an important material for improving the aerodynamic performance of aircraft and reducing fuel consumption, the performance optimization of aviation drag reduction films is directly related to the economy and environmental protection of aircraft. However, after long-term exposure to complex high-altitude environments, various pollutants such as dust, salt spray, oil, moisture, etc. are easily accumulated on the surface of aviation drag reduction films. These pollutants will not only increase flight resistance, but also accelerate the corrosion and aging of the membrane material, seriously affecting its drag reduction effect and service life. Self-cleaning technology makes the aircraft surface anti-fouling, waterproof and automatic cleaning by coating a coating with a special surface structure. It uses the hydrophobicity or hydrophilicity of the coating surface to make it difficult for pollutants to adhere to the surface, or automatically remove pollutants through natural forces such as rain. However, since aircraft fly in different environments, the types and degrees of pollutants on their surfaces will vary. Therefore, how to optimize the performance of self-cleaning coatings so that they can maintain stable self-cleaning and drag reduction effects in a changing environment has become an urgent problem to be solved. Existing self-cleaning coating optimization methods often focus on a specific environment or a specific type of pollutants, lack a comprehensive analysis of the self-cleaning ability of the coating in a complex and changing environment, and rarely consider the interaction between the self-cleaning coating and the drag reduction layer. The self-cleaning coating is mainly used to prevent pollutants from attaching and remove surface pollution, but it may also affect the drag reduction performance of the drag reduction layer.

[0003] Therefore, in the current technologies related to optimizing the self-cleaning performance of drag reduction membranes, there is a technical problem that over-emphasizes the cleaning ability and ignores its potential impact on the drag reduction performance, which in turn leads to the inability of the self-cleaning coating to perform the best cleaning effect in complex environments. Summary of the invention

[0004] The present application provides a method and device for optimizing the self-cleaning performance of aviation drag reduction films, thereby solving the technical problem that the existing self-cleaning of aviation drag reduction films overemphasizes the cleaning ability while ignoring its potential impact on the drag reduction performance, which in turn causes the self-cleaning coating to be unable to perform the optimal cleaning function in complex environments. The application achieves a balance between self-cleaning performance and drag reduction performance, and achieves the technical effect of improving optimization efficiency and accuracy and enhancing the stability and reliability of aviation drag reduction films.

[0005] The present application provides a method for optimizing the self-cleaning performance of an aviation drag reduction film. The method includes: obtaining the self-cleaning coating optimization space of the self-cleaning coating in the aviation drag reduction film and the drag reduction layer characteristic information of the drag reduction functional layer; randomly generating first self-cleaning coating parameters within the self-cleaning coating optimization space, performing self-cleaning performance analysis to obtain first self-cleaning performance parameters, and combining the drag reduction layer characteristic information to perform drag reduction performance impact analysis of the drag reduction film to obtain first drag reduction performance impact parameters; obtaining the operating environment parameters of the aviation drag reduction film, combining the first self-cleaning performance parameters to perform pollutant cleaning prediction to obtain first pollutant cleaning scale parameters; combining the first pollutant cleaning scale parameters and the drag reduction layer characteristic information to perform drag reduction performance improvement analysis of the self-cleaning coating cleaning pollution to obtain first drag reduction performance improvement parameters; calculating the self-cleaning fitness of the first self-cleaning coating parameters according to the first self-cleaning performance parameters, the first drag reduction performance impact parameters, and the first drag reduction performance improvement parameters, and performing iterative optimization of the self-cleaning coating parameters according to the first self-cleaning fitness to obtain optimal self-cleaning coating parameters as the optimization result of the self-cleaning performance of the aviation drag reduction film. Among them, in the iterative optimization, the optimization step size is set according to the self-cleaning performance parameters and the drag reduction performance improvement parameters.

[0006] In a possible implementation manner, when obtaining the self-cleaning coating optimization space of the self-cleaning coating in the aviation drag reduction film and the drag reduction layer characteristic information of the drag reduction functional layer, the following processing is also performed: obtaining the coating thickness adjustment space of the self-cleaning coating in the aviation drag reduction film as the self-cleaning coating optimization space; collecting the surface microstructure information of the drag reduction functional layer in the aviation drag reduction film as the drag reduction layer characteristic information.

[0007] In a possible implementation manner, when randomly generating first self-cleaning coating parameters within the self-cleaning coating optimization space and performing self-cleaning performance analysis to obtain first self-cleaning performance parameters, the following processing is also performed: randomly generating first self-cleaning coating parameters within the self-cleaning coating optimization space; collecting a sample self-cleaning coating parameter set according to the coating test data of the self-cleaning coating, and obtaining the cleaning ratio of the self-cleaning coating with each sample self-cleaning coating parameter for pollution cleaning test in a preset pollution test environment, and marking to obtain a sample self-cleaning performance parameter set; training a self-cleaning performance predictor by using the sample self-cleaning coating parameter set and the sample self-cleaning performance parameter set; and using the self-cleaning performance predictor to perform self-cleaning performance analysis on the first self-cleaning coating parameters to obtain first self-cleaning performance parameters.

[0008] In a possible implementation manner, in combination with the drag reduction layer feature information, perform an analysis on the influence of the drag reduction performance of the drag reduction film to obtain a first drag reduction performance influence parameter, and also perform the following processing: According to the production test data of the aviation drag reduction film, collect a set of sample self-cleaning coating parameters, and respectively test and obtain the change ratio of the drag reduction performance of the aviation drag reduction film under different sample self-cleaning coating parameters, and label to obtain a set of sample drag reduction performance influence parameters; Use the set of sample self-cleaning coating parameters and the set of sample drag reduction performance influence parameters to train a drag reduction performance influence analyzer; Use the drag reduction performance influence analyzer to perform an analysis on the influence of the drag reduction performance of the drag reduction layer feature information on the first self-cleaning coating parameter to obtain a first drag reduction performance influence parameter.

[0009] In a possible implementation manner, obtain the operating environment parameters of the aviation drag reduction film, and in combination with the first self-cleaning performance parameter, perform a pollutant cleaning prediction to obtain a first pollutant cleaning scale parameter, and also perform the following processing: Collect the pollutant content parameter in the operating environment of the aviation drag reduction film as the operating environment parameter; According to the cleaning test data of the aviation drag reduction film, collect a set of sample self-cleaning performance parameters, a set of sample operating environment parameters, and according to the cleaning ratio of pollutants under different sample self-cleaning performance parameters and different sample operating environment parameters, label to obtain a set of sample pollutant cleaning scale parameters; Use the set of sample self-cleaning performance parameters, the set of sample operating environment parameters, and the set of sample pollutant cleaning scale parameters to train a pollutant cleaning predictor; Use the pollutant cleaning predictor to perform a pollutant cleaning prediction on the pollutant content parameter and the first self-cleaning performance parameter to obtain a first pollutant cleaning scale parameter.

