A method, system, device and medium for optimizing extraction of structural parameters of an anti-pollution flashover coating super-hydrophobic texture

Abstract

The application discloses a kind of super-hydrophobic textured structure parameter optimization extraction method, system, equipment and medium of anti-pollution flash coating, method includes constructing texture model, and initializes a set of key geometric parameters;Solid-liquid contact area fraction is calculated;The intrinsic contact angle of droplet is preset, and apparent contact angle is calculated in combination with the solid-liquid contact area fraction;Adhesion work and spreading coefficient are calculated according to solid-liquid contact area fraction and apparent contact angle;Evaluation function is constructed;With the evaluation function as target, iterative optimization is carried out, and the key geometric parameters after optimization are output as final structure parameters.The application realizes the collaborative optimization of multiple structure parameters and the comprehensive trade-off of multiple performance indicators by establishing texture model and quantitative performance analysis, can extract the microstructure parameter combination that makes the hydrophobicity, droplet detachment and anti-spreading of anti-pollution flash coating reach the best balance, improve the design scientificity, optimization efficiency and engineering reliability of anti-pollution flash coating.

Description

Technical Field

[0001] This invention relates to the field of anti-pollution flashover technology for transmission line insulators, and in particular to a method, system, equipment, and medium for optimizing and extracting superhydrophobic texture structure parameters of an anti-pollution flashover coating. Background Technology

[0002] During the operation of transmission lines, dirt easily accumulates on the surface of insulators, which may cause flashover accidents in humid environments, threatening the safety of the power grid. To improve the flashover protection capability of insulators, a coating with superhydrophobic properties is often applied to the surface of the skirts. The surface of the coating is usually designed with micron or nano-scale micro-uneven structures, called texture, which can effectively reduce the contact between water droplets and the surface, thereby inhibiting flashover. The flashover protection performance of the coating directly depends on the geometric characteristics such as the shape, size, spacing and distribution of these microstructures.

[0003] Existing methods for designing and optimizing superhydrophobic textures mostly focus on independently adjusting and optimizing individual geometric parameters, such as width, spacing, or height, without considering the synergistic effect between multiple parameters. This makes it difficult to obtain the overall optimal microstructure combination. Furthermore, when evaluating texture performance, existing methods typically only focus on single indicators such as contact angle, neglecting the balance of multiple key performance factors such as droplet adhesion and spreading tendency on the surface. This results in poor overall hydrophobic performance and durability of the designed textures in real-world environments. Therefore, there is an urgent need for a design method that can coordinate multiple geometric parameters and integrate multiple performance indicators for synergistic optimization, enabling efficient and reliable design of superhydrophobic textures for anti-pollution flashover coatings on insulators. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a method, system, equipment, and medium for optimizing and extracting superhydrophobic texture structure parameters of anti-flashover coatings. This addresses the problem that existing superhydrophobic texture optimization methods often adjust single geometric parameters in isolation, neglecting the synergy between parameters, and that performance evaluation often focuses only on the contact angle, ignoring the balance of key indicators such as adhesion and spreading trend, resulting in poor actual comprehensive performance and durability.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In a first aspect, the present invention provides a method for optimizing and extracting the superhydrophobic texture structure parameters of an anti-flashover coating, comprising: A texture model of the microstructure array on the surface of the anti-flashover coating is constructed, and a set of key geometric parameters are initialized for the texture model; The key geometric parameters are input into the texture model to calculate the solid-liquid contact area ratio. The intrinsic contact angle of the pre-placed droplet is used to calculate the apparent contact angle of the anti-flashover coating surface, which is obtained by combining the solid-liquid contact area ratio. The adhesion work and spreading coefficient are calculated based on the solid-liquid contact area ratio and the apparent contact angle. An evaluation function is constructed based on the apparent contact angle, the adhesion work, and the spreading coefficient. Using the evaluation function as the objective, an optimization algorithm is used to iteratively optimize the key geometric parameters, and the optimized key geometric parameters are output as the final structural parameters.

[0007] As a preferred embodiment of the method for optimizing and extracting the superhydrophobic texture structure parameters of the anti-flashover coating described in this invention, the steps of constructing a texture model of the microstructure array on the surface of the anti-flashover coating and initializing a set of key geometric parameters for the texture model include: A geometric model of a microstructure array with periodic distribution characteristics on the surface of the anti-flashover coating is constructed as a texture model; A set of key geometric parameters is initialized for the texture model, including the feature size of the microstructure unit, the center-to-center distance between adjacent microstructure units, and the protrusion height of the microstructure unit.

