Method for testing hydrophobicity of super-hydrophobic surface structures of energy equipment with different microstructures

Abstract

The invention discloses a method for testing hydrophobicity of super-hydrophobic surface structures of energy equipment with different microstructures, and relates to the technical field of surface hydrophobicity testing. According to the method, environmental adaptation index data and functional characteristic index data of the energy equipment super-hydrophobic surface structures of different microstructures are evaluated respectively, and hydrophobic coefficients of the energy equipment super-hydrophobic surface structures of different microstructures are obtained by constructing a hydrophobic coefficient output model of the super-hydrophobic surface structures and a hydrophobic test correction model of the super-hydrophobic surface structures; according to the method, the convolutional neural network algorithm, the multiple linear regression algorithm, the neural network algorithm, the super-hydrophobic surface structure hydrophobicity testing technology and the super-hydrophobic surface structure hydrophobicity correction technology are closely combined with the modern information technology, and the problems that the hydrophobicity is difficult to comprehensively test and the result is inaccurate in a traditional method are solved.

Description

Technical Field

[0001] This invention relates to the field of surface hydrophobicity testing technology, specifically to a method for testing the hydrophobicity of superhydrophobic surface structures of energy equipment with different microstructures. Background Technology

[0002] Traditional testing techniques for the hydrophobicity of superhydrophobic surface structures in energy equipment are insufficient to comprehensively assess hydrophobicity from multiple dimensions, resulting in inaccurate test results that fail to accurately reflect the performance differences of different microstructures in practical applications. Furthermore, traditional methods do not fully utilize modern information technology, making it difficult to achieve intelligent modeling and correction of the hydrophobicity of complex structures. This leads to a lack of dynamic monitoring and data optimization capabilities in the testing process, making it difficult to meet the high performance and high reliability requirements of superhydrophobic surfaces for energy equipment under extreme operating conditions. Therefore, there is an urgent need to develop a multi-dimensional, intelligent testing method to address the limitations of traditional techniques in data acquisition, analysis, and result correction, thereby improving the accuracy and applicability of hydrophobicity testing.

[0003] Traditional hydrophobicity testing techniques for superhydrophobic surface structures of energy equipment based on different microstructures are insufficient for comprehensive testing of hydrophobicity from multiple dimensions, resulting in inaccurate test results and affecting the application efficiency of subsequent energy equipment. Therefore, the present invention aims to address the problem of how to test the hydrophobicity of superhydrophobic surface structures of energy equipment with different microstructures from both static and dynamic hydrophobicity perspectives, and further correct the test results by combining the environmental adaptability and functional characteristics of superhydrophobic surface structures of energy equipment with different microstructures in practical applications. Summary of the Invention

[0004] To achieve the above objectives, the present invention provides the following technical solution: a method for testing the hydrophobicity of superhydrophobic surface structures of energy equipment with different microstructures, comprising the following steps: Step 1: Collect data on energy equipment with different microstructures. This data includes static hydrophobicity monitoring data, dynamic hydrophobicity monitoring data, environmental adaptability data, functional characteristic data, and microstructure differentiation data, providing a data foundation for subsequent steps. Step 2: Combining static and dynamic hydrophobicity monitoring data, calculate the contact angle change rate and roll-off angle change rate of superhydrophobic surface structures of energy equipment with different microstructures, and then update the environmental adaptability data and functional characteristic data, providing a basis for subsequent environmental adaptability index data and functional characteristic index data of superhydrophobic surface structures of energy equipment with different microstructures. Step 3: Using the updated environmental adaptability data, evaluate the environmental adaptability index data of superhydrophobic surface structures of energy equipment with different microstructures. Through the updated functional characteristic data and combined with the convolutional neural network algorithm, construct a functional characteristic evaluation model and output the functional characteristic index data of superhydrophobic surface structures of energy equipment with different microstructures. This provides data preparation for constructing a hydrophobic test calibration model for superhydrophobic surface structures. Step 4: Use microstructure to distinguish data, and calculate the superhydrophobic surface roughness factor and superhydrophobic surface contact area ratio respectively, which will be used to construct the superhydrophobic surface structure hydrophobic coefficient output model in the future. Step 5: Using static and dynamic hydrophobicity monitoring data, a multivariate linear regression algorithm is used to construct a hydrophobicity coefficient output model for superhydrophobic surface structures, thereby obtaining the hydrophobicity coefficients of superhydrophobic surface structures of energy equipment with different microstructures. Step Six: Combining environmental adaptability index data, functional characteristic index data, superhydrophobic surface roughness factor, and superhydrophobic surface contact area ratio of superhydrophobic surface structures of energy equipment with different microstructures, a neural network algorithm is used to construct a hydrophobic test correction model for superhydrophobic surface structures, outputting the corresponding hydrophobic test correction index, thereby correcting the hydrophobic coefficient of superhydrophobic surface structures of energy equipment with different microstructures, and improving the accuracy of hydrophobicity testing of superhydrophobic surface structures of energy equipment with different microstructures; Step 7: Based on the corrected hydrophobic coefficients of the superhydrophobic surface structures of energy equipment with different microstructures, analyze the hydrophobicity test results of the superhydrophobic surface structures of energy equipment with different microstructures, and display the hydrophobicity test results of the superhydrophobic surface structures of energy equipment with different microstructures through different colored indicator lights.

[0005] A further improvement to the technical solution of this invention lies in that, in step one, the data acquisition process for energy equipment with different microstructures includes: Different types of data acquisition devices are deployed to collect data on energy equipment with different microstructures, including columnar structures, conical structures, porous structures, micro-nano hierarchical structures, dendritic structures, and textured composite structures. The data acquisition devices include contact angle meters, surface energy meters, overall tilt contact angle meters, surface interfacial tension meters, high-speed cameras, electronic balances, X-ray photoelectron spectrometers, optical microscopes, automatic colony counters, mechanical testing instruments, infrared thermal imagers, wireless temperature sensor networks, power analyzers, fouling thermal resistance testers, and high-sensitivity infrared thermometers. Static hydrophobicity monitoring data includes real-time contact angles and surface energies of superhydrophobic surface structures of energy equipment with different microstructures; dynamic hydrophobicity monitoring data includes real-time roll angles, advance angles, and retreat angles of superhydrophobic surface structures of energy equipment with different microstructures when they are in a dynamic state. The environmental adaptability data consists of shock resistance data, temperature tolerance data, corrosion resistance data, and salt spray resistance data. Among them, the shock resistance data includes the fracture length of the superhydrophobic surface structure of energy equipment with different microstructures after being impacted; the temperature tolerance data includes the loss mass of the superhydrophobic surface structure of energy equipment with different microstructures under low temperature and high temperature environments; the corrosion resistance data includes the relative density of hydrophilic groups and the relative density of hydrophobic groups of the superhydrophobic surface structure of energy equipment with different microstructures; and the salt spray resistance data includes the salt crystallization mass per unit area of ​​the superhydrophobic surface structure of energy equipment with different microstructures. The functional performance data consists of self-cleaning data, anti-biofouling data, icing performance data, and heat transfer efficiency data. Specifically, the self-cleaning data includes the initial and residual mass of contaminants on the superhydrophobic surface structures of energy equipment with different microstructures; the anti-biofouling data includes the biofouling density and biofilm thickness on the superhydrophobic surface structures of energy equipment with different microstructures, as well as the number of viable bacteria in the experimental and control groups during the anti-biofouling experiment; the icing performance data includes the time from water droplet contact with the superhydrophobic surface structures of energy equipment with different microstructures to complete freezing and ice melting, as well as the adhesion strength between the ice layer and the superhydrophobic surface structures of energy equipment with different microstructures; and the heat transfer efficiency data includes the boiling critical heat flux density, scaling thermal resistance, and temperature distribution standard deviation of the superhydrophobic surface structures of energy equipment with different microstructures. The data distinguishing the microstructures include the actual surface area and apparent geometric area of ​​the superhydrophobic surface structures of energy equipment with different microstructures, as well as the contact area between the superhydrophobic surface structures of energy equipment with different microstructures and water. Data cleaning and normalization were performed on the collected static hydrophobicity monitoring data, dynamic hydrophobicity monitoring data, environmental adaptation data, functional characteristic data, and microstructure differentiation data. The pre-processed static hydrophobicity monitoring data and dynamic hydrophobicity monitoring data were then integrated to generate a superhydrophobic surface structure hydrophobicity evaluation dataset.

