A method and device for optimizing the self-cleaning performance of 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, and relates to the technical field related to drag reduction films. The method comprises: 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 influencing parameter; obtaining the first pollutant cleaning scale parameter; conducting a drag reduction performance improvement analysis of the self-cleaning coating cleaning pollution, and obtaining the first drag reduction performance improvement parameter; obtaining the optimal self-cleaning coating parameter as the self-cleaning performance optimization result of the aviation drag reduction film. The method solves the technical problem that the existing aviation drag reduction film self-cleaning overemphasizes the cleaning ability and ignores its potential impact on the drag reduction performance, which leads to the inability of the self-cleaning coating to play the best cleaning role in complex environments, achieves a balance between self-cleaning performance and drag reduction performance, and achieves the technical effect of improving optimization efficiency and accuracy and improving 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 comprising: obtaining a self-cleaning coating optimization space of a self-cleaning coating in the aviation drag reduction film, and drag reduction layer characteristic information of a drag reduction functional layer; randomly generating a first self-cleaning coating parameter in the self-cleaning coating optimization space, performing a self-cleaning performance analysis, obtaining 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, obtaining a first drag reduction performance influence parameter; obtaining an operating environment parameter of the aviation drag reduction film, performing a pollutant cleaning prediction in combination with the first self-cleaning performance parameter, and obtaining a first pollutant cleaning scale parameter; number; combining the first pollutant cleaning scale parameter and the drag reduction layer characteristic information, analyzing the drag reduction performance improvement of the self-cleaning coating cleaning pollution is performed to obtain the 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 improvement parameter, the self-cleaning fitness of the first self-cleaning coating parameter is calculated, and according to the first self-cleaning fitness, the self-cleaning coating parameters are iteratively optimized to obtain the optimal self-cleaning coating parameters 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 parameters and the drag reduction performance improvement parameters.

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

[0007] In a possible implementation, a first self-cleaning coating parameter is randomly generated in the self-cleaning coating optimization space, a self-cleaning performance analysis is performed, and a first self-cleaning performance parameter is obtained. The following processing is also performed: a first self-cleaning coating parameter is randomly generated in the self-cleaning coating optimization space; a sample self-cleaning coating parameter set is collected based on the coating test data of the self-cleaning coating, and the cleaning ratio of the self-cleaning coating of each sample self-cleaning coating parameter under a preset pollution test environment is obtained, and the sample self-cleaning performance parameter set is obtained by marking; a self-cleaning performance predictor is trained using the sample self-cleaning coating parameter set and the sample self-cleaning performance parameter set; and 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.

[0008] In a possible implementation, the drag reduction performance influence analysis of the drag reduction film is performed in combination with the drag reduction layer characteristic information to obtain a first drag reduction performance influence parameter, and the following processing is also performed: based on the production test data of the aviation drag reduction film, a sample self-cleaning coating parameter set 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 sample drag reduction performance influence parameter set is obtained by marking; the sample self-cleaning coating parameter set and the sample drag reduction performance influence parameter set are used to train a drag reduction performance influence analyzer; the drag reduction performance influence analyzer is used to perform the drag reduction performance influence analysis of the drag reduction layer characteristic information on the first self-cleaning coating parameter to obtain a first drag reduction performance influence parameter.

[0009] In a possible implementation, the operating environment parameters of the aviation drag reduction film are obtained, and in combination with the first self-cleaning performance parameters, pollutant cleaning prediction is performed to obtain a first pollutant cleaning scale parameter, and the following processing is also performed: the pollutant content parameters in the operating environment of the aviation drag reduction film are collected as operating environment parameters; based on the cleaning test data of the aviation drag reduction film, a sample self-cleaning performance parameter set and a sample operating environment parameter set are collected, and according to different sample self-cleaning performance parameters and pollutant cleaning ratios under different sample operating environment parameters, a sample pollutant cleaning scale parameter set is obtained by marking; the sample self-cleaning performance parameter set, the sample operating environment parameter set and the sample pollutant cleaning scale parameter set are used 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.

