Method and system for testing weather resistance of super-hydrophobic surface of energy equipment in natural environment

Abstract

The invention belongs to the technical field of hydrophobic testing, and particularly discloses a method and system for testing the weather resistance of a super-hydrophobic surface of energy equipment in a natural environment, and the method comprises the steps: collecting the characteristics of the natural environment where the energy equipment is located, constructing a natural environment coupling model, and matching a weather resistance threshold set of the super-hydrophobic surface of the energy equipment; establishing an appearance-wetting-chemical three-axis characteristic space, outputting a dimensionality signal set of the super-hydrophobic surface of the energy equipment, analyzing a failure probability factor, determining a weather fastness judgment early-warning level of the super-hydrophobic surface, collecting geometrical parameters of the super-hydrophobic surface of the energy equipment, establishing a physical constraint boundary, and combining the failure probability factor to judge the weather fastness of the super-hydrophobic surface. And performing energy equipment super-hydrophobic surface optimization feedback. By constructing the natural environment coupling model and the three-axis feature space, the weather resistance of the super-hydrophobic surface of the energy equipment can be comprehensively evaluated, and accurate early warning and optimized feedback are achieved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of hydrophobicity testing, and particularly relates to a method and system for testing the weather resistance of an energy equipment super-hydrophobic surface in a natural environment. BACKGROUND

[0002] The stable operation of energy equipment is crucial for ensuring energy supply. Equipment in the fields of petroleum and chemical industry, power generation, and new energy, etc. are all exposed to complex and harsh natural environmental conditions, such as high temperature, high humidity, salt spray, wind and sand, ultraviolet radiation, etc. These environmental factors can cause erosion, corrosion, and aging on the surface of the equipment, reducing the performance and service life of the equipment. Super-hydrophobic surfaces have excellent water-repellent, anti-fouling, and self-cleaning properties, which can effectively protect the surface of energy equipment from environmental erosion. The application of super-hydrophobic surface technology to energy equipment can reduce surface fouling and corrosion, improve the operation efficiency and reliability of the equipment, and reduce maintenance costs. Weather resistance is an important indicator for measuring the stability of the performance of super-hydrophobic surfaces in actual use environments.

[0003] Current artificial simulation of major aging factors in natural environments may have different mechanisms from natural aging, resulting in deviations between test results and actual conditions. For some complex natural environmental factors, it is difficult to completely simulate them in artificial aging tests. Moreover, only a single factor is focused on when analyzing the weather resistance of super-hydrophobic surfaces of energy equipment, which is not comprehensive enough and has a single early warning feature, making it difficult to accurately assess the special performance changes of super-hydrophobic surfaces. SUMMARY

[0004] In view of the deficiencies of the prior art, the present application provides a method and system for testing the weather resistance of super-hydrophobic surfaces of energy equipment in a natural environment, which can comprehensively evaluate the weather resistance of super-hydrophobic surfaces of energy equipment by constructing a natural environment coupling model and a three-axis feature space, and achieve precise early warning and optimized feedback.

[0005] To achieve the above purpose, the present application realizes the following technical solutions: The method for testing the weather resistance of super-hydrophobic surfaces of energy equipment in a natural environment comprises the following steps: Collecting the natural environmental characteristics of energy equipment, constructing a natural environment coupling model, and matching the weather resistance threshold set of super-hydrophobic surfaces of energy equipment; Collecting the appearance feature data of super-hydrophobic surfaces of energy equipment, the wetting feature data of super-hydrophobic surfaces of energy equipment, and the chemical state data of super-hydrophobic surfaces of energy equipment, establishing a three-axis feature space of appearance-wetting-chemistry, outputting a dimension signal set of super-hydrophobic surfaces of energy equipment, and analyzing the failure probability factor; Based on the dimension signal set of super-hydrophobic surfaces of energy equipment and the failure probability factor, and in combination with the weather resistance threshold set of super-hydrophobic surfaces of energy equipment, the weather resistance of super-hydrophobic surfaces is determined and the early warning level is determined. The geometric parameters of the super-hydrophobic surface of the energy equipment are collected, a physical constraint boundary is established, and an optimization feedback of the super-hydrophobic surface of the energy equipment is performed in combination with a failure probability factor.

[0006] Preferably, the process of collecting the natural environment characteristics of the energy equipment and constructing a natural environment coupling model comprises the following steps: The natural environment characteristics of the energy equipment are collected, and specifically include the natural environment temperature , the natural environment humidity , the natural environment ultraviolet intensity , and the natural environment salt fog concentration . After the natural environment characteristics of the energy equipment are dimensionless processed, a natural environment coupling model is constructed, and a natural environment coupling signal is output, which is expressed as: .

[0007] Preferably, the process of matching the weather resistance threshold set of the super-hydrophobic surface of the energy equipment comprises the following steps: An environment coupling signal-energy equipment super-hydrophobic surface weather resistance threshold set mapping table pre-stored in a database is obtained; Based on the current natural environment coupling signal, a matched energy equipment super-hydrophobic surface weather resistance threshold set is determined; The energy equipment super-hydrophobic surface weather resistance threshold set specifically includes a super-hydrophobic surface appearance threshold, a super-hydrophobic surface wetness threshold, a super-hydrophobic surface chemical state threshold, and a super-hydrophobic surface failure threshold.