[0006] A further improvement to the technical solution of this invention lies in the fact that, in step two, the process of calculating the contact angle change rate and roll-off angle change rate of the superhydrophobic surface structure of energy equipment with different microstructures, and then updating the environmental adaptability data and functional characteristic data, includes: The contact angle variation rates of superhydrophobic surface structures of energy equipment with different microstructures include impact resistance contact angle variation rate, temperature tolerance contact angle variation rate, corrosion resistance contact angle variation rate, and salt spray resistance contact angle variation rate. Among them, the temperature tolerance contact angle includes the temperature rise tolerance contact angle variation rate and the temperature drop tolerance contact angle variation rate. The contact angles of superhydrophobic surface structures of energy equipment with different microstructures were extracted before and after the impact, and the rate of change of the impact-resistant contact angle was calculated. The contact angles of superhydrophobic surface structures of energy equipment with different microstructures were extracted from low temperature to high temperature, and the contact angles corresponding to the low temperature and high temperature were calculated. The contact angle change rate of the temperature rise tolerance was also calculated. The contact angles of superhydrophobic surface structures of energy equipment with different microstructures before and after corrosion were extracted, and the rate of change of corrosion-resistant contact angle was calculated. Salt spray tests were conducted on superhydrophobic surface structures of energy equipment with different microstructures. The contact angles of the superhydrophobic surface structures of energy equipment with different microstructures were extracted before and after the salt spray tests, and the rate of change of the salt spray contact angle was calculated. Select time t1 and time t2, extract the roll angle of the superhydrophobic surface structure of energy equipment with different microstructures at time t1 and time t2 respectively, and calculate the roll angle change rate of the superhydrophobic surface structure of energy equipment with different microstructures; The impact resistance contact angle change rate, temperature tolerance contact angle change rate, corrosion resistance contact angle change rate, salt spray resistance contact angle change rate, and roll angle change rate of superhydrophobic surface structures of energy equipment with different microstructures are integrated into the impact resistance data, temperature tolerance data, corrosion resistance data, salt spray resistance data, and self-cleaning data, thereby updating the environmental adaptability data and functional characteristic data.

[0007] A further improvement to the technical solution of this invention lies in the fact that, in step three, the evaluation process of environmental adaptability index data for superhydrophobic surface structures of energy equipment with different microstructures includes: Environmental adaptability index data for superhydrophobic surface structures of energy equipment with different microstructures include impact resistance index, temperature tolerance index, corrosion resistance index, and salt spray resistance index; Based on the impact resistance characteristics of superhydrophobic surface structures of energy equipment with different microstructures, the weights of the fracture length of superhydrophobic surface structures of energy equipment with different microstructures after impact are determined, and the impact resistance index is evaluated in combination with the impact contact angle change rate. Based on the impact resistance characteristics of superhydrophobic surfaces of energy equipment with different microstructures, the weights of mass loss of superhydrophobic surfaces of energy equipment with different microstructures under low temperature and high temperature environments are determined. The temperature tolerance index is evaluated by combining the temperature rise tolerance contact angle change rate and the temperature drop tolerance contact angle change rate. By using historical corrosion resistance test results of superhydrophobic surface structures of energy equipment with different microstructures, the corrosion rate constant of superhydrophobic surface structures of energy equipment with different microstructures was determined, and the corrosion resistance index was evaluated by combining the updated corrosion resistance data. Based on historical salt spray resistance test results of superhydrophobic surface structures of energy equipment with different microstructures, the salt spray resistance coefficient of superhydrophobic surface structures of energy equipment with different microstructures is determined, and the salt spray resistance index is evaluated by combining the updated salt spray resistance data. The environmental adaptability index data of superhydrophobic surface structures of energy equipment with different microstructures were integrated into the superhydrophobic surface structure hydrophobicity evaluation dataset.

[0008] A further improvement to the technical solution of this invention lies in the fact that, in step three, the process of constructing a functional characteristic evaluation model and outputting functional characteristic index data of superhydrophobic surface structures of energy equipment with different microstructures includes: The functional characteristic index data of superhydrophobic surface structures of energy equipment with different microstructures include self-cleaning index, anti-bioadhesion index, icing index and heat transfer index; Self-cleaning data, anti-bioadhesion data, icing performance data, and heat transfer efficiency data were extracted from the superhydrophobic surface structure hydrophobicity evaluation dataset. The extracted data were then converted into a first training set and a first test set, with the ratio of the first training set data to the first test set data being 8:2. Using a convolutional neural network algorithm, the first training set data is used as input, and the functional characteristic index data of superhydrophobic surface structures of energy equipment with different microstructures are used as output. The first nonlinear relationship, the second nonlinear relationship, the third nonlinear relationship and the fourth nonlinear relationship are learned respectively, and the functional characteristic evaluation model is trained. The first test set data is input into the functional characteristic evaluation model to evaluate the performance of the functional characteristic evaluation model, adjust the parameters of the functional characteristic evaluation model, optimize the functional characteristic evaluation model, and obtain the final functional characteristic evaluation model. The current self-cleaning data, anti-biofouling data, icing performance data, and heat transfer efficiency data are input into the functional characteristic evaluation model, which outputs the corresponding functional characteristic index data of superhydrophobic surface structures of energy equipment with different microstructures. The functional characteristic index data of superhydrophobic surface structures of energy equipment with different microstructures are then integrated into the superhydrophobic surface structure hydrophobicity evaluation dataset.

[0009] A further improvement to the technical solution of this invention lies in that, in step four, the calculation process of the superhydrophobic surface roughness factor and the superhydrophobic surface contact area ratio includes: The roughness factor of the superhydrophobic surface is calculated by the proportion of the actual surface area of ​​the superhydrophobic surface structure of energy equipment with different microstructures to the apparent geometric area of ​​the superhydrophobic surface structure of energy equipment with different microstructures. The superhydrophobic surface contact area ratio is calculated by the proportion of the contact area between the superhydrophobic surface structure of energy equipment with water and the apparent geometric area of ​​the superhydrophobic surface structure of energy equipment with different microstructures. The superhydrophobic surface roughness factor and the superhydrophobic surface contact area ratio are integrated into the superhydrophobic surface structure hydrophobicity evaluation dataset.

[0010] A further improvement to the technical solution of this invention lies in the fact that, in step five, the process of constructing a hydrophobic coefficient output model for the superhydrophobic surface structure, and then obtaining the hydrophobic coefficients of the superhydrophobic surface structures of energy equipment with different microstructures, includes: Static and dynamic hydrophobicity monitoring data were extracted from the superhydrophobic surface structure hydrophobicity evaluation dataset. The extracted data were divided into a second training set and a second test set, with a ratio of 7:3. Using the multiple linear regression algorithm, the second training set data is used as input. The hydrophobic coefficients of the superhydrophobic surface structure of energy equipment with different microstructures are used to learn the linear relationship between static hydrophobicity monitoring data, dynamic hydrophobicity monitoring data and the hydrophobic coefficients of the superhydrophobic surface structure of energy equipment with different microstructures, and the output model of the superhydrophobic surface structure hydrophobic coefficient is trained. The second test set data is input into the trained superhydrophobic surface structure hydrophobic coefficient output model. The regression coefficient intercept term of the superhydrophobic surface structure hydrophobic coefficient output model is adjusted to optimize the performance of the superhydrophobic surface structure hydrophobic coefficient output model and obtain the final superhydrophobic surface structure hydrophobic coefficient output model. The current static hydrophobicity monitoring data and dynamic hydrophobicity monitoring data are input into the hydrophobicity coefficient output model of the superhydrophobic surface structure, and the corresponding hydrophobicity coefficients of the superhydrophobic surface structure of energy equipment with different microstructures are output. The hydrophobicity coefficients of the superhydrophobic surface structure of energy equipment with different microstructures are then integrated into the hydrophobicity evaluation dataset of the superhydrophobic surface structure.