[0008] The beneficial effects of this preferred technical solution are as follows: by establishing a geometric model with periodic distribution characteristics and clearly defining a set of key geometric parameters such as feature size, center spacing and protrusion height, an accurate and complete mathematical model foundation is established for subsequent quantitative analysis and collaborative optimization, ensuring that the optimization process can comprehensively consider all key structural dimensions, which is a prerequisite for achieving multi-parameter collaborative optimization and multi-performance index balance.

[0009] As a preferred embodiment of the method for optimizing and extracting the superhydrophobic texture structure parameters of the anti-flashover coating described in this invention, the step of calculating the solid-liquid contact area fraction includes: Input the feature dimensions and the center spacing into the texture model; Based on the feature size and the center distance, the solid-liquid contact area ratio of the surface of the microstructure array is calculated and used as the solid-liquid contact area fraction.

[0010] The beneficial effects of this preferred technical solution are as follows: the solid-liquid contact area fraction is calculated by using the feature size and center-to-center distance. The solid-liquid contact area fraction is a key physical quantity that connects the microscopic geometry and the macroscopic hydrophobic properties, providing a unified quantitative benchmark for subsequent evaluation of the wetting characteristics of the anti-flashover coating.

[0011] As a preferred embodiment of the method for optimizing and extracting the superhydrophobic texture structure parameters of the anti-flashover coating described in this invention, the step of calculating the apparent contact angle of the anti-flashover coating surface in conjunction with the solid-liquid contact area fraction includes: Intrinsic contact angle of pre-placed droplets on a smooth anti-flashover coating surface; Based on the intrinsic contact angle and the solid-liquid contact area ratio, a calculation relationship for the apparent contact angle is established. Based on the aforementioned calculation relationship, the apparent contact angle of the anti-flashover coating surface is calculated.

[0012] The beneficial effects of this preferred technical solution are as follows: by establishing a quantitative calculation relationship between the intrinsic contact angle and the solid-liquid contact area ratio, the quantitative conversion from microscopic geometry to macroscopic wetting performance, i.e., apparent contact angle, is realized, providing key performance indicators for subsequent comprehensive evaluation of superhydrophobic performance.

[0013] As a preferred embodiment of the method for optimizing and extracting the superhydrophobic texture structure parameters of the anti-flashover coating described in this invention, the step of calculating the adhesion work and spreading coefficient includes: Input the solid-liquid contact area fractions and the apparent contact angle into the texture model; Using the texture model, the adhesion work of the anti-flashover coating to droplets and the spreading coefficient of the anti-flashover coating surface are calculated.

[0014] The beneficial effects of this preferred technical solution are as follows: based on the solid-liquid contact area ratio and apparent contact angle, the adhesion work and spreading coefficient are further calculated through the texture model, which quantifies the adhesion and spreading behavior of droplets on the surface from the perspective of energy and dynamics, providing key data support for a comprehensive and multi-dimensional evaluation of superhydrophobic performance, and serving as an important basis for achieving multi-objective comprehensive optimization.

[0015] As a preferred embodiment of the method for optimizing and extracting the superhydrophobic texture structure parameters of the anti-flashover coating described in this invention, the step of constructing the evaluation function includes: The apparent contact angle, the adhesion work, and the spreading coefficient are combined to obtain a comprehensive result. By combining the protrusion height in the key geometric parameters, an evaluation function is constructed based on the comprehensive results to quantitatively evaluate the superhydrophobic performance of the anti-flashover coating.

[0016] The beneficial effects of this preferred technical solution are as follows: based on the comprehensive apparent contact angle, adhesion work and spreading coefficient, the protrusion height in the key geometric parameters is further combined, so that the evaluation function can more comprehensively characterize the micro-dynamic characteristics and comprehensive hydrophobic performance of the coating surface. This provides a unified and quantitative target that can simultaneously weigh multiple key performance indicators for subsequent iterative optimization, which is the core step to achieve multi-parameter collaborative optimization.

