Optimization verification method and system for antireflection photovoltaic module

Abstract

The invention provides an optimization verification method and system for an antireflection photovoltaic module, and the method comprises the steps: screening a photovoltaic module through graying processing and brightness analysis, carrying out the reflectivity test, and calculating an antireflection evaluation parameter through combining with an environment parameter, so as to systematically evaluate the performance of the module and guide an optimization measure. Through systematic image processing and data analysis, the accuracy and efficiency of photovoltaic module antireflection performance evaluation are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic anti-reflection, and in particular to an anti-reflection photovoltaic component optimization verification method and system. Background Art

[0002] As the global demand for renewable energy grows, the efficiency improvement of photovoltaic modules has become a key research direction in the industry. Anti-reflective coating technology has received widespread attention and application due to its significant contribution to efficiency improvement. In addition, with the advancement of technology, cost optimization and durability improvement, the popularity of anti-reflective coating technology continues to increase. Therefore, it is very important to verify the anti-reflective coating in real time to ensure the high efficiency of photovoltaic modules. Summary of the invention

[0003] The present invention proposes an optimization verification method and system for anti-reflection photovoltaic modules, and the technical solutions adopted are as follows: A method for optimizing and verifying an anti-reflective photovoltaic assembly, the method comprising: S1: Acquire images of a plurality of anti-reflection photovoltaic assemblies, and perform grayscale processing on the images of the anti-reflection photovoltaic assemblies; S2: dividing each grayscale processed image of the anti-reflection photovoltaic assembly into a plurality of small blocks, so that each small block corresponds to an anti-reflection photovoltaic assembly, and calculating the brightness of each anti-reflection photovoltaic assembly through the small blocks to obtain an average brightness value of each anti-reflection photovoltaic assembly; S3: according to the average brightness value distribution of the anti-reflection photovoltaic components, the anti-reflection photovoltaic components corresponding to the maximum value, the minimum value and the intermediate value of the average brightness value are used as marked components, the reflectivity of the marked components is tested, and the reflectivity value corresponding to the marked components is obtained; S4: Obtain anti-reflection evaluation parameters by marking the reflectivity value of the component and the corresponding average brightness value and environmental parameters, and perform anti-reflection evaluation on each anti-reflection photovoltaic component by using the anti-reflection evaluation parameters to obtain evaluation results, and take corresponding measures according to the evaluation results.

[0004] Preferably, the S1 includes: S11: Acquire images of a plurality of anti-reflection photovoltaic modules, and preprocess the images, wherein the preprocessing includes denoising and contrast enhancement, to obtain preprocessed images; S12: gray-scale the pre-processed image to obtain a gray-scale image, and check the gray-scale image to ensure that the brightness value of each pixel in the gray-scale image is within a reasonable range.

[0005] Preferably, S2 includes: S21: marking the rows and columns of the anti-reflection photovoltaic modules in each grayscale image to obtain the position information of each anti-reflection photovoltaic module; S22: dividing each grayscale-processed anti-reflection photovoltaic component image into a grid according to a fixed size of a block of anti-reflection photovoltaic components in the image to obtain a plurality of small blocks, and each small block corresponds to an anti-reflection photovoltaic component with position information attached; S23: calculating the brightness of the anti-reflection photovoltaic module at the same position in each grayscale image to obtain an average brightness value of each anti-reflection photovoltaic module; S24: Compare the average brightness value of each anti-reflection photovoltaic module. When the average brightness value of a certain anti-reflection photovoltaic module is lower than the set normal photovoltaic module threshold, it means that a certain anti-reflection photovoltaic module is blocked, and the blocked anti-reflection photovoltaic module is determined as an abnormal module. At the same time, the location information of the abnormal module is recorded, and the early warning device is used to alert relevant personnel of the abnormality.

[0006] Preferably, S3 includes: S31: Identify the marked abnormal components, remove the abnormal components, arrange the remaining components in ascending order according to the average brightness values, and use the anti-reflection photovoltaic components corresponding to the maximum value, the minimum value and the middle value of the average brightness value as marked components; S32: Decomposing the surface of the marking component into a grid-like structure into a plurality of grid points, measuring the reflectivity of each grid point to obtain a plurality of reflectivity values, and averaging the plurality of reflectivity values ​​to obtain an average reflectivity of the marking component.

[0007] Preferably, the S4 includes: S41: Fitting the reflectivity value of the marking component with the corresponding average brightness value to obtain a polynomial equation for representing the anti-reflection evaluation parameter, and the expression of the polynomial equation is as follows:

[0008] Wherein, R represents the average value of the reflectivity value of the marking component, L represents the average value of the maximum value, the minimum value and the intermediate value of the average brightness value of the marking component, n represents the refractive index of the anti-reflection coating, λ represents the thickness of the anti-reflection layer, θ represents the angle at which the light enters the surface of the marking component, T represents the ambient temperature, A represents the aging degree of the marking component, and the aging degree of the anti-reflection photovoltaic component is obtained by the following formula:

[0009] in, represents the initial aging degree of the marking component, α represents the aging coefficient, the value range of α is [0.1], and t represents the usage time of the marking component; S42: performing anti-reflection evaluation on each anti-reflection photovoltaic module using the anti-reflection evaluation parameter, and classifying the anti-reflection photovoltaic modules according to the evaluation result. When 0.8≤P≤1, it indicates that the anti-reflection performance of the anti-reflection component is good, the light absorption is optimal, and the reflection loss is minimal.