[0008] Preferably, the super-hydrophobic surface appearance feature data specifically includes the number of cracks of the super-hydrophobic surface , the area ratio of the super-hydrophobic surface shedding , and the area ratio of the super-hydrophobic surface corrosion . The super-hydrophobic surface wetness feature data specifically includes the contact angle of the super-hydrophobic surface and the rolling angle of the super-hydrophobic surface . The super-hydrophobic surface chemical state data specifically includes the characteristic peak intensity of the fluorine element of the super-hydrophobic surface and the peak area ratio of the C-F bond to the C-H bond of the super-hydrophobic surface .

[0009] Preferably, the process of establishing an appearance-wetness-chemical three-axis feature space and outputting a super-hydrophobic surface dimension signal set comprises the following steps: The appearance-wetness-chemical three-axis feature space is established: Based on the appearance characteristic data of the super-hydrophobic surface of the energy equipment, a super-hydrophobic surface appearance signal is obtained : ; In the formula, e is a natural constant; Based on the wetting characteristic data of the super-hydrophobic surface of the energy equipment, after dimensionless processing, a super-hydrophobic surface wetting signal is obtained : ; Based on the chemical state data of the super-hydrophobic surface of the energy equipment, after dimensionless processing, a super-hydrophobic surface chemical state signal is obtained : ; In the formula, is the intensity of the characteristic peak of the fluorine element defined in the database; The output energy equipment super-hydrophobic surface dimension signal set includes the super-hydrophobic surface appearance signal, the super-hydrophobic surface wetting signal, and the super-hydrophobic surface chemical state signal.

[0010] Preferably, the process of analyzing the failure probability factor is: Based on the energy equipment super-hydrophobic surface dimension signal set, a failure probability factor is obtained by comprehensive analysis: ; In the formula, is the failure probability factor.

[0011] Preferably, the process of determining the super-hydrophobic surface weather resistance judgment warning level based on the energy equipment super-hydrophobic surface dimension signal set and the failure probability factor, in combination with the energy equipment super-hydrophobic surface weather resistance threshold set, is: The super-hydrophobic surface appearance signal is compared with the super-hydrophobic surface appearance threshold, and if the super-hydrophobic surface appearance signal is higher than the super-hydrophobic surface appearance threshold, the super-hydrophobic surface appearance signal is marked as over-limit; The super-hydrophobic surface wetting signal is compared with the super-hydrophobic surface wetting threshold, and if the super-hydrophobic surface wetting signal is higher than the super-hydrophobic surface wetting threshold, the super-hydrophobic surface wetting signal is marked as over-limit; The super-hydrophobic surface chemical state signal is compared with the super-hydrophobic surface chemical state threshold, and if the super-hydrophobic surface chemical state signal is higher than the super-hydrophobic surface chemical state threshold, the super-hydrophobic surface chemical state signal is marked as over-limit; If any two or more of the super-hydrophobic surface appearance signal, the super-hydrophobic surface wetting signal, and the super-hydrophobic surface chemical state signal are over-limit, the super-hydrophobic surface weather resistance judgment warning level is secondary, and an energy equipment super-hydrophobic surface damage warning is issued; The failure probability factor is compared with the super-hydrophobic surface failure threshold, if the failure probability factor is higher than the super-hydrophobic surface failure threshold, the weather resistance of the super-hydrophobic surface is determined to be updated from the second level to the first level, and a failure warning of the super-hydrophobic surface of the energy equipment is issued.

[0012] Preferably, the process of collecting the geometric parameters of the super-hydrophobic surface of the energy equipment and establishing the physical constraint boundary is: The geometric parameters of the super-hydrophobic surface of the energy equipment are collected, including the super-hydrophobic surface micro-column height , the super-hydrophobic surface column spacing ; The physical constraint boundary is established: ; In the formula, is the minimum super-hydrophobic surface micro-column height, is the maximum super-hydrophobic surface micro-column height, is the minimum super-hydrophobic surface column spacing, is the maximum super-hydrophobic surface column spacing.

[0013] Preferably, the process of combining the failure probability factor to optimize the feedback of the super-hydrophobic surface of the energy equipment is: The objective function J is established, and the objective is to minimize the objective function J: ; ; ; In the formula, is the updated failure probability factor, is the super-hydrophobic surface micro-column height change value, is the super-hydrophobic surface column spacing change value, is the natural environment coupling signal, e is the natural constant, is the failure probability factor, x is the independent variable, wherein, and are dimensionless processed; Gradient sensitivity analysis is performed, and gradient calculation is analyzed: ; ; The constraint Jacobian matrix is established: ; In the formula, is the updated super-hydrophobic surface micro-column height, is the updated super-hydrophobic surface column spacing; Sequential quadratic programming optimization: wherein k is an iteration index, is a micro-pillar height change value of the super-hydrophobic surface in the k+1th iteration, is a pillar spacing change value of the super-hydrophobic surface in the k+1th iteration, is a micro-pillar height change value of the super-hydrophobic surface in the kth iteration, is a pillar spacing change value of the super-hydrophobic surface in the kth iteration, is a step size stored in the database, denotes a Hessian matrix of the objective function J, denotes a gradient vector of the objective function J; a Hessian matrix approximation is performed: a projection correction is performed on the out-of-bound parameters: wherein is an adjusted micro-pillar height change value of the super-hydrophobic surface; wherein is an adjusted pillar spacing change value of the super-hydrophobic surface; the iteration is terminated when any of the following is satisfied: an updated micro-pillar height of the super-hydrophobic surface an updated pillar spacing of the super-hydrophobic surface are outputted.