[0017] As a preferred embodiment of the method for optimizing and extracting the superhydrophobic texture structure parameters of the anti-flashover coating described in this invention, the step of outputting the optimized key geometric parameters as the final structural parameters includes: Using the evaluation function as the optimization objective, the optimization algorithm is used to iteratively optimize the key geometric parameters, and the optimization process state represented by the iterative optimization calculation is obtained. A preset optimization termination criterion is set. If the optimization process state does not meet the optimization termination criterion, iterative optimization calculation continues. When the optimization termination criterion is met, the key geometric parameters corresponding to the current iteration are output as the final structural parameters.

[0018] The beneficial effects of this preferred technical solution are as follows: by iteratively optimizing with the evaluation function as the target and judging whether the optimization termination criterion is met based on the state of the optimization process, intelligent optimization of key geometric parameters can be achieved. This can efficiently search for the key geometric parameter set that makes the overall superhydrophobic performance of the anti-flashover coating optimal, thereby improving design efficiency and reliability.

[0019] Secondly, the present invention provides a superhydrophobic texture structure parameter optimization extraction system for anti-flashover coatings, comprising: The model building module is used to construct a texture model of the microstructure array on the surface of the anti-flashover coating, and initialize a set of key geometric parameters for the texture model; The first calculation module is used to input the key geometric parameters into the texture model and calculate the solid-liquid contact area ratio. The second calculation module is used to calculate the apparent contact angle of the anti-flashover coating surface by combining the intrinsic contact angle of the pre-placed droplet with the solid-liquid contact area ratio. The third calculation module is used to calculate the adhesion work and spreading coefficient based on the solid-liquid contact area fraction and the apparent contact angle. The function construction module is used to construct an evaluation function based on the apparent contact angle, the adhesion work, and the spreading coefficient. The optimization output module is used to iteratively optimize the key geometric parameters using an optimization algorithm with the evaluation function as the objective, and output the optimized key geometric parameters as the final structural parameters.

[0020] Thirdly, the present invention provides an electronic device, comprising: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the method for optimizing and extracting the superhydrophobic texture structure parameters of the anti-flashover coating are implemented.

[0021] Fourthly, the present invention provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the method for optimizing and extracting the superhydrophobic texture structure parameters of the anti-flashover coating.

[0022] Compared with the prior art, the beneficial effects of the present invention are as follows: by establishing a texture model and quantitative performance analysis, the synergistic optimization of multiple structural parameters and the comprehensive balance of multiple performance indicators are achieved, avoiding the limitations of isolated optimization of a single geometric parameter or indicator in the prior art. It can efficiently extract the combination of microstructure parameters that achieves the best balance between the hydrophobicity, droplet detachment and anti-spreading properties of the anti-flashover coating, thereby improving the design scientificity, optimization efficiency and engineering reliability of the anti-flashover coating. Attached Figure Description

[0023] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a schematic diagram of the overall process for optimizing and extracting the superhydrophobic texture structure parameters of the anti-flashover coating according to an embodiment of the present invention.

[0025] Figure 2 Schematic diagram of a cylindrical textured microstructure array established for the superhydrophobic surface of the anti-flashover coating. Detailed Implementation

[0026] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0027] Example 1, referring to Figures 1-2 As an embodiment of the present invention, a method for optimizing and extracting superhydrophobic texture structure parameters of an anti-flashover coating is provided, comprising: S100. Construct a texture model of the microstructure array on the surface of the anti-flashover coating, and initialize a set of key geometric parameters for the texture model.

[0028] S200. Input the key geometric parameters into the texture model to calculate the solid-liquid contact area ratio.

[0029] S300, the intrinsic contact angle of the pre-placed droplet, combined with the solid-liquid contact area ratio, is used to calculate the apparent contact angle of the anti-flashover coating surface.

[0030] S400. Based on the solid-liquid contact area ratio and the apparent contact angle, the adhesion work and spreading coefficient are calculated.

[0031] S500. Construct an evaluation function based on the apparent contact angle, the adhesion work, and the spreading coefficient.

[0032] S600. Using the evaluation function as the objective, an optimization algorithm is used to iteratively optimize the key geometric parameters, and the optimized key geometric parameters are output as the final structural parameters.

[0033] It should be noted that the design of existing superhydrophobic textures for anti-flashover coatings on insulators often involves adjusting single geometric parameters such as width, spacing, or height in isolation, without considering the synergistic effect between parameters, making it difficult to obtain the overall optimal microstructure combination. Furthermore, performance evaluation usually only focuses on the contact angle, ignoring the balance of multiple key indicators such as droplet adhesion and spreading tendency, resulting in poor overall hydrophobic performance and durability in real-world environments.