[0010] In a possible implementation, a drag reduction performance improvement analysis of the self-cleaning coating cleaning pollution is performed in combination with the first pollutant cleaning scale parameter and the drag reduction layer characteristic information to obtain a first drag reduction performance improvement parameter, and the following processing is also performed: based on the test data of the aviation drag reduction film, a sample pollution scale information set is collected, and according to the change ratio of the drag reduction performance of the aviation drag reduction film under different sample pollution scale information, a sample pollution drag reduction performance change parameter set is labeled to obtain; the sample pollution scale information set and the sample pollution drag reduction performance change parameter set are used to train a pollution drag reduction performance impact analyzer; the pollution drag reduction performance impact analyzer is used to perform a pollution drag reduction performance impact analysis using the first pollutant cleaning scale parameter as the pollution scale information, and obtain the first pollution drag reduction performance change parameter 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 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. The following processing is also performed: 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, 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: is a drag reduction performance improvement parameter; according to the self-cleaning performance parameter and the drag reduction performance improvement parameter, a step adjustment parameter is calculated and generated, and the preset step size is adjusted and calculated to obtain an optimized step size; the first self-cleaning coating parameter is adjusted using the optimized step size to obtain a second self-cleaning coating parameter; the self-cleaning coating parameters are iteratively optimized in the self-cleaning coating optimization space until convergence, and the self-cleaning coating parameters with the largest self-cleaning fitness are output to obtain the optimal self-cleaning coating parameters, wherein the preset step size adjustment calculation is set to adjust and optimize the optimization step size in each round of optimization.

[0012] The present application also provides an aviation drag reduction film self-cleaning performance optimization device, including: a self-cleaning coating optimization space acquisition module, used 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; 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 to 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, perform a pollutant cleaning prediction in combination with the first self-cleaning performance parameter, and obtain a first pollutant cleaning parameter. cleaning scale parameters; a drag reduction performance improvement analysis module, used to combine the first pollutant cleaning scale parameters and the drag reduction layer characteristic information to perform a drag reduction performance improvement analysis of the self-cleaning coating cleaning pollution, and obtain the first drag reduction performance improvement parameters; an optimal self-cleaning coating parameter acquisition module, 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 influencing parameters and the first drag reduction performance improvement parameters, and iteratively optimize the self-cleaning coating parameters according to the first self-cleaning fitness to obtain the optimal self-cleaning coating parameters 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 parameters and the drag reduction performance improvement parameters.

[0013] Through the method and device for optimizing the self-cleaning performance of aviation drag reduction film proposed in this application, it is intended to obtain the optimization space of the self-cleaning coating in the aviation drag reduction film, as well as the characteristic information of the drag reduction layer of the drag reduction functional layer; obtain the first drag reduction performance influencing parameter; obtain the first pollutant cleaning scale parameter; conduct drag reduction performance improvement analysis of self-cleaning coating cleaning pollution, obtain the first drag reduction performance improvement parameter; obtain the optimal self-cleaning coating parameter as the self-cleaning performance optimization result of the aviation drag reduction film. It solves the technical problem that the existing aviation drag reduction film self-cleaning overemphasizes the cleaning ability and ignores its potential impact on the drag reduction performance, which leads to the inability of the self-cleaning coating to play the best cleaning role in complex environments, achieves a balance between self-cleaning performance and drag reduction performance, and achieves the technical effect of improving optimization efficiency and accuracy and improving the stability and reliability of aviation drag reduction film. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solution of the embodiment of the present disclosure, the accompanying drawings of the embodiment of the present disclosure will be briefly introduced below. A flow chart is used in the present application to illustrate the operations performed by the device according to the embodiment of the present application. It should be understood that the previous or following operations are not necessarily performed precisely in order. On the contrary, various steps can be processed in reverse order or simultaneously as needed. At the same time, other operations can also be added to these processes, or one or more operations can be removed from these processes.

[0015] Figure 1 A schematic diagram of a process for optimizing the self-cleaning performance of an aviation drag reduction film provided in an embodiment of the present application; Figure 2 A schematic diagram of the structure of a self-cleaning performance optimization device for an aviation drag reduction film provided in an embodiment of the present application.

[0016] Explanation of the 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 DESCRIPTION

[0017] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0018] 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 in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0019] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict, and the terms "first\second" involved are merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, device, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or modules that are 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 those generally understood by technicians in the technical field of this application. The terms used herein are for the purpose of describing the embodiments of the present application only.