[0011] A further improvement to the technical solution of this invention lies in the fact that, in step six, the process of constructing a hydrophobic test correction model for a superhydrophobic surface structure and outputting the corresponding hydrophobic test correction index includes: The environmental adaptability index data, functional characteristic index data, superhydrophobic surface roughness factor and superhydrophobic surface contact area ratio of superhydrophobic surface structures of different microstructures in energy equipment were extracted from the superhydrophobic surface structure hydrophobicity evaluation dataset. The extracted data were then converted into a third training set and a third test set with a ratio of 6:4. Using a neural network algorithm, the third training set data is used as input and the hydrophobic test correction index is used as output. The nonlinear relationship between environmental adaptability index data, functional characteristic index data, superhydrophobic surface roughness factor, superhydrophobic surface contact area ratio and hydrophobic test correction index of superhydrophobic surface structure of energy equipment with different microstructures is learned, and the hydrophobic test correction model of superhydrophobic surface structure is trained. The third test set data is input into the superhydrophobic surface structure hydrophobic test calibration model. The parameters of the superhydrophobic surface structure hydrophobic test calibration model are adjusted to optimize the performance of the superhydrophobic surface structure hydrophobic test calibration model and obtain the final superhydrophobic surface structure hydrophobic test calibration model. By combining the environmental adaptability index data, functional characteristic index data, superhydrophobic surface roughness factor and superhydrophobic surface contact area ratio of superhydrophobic surface structures of energy equipment with different microstructures, the corresponding hydrophobic test correction index is output.

[0012] A further improvement to the technical solution of this invention lies in the fact that, in step six, the correction process for the hydrophobicity coefficient of the superhydrophobic surface structure of energy equipment with different microstructures includes: When the hydrophobicity test correction index is below 0.2, no correction is applied to the hydrophobicity coefficient of the superhydrophobic surface structure of energy equipment with different microstructures; when the hydrophobicity test correction index is between 0.2 and 0.5, it is based on... The formula is used to correct the hydrophobicity coefficient of superhydrophobic surfaces of energy equipment with different microstructures; when the hydrophobicity test correction index is greater than 0.5, it is based on... The formula is used to correct the hydrophobicity coefficient of superhydrophobic surface structures of energy equipment with different microstructures, where... To correct the hydrophobicity coefficient of superhydrophobic surface structures of energy equipment with different microstructures, For hydrophobicity testing correction index, The hydrophobicity coefficients of superhydrophobic surface structures of energy equipment with different microstructures before correction.

[0013] A further improvement to the technical solution of this invention lies in the fact that, in step seven, the process of analyzing the hydrophobicity test results of the superhydrophobic surface structures of energy equipment with different microstructures and displaying the hydrophobicity test results of the superhydrophobic surface structures of energy equipment with different microstructures using different colored indicator lights includes: The hydrophobicity coefficients of superhydrophobic surfaces of energy equipment with different microstructures after correction were analyzed. When the hydrophobicity coefficient of the superhydrophobic surface of energy equipment with different microstructures after correction is higher than 0.8, the corresponding superhydrophobic surface structure of energy equipment with different microstructures is classified as high hydrophobicity level; when the hydrophobicity coefficient of the superhydrophobic surface of energy equipment with different microstructures after correction is between 0.5 and 0.8, the corresponding superhydrophobic surface structure of energy equipment with different microstructures is classified as medium hydrophobicity level; when the hydrophobicity coefficient of the superhydrophobic surface of energy equipment with different microstructures after correction is lower than 0.5, the corresponding superhydrophobic surface structure of energy equipment with different microstructures is classified as low hydrophobicity level. The hydrophobicity test results of superhydrophobic surface structures of energy equipment with different microstructures were obtained. The signal light colors are set to red, yellow, and green. When the signal light displays red, yellow, and green, it corresponds to the low, medium, and high hydrophobicity levels of the superhydrophobic surface structure of energy equipment with different microstructures, respectively. Based on the hydrophobicity test results of the superhydrophobic surface structure of energy equipment with different microstructures, the signal light displays the corresponding color.

[0014] The beneficial effects of this invention are as follows: Compared with traditional methods for testing the hydrophobicity of superhydrophobic surface structures of energy equipment with different microstructures, the method of this invention closely integrates convolutional neural network algorithms, multiple linear regression algorithms, neural network algorithms, superhydrophobic surface structure hydrophobicity testing techniques, and superhydrophobic surface structure hydrophobicity correction techniques with modern information technology. This allows for the accurate capture of static hydrophobicity monitoring data, dynamic hydrophobicity monitoring data, environmental adaptability data, functional characteristic data, and microstructure differentiation data. This leads to the acquisition of environmental adaptability index data, functional characteristic index data, superhydrophobic surface roughness factor, and superhydrophobic surface contact area ratio. By combining static hydrophobicity monitoring data, dynamic hydrophobicity monitoring data, and the multiple linear regression algorithm, a hydrophobicity coefficient output model for the superhydrophobic surface structure is constructed, thereby achieving… A preliminary multi-dimensional assessment of the hydrophobicity of superhydrophobic surfaces is conducted using a neural network algorithm. This algorithm constructs a calibration model for testing the hydrophobicity of superhydrophobic surface structures by incorporating environmental adaptability index data, functional characteristic index data, superhydrophobic surface roughness factor, and superhydrophobic surface contact area ratio. This model then corrects the hydrophobicity coefficient of the superhydrophobic surface structure, improving the accuracy of hydrophobicity testing for energy equipment with different microstructures. It solves the problems of traditional methods' inability to comprehensively test hydrophobicity and inaccurate results. This ensures that the method in this invention can refine the dynamic monitoring standards for testing the hydrophobicity of superhydrophobic surface structures of energy equipment with different microstructures within a more precise range, making the monitored data more accurate indicators under the same conditions. The development and application of this method significantly enhances the intelligence level of the hydrophobicity testing process for superhydrophobic surface structures of energy equipment with different microstructures. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0016] Figure 1 This is a flowchart illustrating the method for testing the hydrophobicity of superhydrophobic surface structures of energy equipment with different microstructures according to the present invention. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] like Figure 1 As shown, this invention provides a method for testing the hydrophobicity of superhydrophobic surface structures of energy equipment with different microstructures, comprising the following steps: Step 101: Collect data on energy equipment with different microstructures. The data on energy equipment with different microstructures includes static hydrophobicity monitoring data, dynamic hydrophobicity monitoring data, environmental adaptability data, functional characteristic data, and microstructure differentiation data, which provides a data foundation for the implementation of subsequent steps. Step 102: Combining static hydrophobicity monitoring data and dynamic hydrophobicity monitoring data, calculate the contact angle change rate and roll-off angle change rate of superhydrophobic surface structures of energy equipment with different microstructures, and then update the environmental adaptability data and functional characteristic data, providing a basis for subsequent environmental adaptability index data and functional characteristic index data of superhydrophobic surface structures of energy equipment with different microstructures. Step 103: Using the updated environmental adaptation data, evaluate the environmental adaptation index data of superhydrophobic surface structures of energy equipment with different microstructures. Through the updated functional characteristic data and combined with the convolutional neural network algorithm, construct a functional characteristic evaluation model and output the functional characteristic index data of superhydrophobic surface structures of energy equipment with different microstructures. This provides data preparation for constructing a hydrophobic test correction model for superhydrophobic surface structures. Step 104: Use microstructure to distinguish data, calculate the superhydrophobic surface roughness factor and superhydrophobic surface contact area ratio respectively, and use them to construct the superhydrophobic surface structure hydrophobic coefficient output model in the future. Step 105: Using static hydrophobicity monitoring data and dynamic hydrophobicity monitoring data, a multivariate linear regression algorithm is used to construct a hydrophobicity coefficient output model for superhydrophobic surface structures, thereby obtaining the hydrophobicity coefficients of superhydrophobic surface structures of energy equipment with different microstructures. Step 106: Combining environmental adaptability index data, functional characteristic index data, superhydrophobic surface roughness factor, and superhydrophobic surface contact area ratio of superhydrophobic surface structures of energy equipment with different microstructures, a neural network algorithm is used to construct a hydrophobic test correction model for superhydrophobic surface structures, outputting the corresponding hydrophobic test correction index, thereby correcting the hydrophobic coefficient of superhydrophobic surface structures of energy equipment with different microstructures, and improving the accuracy of hydrophobicity testing of superhydrophobic surface structures of energy equipment with different microstructures; Step 107: Based on the corrected hydrophobic coefficients of the superhydrophobic surface structures of energy equipment with different microstructures, analyze the hydrophobicity test results of the superhydrophobic surface structures of energy equipment with different microstructures, and display the hydrophobicity test results of the superhydrophobic surface structures of energy equipment with different microstructures through different colored indicator lights.