[0034] Therefore, to address the aforementioned challenges of multi-parameter coordination and poor balance among multiple performance indicators, the following steps (S100-S600) are employed: first, a texture model of a periodic microstructure array is constructed and key geometric parameter sets are initialized; then, based on the texture model, the solid-liquid contact area fraction, apparent contact angle, adhesion work, and spreading coefficient are calculated sequentially, achieving quantitative and multi-dimensional characterization from microstructure to macroscopic performance; finally, an evaluation function is constructed by integrating the aforementioned multiple performance indicators, and the key geometric parameter sets are synergistically optimized and extracted through iterative optimization to obtain the texture structure parameters with optimal overall performance, providing technical support for the efficient and reliable design of anti-pollution flashover coatings for insulators.

[0035] Example 2, refer to Figures 1-2 As an embodiment of the present invention, based on the above embodiment, a method for optimizing and extracting superhydrophobic texture structure parameters of anti-flashover coating is provided.

[0036] In this embodiment, step S100 involves constructing a texture model of the microstructure array on the surface of the anti-flashover coating and initializing a set of key geometric parameters for the texture model. Taking the design of a silicone rubber anti-flashover coating surface as an application scenario, steps A1 to A2 in S100 are specifically implemented as follows: A1. Construct a geometric model of a microstructure array with periodic distribution characteristics on the surface of the anti-flashover coating, as a texture model.

[0037] Specifically, such as Figure 2As shown, this embodiment uses the most common cylindrical protrusion array for illustration. In the texture model, the cylindrical protrusion array is defined to have a square lattice arrangement pattern. Each square lattice unit contains an identical cylindrical microstructure. Based on the characteristics of the cylindrical microstructure, a periodically arranged geometric model is established as the texture model.

[0038] A2. Initialize a set of key geometric parameters for the texture model, including the feature size of the microstructure unit, the center-to-center distance between adjacent microstructure units, and the protrusion height of the microstructure unit.

[0039] Specifically, a set of initial key geometric parameters are assigned to the texture model, and the base radius of the cylindrical microstructure is set. As a characteristic dimension, the distance between the centers of adjacent cylinders The height of the cylinder as the center-to-center distance As the height of the protrusion, the key geometric parameter set is initialized. .

[0040] In an optional implementation, step S100 may further construct a prism-shaped protrusion array as a texture model. The steps are as follows: establish a geometric model in which prisms with square or regular hexagonal cross sections are arranged in a periodic array on the surface of the silicone rubber anti-flashover coating. The corresponding feature dimensions are the side length of the prism cross section or the radius of the circumscribed circle.

[0041] In another optional implementation, step S100 can also construct a frustum-shaped protrusion array as a texture model. The steps are as follows: establish a geometric model in which a frustum with a lower base radius than the lower base radius is arranged in a periodic array on the coating surface. The corresponding key geometric parameters need to include the upper base radius or cone angle of the frustum.

[0042] In this embodiment of the application, S200 involves inputting the key geometric parameters into the texture model to calculate the solid-liquid contact area fraction. Step S200 includes B1~B2: B1. Input the feature dimensions and the center distance into the texture model.

[0043] Specifically, the key geometric parameter set initialized from S100 In the process, feature dimensions and center spacing are extracted and input into the texture model.

[0044] B2. Calculate the solid-liquid contact area ratio of the surface of the microstructure array based on the feature size and the center distance, and use it as the solid-liquid contact area ratio.

[0045] Specifically, based on the geometric relationship of the cylindrical protrusion array, the proportion of the solid surface area (i.e., the area of ​​the top surface of the cylinder in actual contact with the droplet) within each periodic unit is calculated in the texture model of the periodic arrangement, relative to the apparent contact area of ​​that periodic unit. The calculation formula is as follows: ; Will and Substituting, we get: ; in, This refers to the solid-liquid contact area ratio, or the number of parts of the solid-liquid contact area. .

[0046] In an optional implementation, step S200 may also use an equivalent diameter instead of the cylindrical flat surface radius for calculation. The steps are as follows: for non-cylindrical protrusions such as prisms, the cross-sectional area is equivalent to a circle of equal area, and the equivalent radius is calculated. ,in Given the cross-sectional area, calculate the solid-liquid contact area fraction using the same formula. Replace with .