[0010] When 0.5≤P<0.8, the anti-reflection performance of the surface anti-reflection component is general, and it is necessary to analyze the material and thickness of the coating and the incident angle of the light. According to the analysis results, the type and thickness of the coating material and the placement angle of the component are adjusted to optimize the absorption and reflection of light.

[0011] When 0<P<0.5, the anti-reflection performance of the surface anti-reflection component is poor, and an aging test is performed on the anti-reflection layer, and the aged anti-reflection layer is replaced.

[0012] An anti-reflection photovoltaic module optimization verification system, the system comprising: Component image collection system: acquiring images of a plurality of anti-reflection photovoltaic components, and graying the images of the anti-reflection photovoltaic components; Brightness value calculation system: divide each grayscale processed anti-reflection photovoltaic module image into several small blocks, so that each small block corresponds to an anti-reflection photovoltaic module, and calculate the brightness of each anti-reflection photovoltaic module through the small blocks to obtain the average brightness value of each anti-reflection photovoltaic module; Reflectivity test system: according to the average brightness value distribution of the anti-reflective photovoltaic components, the anti-reflective photovoltaic components corresponding to the maximum value, minimum value and intermediate value of the average brightness value are used as marked components, the marked components are tested for reflectivity, and the reflectivity values ​​corresponding to the marked components are obtained; Evaluation system: obtain anti-reflection evaluation parameters by marking the reflectivity value of the component and the corresponding average brightness value and environmental parameters, and perform anti-reflection evaluation on each anti-reflection photovoltaic component according to the anti-reflection evaluation parameters to obtain evaluation results, and take corresponding measures according to the evaluation results.

[0013] Preferably, the component image collection system comprises: Image preprocessing system: acquiring images of a plurality of anti-reflection photovoltaic modules, and preprocessing the images, wherein the preprocessing includes denoising and contrast enhancement, to obtain preprocessed images; Image grayscale system: grayscales the preprocessed image to obtain a grayscale image, and checks the grayscale image to ensure that the brightness value of each pixel in the grayscale image is within a reasonable range.

[0014] Preferably, the brightness value calculation system comprises: Marking system: Mark the rows and columns of the anti-reflection photovoltaic modules in each grayscale image to obtain the position information of each anti-reflection photovoltaic module; Image segmentation system: each grayscale processed anti-reflection photovoltaic module image is segmented into a grid according to the fixed size of a piece of anti-reflection photovoltaic module in the image to obtain a number of small blocks, and each small block corresponds to an anti-reflection photovoltaic module with position information; Average brightness value acquisition system: calculates the brightness of the anti-reflection photovoltaic module at the same position in each grayscale image to obtain the average brightness value of each anti-reflection photovoltaic module; Abnormal component identification system: Compare the average brightness value of each anti-reflection photovoltaic module. When the average brightness value of a certain anti-reflection photovoltaic module is lower than the set normal photovoltaic module threshold, it means that a certain anti-reflection photovoltaic module is blocked, and the blocked anti-reflection photovoltaic module is determined as an abnormal module. At the same time, the location information of the abnormal module is recorded, and the early warning device is used to alert relevant personnel of the abnormality.

[0015] Preferably, the reflectivity testing system comprises: Marking component identification system: Identify the marked abnormal components, remove the abnormal components, arrange the remaining components in ascending order according to the average brightness value, and use the anti-reflection photovoltaic components corresponding to the maximum, minimum and middle values ​​of the average brightness value as marking components; Reflectivity acquisition system: Decompose the surface of the marking component into a grid shape, decompose it into multiple grid points, measure the reflectivity of each grid point to obtain multiple reflectivity values, and average the multiple reflectivity values ​​to obtain the average reflectivity of the marking component.

[0016] Preferably, the evaluation system comprises: Data fitting system: Fitting the reflectivity value of the marking component with the corresponding average brightness value to obtain a polynomial equation for representing the anti-reflection evaluation parameter, and the expression of the polynomial equation is as follows:

[0017] Wherein, R represents the average value of the reflectivity value of the marking component, L represents the average value of the maximum value, the minimum value and the intermediate value of the average brightness value of the marking component, n represents the refractive index of the anti-reflection coating, λ represents the thickness of the anti-reflection layer, θ represents the angle at which the light enters the surface of the marking component, T represents the ambient temperature, A represents the aging degree of the marking component, and the aging degree of the anti-reflection photovoltaic component is obtained by the following formula:

[0018] in, represents the initial aging degree of the marking component, α represents the aging coefficient, the value range of α is [0.1], and t represents the usage time of the marking component; Feedback execution system: perform anti-reflection evaluation on each anti-reflection photovoltaic module through the anti-reflection evaluation parameters, and classify the anti-reflection photovoltaic modules according to the evaluation results. When 0.8≤P≤1, it indicates that the anti-reflection performance of the anti-reflection component is good, the light absorption is optimal, and the reflection loss is minimal.

[0019] When 0.5≤P<0.8, the anti-reflection performance of the surface anti-reflection component is general, and it is necessary to analyze the material and thickness of the coating and the incident angle of the light. According to the analysis results, the type and thickness of the coating material and the placement angle of the component are adjusted to optimize the absorption and reflection of light.

[0020] When 0<P<0.5, the anti-reflection performance of the surface anti-reflection component is poor, and an aging test is performed on the anti-reflection layer, and the aged anti-reflection layer is replaced.