[0019] A further improvement to the technical solution of this invention is that, in step 101, the data acquisition process for energy equipment with different microstructures includes: Different types of data acquisition devices are deployed to collect data on energy equipment with different microstructures, including columnar structures, conical structures, porous structures, micro-nano hierarchical structures, dendritic structures, and textured composite structures. The data acquisition devices include contact angle meters, surface energy meters, overall tilt contact angle meters, surface interfacial tension meters, high-speed cameras, electronic balances, X-ray photoelectron spectrometers, optical microscopes, automatic colony counters, mechanical testing instruments, infrared thermal imagers, wireless temperature sensor networks, power analyzers, fouling thermal resistance testers, and high-sensitivity infrared thermometers. Static hydrophobicity monitoring data includes real-time contact angles and surface energies of superhydrophobic surface structures of energy equipment with different microstructures; dynamic hydrophobicity monitoring data includes real-time roll angles, advance angles, and retreat angles of superhydrophobic surface structures of energy equipment with different microstructures when they are in a dynamic state. The environmental adaptability data consists of shock resistance data, temperature tolerance data, corrosion resistance data, and salt spray resistance data. Among them, the shock resistance data includes the fracture length of the superhydrophobic surface structure of energy equipment with different microstructures after being impacted; the temperature tolerance data includes the loss mass of the superhydrophobic surface structure of energy equipment with different microstructures under low temperature and high temperature environments; the corrosion resistance data includes the relative density of hydrophilic groups and the relative density of hydrophobic groups of the superhydrophobic surface structure of energy equipment with different microstructures; and the salt spray resistance data includes the salt crystallization mass per unit area of ​​the superhydrophobic surface structure of energy equipment with different microstructures. The functional performance data consists of self-cleaning data, anti-biofouling data, icing performance data, and heat transfer efficiency data. Specifically, the self-cleaning data includes the initial and residual mass of contaminants on the superhydrophobic surface structures of energy equipment with different microstructures; the anti-biofouling data includes the biofouling density and biofilm thickness on the superhydrophobic surface structures of energy equipment with different microstructures, as well as the number of viable bacteria in the experimental and control groups during the anti-biofouling experiment; the icing performance data includes the time from water droplet contact with the superhydrophobic surface structures of energy equipment with different microstructures to complete freezing and ice melting, as well as the adhesion strength between the ice layer and the superhydrophobic surface structures of energy equipment with different microstructures; and the heat transfer efficiency data includes the boiling critical heat flux density, scaling thermal resistance, and temperature distribution standard deviation of the superhydrophobic surface structures of energy equipment with different microstructures. The data distinguishing the microstructures include the actual surface area and apparent geometric area of ​​the superhydrophobic surface structures of energy equipment with different microstructures, as well as the contact area between the superhydrophobic surface structures of energy equipment with different microstructures and water. Specifically, real-time contact angles and surface energies of superhydrophobic surfaces of energy equipment with different microstructures were collected using a contact angle meter, surface energy meter, overall tilting contact angle meter, and surface interfacial tension meter. The real-time roll angle, advance angle, and retreat angle of these superhydrophobic surfaces under dynamic conditions were also measured. A high-speed camera was used to capture images at the moment of impact, and MATLAB software was used to calculate the pixel area of ​​the fracture region, thereby obtaining the fracture length of the superhydrophobic surfaces of different microstructures after impact. An electronic balance was used to collect the mass loss of superhydrophobic surfaces of different microstructures under low-temperature and high-temperature environments, the salt crystallization mass per unit area of ​​these surfaces, and the initial and residual mass of contaminants. X-ray photoelectron spectroscopy was used to collect the relative densities of hydrophilic and hydrophobic groups on the superhydrophobic surfaces of different microstructures. Optical microscopy was also used to... The study employed several methods to collect biofilm density and biofilm thickness data on superhydrophobic surfaces of energy equipment with different microstructures. An anti-biofilm experiment was conducted, including experimental and control groups. An automated colony counter was used to measure the number of viable bacteria in both groups. A high-speed camera captured the process of water droplets freezing completely and melting from contact with the superhydrophobic surfaces of different microstructures. The time taken for complete freezing and melting was recorded. A mechanical testing instrument was used to collect the adhesion strength between the ice layer and the superhydrophobic surfaces of different microstructures. An infrared thermal imager, a wireless temperature sensor network, and a power analyzer were used to collect the boiling critical heat flux density of the superhydrophobic surfaces of different microstructures. Finally, a fouling thermal resistance tester and a high-sensitivity infrared thermometer were used to collect the fouling thermal resistance and temperature distribution standard deviation of the superhydrophobic surfaces of different microstructures. Data cleaning and normalization were performed on the collected static hydrophobicity monitoring data, dynamic hydrophobicity monitoring data, environmental adaptation data, functional characteristic data, and microstructure differentiation data. The pre-processed static hydrophobicity monitoring data and dynamic hydrophobicity monitoring data were then integrated to generate a superhydrophobic surface structure hydrophobicity evaluation dataset.

[0020] A further improvement to the technical solution of this invention lies in the fact that, in step 102, the process of calculating the contact angle change rate and roll-off angle change rate of the superhydrophobic surface structure of energy equipment with different microstructures, and then updating the environmental adaptability data and functional characteristic data, includes: The contact angle variation rates of superhydrophobic surface structures of energy equipment with different microstructures include impact resistance contact angle variation rate, temperature tolerance contact angle variation rate, corrosion resistance contact angle variation rate, and salt spray resistance contact angle variation rate. Among them, the temperature tolerance contact angle includes the temperature rise tolerance contact angle variation rate and the temperature drop tolerance contact angle variation rate. The contact angles of superhydrophobic surface structures of energy equipment with different microstructures were extracted before and after the impact, and the rate of change of the impact-resistant contact angle was calculated. The contact angles of superhydrophobic surface structures of energy equipment with different microstructures were extracted from low temperature to high temperature, and the contact angles corresponding to the low temperature and high temperature were calculated. The contact angle change rate of the temperature rise tolerance was also calculated. The contact angles of superhydrophobic surface structures of energy equipment with different microstructures before and after corrosion were extracted, and the rate of change of corrosion-resistant contact angle was calculated. Salt spray tests were conducted on superhydrophobic surface structures of energy equipment with different microstructures. The contact angles of the superhydrophobic surface structures of energy equipment with different microstructures were extracted before and after the salt spray tests, and the rate of change of the salt spray contact angle was calculated. Select time t1 and time t2, extract the roll angle of the superhydrophobic surface structure of energy equipment with different microstructures at time t1 and time t2 respectively, and calculate the roll angle change rate of the superhydrophobic surface structure of energy equipment with different microstructures; The impact resistance contact angle change rate, temperature tolerance contact angle change rate, corrosion resistance contact angle change rate, salt spray resistance contact angle change rate, and roll-off angle change rate of superhydrophobic surface structures of energy equipment with different microstructures are integrated into the impact resistance data, temperature tolerance data, corrosion resistance data, salt spray resistance data, and self-cleaning data, thereby updating the environmental adaptability data and functional characteristic data. The specific calculation process described above includes: in, , , , , and These are the contact angle change rates for impact resistance, temperature rise tolerance, temperature drop tolerance, corrosion resistance, salt spray resistance, and roll-off angle change rates of superhydrophobic surface structures for energy equipment with different microstructures. and These are the contact angles of superhydrophobic surface structures of energy equipment with different microstructures before and after the impact. and The contact angles of superhydrophobic surface structures of energy equipment with different microstructures are shown at high temperature and low temperature, respectively, when the temperature changes from low to high temperature. and The contact angles of superhydrophobic surface structures of energy equipment with different microstructures at low and high temperatures are shown respectively. and These are the contact angles of superhydrophobic surface structures of energy equipment with different microstructures before and after corrosion. and The contact angles at corresponding moments before and after salt spray experiments were obtained for superhydrophobic surface structures of energy equipment with different microstructures. and The rolling angles of the superhydrophobic surface structures of energy equipment with different microstructures at times t1 and t2 are respectively. and The moments before and after the impact on the superhydrophobic surface structure of energy equipment with different microstructures; and These are the low-temperature and high-temperature moments of superhydrophobic surface structures of energy equipment with different microstructures as they change from low to high temperatures. and These correspond to the low-temperature and high-temperature moments of the superhydrophobic surface structures of energy equipment with different microstructures as they change from high to low temperatures. and These correspond to the moments before and after corrosion of the superhydrophobic surface structures of energy equipment with different microstructures. and These are the times before and after salt spray experiments on superhydrophobic surface structures of energy equipment with different microstructures. and These correspond to time points t1 and t2, respectively.