[0010] In a possible implementation manner, in combination with the first pollutant cleaning scale parameter and the drag reduction layer feature information, perform an analysis on the improvement of the drag reduction performance of the self-cleaning coating cleaning pollution to obtain a first drag reduction performance improvement parameter, and also perform the following processing: According to the test data of the aviation drag reduction film, collect a set of sample pollution scale information, and according to the change ratio of the drag reduction performance of the aviation drag reduction film under different sample pollution scale information, label to obtain a set of sample pollution drag reduction performance change parameters; Use the set of sample pollution scale information and the set of sample pollution drag reduction performance change parameters to train a pollution drag reduction performance influence analyzer; Use the pollution drag reduction performance influence analyzer to perform a pollution drag reduction performance influence analysis with the first pollutant cleaning scale parameter as the pollution scale information to obtain a first pollution drag reduction performance change parameter, which is used as the first drag reduction performance improvement parameter.

[0011] In a possible implementation, according to the first self-cleaning performance parameter, the first drag reduction performance influence parameter, and the first drag reduction performance improvement parameter, the self-cleaning fitness of the first self-cleaning coating parameter is calculated. According to the first self-cleaning fitness, iterative optimization of the self-cleaning coating parameter is performed, and the following processing is also executed: According to the first self-cleaning performance parameter, the first drag reduction performance influence parameter, and the first drag reduction performance improvement parameter, the self-cleaning fitness of the first self-cleaning coating parameter is calculated as follows:

[0012] ;

[0013] where SLF is the self-cleaning fitness, 、 and are weights, K is a constant, is the self-cleaning performance parameter, is the drag reduction performance influence parameter, is the drag reduction performance improvement parameter; According to the self-cleaning performance parameter and the drag reduction performance improvement parameter, a step size adjustment parameter is calculated to adjust and calculate the preset step size to obtain an optimized step size; Using the optimized step size, the first self-cleaning coating parameter is adjusted to obtain a second self-cleaning coating parameter; Continue to perform iterative optimization of the self-cleaning coating parameter in the self-cleaning coating optimization space until convergence, output the self-cleaning coating parameter with the maximum self-cleaning fitness, and obtain the optimal self-cleaning coating parameter. Among them, in each round of optimization, the preset step size is adjusted and calculated to set the optimized step size for adjustment and optimization.

[0014] The present application also provides an apparatus for optimizing the self-cleaning performance of an aviation drag reduction film, comprising: a self-cleaning coating optimization space acquisition module for acquiring the self-cleaning coating optimization space of the self-cleaning coating in the aviation drag reduction film and the drag reduction layer characteristic information of the drag reduction functional layer; a self-cleaning performance analysis module for randomly generating first self-cleaning coating parameters within the self-cleaning coating optimization space, performing self-cleaning performance analysis to obtain first self-cleaning performance parameters, and combining the drag reduction layer characteristic information to perform drag reduction performance impact analysis of the drag reduction film to obtain first drag reduction performance impact parameters; a pollutant cleaning prediction module for acquiring the operating environment parameters of the aviation drag reduction film and combining the first self-cleaning performance parameters to perform pollutant cleaning prediction to obtain first pollutant cleaning scale parameters; a drag reduction performance improvement analysis module for combining the first pollutant cleaning scale parameters and the drag reduction layer characteristic information to perform drag reduction performance improvement analysis of the self-cleaning coating cleaning pollution to obtain first drag reduction performance improvement parameters; and an optimal self-cleaning coating parameter acquisition module for calculating the self-cleaning fitness of the first self-cleaning coating parameters according to the first self-cleaning performance parameters, the first drag reduction performance impact parameters, and the first drag reduction performance improvement parameters, and performing iterative optimization of the self-cleaning coating parameters according to the first self-cleaning fitness to obtain optimal self-cleaning coating parameters as the optimization result of the self-cleaning performance of the aviation drag reduction film, wherein, in the iterative optimization, the optimization step size is set according to the self-cleaning performance parameters and the drag reduction performance improvement parameters.

[0015] It is intended to obtain the self-cleaning coating optimization space of the self-cleaning coating in the aviation drag reduction film and the drag reduction layer characteristic information of the drag reduction functional layer through an aviation drag reduction film self-cleaning performance optimization method and apparatus proposed in the present application; obtain first drag reduction performance impact parameters; obtain first pollutant cleaning scale parameters; perform drag reduction performance improvement analysis of the self-cleaning coating cleaning pollution to obtain first drag reduction performance improvement parameters; and obtain optimal self-cleaning coating parameters as the optimization result of the self-cleaning performance of the aviation drag reduction film. This solves the technical problem that the existing self-cleaning of aviation drag reduction films overly emphasizes the cleaning ability while ignoring its potential impact on the drag reduction performance, thereby causing the self-cleaning coating to be unable to play the best cleaning role in complex environments, realizes the balance between the self-cleaning performance and the drag reduction performance, and achieves the technical effects of improving the optimization efficiency and accuracy and enhancing the stability and reliability of the aviation drag reduction film. Brief Description of the Drawings

[0016] To more clearly illustrate the technical solutions of the embodiments of the present disclosure, the accompanying drawings of the embodiments of the present disclosure will be briefly introduced below. Flowcharts are used in this application to illustrate the operations performed by the devices according to the embodiments of the present application. It should be understood that the operations described above or below do not necessarily need to be executed precisely in order. On the contrary, according to the needs, various steps can be executed in reverse order or simultaneously. At the same time, other operations can also be added to these processes, or one or more steps can be removed from these processes.

[0017] Figure 1 Schematic diagram of the process for optimizing the self-cleaning performance of an aviation drag reduction film provided by an embodiment of the present application;

[0018] Figure 2 Schematic diagram of the structure of a device for optimizing the self-cleaning performance of an aviation drag reduction film provided by an embodiment of the present application.

[0019] Explanation of reference numerals: Self-cleaning coating optimization space acquisition module 10, self-cleaning performance analysis module 20, pollutant cleaning prediction module 30, drag reduction performance improvement analysis module 40, optimal self-cleaning coating parameter acquisition module 50. Detailed implementation manners

[0020] The above description is only an overview of the technical solutions of the present application. In order to be able 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 following specifically gives the detailed implementation manners of the present application.

[0021] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limitations of the present application. All other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of the present application.

[0022] In the following description, "some embodiments" are involved, which describe a subset of all possible embodiments. However, it can be understood that "some embodiments" can be the same subset or different subsets of all possible embodiments, and can be combined with each other without conflict. The terms "first / second" involved are only used to distinguish similar objects and do not represent a specific order for the objects. The terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, device, product or server comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules not clearly listed or inherent to these processes, methods, products or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application.

[0023] An embodiment of this application provides an optimization method for the self-cleaning performance of an aviation drag reduction film, as Figure 1 shown. The method includes:

[0024] Step S100, obtaining the self-cleaning coating optimization space of the self-cleaning coating in the aviation drag reduction film and the drag reduction layer characteristic information of the drag reduction functional layer.