[0020] The present application embodiment provides a method for optimizing the self-cleaning performance of an aviation drag reduction film, such as Figure 1 As shown, the method includes: 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.

[0021] 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 refers to analyzing and defining the key parameters of the self-cleaning coating and the drag reduction functional layer respectively in the process of optimizing the self-cleaning performance and drag reduction performance of the aviation drag reduction film. Specifically, the self-cleaning coating optimization space defines the parameter range that can be explored in the performance optimization process of the self-cleaning coating, with the aim of finding the optimal parameter configuration that can improve the self-cleaning performance, which may include multiple coating parameters, such as coating material, coating thickness, surface structure and roughness, etc. Materials with different characteristics, such as hydrophobic coatings, super hydrophobic 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 membrane. Different thicknesses can It can affect the adhesion and removal 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. 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 impact 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 properties, airflow adaptability, etc. The purpose of obtaining the self-cleaning coating optimization space and drag reduction layer characteristic information is 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.

[0022] In a possible implementation, 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.

[0023] Preferably, the coating thickness adjustment space of the self-cleaning coating in the aviation drag reduction film is obtained. 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; and a coating that is too thick may affect the removal efficiency of pollutants and increase material cost and weight. As the optimization space of the self-cleaning coating, for example, the coating thickness is between 1 micron and 10 microns, as the thickness adjustment space, by adjusting different thicknesses and evaluating the performance within this space, the optimal coating thickness is 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 air resistance reduction effect, 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.

[0024] Step S200, 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.

[0025] Preferably, within the self-cleaning coating optimization space, a set of initial self-cleaning coating parameters, i.e., first self-cleaning coating parameters, are randomly generated. These parameters define the basic properties of the coating, such as coating thickness, coating material, and coating surface roughness. After the first self-cleaning coating parameters are generated, their self-cleaning performance is evaluated. Specifically, in a preset simulation environment, specific pollutants (such as dust, oil, water droplets, etc.) are introduced, and the cleaning ability of the coating is tested to simulate the actual environment encountered by the aircraft under different flight conditions, such as humidity, dust, or oil. 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 is used as a self-cleaning performance parameter, reflecting the self-cleaning performance of the coating in 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, certain 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, the surface microstructure of the drag reduction layer may be changed. For example, the drag reduction layer usually relies on its surface smoothness to reduce air friction, while 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 of the aircraft surface. If the coating is too thick and the surface is rough, the calculated air resistance reduction ratio is reduced by 5% or 10%, etc., in order to quantify the impact of the self-cleaning coating on the drag reduction performance, and the reduction ratio of air resistance is used as the drag reduction performance influencing parameter.

[0026] In a possible implementation, step S200 further includes step S210, randomly generating a first self-cleaning coating parameter in the self-cleaning coating optimization space; step S220, 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 of each sample self-cleaning coating parameter under a preset pollution test environment for the pollution cleaning test, and marking the sample self-cleaning performance parameter set; step S230, using the sample self-cleaning coating parameter set and the sample self-cleaning performance parameter set to train a self-cleaning performance predictor; step S240, using the self-cleaning performance predictor, performing a self-cleaning performance analysis on the first self-cleaning coating parameter to obtain a first self-cleaning performance parameter.

[0027] Preferably, within the optimization space, a set of initial self-cleaning coating parameters, such as thickness, composition ratio of coating materials, surface microstructure, etc., are generated by random selection as the first self-cleaning coating parameters; during the test process, different combinations of self-cleaning coating parameters are applied to sample coatings, and coating and performance tests are performed to generate parameter data related to the coating, such as thickness, material composition, surface roughness, etc. A plurality of parameter data obtained through coating tests of different sample coatings constitute a sample self-cleaning coating parameter set, and a pollution cleaning test is performed on the self-cleaning ability of each sample self-cleaning coating parameter under a preset pollution test environment (simulating pollutants in reality, such as dust, oil, etc.), and a test result (expressed as a cleaning ratio) is obtained, that is, the ratio of pollutants removed by the coating under specific conditions ( For example, 80% of the pollutants are removed out of 100%), after each sample coating is tested for pollution cleaning, a set of self-cleaning performance data sets is obtained, which reflects 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.), and the constructed cleaning performance prediction model is trained by taking the sample self-cleaning coating parameter set as input and the sample self-cleaning performance parameter set as output, and the relationship between the coating parameters and the cleaning performance is learned to obtain a self-cleaning performance predictor, which can predict its self-cleaning performance based on the new coating parameters; finally, the first self-cleaning coating parameter is input into the self-cleaning performance predictor to predict the self-cleaning performance under the parameter combination, that is, to predict the cleaning ratio of the coating as the first self-cleaning performance parameter.