[0021] A further improvement to the technical solution of this invention lies in that, in step 103, the evaluation process of environmental adaptability index data of superhydrophobic surface structures of energy equipment with different microstructures includes: Environmental adaptability index data for superhydrophobic surface structures of energy equipment with different microstructures include impact resistance index, temperature tolerance index, corrosion resistance index, and salt spray resistance index; Based on the impact resistance characteristics of superhydrophobic surface structures of energy equipment with different microstructures, the weights of the fracture length of superhydrophobic surface structures of energy equipment with different microstructures after impact are determined. Combined with the impact contact angle change rate, the impact resistance index is evaluated. The evaluation process of the impact resistance index includes: in, Impact resistance index; The rate of change of contact angle against impact; and The fracture lengths and weights of superhydrophobic surface structures of energy equipment with different microstructures after being subjected to impact are respectively. Based on the impact resistance characteristics of superhydrophobic surfaces of energy equipment with different microstructures, the weights of mass loss of superhydrophobic surfaces of energy equipment with different microstructures under low-temperature and high-temperature environments are determined. Combining the temperature tolerance contact angle change rate under temperature rise and the temperature tolerance contact angle change rate under temperature drop, the temperature tolerance index is evaluated. The specific evaluation process includes: in, Temperature tolerance index and These are the rate of change of contact angle with resistance to temperature rise and the rate of change of contact angle with resistance to temperature drop, respectively. and The loss mass and its weight of superhydrophobic surface structures of energy equipment with different microstructures under low-temperature conditions are respectively presented. and The loss mass and its weight of superhydrophobic surface structures of energy equipment with different microstructures under high temperature conditions are respectively. Based on historical corrosion resistance test results of superhydrophobic surface structures of energy equipment with different microstructures, the corrosion rate constants of these superhydrophobic surface structures were determined. Combined with updated corrosion resistance data, the corrosion resistance index was evaluated. The evaluation process for the corrosion resistance index included: in, The corrosion resistance index, To resist the change rate of corrosion contact angle, The corrosion rate constants for superhydrophobic surface structures of energy equipment with different microstructures are given. and These represent the relative densities of hydrophilic and hydrophobic groups on the superhydrophobic surface structures of energy equipment with different microstructures, respectively, where e is a constant. Based on historical salt spray resistance test results of superhydrophobic surface structures of energy equipment with different microstructures, the salt spray resistance coefficients of superhydrophobic surface structures of energy equipment with different microstructures were determined. Combined with updated salt spray resistance data, the salt spray resistance index was evaluated. The evaluation process included: in, Salt spray resistance index; To resist the rate of change of contact angle in salt spray; Salt spray resistance coefficient of superhydrophobic surface structures for energy equipment with different microstructures; The salt crystallization mass per unit area of ​​superhydrophobic surface structures for energy equipment with different microstructures; The environmental adaptability index data of superhydrophobic surface structures of energy equipment with different microstructures were integrated into the superhydrophobic surface structure hydrophobicity evaluation dataset.

[0022] A further improvement to the technical solution of this invention lies in the fact that, in step 103, the process of constructing a functional characteristic evaluation model and outputting functional characteristic index data of superhydrophobic surface structures of energy equipment with different microstructures includes: The functional characteristic index data of superhydrophobic surface structures of energy equipment with different microstructures include self-cleaning index, anti-bioadhesion index, icing index and heat transfer index; Self-cleaning data, anti-bioadhesion data, icing performance data, and heat transfer efficiency data were extracted from the superhydrophobic surface structure hydrophobicity evaluation dataset. The extracted data were then converted into a first training set and a first test set, with the ratio of the first training set data to the first test set data being 8:2. A convolutional neural network algorithm was employed, using the first training set data as input and the functional characteristic index data of superhydrophobic surface structures of energy equipment with different microstructures as output. The algorithm trained a functional characteristic evaluation model by learning the first, second, third, and fourth nonlinear relationships. Specifically, the first nonlinear relationship represents the nonlinear relationship between the roll-off angle change rate, initial contaminant mass, residual contaminant mass, and self-cleaning index of the superhydrophobic surface structures of energy equipment with different microstructures; the second nonlinear relationship represents the biofilm density and biofilm thickness of the superhydrophobic surface structures of energy equipment with different microstructures, as well as the nonlinear relationship between the number of viable bacteria and the anti-biofilm index in the experimental and control groups during the anti-biofilm experiment; the third nonlinear relationship represents the time from water droplet contact with the superhydrophobic surface structures of energy equipment with different microstructures to complete freezing and ice melting, as well as the nonlinear relationship between the adhesion strength between the ice layer and the superhydrophobic surface structures of energy equipment with different microstructures and the freezing index; and the fourth nonlinear relationship represents the nonlinear relationship between the boiling critical heat flux density, scaling thermal resistance, temperature distribution standard deviation, and heat transfer index of the superhydrophobic surface structures of energy equipment with different microstructures. The first test set data is input into the functional characteristic evaluation model to evaluate the performance of the functional characteristic evaluation model, adjust the parameters of the functional characteristic evaluation model, optimize the functional characteristic evaluation model, and obtain the final functional characteristic evaluation model. The current self-cleaning data, anti-biofouling data, icing performance data, and heat transfer efficiency data are input into the functional characteristic evaluation model, which outputs the corresponding functional characteristic index data of superhydrophobic surface structures of energy equipment with different microstructures. The functional characteristic index data of superhydrophobic surface structures of energy equipment with different microstructures are then integrated into the superhydrophobic surface structure hydrophobicity evaluation dataset.

[0023] A further improvement to the technical solution of the present invention is that, in step 104, the calculation process of the superhydrophobic surface roughness factor and the superhydrophobic surface contact area ratio includes: The roughness factor of the superhydrophobic surface is calculated by the proportion of the actual surface area of ​​the superhydrophobic surface structure of energy equipment with different microstructures to the apparent geometric area of ​​the superhydrophobic surface structure of energy equipment with different microstructures. The superhydrophobic surface contact area ratio is calculated by the proportion of the contact area between the superhydrophobic surface structure of energy equipment with water and the apparent geometric area of ​​the superhydrophobic surface structure of energy equipment with different microstructures. The superhydrophobic surface roughness factor and the superhydrophobic surface contact area ratio are integrated into the superhydrophobic surface structure hydrophobicity evaluation dataset.