[0025] Preferably, obtaining the self-cleaning coating optimization space of the self-cleaning coating in the aviation drag reduction film and the drag reduction layer characteristic information of the drag reduction functional layer means that during the process of optimizing the self-cleaning performance and drag reduction performance of the aviation drag reduction film, the key parameters of the self-cleaning coating and the drag reduction functional layer are analyzed and defined respectively. Specifically, the self-cleaning coating optimization space defines the parameter range that the self-cleaning coating can explore during the performance optimization process, aiming to find the optimal parameter configuration that can improve the self-cleaning performance. It may include multiple coating parameters, such as coating material, coating thickness, surface structure, and roughness, etc. Materials with different characteristics, such as hydrophobic coatings, superhydrophobic coatings, or hydrophilic coatings, etc., affect the self-cleaning effect. The thickness of the coating has a direct impact on the self-cleaning effect and the overall performance of the film. Different thicknesses may affect the adhesion and removal effect of dirt. The surface roughness determines the contact angle and self-cleaning performance of the coating. By adjusting the microstructure of the coating surface, its hydrophobicity or hydrophilicity can be changed. The self-cleaning coating optimization space provides a multi-dimensional parameter combination for the self-cleaning coating, and the optimal parameter combination can be selected through calculation and analysis in this space to obtain the best self-cleaning effect; the drag reduction layer characteristic information of the drag reduction functional layer refers to the characteristic parameters related to the drag reduction layer of the aviation drag reduction film, which determines the performance of the drag reduction layer in reducing the air resistance of the aircraft. The characteristic information of the drag reduction layer is used to analyze the influence of the drag reduction coating and ensure that the main function of the drag reduction layer is not weakened while optimizing the self-cleaning performance. It may include surface smoothness, coating material characteristics, air flow adaptability, etc. Obtaining the self-cleaning coating optimization space and the drag reduction layer characteristic information aims to ensure that the self-cleaning and drag reduction functions can be optimized synergistically, and ultimately achieve the best comprehensive performance of the drag reduction film.

[0026] In a possible implementation manner, step S100 further includes step S110, obtaining the coating thickness adjustment space of the self-cleaning coating in the aviation drag reduction film as the self-cleaning coating optimization space; step S120, collecting the surface microstructure information of the drag reduction functional layer in the aviation drag reduction film as the drag reduction layer characteristic information.

[0027] Preferably, obtain the coating thickness adjustment space of the self-cleaning coating inside the aviation drag reduction film. The coating thickness adjustment space refers to the thickness range of the self-cleaning coating and its possible adjustment parameter space. The thickness of the self-cleaning coating affects the self-cleaning performance of the coating and the function of the drag reduction layer. Specifically, if the thickness is too thin, it may not be able to effectively cover or maintain sufficient hydrophobicity / hydrophilicity, resulting in insufficient self-cleaning effect; while an overly thick coating may affect the removal efficiency of pollutants and increase the material cost and weight. As the optimization space of the self-cleaning coating, for example, the coating thickness ranges from 1 micron to 10 microns. As the thickness adjustment space, by making adjustments of different thicknesses and performance evaluations within this space, the optimal coating thickness can be found. The surface microstructure information of the drag reduction functional layer refers to the fine structural characteristics of the surface of the drag reduction layer, which directly affects the effect of reducing air resistance, such as surface roughness, surface microstructure morphology, coating adhesion, etc. As the characteristic information of the drag reduction layer, it is used to analyze the influence of the self-cleaning coating on the drag reduction layer.

[0028] Step S200, randomly generate the first self-cleaning coating parameters within the self-cleaning coating optimization space, conduct self-cleaning performance analysis to obtain the first self-cleaning performance parameters, and combine the drag reduction layer characteristic information to conduct analysis on the influence of the drag reduction performance of the drag reduction film to obtain the first drag reduction performance influence parameters.

[0029] Preferably, within the self-cleaning coating optimization space, a set of initial self-cleaning coating parameters, i.e., the first self-cleaning coating parameters, are randomly generated. These parameters define the basic characteristics of the coating, such as coating thickness, coating material, and coating surface roughness, etc. After generating the first self-cleaning coating parameters, their self-cleaning performance is evaluated. Specifically, in a preset simulation environment, specific pollutants (such as dust, oil stains, water droplets, etc.) are introduced, and the cleaning ability of the coating is tested, simulating the actual environment encountered by the aircraft under different flight conditions, such as humidity, dust, or oil stains. The self-cleaning performance is measured by analyzing the cleaning ratio of the polluted environment. For example, if the pollutants cover 100% of the coating surface and the coating automatically removes 70% of the pollutants, the cleaning ratio is 70%. This pollution cleaning ratio serves as the self-cleaning performance parameter, reflecting the self-cleaning effect of the coating under the corresponding polluted environment. While evaluating the self-cleaning performance, it is also necessary to analyze the impact of the self-cleaning coating on the drag reduction layer. In particular, some designs of the coating (such as an overly thick coating) may have an adverse effect on the surface structure of the drag reduction functional layer, thereby reducing the drag reduction effect. If the self-cleaning coating is too thick, it may change the surface microstructure of the drag reduction layer. For example, the drag reduction layer usually relies on its surface smoothness to reduce air friction, and an overly thick coating may increase the surface roughness, thereby weakening the drag reduction effect. The purpose of the drag reduction performance impact analysis is to evaluate the actual impact of the self-cleaning coating on the drag reduction layer. For example, the main function of the drag reduction layer is to reduce the air resistance on the aircraft surface. If the coating is too thick and causes the surface to be rough, it is calculated that the proportion of air resistance reduction drops by 5% or 10%, etc., to quantify the impact of the self-cleaning coating on the drag reduction performance. The proportion of air resistance reduction serves as the drag reduction performance impact parameter.

[0030] In a possible implementation manner, step S200 further includes step S210 of randomly generating first self-cleaning coating parameters within the self-cleaning coating optimization space; step S220 of collecting a set of sample self-cleaning coating parameters according to the coating test data of the self-cleaning coating, and obtaining the cleaning ratio of the self-cleaning coating for each sample self-cleaning coating parameter in a preset pollution test environment for pollution cleaning tests, and marking to obtain a set of sample self-cleaning performance parameters; step S230 of training a self-cleaning performance predictor by using the set of sample self-cleaning coating parameters and the set of sample self-cleaning performance parameters; step S240 of using the self-cleaning performance predictor to perform self-cleaning performance analysis on the first self-cleaning coating parameters to obtain first self-cleaning performance parameters.

[0031] Preferably, within the optimization space, a set of initial self-cleaning coating parameters, such as thickness, composition ratio of the coating material, surface microstructure, etc., are generated by random selection as the first self-cleaning coating parameters; during the testing process, different combinations of self-cleaning coating parameters are applied to the sample coatings, and coating and performance tests are carried out to generate parameter data related to the coatings, such as thickness, material composition, surface roughness, etc. The multiple parameter data obtained through the coating tests of different sample coatings constitute the sample self-cleaning coating parameter set. And in a preset pollution test environment (simulating real-life pollutants, such as dust, oil stains, etc.), the self-cleaning ability of each sample self-cleaning coating parameter is tested for pollution cleaning to obtain the test results (expressed as the cleaning ratio), that is, the ratio of the coating to remove pollutants under specific conditions (for example, 80% of the 100% pollutants are removed). After each sample coating undergoes the pollution cleaning test, a set of self-cleaning performance data sets are obtained, reflecting the self-cleaning performance corresponding to each sample coating parameter; a cleaning performance prediction model is constructed based on machine learning (such as regression analysis, decision tree, neural network, etc.). By using the sample self-cleaning coating parameter set as the input and the sample self-cleaning performance parameter set as the output, the constructed cleaning performance prediction model is trained to learn the relationship between the coating parameters and the cleaning performance, and a self-cleaning performance predictor is obtained, which can predict the self-cleaning performance based on new coating parameters; finally, the first self-cleaning coating parameters are input into the self-cleaning performance predictor to predict the self-cleaning performance under this parameter combination, that is, to predict the cleaning ratio of the coating as the first self-cleaning performance parameter.