[0021] Beneficial effects of the invention: A method and system for optimizing and verifying anti-reflective photovoltaic modules improves the accuracy and efficiency of evaluation by combining grayscale image processing and reflectivity measurement. The automated process improves test efficiency and reduces costs, thereby enhancing the market competitiveness of modules. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 This is an optimization verification method for an anti-reflective photovoltaic module described in the present invention. DETAILED DESCRIPTION

[0023] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0024] One embodiment of the present invention provides a method for optimizing and verifying an anti-reflective photovoltaic module, the method comprising: S1: Acquire images of a plurality of anti-reflection photovoltaic assemblies, and perform grayscale processing on the images of the anti-reflection photovoltaic assemblies; S2: dividing each grayscale processed image of the anti-reflection photovoltaic assembly into a plurality of small blocks, so that each small block corresponds to an anti-reflection photovoltaic assembly, and calculating the brightness of each anti-reflection photovoltaic assembly through the small blocks to obtain an average brightness value of each anti-reflection photovoltaic assembly; S3: according to the average brightness value distribution of the anti-reflection photovoltaic components, the anti-reflection photovoltaic components corresponding to the maximum value, the minimum value and the intermediate value of the average brightness value are used as marked components, the reflectivity of the marked components is tested, and the reflectivity value corresponding to the marked components is obtained; S4: Obtain anti-reflection evaluation parameters by marking the reflectivity value of the component and the corresponding average brightness value and environmental parameters, and perform anti-reflection evaluation on each anti-reflection photovoltaic component by using the anti-reflection evaluation parameters to obtain evaluation results, and take corresponding measures according to the evaluation results.

[0025] The working principle and effect of the above technical solution are as follows: Images of multiple photovoltaic modules are acquired and grayscaled to simplify the information in the image and highlight the difference in light intensity. The grayscaled image is divided into small blocks so that each image can correspond to an actual physical component. The brightness calculation of these small blocks can obtain the average brightness value of each component, reflecting its light capture and anti-reflection performance. Based on the distribution of the average brightness value, representative components (maximum value, minimum value and intermediate value) are selected for reflectivity testing. These tests help identify components with different anti-reflection performance. The anti-reflection evaluation parameters are calculated using the reflectivity and brightness values ​​of the marked components. These parameters are used to evaluate the anti-reflection effect of each component, thereby guiding further optimization adjustments, including replacing coating materials or adjusting the installation angle of the component. The method improves the accuracy and efficiency of photovoltaic module evaluation through automated and efficient image processing and reflectivity evaluation, supports early fault detection and data-driven optimization decisions, thereby reducing costs and enhancing system reliability.

[0026] In one embodiment of the present invention, the S1 includes: S11: Acquire images of a plurality of anti-reflection photovoltaic modules, and preprocess the images, wherein the preprocessing includes denoising and contrast enhancement, to obtain preprocessed images; S12: gray-scale the pre-processed image to obtain a gray-scale image, and check the gray-scale image to ensure that the brightness value of each pixel in the gray-scale image is within a reasonable range.

[0027] The working principle and effect of the above technical solution are as follows: first, images of multiple anti-reflection photovoltaic modules are obtained. In order to ensure image quality and information reliability, preprocessing is performed. Noise interference in the image is eliminated to reduce subsequent analysis errors. The denoising method includes median filtering, and a specific implementation method includes that each pixel is surrounded by a 3x3 window of a predefined size. All pixel values ​​(brightness values) in the window are sorted, and then the middle value of these pixel values ​​is selected to replace the current value of the center pixel of the window. The window is moved on the image, and this sorting and median replacement process is applied pixel by pixel until each pixel of the entire image is filtered. By enhancing the contrast of the image, the key areas that may have been blurred or unclear are made more prominent, thereby improving the resolution and detail display ability of the image. The preprocessed color image is converted into a grayscale image. The grayscale image is checked to ensure that the brightness value of each pixel is within a reasonable range. By denoising, improving contrast and grayscale processing, the image quality is improved, the processing steps are simplified, the data reliability is ensured, and the analysis efficiency is improved, providing a more efficient basis for subsequent evaluation. In addition, the implementation of median filtering can retain edge details while removing image noise. The filtering method is suitable for the preprocessing stage of photovoltaic module images, ensuring that the subsequent grayscale processing and brightness analysis are based on higher quality image data.

[0028] In one embodiment of the present invention, S2 includes: S21: marking the rows and columns of the anti-reflection photovoltaic modules in each grayscale image to obtain the position information of each anti-reflection photovoltaic module; S22: dividing each grayscale-processed anti-reflection photovoltaic component image into a grid according to a fixed size of a block of anti-reflection photovoltaic components in the image to obtain a plurality of small blocks, and each small block corresponds to an anti-reflection photovoltaic component with position information attached; S23: calculating the brightness of the anti-reflection photovoltaic module at the same position in each grayscale image to obtain an average brightness value of each anti-reflection photovoltaic module; S24: Compare the average brightness value of each anti-reflection photovoltaic module. When the average brightness value of a certain anti-reflection photovoltaic module is lower than the set normal photovoltaic module threshold, it means that a certain anti-reflection photovoltaic module is blocked, and the blocked anti-reflection photovoltaic module is determined as an abnormal module. At the same time, the location information of the abnormal module is recorded, and the early warning device is used to alert relevant personnel of the abnormality.