[0024] A further improvement to the technical solution of this invention is that, in step 105, the process of constructing a hydrophobic coefficient output model for the superhydrophobic surface structure, and then obtaining the hydrophobic coefficients of the superhydrophobic surface structures of energy equipment with different microstructures, includes: Static and dynamic hydrophobicity monitoring data were extracted from the superhydrophobic surface structure hydrophobicity evaluation dataset. The extracted data were divided into a second training set and a second test set, with a ratio of 7:3. Using the multiple linear regression algorithm, the second training set data is used as input. The hydrophobic coefficients of the superhydrophobic surface structure of energy equipment with different microstructures are used to learn the linear relationship between static hydrophobicity monitoring data, dynamic hydrophobicity monitoring data and the hydrophobic coefficients of the superhydrophobic surface structure of energy equipment with different microstructures, and the output model of the superhydrophobic surface structure hydrophobic coefficient is trained. The second test set data is input into the trained superhydrophobic surface structure hydrophobic coefficient output model. The regression coefficient intercept term of the superhydrophobic surface structure hydrophobic coefficient output model is adjusted to optimize the performance of the superhydrophobic surface structure hydrophobic coefficient output model and obtain the final superhydrophobic surface structure hydrophobic coefficient output model. The current static hydrophobicity monitoring data and dynamic hydrophobicity monitoring data are input into the superhydrophobic surface structure hydrophobicity coefficient output model, and the corresponding hydrophobicity coefficients of energy equipment superhydrophobic surface structures with different microstructures are output. The hydrophobicity coefficients of energy equipment superhydrophobic surface structures with different microstructures are integrated into the superhydrophobic surface structure hydrophobicity evaluation dataset. The expression for the hydrophobicity coefficient output model of this superhydrophobic surface structure is as follows: in, The hydrophobicity coefficient of superhydrophobic surface structures for energy equipment with different microstructures, , , , and These are the real-time contact angles and surface energies of superhydrophobic surface structures for energy equipment with different microstructures, as well as the regression coefficients of the real-time roll angle, advance angle, and retreat angle of superhydrophobic surface structures for energy equipment with different microstructures when they are in a dynamic state. , , , and These represent the real-time contact angle and surface energy of superhydrophobic surface structures of energy equipment with different microstructures, as well as the real-time roll angle, advance angle, and retreat angle of superhydrophobic surface structures of energy equipment with different microstructures when they are in a dynamic state. and These are the intercept and error terms of the output model for the hydrophobicity coefficient of the superhydrophobic surface structure, respectively.

[0025] A further improvement to the technical solution of this invention is that, in step 106, the process of constructing a hydrophobic test correction model for a superhydrophobic surface structure and outputting the corresponding hydrophobic test correction index includes: The environmental adaptability index data, functional characteristic index data, superhydrophobic surface roughness factor and superhydrophobic surface contact area ratio of superhydrophobic surface structures of different microstructures in energy equipment were extracted from the superhydrophobic surface structure hydrophobicity evaluation dataset. The extracted data were then converted into a third training set and a third test set with a ratio of 6:4. Using a neural network algorithm, the third training set data is used as input and the hydrophobic test correction index is used as output. The nonlinear relationship between environmental adaptability index data, functional characteristic index data, superhydrophobic surface roughness factor, superhydrophobic surface contact area ratio and hydrophobic test correction index of superhydrophobic surface structure of energy equipment with different microstructures is learned, and the hydrophobic test correction model of superhydrophobic surface structure is trained. The third test set data is input into the superhydrophobic surface structure hydrophobic test calibration model. The parameters of the superhydrophobic surface structure hydrophobic test calibration model are adjusted to optimize the performance of the superhydrophobic surface structure hydrophobic test calibration model and obtain the final superhydrophobic surface structure hydrophobic test calibration model. By combining the environmental adaptability index data, functional characteristic index data, superhydrophobic surface roughness factor and superhydrophobic surface contact area ratio of superhydrophobic surface structures of energy equipment with different microstructures, the corresponding hydrophobic test correction index is output.

[0026] A further improvement to the technical solution of this invention lies in that, in step 106, the correction process for the hydrophobicity coefficient of the superhydrophobic surface structure of energy equipment with different microstructures includes: When the hydrophobicity test correction index is below 0.2, no correction is applied to the hydrophobicity coefficient of the superhydrophobic surface structure of energy equipment with different microstructures; when the hydrophobicity test correction index is between 0.2 and 0.5, it is based on... The formula is used to correct the hydrophobicity coefficient of superhydrophobic surfaces of energy equipment with different microstructures; when the hydrophobicity test correction index is greater than 0.5, it is based on... The formula is used to correct the hydrophobicity coefficient of superhydrophobic surface structures of energy equipment with different microstructures, where... To correct the hydrophobicity coefficient of superhydrophobic surface structures of energy equipment with different microstructures, For hydrophobicity testing correction index, The hydrophobicity coefficients of superhydrophobic surface structures of energy equipment with different microstructures before correction.

[0027] A further improvement to the technical solution of this invention lies in the fact that, in step 107, the process of analyzing the hydrophobicity test results of the superhydrophobic surface structures of energy equipment with different microstructures and displaying the hydrophobicity test results of the superhydrophobic surface structures of energy equipment with different microstructures using different colored indicator lights includes: The hydrophobicity coefficients of superhydrophobic surfaces of energy equipment with different microstructures after correction were analyzed. When the hydrophobicity coefficient of the superhydrophobic surface of energy equipment with different microstructures after correction is higher than 0.8, the corresponding superhydrophobic surface structure of energy equipment with different microstructures is classified as high hydrophobicity level; when the hydrophobicity coefficient of the superhydrophobic surface of energy equipment with different microstructures after correction is between 0.5 and 0.8, the corresponding superhydrophobic surface structure of energy equipment with different microstructures is classified as medium hydrophobicity level; when the hydrophobicity coefficient of the superhydrophobic surface of energy equipment with different microstructures after correction is lower than 0.5, the corresponding superhydrophobic surface structure of energy equipment with different microstructures is classified as low hydrophobicity level. The hydrophobicity test results of superhydrophobic surface structures of energy equipment with different microstructures were obtained. The signal light colors are set to red, yellow, and green. When the signal light displays red, yellow, and green, it corresponds to the low, medium, and high hydrophobicity levels of the superhydrophobic surface structure of energy equipment with different microstructures, respectively. Based on the hydrophobicity test results of the superhydrophobic surface structure of energy equipment with different microstructures, the signal light displays the corresponding color.

[0028] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for testing the hydrophobicity of superhydrophobic surface structures of energy equipment with different microstructures, characterized in that, Includes the following steps: Data on energy equipment with different microstructures are collected, including static hydrophobicity monitoring data, dynamic hydrophobicity monitoring data, environmental adaptability data, functional characteristic data, and microstructure differentiation data. By combining the static hydrophobicity monitoring data and the dynamic hydrophobicity monitoring data, the contact angle change rate and roll-off angle change rate of the superhydrophobic surface structure of energy equipment with different microstructures are calculated, and the environmental adaptation data and the functional characteristic data are updated by combining the contact angle change rate and the roll-off angle change rate. Using the updated environmental adaptation data, the environmental adaptation index data of the superhydrophobic surface structures of energy equipment with different microstructures are evaluated respectively. By using the updated functional characteristic data and combining it with the convolutional neural network algorithm, a functional characteristic evaluation model is constructed, and the functional characteristic index data of the superhydrophobic surface structures of energy equipment with different microstructures are obtained through the functional characteristic evaluation model. Using the microstructure differentiation data, the superhydrophobic surface roughness factor and superhydrophobic surface contact area ratio of the superhydrophobic surface structures of energy equipment with different microstructures are calculated respectively. Using the static hydrophobicity monitoring data and the dynamic hydrophobicity monitoring data, the multivariate linear regression algorithm is used to construct a hydrophobicity coefficient output model for superhydrophobic surface structures, and the hydrophobicity coefficients of superhydrophobic surface structures of energy equipment with different microstructures are obtained through the superhydrophobic surface structure hydrophobicity coefficient output model. Combining the environmental adaptability index data, functional characteristic index data, superhydrophobic surface roughness factor, and superhydrophobic surface contact area ratio of the superhydrophobic surface structures of energy equipment with different microstructures, the neural network algorithm is used to construct a hydrophobic test correction model for the superhydrophobic surface structure. The hydrophobic test correction index corresponding to the superhydrophobic surface structure of energy equipment with different microstructures is obtained through the superhydrophobic test correction model to correct the hydrophobic coefficient of the superhydrophobic surface structure of energy equipment with different microstructures. Based on the corrected hydrophobic coefficients of the superhydrophobic surface structures of energy equipment with different microstructures, the hydrophobicity test results of the superhydrophobic surface structures of energy equipment with different microstructures are analyzed, and the hydrophobicity test results of the superhydrophobic surface structures of energy equipment with different microstructures are displayed by different colored indicator lights.