[0032] In a possible implementation manner, step S200 further includes step S250 of collecting a sample self-cleaning coating parameter set according to the production test data of the aviation drag reduction film, and respectively testing and obtaining the change ratio of the drag reduction performance of the aviation drag reduction film under different sample self-cleaning coating parameters, and marking to obtain a sample drag reduction performance influence parameter set; step S260 of using the sample self-cleaning coating parameter set and the sample drag reduction performance influence parameter set to train a drag reduction performance influence analyzer; step S270 of using the drag reduction performance influence analyzer to perform a drag reduction performance influence analysis on the drag reduction layer characteristic information of the first self-cleaning coating parameter to obtain a first drag reduction performance influence parameter.

[0033] Preferably, a set of self-cleaning coating parameters is collected from the performance evaluation data of the production test of the aviation drag reduction film. Among them, the test data includes various parameters of the coating (such as thickness, material, etc.), and the influence of the coating on the drag reduction performance. Specifically, the test data of different self-cleaning coatings are collected, which may include parameters such as coating thickness, surface roughness, material composition, etc. After testing each sample self-cleaning coating, record its influence on the drag reduction effect of the drag reduction film. The change ratio of the drag reduction performance refers to the change in the drag reduction effect of the aviation drag reduction film before and after the coating is applied. For example, a certain coating may increase the drag reduction effect of the drag reduction film by 5%, while another coating may cause a 2% decrease in performance. Each coating parameter sample corresponds to a change ratio of the drag reduction performance, forming a set of sample drag reduction performance influence parameters; by using the set of sample self-cleaning coating parameters as the input data set and the set of sample drag reduction performance influence parameters as the output data set, a drag reduction performance influence analyzer is constructed based on a machine learning model to learn the relationship between the coating parameters and the influence of the drag reduction performance, aiming to predict the influence of the self-cleaning coating parameters on the drag reduction performance. By inputting the self-cleaning coating parameters, the analyzer can predict the influence of the corresponding coating on the performance of the drag reduction film. For example, the model will identify that coatings of certain thicknesses may significantly reduce the drag reduction effect, while certain material combinations may be very beneficial to the drag reduction effect; input the first self-cleaning coating parameters into the drag reduction performance influence analyzer to predict the influence of this coating combination on the characteristic information of the drag reduction layer, and evaluate whether this coating will have a positive or negative impact on the drag reduction effect, that is, calculate the influence on the drag reduction layer under the first self-cleaning coating parameters, and finally obtain the first drag reduction performance influence parameter, indicating the degree of influence of the coating on the drag reduction performance.

[0034] Step S300, obtain the operating environment parameters of the aviation drag reduction film, and combine the first self-cleaning performance parameters to perform pollutant cleaning prediction to obtain the first pollutant cleaning scale parameter.

[0035] Preferably, obtain the operating environment parameters of the aviation drag reduction film, that is, the environmental conditions in which the aviation drag reduction film is located during actual use, including various environmental factors that affect the performance and self-cleaning effect of the drag reduction film, including atmospheric components (such as pollutants like dust, sand particles, moisture, salt spray, oil stains, etc.), and then combine the obtained first self-cleaning performance parameter to predict the cleaning of pollutants. Specifically, by analyzing the pollutant cleaning effect of the drag reduction film in the operating environment, more accurately predict the performance of the self-cleaning coating in the real environment, so as to understand the cleaning ability of the self-cleaning coating in the actual environment, including testing the pollutant content in the operating environment, such as the concentration of particulate matter in the air, the composition of oil stains, the water vapor content under humidity, etc. Based on the pollutant content in the operating environment and combined with the self-cleaning performance parameter of the coating, predict the proportion of pollutants that the coating can remove. For example, if the average attachment amount of pollutants in the test environment is 10 grams and the self-cleaning coating can remove 6 grams of them, then the cleaning ratio is 60%. Use this cleaning ratio as the pollutant cleaning scale parameter, that is, obtain the first pollutant cleaning scale parameter, which is the quantitative result of the pollutant cleaning ability and represents the overall cleaning effect of the self-cleaning coating in the operating environment.

[0036] In a possible implementation manner, step S300 further includes step S310 of collecting the pollutant content parameter in the operating environment of the aviation drag reduction film as the operating environment parameter; step S320 of collecting the sample self-cleaning performance parameter set, the sample operating environment parameter set according to the cleaning test data of the aviation drag reduction film, and marking to obtain the sample pollutant cleaning scale parameter set according to the cleaning ratio of pollutants under different sample self-cleaning performance parameters and different sample operating environment parameters; step S330 of training the pollutant cleaning predictor using the sample self-cleaning performance parameter set, the sample operating environment parameter set, and the sample pollutant cleaning scale parameter set; step S340 of using the pollutant cleaning predictor to perform pollutant cleaning prediction on the pollutant content parameter and the first self-cleaning performance parameter to obtain the first pollutant cleaning scale parameter.

[0037] Preferably, collect the environmental conditions in the actual application of the aviation drag reduction film, especially the pollutant content parameters in the air, that is, the types and concentrations of pollutants, which may include dust, sand particles, oil stains, moisture, etc. Use these pollutant content parameters as the operating environment parameters, specifically referring to the concentration of pollutants, particle size, humidity, oil content, etc., which reflect the pollutant characteristics of the environment where the drag reduction film is located; According to the cleaning test data of the aviation drag reduction film, collect the self-cleaning performance parameter set of the samples (the self-cleaning ability of different coatings under different operating conditions) and the sample operating environment parameter set (the pollutant parameter set in different test environments). Conduct tests under different sample operating environments and self-cleaning coating parameters, and observe the effect of the coating on removing pollutants within a certain time or under certain conditions, that is, the pollutant cleaning ratio (the percentage of the removed pollutants in the total amount of pollutants). By labeling the self-cleaning performance parameters, operating environment parameters and the corresponding pollutant cleaning ratios, obtain the pollutant cleaning scale parameter set; Use the self-cleaning performance parameter set of the samples (the self-cleaning performance data of the coating), the sample operating environment parameter set (the pollutant data in different environments) and the sample pollutant cleaning scale parameter set (the pollutant cleaning ratio), and train a pollutant cleaning predictor through machine learning algorithms (such as regression models, neural networks, etc.) to learn the relationship between the coating parameters, environmental conditions and the pollutant cleaning ability; Then input the pollutant content parameters and the first self-cleaning performance parameters into the pollutant cleaning predictor for pollutant cleaning prediction. The pollutant cleaning predictor predicts the proportion or cleaning scale of the pollutants that the self-cleaning coating can remove in this environment according to the input data, and obtains the first pollutant cleaning scale parameter, which represents the ability of the current self-cleaning coating to clean pollutants in a specific operating environment. For example, predict that the coating can remove 80% of the pollutants in the current environment.

[0038] Step S400, combine the first pollutant cleaning scale parameter and the drag reduction layer feature information to conduct an analysis on the improvement of the drag reduction performance of the self-cleaning coating for cleaning pollution, and obtain the first drag reduction performance improvement parameter.