[0047] In another optional implementation, step S200 can also be used to calculate the microstructure array that is not square. The steps are as follows: if the microstructure array is arranged in a hexagonal close-packed manner, then the periodic unit is hexagonal. The calculation formula for the solid-liquid contact area ratio needs to be re-derived based on the geometric relationship. The basic principle is still the ratio of the solid surface to the apparent contact area within the periodic unit.

[0048] In this embodiment of the application, in step S300, the intrinsic contact angle of the pre-placed droplet is used to calculate the apparent contact angle of the anti-flashover coating surface based on the solid-liquid contact area ratio. Step S300 includes C1~C3: C1, the intrinsic contact angle of the pre-placed droplet on the smooth anti-flashover coating surface.

[0049] Specifically, intrinsic contact angle It is the intrinsic contact angle of a droplet when it reaches equilibrium on an absolutely smooth coating material surface. The intrinsic contact angle of a droplet on a smooth silicone rubber anti-flashover coating surface is obtained through experimental measurement. And preset.

[0050] C2. Establish the calculation relationship of the apparent contact angle based on the intrinsic contact angle and the solid-liquid contact area ratio.

[0051] Specifically, based on the solid-liquid-gas composite interface wetting theory, when a droplet is in a composite contact state on a superhydrophobic surface, the apparent contact angle... intrinsic contact angle and solid-liquid contact area ratio There is a functional relationship between them, and the formula for calculating the apparent contact angle is as follows: ; in, The apparent contact angle, This refers to the percentage of solid-liquid contact area. This is the intrinsic contact angle.

[0052] C3. Based on the calculation relationship, the apparent contact angle of the anti-flashover coating surface is calculated.

[0053] Specifically, the solid-liquid contact area fractions calculated by S200 ≈0.19635 and the preset intrinsic contact angle Substitute into the formula to calculate: ; The apparent contact angle was calculated. The contact angle is higher than the intrinsic contact angle, indicating that the apparent hydrophobicity of the surface is improved.

[0054] In an optional implementation, step S300 may also employ a modified model that considers the contact line pinning effect. The steps are as follows: when the contact line at the leading edge of the droplet pins at the edge of the microstructure, a pinning force correction factor related to the shape and distribution of the microstructure is introduced into the functional relationship. At this point, the formula for calculating the apparent contact angle is: .

[0055] In another optional implementation, step S300 can also be calculated for different liquid media. The steps are as follows: if the working medium is not pure water, such as salt spray or dirty solution, the intrinsic contact angle of the working medium on the smooth silicone rubber anti-flashover coating surface needs to be re-determined experimentally. and with Intrinsic contact angle in the alternative formula Perform the calculation.

[0056] In this embodiment of the application, step S400 involves calculating the adhesion work and spreading coefficient based on the solid-liquid contact area fraction and the apparent contact angle. Step S400 includes steps D1 to D2: D1. Input the solid-liquid contact area fraction and the apparent contact angle into the texture model.

[0057] Specifically, the solid-liquid contact area fraction is extracted from the calculation results of S200 and S300. With apparent contact angle The data is then input into the texture model established by S100 as input variables for calculating adhesion work and spreading coefficient.

[0058] D2. Using the texture model, the adhesion work of the anti-flashover coating to droplets and the spreading coefficient of the anti-flashover coating surface are calculated.

[0059] Specifically, in the texture model, based on the principle of microscopic energy balance in surface wetting, the adhesion work... With spreading coefficient It can be calculated using the following formula: ; ; in, For adhesion function, This is the spreading coefficient. This refers to the feature size.

[0060] Will , and Substituting the values ​​into the calculation, we obtain the spreading coefficient. Adhesion work : ; ; Among them, the spreading coefficient S≈-1.995S indicates that the surface has extremely strong anti-spreading ability, while the adhesion work W reflects the energy barrier that the droplet needs to overcome to detach from the surface.

[0061] In an optional implementation, step S400 may further calculate the adhesion work that includes the effect of contact angle hysteresis, by: incorporating a correction factor related to the contact angle hysteresis CAH into the adhesion work formula. The calculation formula becomes ,in, It can be obtained by fitting experimental data, which can more accurately characterize the energy dissipation during the wetting process.

[0062] In another optional implementation, step S400 may further calculate the equivalent adhesion work and spreading coefficient for non-cylindrical protrusions, wherein the steps are as follows: for prism or frustum-shaped protrusions, the solid-liquid contact area fraction is derived based on the geometry. The expression, and determine an equivalent radius. Used for energy calculation, substitute and The calculation is performed using the formula.