[0014] A super-hydrophobic surface weather resistance test system for energy equipment in a natural environment is used to implement the above method, and comprises: a surface weather threshold set matching module, configured to collect natural environment characteristics of the energy equipment, construct a natural environment coupling model, and match a super-hydrophobic surface weather resistance threshold set of the energy equipment; a super-hydrophobic surface feature data collection module, configured to collect appearance feature data of the super-hydrophobic surface of the energy equipment, wetting feature data of the super-hydrophobic surface of the energy equipment, and chemical state data of the super-hydrophobic surface of the energy equipment; a three-axis feature space establishment module, configured to establish an appearance-wetting-chemical three-axis feature space, output a dimension signal set of the super-hydrophobic surface of the energy equipment, and analyze a failure probability factor; a weather resistance judgment and early warning level determination module, configured to determine a super-hydrophobic surface weather resistance judgment and early warning level based on the dimension signal set of the super-hydrophobic surface of the energy equipment and the failure probability factor, and in combination with the super-hydrophobic surface weather resistance threshold set of the energy equipment; ​​​​​The super-hydrophobic surface optimization feedback module is used to collect the geometric parameters of the super-hydrophobic surface of the energy equipment, establish a physical constraint boundary, combine a failure probability factor, and perform super-hydrophobic surface optimization feedback of the energy equipment.

[0015] The present application has the following advantages: The present application provides a weather resistance testing method and system for super-hydrophobic surfaces of energy equipment in natural environments, collects natural environment characteristics of the energy equipment and constructs a coupling model, comprehensively considers the influence of various environmental factors on the weather resistance of the super-hydrophobic surface, makes the test results closer to actual use, matches a weather resistance threshold set, and provides a quantitative standard for subsequent analysis and judgment. The appearance feature data, wetting feature data, and chemical state data of the super-hydrophobic surface are collected, and a three-axis feature space of appearance-wetting-chemistry is established, which can comprehensively reflect the state of the super-hydrophobic surface from multiple dimensions. The output dimension signal set is analyzed to determine the failure probability factor, which helps to deeply understand the internal mechanism of the surface performance change and discover potential failure risks in advance.

[0016] The present application determines the weather resistance judgment warning level based on the dimension signal set, the failure probability factor, and the weather resistance threshold set, can accurately evaluate the weather resistance of the super-hydrophobic surface, and timely issues a warning, provides a scientific basis for equipment maintenance and repair, avoids energy equipment failure or failure caused by surface performance degradation, collects the geometric parameters of the super-hydrophobic surface, establishes a physical constraint boundary, combines the failure probability factor to perform optimization feedback, helps to guide the design and improvement of the super-hydrophobic surface, improves the weather resistance and reliability, prolongs the service life of the energy equipment, and reduces the maintenance cost. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 The present application is a method step flowchart; Figure 2 The present application is a system module connection schematic diagram; Figure 3 The present application is a test flowchart. DETAILED DESCRIPTION

[0018] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application.

[0019] Embodiment 1: As shown in Figure 1 and Figure 3 The weather resistance testing method for super-hydrophobic surfaces of energy equipment in natural environments includes the following steps: Collect the natural environment characteristics of the energy equipment, construct a natural environment coupling model, and match a weather resistance threshold set of the super-hydrophobic surface of the energy equipment; Collecting appearance feature data, wetting feature data and chemical state data of the super-hydrophobic surface of the energy equipment; Establishing an appearance-wetting-chemical three-axis feature space, outputting a dimension signal set of the super-hydrophobic surface of the energy equipment, and analyzing a failure probability factor; Based on the dimension signal set of the super-hydrophobic surface of the energy equipment and the failure probability factor, combining the weather resistance threshold set of the super-hydrophobic surface of the energy equipment, determining a weather resistance judgment and early warning level of the super-hydrophobic surface; Collecting geometric parameters of the super-hydrophobic surface of the energy equipment, establishing a physical constraint boundary, combining the failure probability factor, and performing optimization feedback of the super-hydrophobic surface of the energy equipment.

[0020] The process of collecting natural environment features of the energy equipment to construct a natural environment coupling model is as follows: Collecting natural environment features of the energy equipment, specifically including natural environment temperature , natural environment humidity , natural environment ultraviolet intensity , and natural environment salt fog concentration ; The collection can be performed by using a known sensor, for example, temperature and humidity can be collected by a temperature and humidity integrated sensor, ultraviolet intensity can be collected by an ultraviolet radiation meter (including a corresponding sensor module), and fog concentration can be collected by a salt fog concentration sensor or a salt content analyzer.