[0039] Preferably, by combining the first pollutant cleaning scale parameter and the drag reduction layer characteristic information, an analysis is conducted on the improvement of the drag reduction performance of the self-cleaning coating in cleaning pollution, and the improvement effect of the self-cleaning coating on the drag reduction performance after removing pollutants is analyzed, so as to obtain the first drag reduction performance improvement parameter. Specifically, the impact of pollutant removal on the drag reduction layer is evaluated, and the degree of improvement in the drag reduction performance is quantified. The attachment of pollutants on the surface of the aviation drag reduction film will affect the function of the drag reduction layer. The increase in pollutants will lead to an increase in surface roughness, thereby reducing the drag reduction effect. Therefore, after the self-cleaning coating effectively removes pollutants, the surface smoothness is restored, and the drag reduction performance will also be improved. The drag reduction layer characteristic information determines how the drag reduction layer reduces air resistance under ideal conditions (i.e., without pollutants). After the self-cleaning coating cleans the pollutants, it can restore the smooth surface of the drag reduction layer, thereby reducing air resistance. Analyzing the improvement of the drag reduction performance includes analyzing the impact of pollutant cleaning on the drag reduction performance, that is, when the pollutants are removed, the drag reduction layer restores its original drag reduction function. By combining the first pollutant cleaning scale parameter, the restoration effect of the self-cleaning coating on the surface smoothness when removing different amounts of pollutants is analyzed, and the improvement of the drag reduction performance is quantified. If the air resistance increases by 10% due to the presence of pollutants in the drag reduction layer, then the resistance reduction ratio after cleaning may return to near the original level (such as reducing the resistance by 10%). According to the surface condition after pollutant cleaning, the improvement amplitude of the drag reduction performance is calculated. The drag reduction performance improvement ratio can be the percentage of air resistance reduction, or the degree to which the drag reduction layer restores its drag reduction ability after being cleaned by the self-cleaning coating. For example, if the pollutant causes a 5% reduction in the drag reduction performance and the performance improves by 4% after the coating is cleaned, then the drag reduction performance improvement ratio is 80%. This drag reduction performance improvement ratio is used as the first drag reduction performance improvement parameter to represent the improvement effect of the self-cleaning coating on the drag reduction performance after cleaning pollutants.

[0040] In a possible implementation manner, step S400 further includes step S410, according to the test data of the aviation drag reduction film, collecting a sample pollution scale information set, and labeling to obtain a sample pollution drag reduction performance change parameter set according to the change ratio of the drag reduction performance of the aviation drag reduction film under different sample pollution scale information; step S420, using the sample pollution scale information set and the sample pollution drag reduction performance change parameter set to train a pollution drag reduction performance impact analyzer; step S430, using the pollution drag reduction performance impact analyzer, taking the first pollutant cleaning scale parameter as the pollution scale information to conduct a pollution drag reduction performance impact analysis, and obtaining a first pollution drag reduction performance change parameter as the first drag reduction performance improvement parameter.

[0041] Preferably, the pollution conditions in different environments are collected through the test data of the aviation drag reduction film to obtain a sample pollution scale information set, which may include the concentration of pollutants, the size of particulate matter, the pollution area, the types of pollutants (such as dust, oil stains, etc.), etc. It reflects the degree and types of pollution of the aviation drag reduction film in different environments. Under different pollution scales, the drag reduction performance of the aviation drag reduction film will be affected. The change ratio of the drag reduction performance refers to the difference in the drag reduction effect before and after the attachment of pollutants. For example, pollution causes an increase in the air resistance of the drag reduction film and a 5% decrease in the drag reduction performance. According to the change ratio of the drag reduction performance of the aviation drag reduction film under different sample pollution scale information, the sample pollution scale information set is labeled to obtain a sample pollution drag reduction performance change parameter set, that is, the change ratio of the drag reduction performance corresponding to each pollution scale, and the degree of influence of pollution on the performance of the drag reduction film is recorded; using the sample pollution scale information set as input data and the sample pollution drag reduction performance change parameter set as output data, based on a machine learning model (such as a decision tree, neural network, etc.), an analyzer for the influence of pollution on drag reduction performance is trained to learn how the pollution scale affects the performance of the drag reduction film. The analyzer for the influence of pollution on drag reduction performance can identify the extent of the decrease in the drag reduction performance under specific particulate matter concentrations and pollutant areas; taking the first pollutant cleaning scale parameter as pollution scale information and inputting it into the analyzer for the influence of pollution on drag reduction performance for analysis of the influence of pollution on drag reduction performance, that is, analyzing the influence of the current pollution scale on the performance of the drag reduction film, and obtaining the change in the drag reduction performance of the drag reduction film under the current pollution scale, which represents the degree of improvement or decrease in the drag reduction effect after pollution removal, as the final first drag reduction performance improvement parameter, indicating the extent of the improvement in the drag reduction effect of the aviation drag reduction film after the self-cleaning coating removes some pollutants.

[0042] In step S500, according to the first self-cleaning performance parameter, the first drag reduction performance influence parameter, and the first drag reduction performance improvement parameter, the self-cleaning fitness of the first self-cleaning coating parameter is calculated, and the self-cleaning coating parameter is iteratively optimized according to the first self-cleaning fitness to obtain the optimal self-cleaning coating parameter as the self-cleaning performance optimization result of the aviation drag reduction film. Among them, in the iterative optimization, the optimization step size is set according to the self-cleaning performance parameter and the drag reduction performance improvement parameter.

[0043] Preferably, by calculating the self-cleaning fitness and iteratively optimizing the coating parameters according to this fitness and relevant performance parameters, the best self-cleaning performance and drag reduction performance can be achieved. Specifically, according to the first self-cleaning performance parameter, the first drag reduction performance influence parameter, and the first drag reduction performance improvement parameter, the self-cleaning fitness of the first self-cleaning coating parameters is calculated, which reflects the comprehensive performance between the self-cleaning performance and the drag reduction performance of the coating, that is, it measures the performance of a certain set of coating parameters in terms of self-cleaning ability and drag reduction performance. After calculating the self-cleaning fitness of each set of coating parameters, the parameter combination of the coating is gradually adjusted to improve the overall performance. Specifically, a set of self-cleaning coating parameters is randomly selected in the optimization space, and the corresponding self-cleaning fitness is calculated. According to the calculation result of the fitness, the performance of the current parameters is judged, and which parameters need to be adjusted is selected. For example, if the coating of a certain set of parameters is too thick, resulting in a greater impact on the drag reduction performance, the coating thickness will be reduced in the next round of optimization. According to the optimization result, the parameter combination of the self-cleaning coating (such as material, thickness, surface roughness, etc.) is adjusted. Among them, the adjustment amplitude of the self-cleaning coating parameters in each iteration, that is, the adjustment optimization step size is dynamically set according to the self-cleaning performance parameter and the drag reduction performance improvement parameter. For example, in the initial stage, the step size can be slightly larger to quickly explore a wide parameter space. When the self-cleaning fitness gradually approaches the optimal value, the step size is reduced to make the optimization process more accurate and gradually approach the optimal solution. Then, its self-cleaning fitness is calculated again. Through multiple rounds of iterative optimization of the self-cleaning coating parameters, its fitness is continuously improved, and finally the optimal self-cleaning coating parameters are found, which not only perform excellently in self-cleaning ability, but also can maximize the drag reduction performance and minimize the adverse impact on the drag reduction layer. As the optimization result of the self-cleaning performance of the aviation drag reduction film, in practical applications, the best self-cleaning and drag reduction effects of the aviation drag reduction film are achieved.