[0028] In a possible implementation, step S200 further includes step S250, collecting a sample self-cleaning coating parameter set based on the production test data of the aviation drag reduction film, and testing respectively to obtain the drag reduction performance change ratio of the aviation drag reduction film under different sample self-cleaning coating parameters, and marking to obtain the sample drag reduction performance influencing parameter set; step S260, using the sample self-cleaning coating parameter set and the sample drag reduction performance influencing parameter set to train a drag reduction performance impact analyzer; step S270, using the drag reduction performance impact analyzer to perform a drag reduction performance impact analysis of the drag reduction layer characteristic information on the first self-cleaning coating parameter to obtain the first drag reduction performance influencing parameter.

[0029] Preferably, a sample self-cleaning coating parameter set is collected from performance evaluation data of production tests of aviation drag reduction films, wherein 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, test data of different self-cleaning coatings are collected, which may include parameters such as coating thickness, surface roughness, and material composition. After testing each sample self-cleaning coating, its influence on the drag reduction effect of the drag reduction film is recorded. The drag reduction performance change ratio 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 improve the drag reduction effect of the drag reduction film by 5%, while another coating may cause the performance to decrease by 2%. Each coating parameter sample corresponds to a drag reduction performance change ratio, which constitutes a sample drag reduction performance influencing parameter set; by taking the sample self-cleaning coating parameter set as the input data set and taking the sample The set of parameters affecting drag reduction performance is used as the output data set. A drag reduction performance impact analyzer is constructed and trained based on the machine learning model to learn the relationship between coating parameters and drag reduction performance, aiming to predict the impact of self-cleaning coating parameters on drag reduction performance. By inputting the self-cleaning coating parameters, the analyzer can predict the impact of the corresponding coating on the performance of the drag reduction film. For example, the model will recognize 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. The first self-cleaning coating parameters are input into the drag reduction performance impact analyzer to predict the impact of the coating combination on the characteristic information of the drag reduction layer, and to evaluate whether the coating will have a positive or negative impact on the drag reduction effect, that is, to calculate the impact on the drag reduction layer under the first self-cleaning coating parameters, and finally obtain the first drag reduction performance impact parameter, which represents the degree of influence of the coating on the drag reduction performance.

[0030] Step S300, obtaining the operating environment parameters of the aviation drag reduction film, and performing pollutant cleaning prediction in combination with the first self-cleaning performance parameters to obtain the first pollutant cleaning scale parameters.

[0031] Preferably, the operating environment parameters of the aviation drag reduction film are obtained, 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 dust, sand, moisture, salt spray, oil and other pollutants), and then combined with the first self-cleaning performance parameters obtained, the pollutant cleaning is predicted. Specifically, by analyzing the pollutant cleaning effect of the drag reduction film in the operating environment, the performance of the self-cleaning coating in the real environment is more accurately predicted, 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 oil composition, the water vapor content under humidity, etc. Based on the pollutant content in the operating environment, combined with the self-cleaning performance parameters of the coating, the proportion of pollutants that the coating can remove is predicted. 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%. This cleaning ratio is used as the pollutant cleaning scale parameter, that is, the first pollutant cleaning scale parameter is obtained, which is a quantified result of the pollutant cleaning ability, indicating the overall cleaning effect of the self-cleaning coating in the operating environment.

[0032] In a possible implementation, step S300 further includes step S310, collecting pollutant content parameters in the operating environment of the aviation drag reduction film as operating environment parameters; step S320, collecting a sample self-cleaning performance parameter set and a sample operating environment parameter set based on the cleaning test data of the aviation drag reduction film, and annotating a sample pollutant cleaning scale parameter set according to the cleaning ratios of pollutants under different sample self-cleaning performance parameters and different sample operating environment parameters; step S330, 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; step S340, 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.