[0029] The working principle and effect of the above technical solution are as follows: First, each grayscale processed image is marked in rows and columns to clarify the specific position of each anti-reflection photovoltaic component in the image. The grayscale image is segmented according to the fixed size of the component so that each sub-block corresponds accurately to the actual component area. The brightness of each small component block after segmentation is calculated to obtain its average brightness value. These values ​​directly reflect the light capture and anti-reflection performance of each component. The average brightness value of each component is compared with the set normal threshold. If the brightness value of a component is lower than this threshold, it is considered abnormal, which may be blocked by foreign objects. The system will record the location information of the abnormal component and notify the relevant personnel to take necessary measures through the early warning device. The precise positioning and tracking of each photovoltaic component is ensured by row and column marking, making the monitoring and management of the entire system more efficient. Through grid segmentation, individual components can be analyzed independently and finely, and the performance indicators of each component can be quickly obtained, thereby improving the overall evaluation efficiency. Automated brightness calculation and analysis further speeds up data processing. In addition, through the automated early warning mechanism, the frequency of manual inspections is reduced, and the maintenance efficiency and response speed of the system are improved.

[0030] In one embodiment of the present invention, S3 includes: S31: Identify the marked abnormal components, remove the abnormal components, arrange the remaining components in ascending order according to the average brightness values, and use the anti-reflection photovoltaic components corresponding to the maximum value, the minimum value and the middle value of the average brightness value as marked components; S32: Decomposing the surface of the marking component into a grid-like structure into a plurality of grid points, measuring the reflectivity of each grid point to obtain a plurality of reflectivity values, and averaging the plurality of reflectivity values ​​to obtain an average reflectivity of the marking component.

[0031] The working principle and effect of the above technical solution are as follows: First, all photovoltaic components that have been marked as abnormal are identified from the previous steps, and the remaining normal components are sorted according to their average brightness values. The components corresponding to the maximum, minimum and intermediate values ​​of the brightness values ​​are selected as marked components. The surface of each marked component is grid-decomposed, and at each grid point, reflectivity measurement is performed to obtain the reflection parameters of the subdivided area. The reflectivity values ​​of all grid points are collected, and the average of these values ​​is calculated to determine the average reflectivity of the marked components. The method improves the evaluation accuracy by removing abnormal components and selecting components with representative brightness values ​​for comprehensive performance analysis, using grid decomposition for detailed reflectivity measurement, obtaining reliable optical performance data, and providing a scientific basis for optimization guidance, thereby improving the reliability and efficiency of photovoltaic systems.

[0032] In one embodiment of the present invention, the S4 includes: S41: Fitting the reflectivity value of the marking component with the corresponding average brightness value to obtain a polynomial equation for representing the anti-reflection evaluation parameter, and the expression of the polynomial equation is as follows:

[0033] Wherein, R represents the average value of the reflectivity value of the marking component, L represents the average value of the maximum value, the minimum value and the intermediate value of the average brightness value of the marking component, n represents the refractive index of the anti-reflection coating, λ represents the thickness of the anti-reflection layer, θ represents the angle at which the light enters the surface of the marking component, T represents the ambient temperature, A represents the aging degree of the marking component, and the aging degree of the anti-reflection photovoltaic component is obtained by the following formula:

[0034] in, represents the initial aging degree of the marking component, α represents the aging coefficient, the value range of α is [0.1], and t represents the usage time of the marking component; S42: performing anti-reflection evaluation on each anti-reflection photovoltaic module using the anti-reflection evaluation parameter, and classifying the anti-reflection photovoltaic modules according to the evaluation result. When 0.8≤P≤1, it indicates that the anti-reflection performance of the anti-reflection component is good, the light absorption is optimal, and the reflection loss is minimal.

[0035] When 0.5≤P<0.8, the anti-reflection performance of the surface anti-reflection component is general, and it is necessary to analyze the material and thickness of the coating and the incident angle of the light. According to the analysis results, the type and thickness of the coating material and the placement angle of the component are adjusted to optimize the absorption and reflection of light.

[0036] When 0<P<0.5, the anti-reflection performance of the surface anti-reflection component is poor, and an aging test is performed on the anti-reflection layer, and the aged anti-reflection layer is replaced.

[0037] The working principle and effect of the above technical solution are: Fit the reflectivity value of the marked component with its corresponding average brightness value. Express the relationship between the two as a polynomial equation. The purpose of the fitting process is to find a mathematical expression that can best describe the correlation between brightness and reflectivity. The formula introduces optical properties (refractive index, reflectivity) and environmental influences (temperature, aging, etc.), which can dynamically reflect the performance changes of components under different conditions. Represents the basic optical properties of the material. The higher the refractive index and the lower the reflectivity, the better the light transmittance and anti-reflection of the component, and the improved light transmission efficiency. By combining the refractive index and reflectivity, unnecessary light loss is prevented, power loss caused by excessive reflection is avoided, and the complexity of component material selection is reduced. Reflects the attenuation characteristics of light in the material. The exponential function is used to describe the attenuation of light with distance. In addition, the exponential function is used in the formula to improve the simulation accuracy of the attenuation process of light in the material, prevent the omission of light loss calculation, avoid the evaluation error caused by wavelength difference, and reduce the complexity of long-term light efficiency loss estimation. The angle variable is added to the formula to improve the accurate measurement of the impact of the incident angle of light on performance, prevent the angle effect from being ignored, avoid the overestimation of reflection loss, and reduce the uncertainty of installation angle selection. In photovoltaic modules, temperature has an impact on the performance of the module. As the temperature increases, the efficiency of the module will decrease because high temperature may cause changes in electrical performance. In the formula, the temperature T is in the denominator, indicating that the higher the temperature, the worse the performance of the photovoltaic module, reflecting the real physical phenomenon - the negative effect of temperature on performance. The temperature variable is added to the formula to reduce the judgment error of environmental adaptability. The effect of the aging coefficient A on performance is considered, and it is smoothed by log (A+1), so that the performance changes of the module after long-term use can be reflected. The growth rate of the logarithmic function slows down as the input value increases. Initially, as A (aging) increases, the value of the logarithmic function rises rapidly, but when A becomes very large, the function value grows slowly. This property simulates the gradual degradation of PV module performance very well: early aging may have a more obvious effect on performance, but as the module approaches the end of its life, the change may tend to stabilize. The use of logarithms can suppress the nonlinear effect of extreme values ​​on the evaluation. If aging accelerates or data is abnormal at a certain moment, the logarithmic function can smooth its effect, making the performance evaluation more robust. The introduction of the aging coefficient in the formula improves the robustness of the evaluation of the impact of module aging, prevents the impact of aging factors from being ignored, avoids evaluation errors in the rapid aging change stage, and reduces the uncertainty in long-term performance estimation. The entire polynomial equation comprehensively captures the performance of the module under field conditions by integrating factors such as refractive index, reflectivity, brightness, wavelength, angle, temperature, and aging, and provides an accurate evaluation of the optical performance of the module. Taking into account the interaction of multiple variables, it prevents performance misjudgment caused by ignoring specific factors (such as temperature or incident angle), making the evaluation results more reliable. The brightness data of all photovoltaic modules are converted using the fitted polynomial equation to obtain the anti-reflection evaluation parameters of each module. The photovoltaic modules are classified according to the calculated anti-reflection evaluation parameters. Thresholds or intervals are set, including high-performance modules, medium-performance modules and low-performance modules, to facilitate further decision-making guidance. The method obtains a polynomial equation by fitting the reflectivity value of the marked module with the average brightness value, thereby achieving a quantitative evaluation of the anti-reflection performance of each photovoltaic module, thereby enabling accurate classification and optimization of performance.