[0044] In a possible implementation manner, step S500 further includes step S510, and according to the first self-cleaning performance parameter, the first drag reduction performance influence parameter, and the first drag reduction performance improvement parameter, the self-cleaning fitness of the first self-cleaning coating parameters is calculated as follows:

[0045] ;

[0046] Wherein, SLF is the self-cleaning fitness, , and are weights, K is a constant, is the self-cleaning performance parameter, is the drag reduction performance influence parameter, is the drag reduction performance improvement parameter.

[0047] It further includes step S520 of calculating and generating a step size adjustment parameter according to the self-cleaning performance parameter and the drag reduction performance improvement parameter, adjusting and calculating the preset step size to obtain an optimized step size; step S530 of using the optimized step size to adjust the first self-cleaning coating parameter to obtain a second self-cleaning coating parameter; and step S540 of continuing to perform iterative optimization of the self-cleaning coating parameter within the self-cleaning coating optimization space until convergence, outputting the self-cleaning coating parameter with the maximum self-cleaning fitness to obtain the optimal self-cleaning coating parameter, wherein in each round of optimization, the optimized step size is set for the adjustment calculation of the preset step size for adjustment and optimization.

[0048] Preferably, a step size adjustment parameter is calculated and generated according to the self-cleaning performance parameter and the drag reduction performance improvement parameter, which is used to determine how to adjust the step size to ensure that the parameter adjustment amplitude at different stages is appropriate during the optimization process, enabling both rapid finding of better solutions and ensuring the accuracy of optimization. For example, the mean value of the self-cleaning performance parameter and the drag reduction performance improvement parameter is calculated as the step size adjustment parameter, and then its product with the preset step size is used as the optimized step size, where the preset step size is the initial step size set during the optimization process; according to the magnitude of the optimized step size, each dimension of the first self-cleaning coating parameter is adjusted, such as adjusting the coating thickness and changing the surface roughness, to improve the drag reduction performance and self-cleaning effect, and the adjusted parameter combination is called the second self-cleaning coating parameter; then continue to perform iterative optimization of the self-cleaning coating parameter within the self-cleaning coating optimization space, that is, adjust the self-cleaning coating parameter multiple times to gradually improve the self-cleaning performance and drag reduction performance, and continuously search for a better parameter combination. Specifically, in each iteration, the self-cleaning fitness is calculated according to the current self-cleaning performance and drag reduction performance improvement situation, and according to the fitness result, it is decided whether to continue to adjust the parameter. If so, the step size is further adjusted to enter the next round of optimization until convergence, and the corresponding self-cleaning coating parameter with the maximum self-cleaning fitness is output, that is, the coating design that is optimal in terms of self-cleaning performance and drag reduction performance. Among them, during each round of optimization process, the step size will be dynamically adjusted according to the progress of the optimization. For example, if the current coating parameter is still far from the optimal solution, a larger step size is used to quickly adjust the parameter. When approaching the optimal solution, the step size will gradually decrease to facilitate fine-tuning and prevent missing the optimal parameter, ensuring the efficiency and accuracy of the optimization process.

[0049] In the above text, with reference to Figure 1 a method for optimizing the self-cleaning performance of an aviation drag reduction film according to an embodiment of the present invention is described in detail. Next, with reference to Figure 2 a device for optimizing the self-cleaning performance of an aviation drag reduction film according to an embodiment of the present invention will be described.

[0050] An optimization device for the self-cleaning performance of an aviation drag reduction film according to an embodiment of the present invention is used to solve the technical problem that the existing self-cleaning of aviation drag reduction films overemphasizes the cleaning ability while ignoring its potential impact on the drag reduction performance, resulting in the inability of the self-cleaning coating to play the best cleaning role in a complex environment. It realizes the balance between the self-cleaning performance and the drag reduction performance, and achieves the technical effects of improving the optimization efficiency and accuracy, as well as the stability and reliability of the aviation drag reduction film. An optimization device for the self-cleaning performance of an aviation drag reduction film includes: a self-cleaning coating optimization space acquisition module 10, a self-cleaning performance analysis module 20, a pollutant cleaning prediction module 30, a drag reduction performance improvement analysis module 40, and an optimal self-cleaning coating parameter acquisition module 50.

[0051] The self-cleaning coating optimization space acquisition module 10 is used to acquire the self-cleaning coating optimization space of the self-cleaning coating in the aviation drag reduction film, as well as the drag reduction layer characteristic information of the drag reduction functional layer; the self-cleaning performance analysis module 20 is used to randomly generate first self-cleaning coating parameters within the self-cleaning coating optimization space, conduct self-cleaning performance analysis to obtain first self-cleaning performance parameters, and combine the drag reduction layer characteristic information to conduct drag reduction performance impact analysis of the drag reduction film to obtain first drag reduction performance impact parameters; the pollutant cleaning prediction module 30 is used to acquire the operating environment parameters of the aviation drag reduction film, and combine the first self-cleaning performance parameters to conduct pollutant cleaning prediction to obtain first pollutant cleaning scale parameters; the drag reduction performance improvement analysis module 40 is used to combine the first pollutant cleaning scale parameters and the drag reduction layer characteristic information to conduct drag reduction performance improvement analysis of the self-cleaning coating cleaning pollution to obtain first drag reduction performance improvement parameters; the optimal self-cleaning coating parameter acquisition module 50 is used to calculate the self-cleaning fitness of the first self-cleaning coating parameters according to the first self-cleaning performance parameters, the first drag reduction performance impact parameters, and the first drag reduction performance improvement parameters, and conduct iterative optimization of the self-cleaning coating parameters according to the first self-cleaning fitness to obtain optimal self-cleaning coating parameters as the optimization result of the self-cleaning performance of the aviation drag reduction film. Among them, in the iterative optimization, the optimization step size is set according to the self-cleaning performance parameters and the drag reduction performance improvement parameters.

[0052] Next, the specific configuration of the self-cleaning coating optimization space acquisition module 10 will be described in detail. The self-cleaning coating optimization space acquisition module 10 may further include: acquiring the coating thickness adjustment space of the self-cleaning coating in the aviation drag reduction film as the self-cleaning coating optimization space; collecting the surface microstructure information of the drag reduction functional layer in the aviation drag reduction film as the drag reduction layer characteristic information.

[0053] Next, the specific configuration of the self-cleaning performance analysis module 20 will be described in detail. The self-cleaning performance analysis module 20 may further include: randomly generating first self-cleaning coating parameters within the self-cleaning coating optimization space; collecting a set of sample self-cleaning coating parameters according to the coating test data of the self-cleaning coating, and obtaining the cleaning ratio of the self-cleaning coating with each sample self-cleaning coating parameter in a preset pollution test environment, and marking to obtain a set of sample self-cleaning performance parameters; using the set of sample self-cleaning coating parameters and the set of sample self-cleaning performance parameters to train a self-cleaning performance predictor; using the self-cleaning performance predictor to perform self-cleaning performance analysis on the first self-cleaning coating parameters to obtain first self-cleaning performance parameters.