[0033] Preferably, the environmental conditions of the aviation drag reduction film in actual application are collected, especially the parameters of pollutant content in the air, that is, the types of pollutants and their concentrations, which may include dust, sand, oil, moisture, etc., and these pollutant content parameters are used as operating environment parameters, specifically referring to the concentration of pollutants, particle size, humidity, grease content, etc., which reflect the pollutant characteristics of the environment in which the drag reduction film is located; based on the cleaning test data of the aviation drag reduction film, a set of sample self-cleaning performance parameters (the self-cleaning ability of different coatings under different operating conditions) and a set of sample operating environment parameters (a set of pollutant parameters in different test environments) are collected, and tests are carried out under different sample operating environments and self-cleaning coating parameters to observe the effect of the coating in removing pollutants within a certain period of time or under certain conditions, that is, the pollutant cleaning ratio (the percentage of removed pollutants in the total amount of pollutants). By comparing the self-cleaning performance parameters, operating environment parameters and corresponding pollutant cleaning parameters, the self-cleaning performance parameters and operating environment parameters are compared with each other. The pollutant cleaning ratio is marked and a pollutant cleaning scale parameter set is formed; a pollutant cleaning predictor is trained by a machine learning algorithm (such as a regression model, a neural network, etc.) using a sample self-cleaning performance parameter set (self-cleaning performance data of the coating), a sample operating environment parameter set (pollutant data under different environments) and a sample pollutant cleaning scale parameter set (pollutant cleaning ratio) to learn the relationship between coating parameters, environmental conditions and pollutant cleaning ability; then the pollutant content parameter and the first self-cleaning performance parameter are input into the pollutant cleaning predictor to perform pollutant cleaning prediction. The pollutant cleaning predictor predicts the pollutant ratio or cleaning scale that the self-cleaning coating can remove in this environment based on the input data, and obtains a 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, it is predicted that the coating can remove 80% of pollutants in the current environment.

[0034] Step S400, combining the first pollutant cleaning scale parameter and the drag reduction layer characteristic information, performing drag reduction performance improvement analysis of the self-cleaning coating cleaning pollution, and obtaining a first drag reduction performance improvement parameter.

[0035] Preferably, the drag reduction performance improvement analysis of the self-cleaning coating cleaning pollution is carried out in combination with the first pollutant cleaning scale parameter and the drag reduction layer characteristic information, and the improvement effect of the self-cleaning coating on the drag reduction performance after removing the 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 drag reduction performance is quantified. Pollutants attached to 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 is also improved. The drag reduction layer characteristic information determines how the drag reduction layer reduces air resistance under ideal conditions (i.e., in the absence of pollutants). After cleaning the pollutants, the self-cleaning coating can restore the smooth surface of the drag reduction layer, thereby reducing air resistance. Analysis of drag reduction performance improvement includes analyzing the impact of pollutant cleaning on drag reduction performance, i.e., when pollutants are After cleaning, the drag reduction layer restores its original drag reduction function. By combining the first pollutant cleaning scale parameter, the effect of the self-cleaning coating on restoring surface smoothness when removing different amounts of pollutants is analyzed to quantify the improvement in drag reduction performance. If the drag reduction layer causes a 10% increase in air resistance in the presence of pollutants, then the drag reduction ratio after cleaning may be restored to a level close to the original level (such as a 10% reduction in drag). The improvement in drag reduction performance is calculated based on the surface condition after cleaning the pollutants. 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 cleaning with the self-cleaning coating. For example, if pollutants cause a 5% decrease in drag reduction performance, and the coating improves by 4% after cleaning, 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, which represents the effect of the self-cleaning coating on improving drag reduction performance after cleaning the pollutants.

[0036] In a possible implementation, step S400 further includes step S410, collecting a sample pollution scale information set based on the test data of the aviation drag reduction film, and marking 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, using the first pollutant cleaning scale parameter as the pollution scale information to perform pollution drag reduction performance impact analysis, and obtaining a first pollution drag reduction performance change parameter as a first drag reduction performance improvement parameter.