[0063] In this embodiment of the application, step S500 involves constructing an evaluation function based on the apparent contact angle, the adhesion work, and the spreading coefficient. Step S500 includes steps E1 to E2: E1. The apparent contact angle, the adhesion work, and the spreading coefficient are combined to obtain a comprehensive result.

[0064] Specifically, the apparent contact angle obtained based on S300 Calculate the contact angle hysteresis CAH and the change in free energy ΔA. The formula for calculating the contact angle hysteresis CAH is: ; Substitute the apparent contact angle The calculation yields: ° The formula for calculating the change in free energy ΔA is: ; in Substituting into the calculation, we get: ; The apparent contact angle θ and the change in free energy ΔA are combined as the primary performance index. The contact angle hysteresis (CAH), adhesion work (W), and spreading coefficient (S) are combined into a second performance index. Using preset weighting factors m and n, in this example m=n=0.5, for η and We perform a weighted aggregation to obtain the overall result.

[0065] E2. Combining the protrusion height in the key geometric parameters, construct an evaluation function based on the comprehensive results to quantitatively evaluate the superhydrophobic performance of the anti-flashover coating.

[0066] Specifically, the evaluation function Defined as: ; in, As the primary performance indicator, As the second performance indicator, in this embodiment, let Both should be as large as possible; If all three are as small as possible, then the evaluation function is: ; Evaluation function: Overall apparent hydrophobicity ( ), droplet detachment capability ( ) and anti-adhesion and anti-spreading capabilities ( And by using weighting factors to balance the importance of each indicator, the emphasis is on... As a key geometric parameter, it affects the change of free energy. With contact angle hysteresis The calculation, such as Changes in this state may lead to a shift in the wetted state.

[0067] In an optional implementation, step S500 may also use a linear weighted sum as the evaluation function, wherein the steps are: normalizing each performance index and then directly performing a linear weighted sum, i.e. ,in The value is the weighting coefficient, and the subscript norm indicates the normalized value.

[0068] In another optional implementation, step S500 can also construct an evaluation function based on a response surface model, the steps of which are: obtaining performance data under different combinations of geometric parameters through experimental design such as orthogonal experiments, and establishing an evaluation function based on key geometric parameter groups using multiple regression or neural networks. The response surface model is used as the input and the output is determined by a comprehensive performance score based on expert experience or principal component analysis. This response surface model is then used as the evaluation function. It is used for direct prediction and optimization.

[0069] In this embodiment, step S600 involves using the evaluation function as the objective and employing an optimization algorithm to iteratively optimize the key geometric parameters, outputting the optimized key geometric parameters as the final structural parameters. Step S600 includes F1~F2: F1. Using the evaluation function as the optimization objective, the optimization algorithm is used to iteratively optimize the key geometric parameters, and the optimization process state represented by the iterative optimization calculation is obtained.

[0070] Specifically, this embodiment uses a greedy algorithm as the optimization algorithm, based on the evaluation function constructed in S500. To optimize the objective, the initial key geometric parameters are... Iterative optimization is performed, and the state of the optimization process is determined by the candidate parameter set in the current iteration. and the corresponding evaluation function value Common characteristics, among which This represents the number of iterations. In each iteration, the optimization algorithm performs a local search around the current optimal solution in the parameter space, attempting to fine-tune the algorithm. One or more key geometric parameters are selected, and the S200 to S500 calculations are re-executed to determine the new evaluation function. Value, selection The direction of the update for the key geometric parameter with the largest value is determined by recording the value of each iteration. and parameter change Obtain the status of the optimization process.

[0071] F2. Preset optimization termination criteria: If the optimization process state does not meet the optimization termination criteria, iterative optimization calculation continues. When the optimization termination criteria are met, the key geometric parameters corresponding to the current iteration are output as the final structural parameters.

[0072] Specifically, the preset optimization termination criterion is: the relative improvement rate of the evaluated function value is less than 0.1% for three consecutive times, i.e. The condition is met three times consecutively; During the iteration process, if the optimization process state, i.e., the relative improvement rate of the calculated evaluation function value, does not meet the optimization termination criterion, the next iteration of optimization calculation continues; if the optimization process state meets the optimization termination criterion, it is determined that the optimization process has converged to a local optimum, and the key geometric parameters corresponding to the current iteration step are... As the final structural parameters output; The optimal parameter solution obtained after optimization using a greedy algorithm is approximately: base radius center spacing Protrusion height The optimal parameter solution is the final structural parameter output extracted through multi-objective collaborative optimization, which optimizes the overall superhydrophobic performance of the anti-flashover coating.