[0021] Based on the natural environment features of the energy equipment, after dimensionless processing (this embodiment adopts normalization processing), a natural environment coupling model is constructed, and a natural environment coupling signal is output, which is represented as: .

[0022] The weather resistance failure (such as coating peeling, hydrophilicity enhancement) of the super-hydrophobic surface is the result of the synergistic effect of multiple environmental factors (rather than a single factor), for example, high temperature can accelerate coating aging, salt fog can accelerate corrosion, and ultraviolet light can damage the chemical structure. By integrating key environmental parameters such as temperature, humidity, ultraviolet intensity, and salt fog concentration through dimensionless processing, the principle is consistent with the scientific understanding of the influence of multiple environmental factors on weather resistance, avoiding the one-sidedness of single environmental parameter evaluation, and can more truly reflect the service conditions of the energy equipment in the actual natural environment.

[0023] The process of matching the weather resistance threshold set of the super-hydrophobic surface of the energy equipment is as follows: Obtaining a pre-stored natural environment coupling signal-energy equipment super-hydrophobic surface weather resistance threshold set mapping table in the database; Based on the current natural environment coupling signal, determining the matched weather resistance threshold set of the super-hydrophobic surface of the energy equipment; The energy equipment super-hydrophobic surface weathering threshold set specifically includes a super-hydrophobic surface appearance threshold, a super-hydrophobic surface wetting threshold, a super-hydrophobic surface chemical state threshold, and a super-hydrophobic surface failure threshold.

[0024] The natural environment coupling model is constructed by comprehensively collecting natural environment temperature, humidity, ultraviolet intensity, salt mist concentration and other characteristics. By calculating the coupling signal through a specific formula, the effect of complex natural environment on the super-hydrophobic surface can be comprehensively and quantitatively reflected. Compared with single factor consideration, the actual environmental impact is more accurately presented, and the subsequent weathering evaluation is based on data more consistent with real scenarios. According to the natural environment coupling signal, the threshold set of super-hydrophobic surface appearance, wetting, chemical state, and failure is matched. This provides a clear quantitative reference for judging the performance state of the super-hydrophobic surface in a specific environment, and facilitates accurate definition of the degree of surface performance change, making the weathering evaluation results more objective and comparable.

[0025] The energy equipment super-hydrophobic surface appearance feature data specifically includes super-hydrophobic surface crack number , super-hydrophobic surface shedding area ratio , and super-hydrophobic surface corrosion area ratio . The energy equipment super-hydrophobic surface wetting feature data specifically includes super-hydrophobic surface contact angle and super-hydrophobic surface rolling angle . The energy equipment super-hydrophobic surface chemical state data specifically includes super-hydrophobic surface fluorine element characteristic peak intensity and super-hydrophobic surface C-F bond to C-H bond peak area ratio .

[0026] The super-hydrophobic surface crack number can be obtained based on optical microscopy, industrial CT scanning; the super-hydrophobic surface shedding area ratio and the super-hydrophobic surface corrosion area ratio can be obtained based on visual image analysis, laser scanning; the super-hydrophobic surface contact angle and the super-hydrophobic surface rolling angle can be obtained based on contact angle measurement, video optical contact angle measurement; the super-hydrophobic surface fluorine element characteristic peak intensity and the super-hydrophobic surface C-F bond to C-H bond peak area ratio can be obtained based on X-ray photoelectron spectroscopy, Fourier transform infrared spectroscopy.

[0027] A three-axis feature space of appearance-wetting-chemistry is established, and an energy equipment super-hydrophobic surface dimension signal set is output, and the specific analysis process is as follows: The three-axis feature space of appearance-wetting-chemistry is established: Based on the appearance feature data of the super-hydrophobic surface of the energy equipment, a super-hydrophobic surface appearance signal is obtained : ; In the formula, e is a natural constant; Based on the wetting feature data of the super-hydrophobic surface of the energy equipment, after dimensionless processing, a super-hydrophobic surface wetting signal is obtained : ; Based on the chemical state data of the super-hydrophobic surface of the energy equipment, after dimensionless processing, a super-hydrophobic surface chemical state signal is obtained : ; In the formula, is the intensity of the characteristic peak of the fluorine element defined in the database (also dimensionless processed); The performance failure of the super-hydrophobic surface can be reflected through the three core dimensions of structure, performance and chemistry: the appearance (cracks, shedding) directly reflects the physical structure integrity (structure dimension); the contact angle / rolling angle is the core index of wetting performance (performance dimension), and its change directly represents the loss degree of the super-hydrophobic function; the peak intensity of fluorine element and the area ratio of C-F / C-H peak are directly related to the chemical stability of the super-hydrophobic coating (chemical dimension, fluorine element is the key functional component of the super-hydrophobic coating, and the rupture of C-F bond will cause the decrease of hydrophobicity). The above formula constructs a signal model from the three dimensions of structure, performance and chemistry, covering the core failure mechanism of the super-hydrophobic surface.