[0054] Next, the specific configuration of the self-cleaning performance analysis module 20 will be further described in detail. The self-cleaning performance analysis module 20 may further include: collecting a set of sample self-cleaning coating parameters according to the production test data of the aviation drag reduction film, and respectively testing and obtaining the change ratio of the drag reduction performance of the aviation drag reduction film under different sample self-cleaning coating parameters, and marking to obtain a set of sample drag reduction performance influence parameters; using the set of sample self-cleaning coating parameters and the set of sample drag reduction performance influence parameters to train a drag reduction performance influence analyzer; using the drag reduction performance influence analyzer to perform drag reduction performance influence analysis on the drag reduction layer characteristic information of the first self-cleaning coating parameters to obtain first drag reduction performance influence parameters.

[0055] Next, the specific configuration of the pollutant cleaning prediction module 30 will be described in detail. The pollutant cleaning prediction module 30 may further include: collecting the pollutant content parameters in the operating environment of the aviation drag reduction film as operating environment parameters; collecting a set of sample self-cleaning performance parameters, a set of sample operating environment parameters according to the cleaning test data of the aviation drag reduction film, and the cleaning ratio of pollutants under different sample self-cleaning performance parameters and different sample operating environment parameters, and marking to obtain a set of sample pollutant cleaning scale parameters; using the set of sample self-cleaning performance parameters, the set of sample operating environment parameters and the set of sample pollutant cleaning scale parameters to train a pollutant cleaning predictor; using the pollutant cleaning predictor to perform pollutant cleaning prediction on the pollutant content parameters and the first self-cleaning performance parameters to obtain first pollutant cleaning scale parameters.

[0056] Next, the specific configuration of the drag reduction performance improvement analysis module 40 will be described in detail. The drag reduction performance improvement analysis module 40 further includes: collecting a set of sample pollution scale information according to the test data of the aviation drag reduction film, and labeling to obtain a set of sample pollution drag reduction performance change parameters according to the change ratio of the drag reduction performance of the aviation drag reduction film under different sample pollution scale information; using the set of sample pollution scale information and the set of sample pollution drag reduction performance change parameters to train a pollution drag reduction performance impact analyzer; using the pollution drag reduction performance impact analyzer, performing pollution drag reduction performance impact analysis with the first pollutant cleaning scale parameter as the pollution scale information to obtain a first pollution drag reduction performance change parameter, which is used as the first drag reduction performance improvement parameter.

[0057] Next, the specific configuration of the optimal self-cleaning coating parameter obtaining module 50 will be described in detail. The optimal self-cleaning coating parameter obtaining module 50 further includes: calculating the self-cleaning fitness of the first self-cleaning coating parameter according to the first self-cleaning performance parameter, the first drag reduction performance impact parameter, and the first drag reduction performance improvement parameter, as shown in the following formula:

[0058] ;

[0059] where SLF is the self-cleaning fitness, , and are weights, K is a constant, is the self-cleaning performance parameter, is the drag reduction performance impact parameter, is the drag reduction performance improvement parameter; calculating and generating a step size adjustment parameter according to the self-cleaning performance parameter and the drag reduction performance improvement parameter, adjusting and calculating the preset step size to obtain an optimized step size; using the optimized step size to adjust the first self-cleaning coating parameter to obtain a second self-cleaning coating parameter; continuing to perform iterative optimization of the self-cleaning coating parameters in the self-cleaning coating optimization space until convergence, and outputting the self-cleaning coating parameter with the maximum self-cleaning fitness to obtain the optimal self-cleaning coating parameter, where in each round of optimization, the preset step size is adjusted and calculated to set the optimized step size for adjustment and optimization.

[0060] An aviation drag reduction film self-cleaning performance optimization device provided by an embodiment of the present invention can execute an aviation drag reduction film self-cleaning performance optimization method provided by any embodiment of the present invention, and has corresponding functional modules and beneficial effects for executing the method.

[0061] Although the present application makes various references to certain modules in the apparatus according to the embodiments of the present application, however, any number of different modules can be used and run on the user terminal and / or the server. The various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be realized; in addition, the specific names of the functional units are only for the convenience of mutual distinction and do not limit the protection scope of the present invention.

[0062] The above specific embodiments do not constitute a limitation on the protection scope of the present application. Those skilled in the art should understand that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principle of the present application shall be included within the protection scope of the present application.

Claims

1. A method for optimizing the self-cleaning performance of an aviation drag reduction film, characterized in that: The method comprises: Obtain the self-cleaning coating optimization space of the self-cleaning coating in the aviation drag reduction film and the drag reduction layer characteristic information of the drag reduction functional layer. The self-cleaning coating optimization space defines the parameter range explored by the self-cleaning coating during the performance optimization process; Randomly generating a first self-cleaning coating parameter in the self-cleaning coating optimization space, performing a self-cleaning performance analysis to obtain a first self-cleaning performance parameter, and performing a drag reduction performance influence analysis of the drag reduction film in combination with the drag reduction layer characteristic information to obtain a first drag reduction performance influence parameter; Acquire the operating environment parameters of the aviation drag reduction film, and perform pollutant cleaning prediction in combination with the first self-cleaning performance parameters to obtain the first pollutant cleaning scale parameter; In combination with the first pollutant cleaning scale parameter and the drag reduction layer characteristic information, a drag reduction performance improvement analysis of the self-cleaning coating cleaning pollution is performed to obtain a first drag reduction performance improvement parameter; According to the first self-cleaning performance parameter, the first drag reduction performance influencing parameter and the first drag reduction performance improving parameter, the self-cleaning fitness of the first self-cleaning coating parameter is calculated, and the self-cleaning coating parameter is iteratively optimized according to the first self-cleaning fitness to obtain the optimal self-cleaning coating parameter as the self-cleaning performance optimization result of the aviation drag reduction film, wherein in the iterative optimization, the optimization step size is set according to the self-cleaning performance parameter and the drag reduction performance improving parameter; According to the first self-cleaning performance parameter, the first drag reduction performance influencing parameter and the first drag reduction performance improving parameter, the self-cleaning fitness of the first self-cleaning coating parameter is calculated, and the self-cleaning coating parameter is iteratively optimized according to the first self-cleaning fitness, including: According to the first self-cleaning performance parameter, the first drag reduction performance influencing parameter and the first drag reduction performance improving parameter, the self-cleaning adaptability of the first self-cleaning coating parameter is calculated as follows: ； Among them, SLF is the self-cleaning fitness, , and is the weight, K is a constant, is the self-cleaning performance parameter, Parameters affecting drag reduction performance: Improve parameters for drag reduction performance; According to the self-cleaning performance parameters and the drag reduction performance improvement parameters, a step length adjustment parameter is calculated and generated, and the preset step length is adjusted and calculated to obtain an optimized step length; Using the optimization step length, adjusting the first self-cleaning coating parameter to obtain a second self-cleaning coating parameter; Continue to iteratively optimize the self-cleaning coating parameters in the self-cleaning coating optimization space until convergence, output the self-cleaning coating parameters with the maximum self-cleaning fitness, and obtain the optimal self-cleaning coating parameters, wherein the preset step size adjustment calculation setting optimization step size is adjusted and optimized in each round of optimization.