[0073] In an optional implementation, step S600 may further employ a genetic algorithm for iterative optimization, the steps of which are: setting key geometric parameters... Encoded as chromosomes for evaluating functions. As a fitness function, the population is iteratively evolved through operations such as selection, crossover, and mutation. Convergence criteria such as the maximum number of generations or the average fitness change rate of the population are defined as optimization termination criteria, and the parameters of the optimal individual are output.

[0074] In another optional implementation, step S600 can also employ a particle swarm optimization algorithm for iterative optimization, the steps of which are: setting each key geometric parameter group Consider it as the position of a particle in a multidimensional space to evaluate the function. The particle fitness is calculated by updating the particle velocity and position by tracking the individual historical best and the group historical best, and termination criteria such as the maximum number of iterations or the number of steps to stabilize at the global optimal fitness are defined. Finally, the parameter set corresponding to the global optimal particle position is output.

[0075] In summary, this invention constructs a texture model of a periodic microstructure array, initializes key geometric parameters, calculates indicators such as the solid-liquid contact area fraction, apparent contact angle, adhesion work, and spreading coefficient, and integrates these indicators to construct an evaluation function. The final structural parameters are obtained through iterative optimization using an optimization algorithm, achieving quantitative synergistic optimization from microstructure to macroscopic performance. This provides a complete technical solution and intelligent support for the efficient and reliable design of superhydrophobic textures for anti-pollution flashover coatings of insulators.

[0076] Example 3 illustrates a schematic scheme for optimizing and extracting superhydrophobic texture parameters of an anti-flashover coating. It should be noted that the technical solution of this system for optimizing and extracting superhydrophobic texture parameters of an anti-flashover coating is based on the same concept as the aforementioned method for optimizing and extracting superhydrophobic texture parameters of an anti-flashover coating. Details not described in detail in this embodiment can be found in the description of the aforementioned method for optimizing and extracting superhydrophobic texture parameters of an anti-flashover coating.

[0077] This embodiment also provides a system for optimizing and extracting the superhydrophobic texture structure parameters of an anti-flashover coating, including: The model building module is used to construct a texture model of the microstructure array on the surface of the anti-flashover coating, and initialize a set of key geometric parameters for the texture model; The first calculation module is used to input the key geometric parameters into the texture model and calculate the solid-liquid contact area ratio. The second calculation module is used to calculate the apparent contact angle of the anti-flashover coating surface by combining the intrinsic contact angle of the pre-placed droplet with the solid-liquid contact area ratio. The third calculation module is used to calculate the adhesion work and spreading coefficient based on the solid-liquid contact area fraction and the apparent contact angle. The function construction module is used to construct an evaluation function based on the apparent contact angle, the adhesion work, and the spreading coefficient. The optimization output module is used to iteratively optimize the key geometric parameters using an optimization algorithm with the evaluation function as the objective, and output the optimized key geometric parameters as the final structural parameters.

[0078] This embodiment also provides an electronic device suitable for optimizing the extraction of superhydrophobic texture parameters of anti-flashover coatings, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to realize the method for optimizing the extraction of superhydrophobic texture parameters of anti-flashover coatings as proposed in the above embodiment.

[0079] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements the method for optimizing and extracting superhydrophobic texture parameters for anti-flashover coatings as proposed in the above embodiments.

[0080] The storage medium proposed in this embodiment and the method for optimizing and extracting superhydrophobic texture structure parameters for achieving anti-flashover coating proposed in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0081] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0082] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for optimizing and extracting superhydrophobic texture structure parameters of an anti-flashover coating, characterized in that, include: A texture model of the microstructure array on the surface of the anti-flashover coating is constructed, and a set of key geometric parameters are initialized for the texture model; The key geometric parameters are input into the texture model to calculate the solid-liquid contact area ratio. The intrinsic contact angle of the pre-placed droplet is used to calculate the apparent contact angle of the anti-flashover coating surface, which is obtained by combining the solid-liquid contact area ratio. The adhesion work and spreading coefficient are calculated based on the solid-liquid contact area ratio and the apparent contact angle. An evaluation function is constructed based on the apparent contact angle, the adhesion work, and the spreading coefficient. Using the evaluation function as the objective, an optimization algorithm is used to iteratively optimize the key geometric parameters, and the optimized key geometric parameters are output as the final structural parameters.