[0038] One embodiment of the present invention provides an optimization verification system for anti-reflection photovoltaic modules, the system comprising: Component image collection system: acquiring images of a plurality of anti-reflection photovoltaic components, and graying the images of the anti-reflection photovoltaic components; Brightness value calculation system: divide each grayscale processed anti-reflection photovoltaic module image into several small blocks, so that each small block corresponds to an anti-reflection photovoltaic module, and calculate the brightness of each anti-reflection photovoltaic module through the small blocks to obtain the average brightness value of each anti-reflection photovoltaic module; Reflectivity test system: according to the average brightness value distribution of the anti-reflective photovoltaic components, the anti-reflective photovoltaic components corresponding to the maximum value, minimum value and intermediate value of the average brightness value are used as marked components, the marked components are tested for reflectivity, and the reflectivity values ​​corresponding to the marked components are obtained; Evaluation system: obtain anti-reflection evaluation parameters by marking the reflectivity value of the component and the corresponding average brightness value and environmental parameters, and perform anti-reflection evaluation on each anti-reflection photovoltaic component according to the anti-reflection evaluation parameters to obtain evaluation results, and take corresponding measures according to the evaluation results.

[0039] The working principle and effect of the above technical solution are: images of multiple photovoltaic components are obtained and grayscaled to simplify the information in the image and highlight the difference in light intensity. The grayscale image is divided into small blocks so that each image can correspond to an actual physical component. The brightness calculation of these small blocks can obtain the average brightness value of each component, reflecting its light capture and anti-reflection performance. Based on the distribution of the average brightness value, representative components (maximum value, minimum value and intermediate value) are selected for reflectivity testing. These tests help identify components with different anti-reflection performance. The anti-reflection evaluation parameters are calculated using the reflectivity and brightness values ​​of the marked components. These parameters are used to evaluate the anti-reflection effect of each component, thereby guiding further optimization adjustments, including replacing coating materials or adjusting the component installation angle. The method improves the accuracy and efficiency of photovoltaic component evaluation through automated and efficient image processing and reflectivity evaluation, supports early fault detection and data-driven optimization decisions, thereby reducing costs and enhancing system reliability.

[0040] In one embodiment of the present invention, the component image collection system comprises: Image preprocessing system: acquiring images of a plurality of anti-reflection photovoltaic modules, and preprocessing the images, wherein the preprocessing includes denoising and contrast enhancement, to obtain preprocessed images; Image grayscale system: grayscales the preprocessed image to obtain a grayscale image, and checks the grayscale image to ensure that the brightness value of each pixel in the grayscale image is within a reasonable range.

[0041] The working principle and effect of the above technical solution are as follows: first, images of multiple anti-reflection photovoltaic modules are obtained. In order to ensure image quality and information reliability, preprocessing is performed. Noise interference in the image is eliminated to reduce subsequent analysis errors. The denoising method includes median filtering, and a specific implementation method includes that each pixel is surrounded by a 3x3 window of a predefined size. All pixel values ​​(brightness values) in the window are sorted, and then the middle value of these pixel values ​​is selected to replace the current value of the center pixel of the window. The window is moved on the image, and this sorting and median replacement process is applied pixel by pixel until each pixel of the entire image is filtered. By enhancing the contrast of the image, the key areas that may have been blurred or unclear are made more prominent, thereby improving the resolution and detail display ability of the image. The preprocessed color image is converted into a grayscale image. The grayscale image is checked to ensure that the brightness value of each pixel is within a reasonable range. By denoising, improving contrast and grayscale processing, the image quality is improved, the processing steps are simplified, the data reliability is ensured, and the analysis efficiency is improved, providing a more efficient basis for subsequent evaluation. In addition, the implementation of median filtering can retain edge details while removing image noise. The filtering method is suitable for the preprocessing stage of photovoltaic module images, ensuring that the subsequent grayscale processing and brightness analysis are based on higher quality image data.