2. The method for testing the hydrophobicity of superhydrophobic surface structures of energy equipment with different microstructures according to claim 1, characterized in that, The data acquisition process for energy equipment with different microstructures includes: Different types of data acquisition devices are deployed to collect data on energy equipment with different microstructures, including columnar structures, conical structures, porous structures, micro-nano hierarchical structures, dendritic structures, and textured composite structures. The data acquisition devices include contact angle measuring instruments, surface energy testers, overall tilt contact angle measuring instruments, surface interfacial tension meters, high-speed cameras, electronic balances, X-ray photoelectron spectrometers, optical microscopes, automatic colony counters, mechanical testing instruments, infrared thermal imagers, wireless temperature sensor networks, power analyzers, fouling thermal resistance testers, and high-sensitivity infrared thermometers. The static hydrophobicity monitoring data includes the real-time contact angle and surface energy of the superhydrophobic surface structure of energy equipment with different microstructures; the dynamic hydrophobicity monitoring data includes the real-time roll angle, advance angle, and retreat angle of the superhydrophobic surface structure of energy equipment with different microstructures when it is in a dynamic state. The environmental adaptability data consists of impact resistance data, temperature tolerance data, corrosion resistance data, and salt spray resistance data. Specifically, the impact resistance data includes the fracture length of the superhydrophobic surface structure of energy equipment with different microstructures after being impacted; the temperature tolerance data includes the loss mass of the superhydrophobic surface structure of energy equipment with different microstructures under low-temperature and high-temperature environments; the corrosion resistance data includes the relative density of hydrophilic groups and the relative density of hydrophobic groups of the superhydrophobic surface structure of energy equipment with different microstructures; and the salt spray resistance data includes the salt crystallization mass per unit area of ​​the superhydrophobic surface structure of energy equipment with different microstructures. The functional characteristic data consists of self-cleaning data, anti-biofilm data, icing performance data, and heat transfer efficiency data. Specifically, the self-cleaning data includes the initial and residual mass of contaminants on the superhydrophobic surface structures of energy equipment with different microstructures; the anti-biofilm data includes the biofilm density and biofilm thickness on the superhydrophobic surface structures of energy equipment with different microstructures, as well as the number of viable bacteria in the experimental and control groups during the anti-biofilm experiment; the icing performance data includes the time from water droplet contact with the superhydrophobic surface structures of energy equipment with different microstructures to complete freezing and ice melting, as well as the adhesion strength between the ice layer and the superhydrophobic surface structures of energy equipment with different microstructures; and the heat transfer efficiency data includes the boiling critical heat flux density, scaling thermal resistance, and temperature distribution standard deviation of the superhydrophobic surface structures of energy equipment with different microstructures. The microstructure differentiation data includes the actual surface area and apparent geometric area of ​​the superhydrophobic surface structure of energy equipment with different microstructures, as well as the contact area between the superhydrophobic surface structure of energy equipment with water and the microstructures of energy equipment with different microstructures. The collected static hydrophobicity monitoring data, dynamic hydrophobicity monitoring data, environmental adaptation data, functional characteristic data, and microstructure differentiation data are cleaned and normalized. The processed static hydrophobicity monitoring data and dynamic hydrophobicity monitoring data are then integrated to generate a superhydrophobic surface structure hydrophobicity evaluation dataset.

3. The method for testing the hydrophobicity of superhydrophobic surface structures of energy equipment with different microstructures according to claim 2, characterized in that, The process of calculating the contact angle change rate and roll-off angle change rate of superhydrophobic surface structures of energy equipment with different microstructures, and updating the environmental adaptation data and functional characteristic data by combining the contact angle change rate and roll-off angle change rate, includes: The contact angle variation rates of the superhydrophobic surface structures of energy equipment with different microstructures include impact resistance contact angle variation rate, temperature tolerance contact angle variation rate, corrosion resistance contact angle variation rate, and salt spray resistance contact angle variation rate. The temperature tolerance contact angle variation rate includes heating resistance contact angle variation rate and cooling resistance contact angle variation rate. The contact angles of the superhydrophobic surface structures of energy equipment with different microstructures before and after the impact are extracted, and the rate of change of the impact-resistant contact angle is calculated. The contact angles of the superhydrophobic surface structures of energy equipment with different microstructures are extracted when the temperature changes from low to high, and the contact angles at low and high temperatures are calculated. The rate of change of the temperature-resistant contact angle is also calculated. The contact angles of the superhydrophobic surface structures of energy equipment with different microstructures before and after corrosion are extracted, and the rate of change of the corrosion-resistant contact angle is calculated. Salt spray tests were conducted on the superhydrophobic surface structures of energy equipment with different microstructures. The contact angles of the superhydrophobic surface structures of energy equipment with different microstructures before and after the salt spray tests were extracted, and the salt spray contact angle change rate was calculated. Select time t1 and time t2, extract the roll angle of the superhydrophobic surface structure of the energy equipment with different microstructures corresponding to time t1 and time t2 respectively, and calculate the roll angle change rate of the superhydrophobic surface structure of the energy equipment with different microstructures; By integrating the impact resistance contact angle change rate, the temperature tolerance contact angle change rate, the corrosion resistance contact angle change rate, the salt spray resistance contact angle change rate, and the roll-off angle change rate of the superhydrophobic surface structure of energy equipment with different microstructures into the impact resistance data, the temperature tolerance data, the corrosion resistance data, the salt spray resistance data, and the self-cleaning data, the environmental adaptability data and the functional characteristic data are updated.

4. The method for testing the hydrophobicity of superhydrophobic surface structures of energy equipment with different microstructures according to claim 3, characterized in that, The evaluation process for the environmental adaptability index data of the superhydrophobic surface structures of energy equipment with different microstructures includes: The environmental adaptability index data of the superhydrophobic surface structures of energy equipment with different microstructures include impact resistance index, temperature tolerance index, corrosion resistance index, and salt spray resistance index. Based on the impact resistance characteristics of the superhydrophobic surface structures of energy equipment with different microstructures, the weights of the fracture lengths of the superhydrophobic surface structures of energy equipment with different microstructures after impact are determined, and the impact resistance index is evaluated in combination with the impact contact angle change rate. Based on the impact resistance characteristics of the superhydrophobic surface structures of energy equipment with different microstructures, the weights of the mass loss of the superhydrophobic surface structures of energy equipment with different microstructures under low temperature and high temperature environments are determined. Combined with the temperature rise tolerance contact angle change rate and the temperature drop tolerance contact angle change rate, the temperature tolerance index is evaluated. Based on the historical corrosion resistance test results of the superhydrophobic surface structures of energy equipment with different microstructures, the corrosion rate constant of the superhydrophobic surface structures of energy equipment with different microstructures is determined, and the corrosion resistance index is evaluated by combining the updated corrosion resistance data. Based on the historical salt spray resistance test results of the superhydrophobic surface structures of energy equipment with different microstructures, the salt spray resistance coefficient of the superhydrophobic surface structures of energy equipment with different microstructures is determined, and the salt spray resistance index is evaluated by combining the updated salt spray resistance data. The environmental adaptability index data of the different microstructures of the superhydrophobic surface structures of energy equipment are integrated into the superhydrophobic surface structure hydrophobicity evaluation dataset.

5. The method for testing the hydrophobicity of superhydrophobic surface structures of energy equipment with different microstructures according to claim 4, characterized in that, The process of constructing a functional characteristic evaluation model and obtaining functional characteristic index data of the superhydrophobic surface structures of energy equipment with different microstructures through the functional characteristic evaluation model includes: The functional characteristic index data of the superhydrophobic surface structures of energy equipment with different microstructures include self-cleaning index, anti-bioadhesion index, icing index and heat transfer index. The self-cleaning data, anti-bioattachment data, icing performance data, and heat transfer efficiency data are extracted from the superhydrophobic surface structure hydrophobicity evaluation dataset and converted into a first training set and a first test set. Using the convolutional neural network algorithm, the first training set data is taken as input, and the functional characteristic index data of the superhydrophobic surface structure of energy equipment with different microstructures are taken as output. The first nonlinear relationship, the second nonlinear relationship, the third nonlinear relationship and the fourth nonlinear relationship are learned respectively to obtain the trained functional characteristic evaluation model. The first test set data is input into the functional characteristic evaluation model to evaluate the performance of the functional characteristic evaluation model. The parameters of the functional characteristic evaluation model are adjusted to optimize the functional characteristic evaluation model and obtain the final functional characteristic evaluation model. The current self-cleaning data, anti-biofouling data, icing performance data, and heat transfer efficiency data are input into the final functional characteristic evaluation model, and the functional characteristic index data of the current superhydrophobic surface structure of energy equipment with different microstructures are output. The functional characteristic index data of the current superhydrophobic surface structure of energy equipment with different microstructures are integrated into the superhydrophobic surface structure hydrophobicity evaluation dataset.