[0028] The output energy equipment super-hydrophobic surface dimension signal set includes the super-hydrophobic surface appearance signal, the super-hydrophobic surface wetting signal and the super-hydrophobic surface chemical state signal.

[0029] The process of analyzing the failure probability factor is as follows: Based on the energy equipment super-hydrophobic surface dimension signal set, the failure probability factor is obtained through comprehensive analysis: ; In the formula, is the failure probability factor. The formula is based on the three-dimensional surface signal for comprehensive calculation, and the output failure probability factor directly quantifies the failure possibility.

[0030] Data is collected from three dimensions of appearance, wetting and chemical state. On the appearance, the number of cracks, shedding and corrosion area ratio are concerned; the wetting involves the contact angle and the rolling angle; the chemical state focuses on the intensity of the characteristic peak of the fluorine element and the area ratio of C-F bond and C-H bond. The aspects possibly involved in the performance change of the super-hydrophobic surface are comprehensively covered, the surface state information can be completely captured, and the one-sidedness of single-dimensional evaluation is avoided.

[0031] The contact angle and the roll-off angle directly reflect the hydrophobicity and self-cleaning ability; the fluorine element related data are closely related to the surface low-energy material characteristics. Precise selection of these features can accurately reflect the performance evolution of the super-hydrophobic surface in the natural environment.

[0032] A three-axis feature space of appearance-moisture-chemistry is established to integrate different dimensional data. The appearance, moisture and chemical state signals are calculated by specific formulas to form a dimensional signal set. The performance of the super-hydrophobic surface is analyzed from a comprehensive perspective, and the influence of the potential correlation and interaction between different features on the surface performance is explored.

[0033] The failure probability factor is calculated based on the dimensional signal set to quantitatively represent the failure possibility of the super-hydrophobic surface. Compared with qualitative judgment, it is more scientific and accurate, and provides clear and comparable quantitative basis for evaluating the weather resistance of the super-hydrophobic surface, which is convenient for early risk prediction.

[0034] Based on the dimensional signal set and the failure probability factor of the super-hydrophobic surface of the energy equipment, and combined with the weather resistance threshold set of the super-hydrophobic surface of the energy equipment, the process of determining the warning level of the weather resistance of the super-hydrophobic surface is as follows: The appearance signal of the super-hydrophobic surface is compared with the appearance threshold of the super-hydrophobic surface. If the appearance signal of the super-hydrophobic surface is higher than the appearance threshold of the super-hydrophobic surface, the appearance signal of the super-hydrophobic surface is marked as over-limit; The moisture signal of the super-hydrophobic surface is compared with the moisture threshold of the super-hydrophobic surface. If the moisture signal of the super-hydrophobic surface is higher than the moisture threshold of the super-hydrophobic surface, the moisture signal of the super-hydrophobic surface is marked as over-limit; The chemical state signal of the super-hydrophobic surface is compared with the chemical state threshold of the super-hydrophobic surface. If the chemical state signal of the super-hydrophobic surface is higher than the chemical state threshold of the super-hydrophobic surface, the chemical state signal of the super-hydrophobic surface is marked as over-limit; If any two or more of the appearance signal of the super-hydrophobic surface, the moisture signal of the super-hydrophobic surface and the chemical state signal of the super-hydrophobic surface are over-limit, the weather resistance determination warning level of the super-hydrophobic surface is level two, and the damage warning of the super-hydrophobic surface of the energy equipment is issued; The failure probability factor is compared with the failure threshold of the super-hydrophobic surface. If the failure probability factor is higher than the failure threshold of the super-hydrophobic surface, the weather resistance determination warning level of the super-hydrophobic surface is updated from level two to level one, and the failure warning of the super-hydrophobic surface of the energy equipment is issued.

[0035] By comparing the appearance, moisture and chemical state signals of the super-hydrophobic surface with the corresponding thresholds, it can be determined whether each dimension performance is over-limit. According to the over-limit condition, the warning level is divided. When multiple dimensions are over-limit and the failure probability factor is over-limit, the warning level is upgraded from level two to level one, realizing the hierarchical warning from local performance anomaly to overall failure risk of the super-hydrophobic surface, which is high in precision and convenient for timely taking corresponding maintenance measures.

[0036] The quantitative signal value and the threshold value are compared as the basis for determination, avoiding subjective judgment, making the weather resistance determination early warning more objective, scientific and reliable. The clear rules facilitate different personnel operation and understanding, ensure the consistency of the determination results, and provide solid data support for energy equipment maintenance decision.

[0037] The process of collecting the geometric parameters of the super-hydrophobic surface of the energy equipment and establishing the physical constraint boundary is as follows: The geometric parameters of the super-hydrophobic surface of the energy equipment are collected, including the micro-column height of the super-hydrophobic surface , the column spacing of the super-hydrophobic surface ; ; In the formula, is the minimum micro-column height of the super-hydrophobic surface, is the maximum micro-column height of the super-hydrophobic surface, is the minimum column spacing of the super-hydrophobic surface, is the maximum column spacing of the super-hydrophobic surface.