[0037] 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, particle size, pollution area, type of pollutants (such as dust, oil, etc.), etc., reflecting the degree and type of pollution of the aviation drag reduction film in different environments. The drag reduction performance of the aviation drag reduction film will be affected under different pollution scales. The drag reduction performance change ratio refers to the difference in drag reduction effect before and after the attachment of pollutants. For example, pollution causes the air resistance of the drag reduction film to increase, and the drag reduction performance decreases by 5%. According to the drag reduction performance change ratio of the aviation drag reduction film under different sample pollution scale information, the sample pollution scale information set is annotated to obtain the sample pollution drag reduction performance change parameter set, that is, the drag reduction performance change ratio corresponding to each pollution scale, and the degree of influence of pollution on the performance of the drag reduction film is recorded; the sample pollution The pollution scale information set is used as input data, and the sample pollution drag reduction performance change parameter set is used as output data. The pollution drag reduction performance impact analyzer is trained based on machine learning models (such as decision trees, neural networks, etc.) to learn how the pollution scale affects the performance of the drag reduction membrane. The pollution drag reduction performance impact analyzer can identify the extent of the decline in drag reduction performance under specific particle concentrations and pollutant areas. The first pollutant cleaning scale parameter is used as the pollution scale information and input into the pollution drag reduction performance impact analyzer to perform pollution drag reduction performance impact analysis, that is, to analyze the impact of the current pollution scale on the performance of the drag reduction membrane, and to obtain the drag reduction performance change of the drag reduction membrane under the current pollution scale, which indicates the degree of improvement or decrease in the drag reduction effect after the pollution is removed. It is used as the final first drag reduction performance improvement parameter, which indicates the extent to which the drag reduction effect of the aviation drag reduction membrane is improved after the self-cleaning coating removes some pollutants.

[0038] Step S500, according to the first self-cleaning performance parameter, the first drag reduction performance influencing parameter and the first drag reduction performance improving parameter, calculate the self-cleaning fitness of the first self-cleaning coating parameter, perform iterative optimization of the self-cleaning coating parameter according to the first self-cleaning fitness, and 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.

[0039] Preferably, the self-cleaning fitness is calculated, and the coating parameters are iteratively optimized according to the fitness and related performance parameters to achieve the best self-cleaning performance and drag reduction performance. Specifically, the self-cleaning fitness of the first self-cleaning coating parameter is calculated according to the first self-cleaning performance parameter, the first drag reduction performance influencing parameter and the first drag reduction performance improving parameter, which reflects the comprehensive performance of the coating between the self-cleaning performance and the drag reduction performance, that is, the performance of a certain group of coating parameters in terms of self-cleaning ability and drag reduction performance is measured. After calculating the self-cleaning fitness of each group of coating parameters, the parameter combination of the coating is gradually adjusted to improve the overall performance. Specifically, a group of self-cleaning coating parameters is randomly selected in the optimization space, and the corresponding self-cleaning fitness is calculated. According to the calculation results of the fitness, the performance of the current parameters is judged, and the parameters that need to be adjusted are selected. For example, if the coating of a certain group of parameters is too thick, resulting in a large impact on the drag reduction performance, then the self-cleaning fitness is adjusted in the next round of optimization. The coating thickness is reduced in the optimization process, and the parameter combination of the self-cleaning coating (such as material, thickness, surface roughness, etc.) is adjusted according to the optimization results. The amplitude of the adjustment of the self-cleaning coating parameters in each iteration, that is, the optimization step size is dynamically set and adjusted according to the self-cleaning performance parameters and the drag reduction performance improvement parameters. For example, in the initial stage, the step size can be slightly larger to quickly explore a wide range of parameter spaces. 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. It not only performs well in self-cleaning ability, but also can maximize the drag reduction performance and minimize the adverse effects 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.

[0040] In a possible implementation, step S500 further includes step S510, calculating the self-cleaning adaptability 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, as shown in the following formula: ; 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.

[0041] The method also includes step S520, calculating and generating step adjustment parameters according to the self-cleaning performance parameters and the drag reduction performance improvement parameters, adjusting and calculating the preset step length to obtain the optimized step length; step S530, using the optimized step length to adjust the first self-cleaning coating parameters to obtain the second self-cleaning coating parameters; step S540, continuing to iteratively optimize the self-cleaning coating parameters in the self-cleaning coating optimization space until convergence, outputting the self-cleaning coating parameters with the maximum self-cleaning fitness, and obtaining the optimal self-cleaning coating parameters, wherein the preset step length adjustment calculation is set to adjust and optimize the optimization step length in each round of optimization.