6. The method for testing the hydrophobicity of superhydrophobic surface structures of energy equipment with different microstructures according to claim 5, characterized in that, The calculation process for the superhydrophobic surface roughness factor and superhydrophobic surface contact area ratio of the superhydrophobic surface structures of energy equipment with different microstructures includes: The superhydrophobic surface roughness factor of the superhydrophobic surface structure of the energy equipment with different microstructures is calculated by the proportion of the actual surface area of ​​the superhydrophobic surface structure of the energy equipment with different microstructures to the apparent geometric area of ​​the superhydrophobic surface structure of the energy equipment with different microstructures. The superhydrophobic surface contact area ratio of the superhydrophobic surface structures of the energy equipment with different microstructures is calculated by taking the proportion of the contact area between the superhydrophobic surface structure of the energy equipment with water and the apparent geometric area of ​​the superhydrophobic surface structure of the energy equipment with different microstructures. The superhydrophobic surface roughness factor and superhydrophobic surface contact area ratio of the superhydrophobic surface structures of energy equipment with different microstructures are integrated into the superhydrophobicity evaluation dataset of the superhydrophobic surface structure.

7. The method for testing the hydrophobicity of superhydrophobic surface structures of energy equipment with different microstructures according to claim 6, characterized in that, The process of constructing a hydrophobic coefficient output model for superhydrophobic surface structures and obtaining the hydrophobic coefficients of superhydrophobic surface structures of energy equipment with different microstructures through the superhydrophobic surface structure hydrophobic coefficient output model includes: The static hydrophobicity monitoring data and the dynamic hydrophobicity monitoring data in the superhydrophobic surface structure hydrophobicity evaluation dataset are extracted and divided into a second training set and a second test set. Using the aforementioned multiple linear regression algorithm, the second training set data is taken as input, and the hydrophobic coefficients of the superhydrophobic surface structures of energy equipment with different microstructures are taken as output. The linear relationship between the static hydrophobicity monitoring data, the dynamic hydrophobicity monitoring data, and the hydrophobic coefficients of the superhydrophobic surface structures of energy equipment with different microstructures is learned, and the trained output model of the hydrophobic coefficients of the superhydrophobic surface structures is obtained. The second test set data is input into the trained superhydrophobic surface structure hydrophobic coefficient output model, and the regression coefficient intercept term of the superhydrophobic surface structure hydrophobic coefficient output model is adjusted to optimize the performance of the superhydrophobic surface structure hydrophobic coefficient output model and obtain the final superhydrophobic surface structure hydrophobic coefficient output model. The current static hydrophobicity monitoring data and the current dynamic hydrophobicity monitoring data are input into the final superhydrophobic surface structure hydrophobicity coefficient output model, which outputs the hydrophobicity coefficient of the current superhydrophobic surface structure of energy equipment with different microstructures, and integrates the current hydrophobicity coefficients of the current superhydrophobic surface structure of energy equipment with different microstructures into the superhydrophobic surface structure hydrophobicity evaluation dataset.

8. The method for testing the hydrophobicity of superhydrophobic surface structures of energy equipment with different microstructures according to claim 7, characterized in that, The process of constructing a hydrophobic test calibration model for superhydrophobic surface structures and obtaining the hydrophobic test calibration index corresponding to the superhydrophobic surface structures of energy equipment with different microstructures through the superhydrophobic test calibration model includes: The environmental adaptability index data, functional characteristic index data, superhydrophobic surface roughness factor and superhydrophobic surface contact area ratio of the superhydrophobic surface structure of energy equipment with different microstructures are extracted from the superhydrophobic surface structure hydrophobicity evaluation dataset, and converted into a third training set and a third test set, wherein the ratio of the third training set to the three test sets is 6:

4. Using the neural network algorithm, the third training set data is taken as input and the hydrophobic test correction index is taken as output. The nonlinear relationship between the environmental adaptability index data, functional characteristic index data, superhydrophobic surface roughness factor, superhydrophobic surface contact area ratio and the hydrophobic test correction index of the superhydrophobic surface structure of energy equipment with different microstructures is learned, and a hydrophobic test correction model of superhydrophobic surface structure is trained. The third test set data is input into the superhydrophobic surface structure hydrophobic test calibration model, and the parameters of the superhydrophobic surface structure hydrophobic test calibration model are adjusted to optimize the performance of the superhydrophobic surface structure hydrophobic test calibration model and obtain the final superhydrophobic surface structure hydrophobic test calibration model. The environmental adaptability index data, functional characteristic index data, superhydrophobic surface roughness factor, and superhydrophobic surface contact area ratio of the current superhydrophobic surface structures of different microstructures are input into the final hydrophobic test correction model of the superhydrophobic surface structure, and the current hydrophobic test correction index is output.

9. The method for testing the hydrophobicity of superhydrophobic surface structures of energy equipment with different microstructures according to claim 8, characterized in that, The correction process for the hydrophobicity coefficient of the superhydrophobic surface structure of the energy equipment with different microstructures includes: When the current hydrophobicity test correction index is less than 0.2, no correction processing is performed on the hydrophobicity coefficient of the superhydrophobic surface structure of the energy equipment with different microstructures; When the current hydrophobicity test correction index is between 0.2 and 0.5, according to The formula is used to correct the hydrophobicity coefficient of the superhydrophobic surface structure of energy equipment with different microstructures; when the current hydrophobicity test correction index is greater than 0.5, according to... The formula is used to correct the hydrophobicity coefficient of the superhydrophobic surface structure of energy equipment with different microstructures, wherein... To correct the hydrophobicity coefficient of the superhydrophobic surface structures of energy equipment with different microstructures, This is the current hydrophobicity test correction index. The hydrophobicity coefficient of the superhydrophobic surface structure of the energy equipment with different microstructures before correction.

10. The method for testing the hydrophobicity of superhydrophobic surface structures of energy equipment with different microstructures according to claim 9, characterized in that, The process of analyzing the hydrophobicity test results of the superhydrophobic surface structures of energy equipment with different microstructures, and displaying the hydrophobicity test results of the superhydrophobic surface structures of energy equipment with different microstructures using different colored indicator lights, includes: The hydrophobicity coefficients of the superhydrophobic surface structures of energy equipment with different microstructures after correction were analyzed. When the hydrophobicity coefficients of the superhydrophobic surface structures of energy equipment with different microstructures after correction are higher than 0.8, the hydrophobicity test results of the superhydrophobic surface structures of energy equipment with different microstructures are of high hydrophobicity level. When the hydrophobicity coefficient of the superhydrophobic surface structure of the energy equipment with different microstructures after correction is between 0.5 and 0.8, the hydrophobicity test result of the superhydrophobic surface structure of the energy equipment with different microstructures is medium hydrophobicity level. When the hydrophobic coefficient of the corrected superhydrophobic surface structure of the energy equipment with different microstructures is less than 0.5, the hydrophobicity test result of the superhydrophobic surface structure of the energy equipment with different microstructures is a low hydrophobicity level. The signal light colors are set to red, yellow, and green. When the signal light displays red, yellow, and green, the hydrophobicity test results of the superhydrophobic surface structure of the energy equipment with different microstructures are respectively assigned as low hydrophobicity level, medium hydrophobicity level, and high hydrophobicity level. Based on the hydrophobicity test results of the superhydrophobic surface structure of the energy equipment with different microstructures, the signal light is scheduled to display the corresponding color.

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