[0038] The micro-column structure (height, spacing) of the super-hydrophobic surface is the physical basis for realizing the super-hydrophobic function. Too high / low height and too dense / scarce spacing will destroy the super-hydrophobic effect (for example, too large spacing is easy to cause liquid droplets to penetrate, and insufficient height cannot support liquid droplets).

[0039] The process of combining the failure probability factor to perform the optimization feedback of the super-hydrophobic surface of the energy equipment is as follows: A target function J is established, and the target is to minimize the target function J: ; ; ; In the formula, is the updated failure probability factor, is the micro-column height change value of the super-hydrophobic surface, is the column spacing change value of the super-hydrophobic surface, is the natural environment coupling signal, e is the natural constant, is the failure probability factor, x is the independent variable, wherein, and are dimensionless processed; In subsequent calculations, , , , , and Also, the non-dimensionalization process can be continued to simplify the calculation; Gradient sensitivity analysis is performed to analyze the gradient calculation: ; ; The constraint Jacobian matrix is established : ; In the formula, is the updated super-hydrophobic surface micro-column height, is the updated super-hydrophobic surface column spacing; Sequential quadratic programming optimization: ; In the formula, k is the iteration index, is the (k+1)th iteration super-hydrophobic surface micro-column height change value, is the (k+1)th iteration super-hydrophobic surface column spacing change value, is the kth iteration super-hydrophobic surface micro-column height change value, is the kth iteration super-hydrophobic surface column spacing change value, is the step size stored in the database, denotes the Hessian matrix of the objective function J, denotes the gradient vector of the objective function J; Hessian matrix approximation is performed: ; Projection correction is performed on the out-of-bound parameters: ; In the formula, is the adjusted super-hydrophobic surface micro-column height change value; ; In the formula, is the adjusted super-hydrophobic surface column spacing change value; The iteration is terminated when any of the following conditions is met: ; The updated super-hydrophobic surface micro-column height , the updated super-hydrophobic surface column spacing are output.

[0040] The core of engineering optimization is to minimize the target risk under the constraint condition, the gradient is used to measure the sensitivity of the parameter change to the target function, and the Hessian matrix is used to judge the second order of the optimization direction (to ensure convergence to the optimal solution rather than a local extreme value).

[0041] The geometric parameters of the super-hydrophobic surface, such as the micro-column height and the column spacing, are collected, and it is clear that they are closely related to the surface performance. By establishing a physical constraint boundary, the parameter adjustment range is defined, and combined with the failure probability factor, the key geometric parameters are optimized and fed back directly to the core elements affecting the weather resistance and performance of the super-hydrophobic surface.

[0042] By using mathematical methods such as establishing a target function and optimizing through sequential quadratic programming, considering factors such as failure probability factor, natural environment coupling signal, and parameter variation, the geometric parameters of the super-hydrophobic surface are systematically and scientifically optimized. In the optimization process, the approximation of the Hessian matrix and the projection correction of the out-of-bound parameters are used to ensure the stability of the optimization process and the parameters within a reasonable range, making the optimization results more reliable and effective.

[0043] Through the above optimization feedback process, the failure probability of the super-hydrophobic surface can be reduced, and its weather resistance and functionality in the natural environment can be improved.

[0044] By optimizing in combination with the natural environment coupling signal, the super-hydrophobic surface can better adapt to different natural environmental conditions. By dynamically adjusting the surface geometric parameters according to environmental temperature, humidity, ultraviolet intensity, and salt fog concentration, the surface can always maintain good performance in complex and changing natural environments, improving the reliability and stability of energy equipment operation.

[0045] In Example 2, based on the consideration of temperature, humidity, ultraviolet intensity, and salt fog concentration in Example 1 to construct the natural environment coupling signal, historical data and real-time feedback mechanisms are introduced: The historical data of the failure of the super-hydrophobic surface of different energy equipment in various natural environments is collected, and a dynamic weight model of the influence of each environmental factor on the weather resistance of the super-hydrophobic surface in different time scales and different climate regions is established using deep learning algorithms such as recurrent neural networks (RNN) or long short-term memory networks (LSTM). According to the real-time collected environmental parameters, the weight of each environmental factor in the calculation of the natural environment coupling signal is adjusted in real time according to the dynamic weight model. For example, in the coastal area during the summer high temperature period, the influence weight of salt fog concentration and temperature on the surface failure increases; in the high-altitude strong ultraviolet region, the weight of ultraviolet intensity is significantly increased.

[0046] As shown in Figure 2 , a natural environment energy equipment super-hydrophobic surface weather resistance test system for implementing the method in Example 1 or Example 2 includes: A surface weather threshold set matching module is used to collect the characteristics of the natural environment where the energy equipment is located, construct a natural environment coupling model, and match the weather threshold set of the super-hydrophobic surface of the energy equipment.

[0047] The super-hydrophobic surface feature data collection module is configured to collect appearance feature data, wetting feature data, and chemical state data of the super-hydrophobic surface of the energy equipment.