[0042] In one embodiment of the present invention, the brightness value calculation system includes: Marking system: Mark the rows and columns of the anti-reflection photovoltaic modules in each grayscale image to obtain the position information of each anti-reflection photovoltaic module; Image segmentation system: each grayscale processed anti-reflection photovoltaic module image is segmented into a grid according to the fixed size of a piece of anti-reflection photovoltaic module in the image to obtain a number of small blocks, and each small block corresponds to an anti-reflection photovoltaic module with position information; Average brightness value acquisition system: calculates the brightness of the anti-reflection photovoltaic module at the same position in each grayscale image to obtain the average brightness value of each anti-reflection photovoltaic module; Abnormal component identification system: Compare the average brightness value of each anti-reflection photovoltaic module. When the average brightness value of a certain anti-reflection photovoltaic module is lower than the set normal photovoltaic module threshold, it means that a certain anti-reflection photovoltaic module is blocked, and the blocked anti-reflection photovoltaic module is determined as an abnormal module. At the same time, the location information of the abnormal module is recorded, and the early warning device is used to alert relevant personnel of the abnormality.

[0043] The working principle and effect of the above technical solution are as follows: First, each grayscale processed image is marked in rows and columns to clarify the specific position of each anti-reflection photovoltaic component in the image. The grayscale image is segmented according to the fixed size of the component so that each sub-block corresponds accurately to the actual component area. The brightness of each small component block after segmentation is calculated to obtain its average brightness value. These values ​​directly reflect the light capture and anti-reflection performance of each component. The average brightness value of each component is compared with the set normal threshold. If the brightness value of a component is lower than this threshold, it is considered abnormal, which may be blocked by foreign objects. The system will record the location information of the abnormal component and notify the relevant personnel to take necessary measures through the early warning device. The precise positioning and tracking of each photovoltaic component is ensured by row and column marking, making the monitoring and management of the entire system more efficient. Through grid segmentation, individual components can be analyzed independently and finely, and the performance indicators of each component can be quickly obtained, thereby improving the overall evaluation efficiency. Automated brightness calculation and analysis further speeds up data processing. In addition, through the automated early warning mechanism, the frequency of manual inspections is reduced, and the maintenance efficiency and response speed of the system are improved.

[0044] In one embodiment of the present invention, the reflectivity testing system comprises: Marking component identification system: Identify the marked abnormal components, remove the abnormal components, arrange the remaining components in ascending order according to the average brightness value, and use the anti-reflection photovoltaic components corresponding to the maximum, minimum and middle values ​​of the average brightness value as marking components; Reflectivity acquisition system: Decompose the surface of the marking component into a grid shape, decompose it into multiple grid points, measure the reflectivity of each grid point to obtain multiple reflectivity values, and average the multiple reflectivity values ​​to obtain the average reflectivity of the marking component.

[0045] The working principle and effect of the above technical solution are as follows: First, all photovoltaic components that have been marked as abnormal are identified from the previous steps, and the remaining normal components are sorted according to their average brightness values. The components corresponding to the maximum, minimum and intermediate values ​​of the brightness values ​​are selected as marked components. The surface of each marked component is grid-decomposed, and at each grid point, reflectivity measurement is performed to obtain the reflection parameters of the subdivided area. The reflectivity values ​​of all grid points are collected, and the average of these values ​​is calculated to determine the average reflectivity of the marked components. The method improves the evaluation accuracy by removing abnormal components and selecting components with representative brightness values ​​for comprehensive performance analysis, using grid decomposition for detailed reflectivity measurement, obtaining reliable optical performance data, and providing a scientific basis for optimization guidance, thereby improving the reliability and efficiency of the photovoltaic system.

[0046] In one embodiment of the present invention, the evaluation system comprises: Data fitting system: Fitting the reflectivity value of the marking component with the corresponding average brightness value to obtain a polynomial equation for representing the anti-reflection evaluation parameter, and the expression of the polynomial equation is as follows:

[0047] Wherein, R represents the average value of the reflectivity value of the marking component, L represents the average value of the maximum value, the minimum value and the intermediate value of the average brightness value of the marking component, n represents the refractive index of the anti-reflection coating, λ represents the thickness of the anti-reflection layer, θ represents the angle at which the light enters the surface of the marking component, T represents the ambient temperature, A represents the aging degree of the marking component, and the aging degree of the anti-reflection photovoltaic component is obtained by the following formula:

[0048] in, represents the initial aging degree of the marking component, α represents the aging coefficient, the value range of α is [0.1], and t represents the usage time of the marking component; Feedback execution system: perform anti-reflection evaluation on each anti-reflection photovoltaic module through the anti-reflection evaluation parameters, and classify the anti-reflection photovoltaic modules according to the evaluation results. When 0.8≤P≤1, it indicates that the anti-reflection performance of the anti-reflection component is good, the light absorption is optimal, and the reflection loss is minimal.

[0049] When 0.5≤P<0.8, the anti-reflection performance of the surface anti-reflection component is general, and it is necessary to analyze the material and thickness of the coating and the incident angle of the light. According to the analysis results, the type and thickness of the coating material and the placement angle of the component are adjusted to optimize the absorption and reflection of light.

[0050] When 0<P<0.5, the anti-reflection performance of the surface anti-reflection component is poor, and an aging test is performed on the anti-reflection layer, and the aged anti-reflection layer is replaced.