[0042] Preferably, a step adjustment parameter is calculated based on the self-cleaning performance parameter and the drag reduction performance improvement parameter to determine how to adjust the step size, so as to ensure that the parameter adjustment range at different stages during the optimization process is appropriate, so as to quickly find a better solution and ensure the accuracy of the optimization. For example, the mean of the self-cleaning performance parameter and the drag reduction performance improvement parameter is calculated as the step adjustment parameter, and then the product of the mean and the preset step size is used as the optimization step size, wherein the preset step size is the initial step size set during the optimization process; according to the size of the optimization step size, various dimensions of the first self-cleaning coating parameter are adjusted, such as adjusting the coating thickness and changing the surface roughness to improve the drag reduction performance and the self-cleaning effect, and the adjusted parameter combination is called the second self-cleaning coating parameter; then, the self-cleaning coating parameter is iteratively optimized within the self-cleaning coating optimization space, i.e., multiple times. Adjust the self-cleaning coating parameters, gradually improve the self-cleaning performance and drag reduction performance, and continuously search for better parameter combinations. Specifically, in each iteration, calculate the self-cleaning fitness according to the current self-cleaning performance and drag reduction performance improvement. According to the fitness results, decide whether to continue adjusting the parameters. If so, further adjust the step size and enter the next round of optimization until convergence. Output the corresponding self-cleaning coating parameters with the largest self-cleaning fitness, that is, the coating design with the best self-cleaning performance and drag reduction performance. In each round of optimization, the step size will be dynamically adjusted according to the progress of the optimization. For example, if the current coating parameters are still far from the optimal solution, use a larger step size to quickly adjust the parameters. When approaching the optimal solution, the step size will gradually decrease to allow for refined adjustments to prevent missing the optimal parameters, thereby ensuring the efficiency and accuracy of the optimization process.

[0043] In the above, refer 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. Figure 2 A device for optimizing the self-cleaning performance of an aviation drag reduction film according to an embodiment of the present invention is described.

[0044] According to an embodiment of the present invention, an aviation drag reduction film self-cleaning performance optimization device is used to solve the technical problem that the existing aviation drag reduction film self-cleaning overemphasizes the cleaning ability and ignores its potential impact on the drag reduction performance, which leads to the inability of the self-cleaning coating to play the best cleaning role in complex environments, and achieves a balance between self-cleaning performance and drag reduction performance, and achieves the technical effect of improving optimization efficiency and accuracy and improving the stability and reliability of the aviation drag reduction film. An aviation drag reduction film self-cleaning performance optimization device 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.

[0045] The self-cleaning coating optimization space acquisition module 10 is used 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; the self-cleaning performance analysis module 20 is 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 to obtain a first drag reduction performance influence parameter; the pollutant cleaning prediction module 30 is used to obtain the operating environment parameters of the aviation drag reduction film, perform a pollutant cleaning prediction in combination with the first self-cleaning performance parameter, and obtain a first pollutant cleaning scale parameter; the drag reduction performance is improved The analysis module 40 is used to combine the first pollutant cleaning scale parameter and the drag reduction layer characteristic information to perform drag reduction performance improvement analysis of the self-cleaning coating cleaning pollution and obtain the first drag reduction performance improvement parameter; the optimal self-cleaning coating parameter acquisition module 50 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 improvement parameter, and iteratively optimize the self-cleaning coating parameters according to the first self-cleaning fitness to obtain the optimal self-cleaning coating parameters 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 parameters and the drag reduction performance improvement parameters.

[0046] The specific configuration of the self-cleaning coating optimization space acquisition module 10 will be described in detail below. 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.

[0047] The specific configuration of the self-cleaning performance analysis module 20 will be described in detail below. The self-cleaning performance analysis module 20 may further include: randomly generating a first self-cleaning coating parameter in 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 of each sample self-cleaning coating parameter under a preset pollution test environment for the pollution cleaning test, and marking the obtained sample self-cleaning performance parameter set; using the sample self-cleaning coating parameter set and the sample self-cleaning performance parameter set, training a self-cleaning performance predictor; using the self-cleaning performance predictor, performing a self-cleaning performance analysis on the first self-cleaning coating parameter to obtain a first self-cleaning performance parameter.