[0048] The tri-axial feature space establishment module is configured to establish an appearance-wetting-chemical tri-axial feature space, output a dimensionality signal set of the super-hydrophobic surface of the energy equipment, and analyze a failure probability factor.

[0049] The weather resistance determination and early warning level determination module is configured to determine a weather resistance determination and early warning level of the super-hydrophobic surface of the energy equipment based on the dimensionality signal set of the super-hydrophobic surface of the energy equipment and the failure probability factor, and in combination with a weather resistance threshold set of the super-hydrophobic surface of the energy equipment.

[0050] The super-hydrophobic surface optimization feedback module is configured to collect geometric parameters of the super-hydrophobic surface of the energy equipment, establish a physical constraint boundary, and in combination with the failure probability factor, perform super-hydrophobic surface optimization feedback of the energy equipment.

Claims

1. A method for testing the weather resistance of an energy equipment super-hydrophobic surface in a natural environment, characterized in that, The method comprises the following steps: Collecting natural environment characteristics of the energy equipment, constructing a natural environment coupling model, and matching the super-hydrophobic surface weatherability threshold set of the energy equipment; Collecting appearance feature data, wetting feature data, and chemical state data of the super-hydrophobic surface of the energy equipment, establishing a three-axis feature space of appearance-wetting-chemistry, outputting a dimension signal set of the super-hydrophobic surface of the energy equipment, and analyzing a failure probability factor; Based on the dimension signal set of the super-hydrophobic surface of the energy equipment and the failure probability factor, the super-hydrophobic surface weatherability judgment and early warning level is determined in combination with the super-hydrophobic surface weatherability threshold set of the energy equipment; Collecting geometric parameters of the super-hydrophobic surface of the energy equipment, establishing a physical constraint boundary, and combining the failure probability factor to perform super-hydrophobic surface optimization feedback of the energy equipment.

2. The method of claim 1, wherein: The process of collecting natural environment characteristics of the energy equipment and constructing a natural environment coupling model comprises the following steps: Collecting natural environment features where the energy equipment is located, specifically including natural environment temperature , natural environment humidity , natural environment ultraviolet intensity , natural environment salt fog concentration ; Based on the natural environment characteristics of the energy equipment, a natural environment coupling model is constructed after dimensionless processing, and a natural environment coupling signal is output , which is represented as: 。 3. The method of claim 2, wherein the method is performed in a natural environment. The process of matching the super-hydrophobic surface weatherability threshold set of the energy equipment comprises the following steps: Obtaining a pre-stored natural environment coupling signal-super-hydrophobic surface weatherability threshold set mapping table in a database; Based on the current natural environment coupling signal, the matched super-hydrophobic surface weatherability threshold set of the energy equipment is determined; The super-hydrophobic surface weatherability threshold set of the energy equipment specifically comprises a super-hydrophobic surface appearance threshold, a super-hydrophobic surface wetting threshold, a super-hydrophobic surface chemical state threshold, and a super-hydrophobic surface failure threshold.

4. The method of claim 1, wherein: The appearance feature data of the energy equipment super-hydrophobic surface specifically includes the number of cracks of the super-hydrophobic surface , the area percentage of the super-hydrophobic surface flaking , and the area percentage of the super-hydrophobic surface corrosion ; The energy equipment super-hydrophobic surface wetting characteristic data specifically includes a super-hydrophobic surface contact angle , a super-hydrophobic surface rolling angle ; The energy equipment super-hydrophobic surface chemical state data specifically includes super-hydrophobic surface fluorine element characteristic peak intensity , peak area ratio of super-hydrophobic surface C-F bond and C-H bond .

5. The method of claim 4, wherein: The specific analysis process of establishing a three-axis feature space of appearance-wetting-chemistry and outputting a dimension signal set of the super-hydrophobic surface of the energy equipment comprises the following steps: Establishing a three-axis feature space of appearance-wetting-chemistry: Based on the appearance feature data of the energy equipment super-hydrophobic surface, the appearance signal of the super-hydrophobic surface is obtained : ； In the formula, e is a natural constant; Based on the wetting characteristic data of the energy equipment super-hydrophobic surface, after non-dimensional processing, the wetting signal of the super-hydrophobic surface is obtained : ； Based on the chemical state data of the energy equipment super-hydrophobic surface, after non-dimensional processing, the super-hydrophobic surface chemical state signal is obtained : ； In the formula, is the intensity of the characteristic peak of the fluorine element defined in the database; Outputting a dimension signal set of the super-hydrophobic surface of the energy equipment, which comprises a super-hydrophobic surface appearance signal, a super-hydrophobic surface wetting signal, and a super-hydrophobic surface chemical state signal.

6. The method of claim 5, wherein: The process of analyzing the failure probability factor comprises the following steps: Based on the dimension signal set of the super-hydrophobic surface of the energy equipment, the failure probability factor is comprehensively analyzed: ； In the formula, is the failure probability factor.