[0051] The working principle and effect of the above technical solution are as follows: the reflectivity value of the marked component is fitted with its corresponding average brightness value. The relationship between the two is expressed as a polynomial equation. The purpose of the fitting process is to find a mathematical expression that can best describe the correlation between brightness and reflectivity. The formula introduces optical properties (refractive index, reflectivity) and environmental influences (temperature, aging, etc.), which can dynamically reflect the performance changes of components under different conditions. The brightness value L and cosθ jointly consider the efficiency of light absorption and the influence of the incident angle on the reflection loss, which is directly related to the photoelectric conversion capacity of the component. The effect of the aging coefficient A on the performance is considered, and it is smoothed by log (A+1), so that the performance changes of the component after long-term use can be reflected. Using the fitted polynomial equation, the brightness data of all photovoltaic components are converted to obtain the anti-reflection evaluation parameters of each component. According to the calculated anti-reflection evaluation parameters, the photovoltaic components are classified. Thresholds or intervals are set, including high-performance components, medium-performance components and low-performance components, to facilitate further decision-making guidance. The method obtains a polynomial equation by fitting the reflectivity value of the marked component with the average brightness value, thereby achieving a quantitative evaluation of the anti-reflection performance of each photovoltaic component, thereby enabling accurate classification and optimization of performance.

[0052] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for optimizing and verifying an anti-reflective photovoltaic module, characterized in that: The method comprises: S1: Acquire images of a plurality of anti-reflection photovoltaic assemblies, and perform grayscale processing on the images of the anti-reflection photovoltaic assemblies; S2: dividing each grayscale processed image of the anti-reflection photovoltaic assembly into a plurality of small blocks, so that each small block corresponds to an anti-reflection photovoltaic assembly, and calculating the brightness of each anti-reflection photovoltaic assembly through the small blocks to obtain an average brightness value of each anti-reflection photovoltaic assembly; S3: according to the average brightness value distribution of the anti-reflection photovoltaic components, the anti-reflection photovoltaic components corresponding to the maximum value, the minimum value and the intermediate value of the average brightness value are used as marked components, the reflectivity of the marked components is tested, and the reflectivity value corresponding to the marked components is obtained; S4: Obtain anti-reflection evaluation parameters by marking the reflectivity value of the component and the corresponding average brightness value and environmental parameters, and perform anti-reflection evaluation on each anti-reflection photovoltaic component by using the anti-reflection evaluation parameters to obtain evaluation results, and take corresponding measures according to the evaluation results.

2. The method for optimizing and verifying an anti-reflective photovoltaic module according to claim 1, characterized in that: The S1 includes: S11: Acquire images of a plurality of anti-reflection photovoltaic modules, and preprocess the images, wherein the preprocessing includes denoising and contrast enhancement, to obtain preprocessed images; S12: gray-scale the pre-processed image to obtain a gray-scale image, and check the gray-scale image to ensure that the brightness value of each pixel in the gray-scale image is within a reasonable range.

3. The method for optimizing and verifying an anti-reflective photovoltaic module according to claim 1, characterized in that: The S2 includes: S21: marking the rows and columns of the anti-reflection photovoltaic modules in each grayscale image to obtain the position information of each anti-reflection photovoltaic module; S22: dividing each grayscale-processed anti-reflection photovoltaic component image into a grid according to a fixed size of a block of anti-reflection photovoltaic components in the image to obtain a plurality of small blocks, and each small block corresponds to an anti-reflection photovoltaic component with position information attached; S23: calculating the brightness of the anti-reflection photovoltaic module at the same position in each grayscale image to obtain an average brightness value of each anti-reflection photovoltaic module; S24: Compare the average brightness value of each anti-reflection photovoltaic module. When the average brightness value of a certain anti-reflection photovoltaic module is lower than the set normal photovoltaic module threshold, it means that a certain anti-reflection photovoltaic module is blocked, and the blocked anti-reflection photovoltaic module is determined as an abnormal module. At the same time, the location information of the abnormal module is recorded, and the early warning device is used to alert relevant personnel of the abnormality.

4. The method for optimizing and verifying an anti-reflective photovoltaic assembly according to claim 1, characterized in that: The S3 includes: S31: Identify the marked abnormal components, remove the abnormal components, arrange the remaining components in ascending order according to the average brightness values, and use the anti-reflection photovoltaic components corresponding to the maximum value, the minimum value and the middle value of the average brightness value as marked components; S32: Decomposing the surface of the marking component into a grid-like structure into a plurality of grid points, measuring the reflectivity of each grid point to obtain a plurality of reflectivity values, and averaging the plurality of reflectivity values ​​to obtain an average reflectivity of the marking component.

5. The method for optimizing and verifying an anti-reflective photovoltaic assembly according to claim 1, characterized in that: The S4 includes: S41: Fitting the reflectivity value of the marking component with the corresponding average brightness value to obtain a polynomial equation for representing the anti-reflection evaluation parameter, and the expression of the polynomial equation is as follows: ； Wherein, R represents the average value of the reflectivity value of the marking component, L represents the average value of the maximum value, the minimum value and the intermediate value of the average brightness value of the marking component, n represents the refractive index of the anti-reflection coating, λ represents the thickness of the anti-reflection layer, θ represents the angle at which the light enters the surface of the marking component, T represents the ambient temperature, A represents the aging degree of the marking component, and the aging degree of the anti-reflection photovoltaic component is obtained by the following formula: ； in, represents the initial aging degree of the marker component, α represents the aging coefficient, the value range of α is [0.1], and t represents the usage time of the marker component; S42: performing anti-reflection evaluation on each anti-reflection photovoltaic module using the anti-reflection evaluation parameter, and classifying the anti-reflection photovoltaic modules according to the evaluation results; When 0.8≤P≤1, it indicates that the anti-reflection performance of the anti-reflection component is good, the light absorption is optimal, and the reflection loss is minimal; When 0.5≤P<0.8, the anti-reflection performance of the surface anti-reflection component is average, and it is necessary to analyze the material and thickness of the coating and the incident angle of the light. According to the analysis results, adjust the type and thickness of the coating material and the placement angle of the component to optimize the absorption and reflection of light; When 0<P<0.5, the anti-reflection performance of the surface anti-reflection component is poor, and an aging test is performed on the anti-reflection layer, and the aged anti-reflection layer is replaced.