[0048] The specific configuration of the self-cleaning performance analysis module 20 will be described in detail below. The self-cleaning performance analysis module 20 may further include: collecting a sample self-cleaning coating parameter set according to the production test data of the aviation drag reduction film, and testing and obtaining the drag reduction performance change ratio of the aviation drag reduction film under different sample self-cleaning coating parameters, and marking and obtaining a sample drag reduction performance influencing parameter set; using the sample self-cleaning coating parameter set and the sample drag reduction performance influencing parameter set to train a drag reduction performance influencing analyzer; using the drag reduction performance influencing analyzer, performing a drag reduction performance influencing analysis of the drag reduction layer characteristic information on the first self-cleaning coating parameter, and obtaining a first drag reduction performance influencing parameter.

[0049] The specific configuration of the pollutant cleaning prediction module 30 will be described in detail below. The pollutant cleaning prediction module 30 may further include: collecting pollutant content parameters in the working environment of the aviation drag reduction film as working environment parameters; collecting sample self-cleaning performance parameter sets and sample working environment parameter sets according to the cleaning test data of the aviation drag reduction film, and annotating and obtaining sample pollutant cleaning scale parameter sets according to the cleaning ratios of pollutants under different sample self-cleaning performance parameters and different sample working environment parameters; using the sample self-cleaning performance parameter set, the sample working environment parameter set and the sample pollutant cleaning scale parameter set to train a pollutant cleaning predictor; 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.

[0050] The specific configuration of the drag reduction performance improvement analysis module 40 will be described in detail below. The drag reduction performance improvement analysis module 40 further includes: collecting a sample pollution scale information set according to the test data of the aviation drag reduction film, and annotating and obtaining a sample pollution drag reduction performance change parameter set according to the drag reduction performance change ratio of the aviation drag reduction film under different sample pollution scale information; 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; using the pollution drag reduction performance impact analyzer, using the first pollutant cleaning scale parameter as the pollution scale information to perform pollution drag reduction performance impact analysis, and obtaining a first pollution drag reduction performance change parameter as a first drag reduction performance improvement parameter.

[0051] The specific configuration of the optimal self-cleaning coating parameter acquisition module 50 will be described in detail below. The optimal self-cleaning coating parameter acquisition module 50 further includes: according to the first self-cleaning performance parameter, the first drag reduction performance influencing parameter and the first drag reduction performance improving parameter, calculating the self-cleaning adaptability of the first self-cleaning coating parameter, as shown in the following formula: ; 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: is a drag reduction performance improvement parameter; according to the self-cleaning performance parameter and the drag reduction performance improvement parameter, a step adjustment parameter is calculated and generated, and the preset step size is adjusted and calculated to obtain an optimized step size; the first self-cleaning coating parameter is adjusted using the optimized step size to obtain a second self-cleaning coating parameter; the self-cleaning coating parameters are iteratively optimized in the self-cleaning coating optimization space until convergence, and the self-cleaning coating parameters with the largest self-cleaning fitness are output to obtain the optimal self-cleaning coating parameters, wherein the preset step size adjustment calculation is set to adjust and optimize the optimization step size in each round of optimization.

[0052] An aviation drag reduction film self-cleaning performance optimization device provided in an embodiment of the present invention can execute an aviation drag reduction film self-cleaning performance optimization method provided in any embodiment of the present invention, and has functional modules and beneficial effects corresponding to the execution method.

[0053] Although the present application makes various references to certain modules in the device according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, and 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 achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0054] The above specific implementations do not constitute a limitation on the protection scope of this application. It should be understood by those skilled in the art 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 principles of this application should be included in the protection scope of this 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; 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 parameters are iteratively optimized according to the first self-cleaning fitness to obtain the optimal self-cleaning coating parameters 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 parameters and the drag reduction performance improving parameters.

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: In combination 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 the 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. The method for optimizing the self-cleaning performance of an aviation drag reduction film according to claim 1, characterized in that: The method further comprises: 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 influencing parameter and the first drag reduction performance improving parameter, and iteratively optimizing the self-cleaning coating parameter 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.

8. 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 7, 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; The 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.

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