7. The method of claim 1, wherein: The process of determining the super-hydrophobic surface weatherability judgment and early warning level based on the dimension signal set of the super-hydrophobic surface of the energy equipment and the failure probability factor in combination with the super-hydrophobic surface weatherability threshold set of the energy equipment comprises the following steps: Comparing the super-hydrophobic surface appearance signal with the super-hydrophobic surface appearance threshold, if the super-hydrophobic surface appearance signal is higher than the super-hydrophobic surface appearance threshold, the super-hydrophobic surface appearance signal is marked as over-limit; Comparing the super-hydrophobic surface wetting signal with the super-hydrophobic surface wetting threshold, if the super-hydrophobic surface wetting signal is higher than the super-hydrophobic surface wetting threshold, the super-hydrophobic surface wetting signal is marked as over-limit; Comparing the super-hydrophobic surface chemical state signal with the super-hydrophobic surface chemical state threshold, if the super-hydrophobic surface chemical state signal is higher than the super-hydrophobic surface chemical state threshold, the super-hydrophobic surface chemical state signal is marked as over-limit; If any two or more of the super-hydrophobic surface appearance signal, the super-hydrophobic surface wetting signal, and the super-hydrophobic surface chemical state signal are over-limit, the super-hydrophobic surface weatherability judgment and early warning level is level two, and the super-hydrophobic surface damage early warning of the energy equipment is issued. The failure probability factor is compared with the super-hydrophobic surface failure threshold, if the failure probability factor is higher than the super-hydrophobic surface failure threshold, the weather resistance judgment warning level of the super-hydrophobic surface is updated from level two to level one, and the energy equipment super-hydrophobic surface failure warning is issued.

8. The method of claim 1, wherein: The process of collecting the geometric parameters of the super-hydrophobic surface of the energy equipment and establishing the physical constraint boundary is: Collecting energy equipment super-hydrophobic surface geometry parameters, including super-hydrophobic surface micro-column height , super-hydrophobic surface column spacing ; Establishing the physical constraint boundary: ； wherein is the minimum superhydrophobic surface micro-pillar height, is the maximum superhydrophobic surface micro-pillar height, is the minimum superhydrophobic surface pillar spacing, is the maximum superhydrophobic surface pillar spacing.

9. The method of claim 8, wherein the method further comprises: The process of combining the failure probability factor to perform the optimization feedback of the super-hydrophobic surface of the energy equipment is: The objective function J is established, and the target is to minimize the objective function J: ； ； ； In the formula, is the update failure probability factor, is the micro-column height change value of the super-hydrophobic surface, is the column spacing change value of the super-hydrophobic surface, is the natural environment coupling signal, e is a natural constant, is the failure probability factor, x is the independent variable, wherein, the independent variable x is dimensionless processing of and ; Gradient sensitivity analysis is performed, and gradient calculation is analyzed: ； ； establishing a constraint jacobian matrix : ； wherein to update the micro-pillar height of the superhydrophobic surface, to update the inter-pillar spacing of the superhydrophobic surface; Sequential quadratic programming optimization: ； where k is an iteration index, is a change in micro-pillar height of the super-hydrophobic surface for the k+1th iteration, is a change in pitch of the super-hydrophobic surface for the k+1th iteration, is a change in micro-pillar height of the super-hydrophobic surface for the kth iteration, is a change in pitch of the super-hydrophobic surface for the kth iteration, is a step size stored in a database, denotes a Hessian matrix of the objective function J, denotes a gradient vector of the objective function J; Hessian matrix approximation is performed: ； Projection correction is performed on the out-of-bound parameters: ； In the formula, is the adjusted micro-pillar height variation value of the super-hydrophobic surface. ； In the formula, is the adjusted superhydrophobic surface column spacing change value; When any of the following conditions is met, the iteration is terminated: ； Output updating micro-pillar height of superhydrophobic surface Output updating inter-pillar spacing of superhydrophobic surface .

10. A system for testing the weather resistance of an energy equipment superhydrophobic surface in a natural environment, for implementing the method according to any one of claims 1 to 9, characterized in that it comprises: Including: The surface weathering threshold set matching module is used to collect the natural environment characteristics of the energy equipment, construct a natural environment coupling model, and match the super-hydrophobic surface weathering threshold set of the energy equipment; The super-hydrophobic surface feature data collection module is used to collect the appearance feature data of the super-hydrophobic surface of the energy equipment, the wetting feature data of the super-hydrophobic surface of the energy equipment, and the chemical state data of the super-hydrophobic surface of the energy equipment; The three-axis feature space establishment module is used to establish an appearance-wetting-chemical three-axis feature space, output the dimension signal set of the super-hydrophobic surface of the energy equipment, and analyze the failure probability factor; The weather resistance judgment warning level determination module is used to determine the super-hydrophobic surface weather resistance judgment warning level based on the dimension signal set of the super-hydrophobic surface of the energy equipment and the failure probability factor, combined with the super-hydrophobic surface weathering threshold set of the energy equipment; The super-hydrophobic surface optimization feedback module is used to collect the geometric parameters of the super-hydrophobic surface of the energy equipment, establish the physical constraint boundary, combine the failure probability factor, and perform the optimization feedback of the super-hydrophobic surface of the energy equipment.

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