2. The method for optimizing and extracting superhydrophobic texture structure parameters of the anti-flashover coating as described in claim 1, characterized in that, The steps of constructing a textured model of the microstructure array on the surface of the anti-flashover coating and initializing a set of key geometric parameters for the textured model include: A geometric model of a microstructure array with periodic distribution characteristics on the surface of the anti-flashover coating is constructed as a texture model; A set of key geometric parameters is initialized for the texture model, including the feature size of the microstructure unit, the center-to-center distance between adjacent microstructure units, and the protrusion height of the microstructure unit.

3. The method for optimizing and extracting the superhydrophobic texture structure parameters of the anti-flashover coating as described in claim 2, characterized in that, The steps for calculating the solid-liquid contact area fractions include: Input the feature dimensions and the center spacing into the texture model; Based on the feature size and the center distance, the solid-liquid contact area ratio of the surface of the microstructure array is calculated and used as the solid-liquid contact area fraction.

4. The method for optimizing and extracting superhydrophobic texture structure parameters of the anti-flashover coating as described in claim 3, characterized in that, The step of calculating the apparent contact angle of the anti-flashover coating surface based on the solid-liquid contact area ratio includes: Intrinsic contact angle of pre-placed droplets on a smooth anti-flashover coating surface; Based on the intrinsic contact angle and the solid-liquid contact area ratio, a calculation relationship for the apparent contact angle is established. Based on the aforementioned calculation relationship, the apparent contact angle of the anti-flashover coating surface is calculated.

5. The method for optimizing and extracting the superhydrophobic texture structure parameters of the anti-flashover coating as described in claim 4, characterized in that, The steps for calculating the adhesion work and spreading coefficient include: Input the solid-liquid contact area fractions and the apparent contact angle into the texture model; Using the texture model, the adhesion work of the anti-flashover coating to droplets and the spreading coefficient of the anti-flashover coating surface are calculated.

6. The method for optimizing and extracting the superhydrophobic texture structure parameters of the anti-flashover coating as described in claim 5, characterized in that, The steps to construct the evaluation function include: The apparent contact angle, the adhesion work, and the spreading coefficient are combined to obtain a comprehensive result. By combining the protrusion height in the key geometric parameters, an evaluation function is constructed based on the comprehensive results to quantitatively evaluate the superhydrophobic performance of the anti-flashover coating.

7. The method for optimizing and extracting superhydrophobic texture structure parameters of the anti-flashover coating as described in claim 6, characterized in that, The step of outputting the optimized key geometric parameters as the final structural parameters includes: Using the evaluation function as the optimization objective, the optimization algorithm is used to iteratively optimize the key geometric parameters, and the optimization process state represented by the iterative optimization calculation is obtained. A preset optimization termination criterion is set. If the optimization process state does not meet the optimization termination criterion, iterative optimization calculation continues. When the optimization termination criterion is met, the key geometric parameters corresponding to the current iteration are output as the final structural parameters.

8. A superhydrophobic texture structure parameter optimization extraction system for an anti-flashover coating, using the method described in any one of claims 1-7, characterized in that, include: The model building module is used to construct a texture model of the microstructure array on the surface of the anti-flashover coating, and initialize a set of key geometric parameters for the texture model; The first calculation module is used to input the key geometric parameters into the texture model and calculate the solid-liquid contact area ratio. The second calculation module is used to calculate the apparent contact angle of the anti-flashover coating surface by combining the intrinsic contact angle of the pre-placed droplet with the solid-liquid contact area ratio. The third calculation module is used to calculate the adhesion work and spreading coefficient based on the solid-liquid contact area fraction and the apparent contact angle. The function construction module is used to construct an evaluation function based on the apparent contact angle, the adhesion work, and the spreading coefficient. The optimization output module is used to iteratively optimize the key geometric parameters using an optimization algorithm with the evaluation function as the objective, and output the optimized key geometric parameters as the final structural parameters.

9. An electronic device, comprising: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, they implement the steps of the method for optimizing and extracting the superhydrophobic texture structure parameters of the anti-flashover coating according to any one of claims 1 to 7.

10. A computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the steps of the method for optimizing and extracting superhydrophobic texture parameters of the anti-flashover coating according to any one of claims 1 to 7.

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