2. The method for optimizing the self-cleaning performance of an aviation drag reduction film according to claim 1, characterized in that: Obtain the self-cleaning coating optimization space of the self-cleaning coating in the aviation drag reduction film, and the drag reduction layer characteristic information of the drag reduction functional layer, including: Obtain the coating thickness adjustment space of the self-cleaning coating in the aviation drag reduction film as the optimization space of the self-cleaning coating; The surface microstructure information of the drag reduction functional layer in the aviation drag reduction film is collected as the characteristic information of the drag reduction layer.

3. The method for optimizing the self-cleaning performance of an aviation drag reduction film according to claim 1, characterized in that: Randomly generating a first self-cleaning coating parameter in the self-cleaning coating optimization space, performing a self-cleaning performance analysis, and obtaining a first self-cleaning performance parameter, including: Randomly generating a first self-cleaning coating parameter in the self-cleaning coating optimization space; According to the coating test data of the self-cleaning coating, a set of sample self-cleaning coating parameters is collected, and the cleaning ratio of the self-cleaning coating of each sample self-cleaning coating parameter in the pollution cleaning test under the preset pollution test environment is obtained, and the sample self-cleaning performance parameter set is obtained by marking; Using the sample self-cleaning coating parameter set and the sample self-cleaning performance parameter set, training a self-cleaning performance predictor; The self-cleaning performance predictor is used to perform a self-cleaning performance analysis on the first self-cleaning coating parameter to obtain a first self-cleaning performance parameter.

4. The method for optimizing the self-cleaning performance of an aviation drag reduction film according to claim 1, characterized in that: Combined with the characteristic information of the drag reduction layer, the drag reduction performance influence analysis of the drag reduction film is performed to obtain the first drag reduction performance influence parameter, including: According to the production test data of the aviation drag reduction film, a set of sample self-cleaning coating parameters is collected, and the drag reduction performance change ratio of the aviation drag reduction film under different sample self-cleaning coating parameters is tested respectively, and the set of parameters affecting the sample drag reduction performance is marked; Using the sample self-cleaning coating parameter set and the sample drag reduction performance impact parameter set, training a drag reduction performance impact analyzer; The drag reduction performance impact analyzer is used to perform drag reduction performance impact analysis of the drag reduction layer characteristic information on the first self-cleaning coating parameter to obtain a first drag reduction performance impact parameter.

5. The method for optimizing the self-cleaning performance of an aviation drag reduction film according to claim 1, characterized in that: The operating environment parameters of the aviation drag reduction film are obtained, and the pollutant cleaning prediction is performed in combination with the first self-cleaning performance parameters to obtain the first pollutant cleaning scale parameters, including: Collecting pollutant content parameters in the working environment of the aviation drag reduction film as working environment parameters; According to the cleaning test data of aviation drag reduction film, the sample self-cleaning performance parameter set and the sample operating environment parameter set are collected, and according to the cleaning ratio of pollutants under different sample self-cleaning performance parameters and different sample operating environment parameters, the sample pollutant cleaning scale parameter set is obtained by annotation; Using the sample self-cleaning performance parameter set, the sample operating environment parameter set and the sample pollutant cleaning scale parameter set to train a pollutant cleaning predictor; The pollutant cleaning predictor is used to perform pollutant cleaning prediction on the pollutant content parameter and the first self-cleaning performance parameter to obtain a first pollutant cleaning scale parameter.

6. The method for optimizing the self-cleaning performance of an aviation drag reduction film according to claim 1, characterized in that: In combination with the first pollutant cleaning scale parameter and the drag reduction layer characteristic information, a drag reduction performance improvement analysis of the self-cleaning coating cleaning pollution is performed to obtain a first drag reduction performance improvement parameter, including: According to the test data of aviation drag reduction film, a set of sample pollution scale information is collected, and according to the change ratio of the drag reduction performance of aviation drag reduction film under different sample pollution scale information, a set of sample pollution drag reduction performance change parameters is obtained by marking; Using the sample pollution scale information set and the sample pollution drag reduction performance change parameter set to train a pollution drag reduction performance impact analyzer; The pollution drag reduction performance impact analyzer is used to perform pollution drag reduction performance impact analysis using the first pollutant cleaning scale parameter as pollution scale information, and a first pollution drag reduction performance change parameter is obtained as a first drag reduction performance improvement parameter.

7. An aviation drag reduction film self-cleaning performance optimization device, characterized in that: The device is used to implement the method for optimizing the self-cleaning performance of an aviation drag reduction film according to any one of claims 1 to 6, and the device comprises: A self-cleaning coating optimization space acquisition module, which is used to obtain the self-cleaning coating optimization space of the self-cleaning coating in the aviation drag reduction film, as well as the drag reduction layer characteristic information of the drag reduction functional layer; A self-cleaning performance analysis module, used to randomly generate a first self-cleaning coating parameter in the self-cleaning coating optimization space, perform a self-cleaning performance analysis, obtain a first self-cleaning performance parameter, and perform a drag reduction performance influence analysis of the drag reduction film in combination with the drag reduction layer characteristic information, and obtain a first drag reduction performance influence parameter; A pollutant cleaning prediction module, used to obtain the operating environment parameters of the aviation drag reduction film, and perform pollutant cleaning prediction in combination with the first self-cleaning performance parameter to obtain a first pollutant cleaning scale parameter; a drag reduction performance improvement analysis module, configured to perform drag reduction performance improvement analysis of self-cleaning coating cleaning pollution in combination with the first pollutant cleaning scale parameter and the drag reduction layer characteristic information, and obtain a first drag reduction performance improvement parameter; An optimal self-cleaning coating parameter acquisition module is used to calculate the self-cleaning fitness of the first self-cleaning coating parameter according to the first self-cleaning performance parameter, the first drag reduction performance influencing parameter and the first drag reduction performance improving parameter, and iteratively optimize the self-cleaning coating parameter according to the first self-cleaning fitness to obtain the optimal self-cleaning coating parameter as the self-cleaning performance optimization result of the aviation drag reduction film, wherein, in the iterative optimization, the optimization step size is set according to the self-cleaning performance parameter and the drag reduction performance improving parameter; Wherein, the optimal self-cleaning coating parameter acquisition module is also used for: According to the first self-cleaning performance parameter, the first drag reduction performance influencing parameter and the first drag reduction performance improving parameter, the self-cleaning adaptability of the first self-cleaning coating parameter is calculated as follows: ； Among them, SLF is the self-cleaning fitness, , and is the weight, K is a constant, is the self-cleaning performance parameter, Parameters affecting drag reduction performance: Improve parameters for drag reduction performance; According to the self-cleaning performance parameters and the drag reduction performance improvement parameters, a step length adjustment parameter is calculated and generated, and the preset step length is adjusted and calculated to obtain an optimized step length; Using the optimization step length, adjusting the first self-cleaning coating parameter to obtain a second self-cleaning coating parameter; Continue to iteratively optimize the self-cleaning coating parameters in the self-cleaning coating optimization space until convergence, output the self-cleaning coating parameters with the maximum self-cleaning fitness, and obtain the optimal self-cleaning coating parameters, wherein the preset step size adjustment calculation setting optimization step size is adjusted and optimized in each round of optimization.

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