6. An anti-reflection photovoltaic module optimization verification system, characterized in that: The system comprises: Component image collection system: acquiring images of a plurality of anti-reflection photovoltaic components, and graying the images of the anti-reflection photovoltaic components; Brightness value calculation system: divide each grayscale processed anti-reflection photovoltaic module image into several small blocks, so that each small block corresponds to an anti-reflection photovoltaic module, and calculate the brightness of each anti-reflection photovoltaic module through the small blocks to obtain the average brightness value of each anti-reflection photovoltaic module; Reflectivity test system: according to the average brightness value distribution of the anti-reflective photovoltaic components, the anti-reflective photovoltaic components corresponding to the maximum value, minimum value and intermediate value of the average brightness value are used as marked components, the marked components are tested for reflectivity, and the reflectivity values ​​corresponding to the marked components are obtained; Evaluation system: obtain anti-reflection evaluation parameters by marking the reflectivity value of the component and the corresponding average brightness value and environmental parameters, and perform anti-reflection evaluation on each anti-reflection photovoltaic component according to the anti-reflection evaluation parameters to obtain evaluation results, and take corresponding measures according to the evaluation results.

7. The anti-reflection photovoltaic module optimization verification system according to claim 6, characterized in that: The component image collection system comprises: Image preprocessing system: acquiring images of a plurality of anti-reflection photovoltaic modules, and preprocessing the images, wherein the preprocessing includes denoising and contrast enhancement, to obtain preprocessed images; Image grayscale system: grayscales the preprocessed image to obtain a grayscale image, and checks the grayscale image to ensure that the brightness value of each pixel in the grayscale image is within a reasonable range.

8. The anti-reflection photovoltaic module optimization verification system according to claim 6, characterized in that: The brightness value calculation system comprises: Marking system: Mark the rows and columns of the anti-reflection photovoltaic modules in each grayscale image to obtain the position information of each anti-reflection photovoltaic module; Image segmentation system: each grayscale processed anti-reflection photovoltaic module image is segmented into a grid according to the fixed size of a piece of anti-reflection photovoltaic module in the image to obtain a number of small blocks, and each small block corresponds to an anti-reflection photovoltaic module with position information; Average brightness value acquisition system: calculates the brightness of the anti-reflection photovoltaic module at the same position in each grayscale image to obtain the average brightness value of each anti-reflection photovoltaic module; Abnormal component identification system: Compare the average brightness value of each anti-reflection photovoltaic module. When the average brightness value of a certain anti-reflection photovoltaic module is lower than the set normal photovoltaic module threshold, it means that a certain anti-reflection photovoltaic module is blocked, and the blocked anti-reflection photovoltaic module is determined as an abnormal module. At the same time, the location information of the abnormal module is recorded, and the early warning device is used to alert relevant personnel of the abnormality.

9. The anti-reflection photovoltaic module optimization verification system according to claim 6, characterized in that: The reflectivity testing system comprises: Marking component identification system: Identify the marked abnormal components, remove the abnormal components, arrange the remaining components in ascending order according to the average brightness value, and use the anti-reflection photovoltaic components corresponding to the maximum, minimum and middle values ​​of the average brightness value as marking components; Reflectivity acquisition system: Decompose the surface of the marking component into a grid shape, decompose it into multiple grid points, measure the reflectivity of each grid point to obtain multiple reflectivity values, and average the multiple reflectivity values ​​to obtain the average reflectivity of the marking component.

10. The anti-reflection photovoltaic module optimization verification system according to claim 6, characterized in that: The evaluation system comprises: Data fitting system: Fitting the reflectivity value of the marking component with the corresponding average brightness value to obtain a polynomial equation for representing the anti-reflection evaluation parameter, and the expression of the polynomial equation is as follows: ； Wherein, R represents the average value of the reflectivity value of the marking component, L represents the average value of the maximum value, the minimum value and the intermediate value of the average brightness value of the marking component, n represents the refractive index of the anti-reflection coating, λ represents the thickness of the anti-reflection layer, θ represents the angle at which the light enters the surface of the marking component, T represents the ambient temperature, A represents the aging degree of the marking component, and the aging degree of the anti-reflection photovoltaic component is obtained by the following formula: ； in, represents the initial aging degree of the marker component, α represents the aging coefficient, the value range of α is [0.1], and t represents the usage time of the marker component; Feedback execution system: performing anti-reflection evaluation on each anti-reflection photovoltaic module according to the anti-reflection evaluation parameters, and classifying the anti-reflection photovoltaic modules according to the evaluation results; When 0.8≤P≤1, it indicates that the anti-reflection performance of the anti-reflection component is good, the light absorption is optimal, and the reflection loss is minimal; When 0.5≤P<0.8, the anti-reflection performance of the surface anti-reflection component is average, and it is necessary to analyze the material and thickness of the coating and the incident angle of the light. According to the analysis results, adjust the type and thickness of the coating material and the placement angle of the component to optimize the absorption and reflection of light; When 0<P<0.5, the anti-reflection performance of the surface anti-reflection component is poor, and an aging test is performed on the anti-reflection layer, and the aged anti-reflection layer is replaced.

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